the representation of body parts in primary sensory cortex is

Somatosensory Cortex Function and Location

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

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Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

On This Page:

The somatosensory cortex is a region of the brain located in the parietal lobe  and lies behind the primary  motor cortex of the frontal lobe . It is responsible for processing sensory information from the body.

The cortex interprets tactile stimuli, such as touch, temperature, pain, and proprioception (awareness of body position).

Different areas of the somatosensory cortex correspond to specific body parts, creating a mapped representation of the body’s sensory surface.

The somatosensory cortex receives tactile information from the body, including sensations such as touch, pressure, temperature, and pain. This sensory information is then carried to the brain via neural pathways to the spinal cord, brainstem, and thalamus.

This information is then projected to the somatosensory cortex, which has numerous connections with other brain areas to process the sensory information.

The somatosensory cortex uses sensory information to initiate important movements that may be required to deal with particular situations.

Somatosensory Pathway

Somatosensory pathways are typically comprised of three neurons : primary, secondary, and tertiary.

The primary neurons are the sensory receptors within the periphery of the somatosensory cortex, which can detect various stimuli such as touch or temperature.

The secondary neurons are located within the spinal cord and brainstem and act as a relay station.

Afferent pathways (which carry signals to the central nervous system) in the spinal cord and brainstem work by passing information from the periphery and the rest of the body to the brain.

These will then terminate in either the thalamus or the cerebellum.

The tertiary neurons, located within the thalamus and cerebellum , will then project to the somatosensory cortex. This will then aid in forming a sensory homunculus, a representational map of the body.

Somatosensory Cortex Area Function

It comprises the primary somatosensory cortex and the secondary somatosensory cortex.

Primary Somatosensory Cortex

The primary somatosensory cortex, also referred to as S1, is found in a ridge of the cerebral cortex known as the postcentral gyrus.

Located just posterior to the central sulcus, a fissure that runs down the side of the cerebral cortex, the primary somatosensory cortex comprises Brodmann’s areas 3a, 3b, 1, and 2.

The primary somatosensory cortex receives projections from nuclei of the thalamus of the brain .

These nuclei receive fibers from the contralateral half of the body, meaning the opposite side of the body from which the area is located in the brain.

Overall, the primary somatosensory cortex is responsible for processing sensations from the body.

These sensations are received through receptors located throughout the body responsible for detecting sensations such as touch, pain, temperature, and proprioception (the body’s ability to perceive its position in space).

Brodmann’s area 3 is responsible for receiving most of the somatosensory input from the thalamus, with the initial information processing occurring here.

Brodmann area 3b is responsible for processing the basics of touch sensations, whilst area 3a responds to information from proprioceptors (receptors responsible for proprioception). Area 3b is also connected to areas 1 and 2 which is where more complex processing takes place.

Area 1 specifically is important in sensing the texture of an object. Area 2, however, has a role in perceiving the shape and size of objects and being involved with proprioception.

An important function of the primary somatosensory cortex is the ability for it to locate where specific sensations arise in the body. This allows us to pinpoint the exact location of touch, pain, and pressure for instance.

This region is also responsible for perceiving pressure by judging the degrees of pressure put on the body. Another function of this area is that it can help us determine the weight of an object by looking at it.

This is useful so that we can determine if we can carry something and give a better idea of whether extra effort is required to carry it. Likewise, the primary somatosensory cortex can help us judge the shapes of objects with our eyes closed and be able to identify objects through touch.

For instance, you could hold a book in your hands and be able to identify the object with your eyes closed based on how this object feels. Similarly, this region would help us judge the texture of objects, which would be dependant on the movement of the fingers and hands over the surface of an object.

Therefore, you could run your fingers over the book and the pages within and know what this object was by the texture.

Secondary Somatosensory Cortex

Posterior to the primary somatosensory cortex lies the secondary somatosensory cortex. This region of the Parietal lobe seems to be serve as an association area for sensory input. It is involved with episodic memory, visuospatial processing, reflections upon self, and aspects of consciousness.

The secondary somatosensory cortex, also referred to as S2 is not as well understood as the primary somatosensory cortex, and it is believed that many of the fibers in this area come from the primary somatosensory area.

The secondary somatosensory cortex is located adjacent to the primary somatosensory cortex in the upper part of the lateral sulcus, a fissure in the cortex that separates the frontal and parietal lobes from the temporal lobes.

This region is believed to not only be connected to the primary somatosensory cortex but also receives direct projections from the thalamus.

The secondary somatosensory cortex is believed to be involved in tactile object recognition and memory. It is suggested that whilst the primary area receives peripheral sensory information, it requires the secondary area to store, process, and retain this information.

This area has been shown to contain many somatotopic representations of the body, which are complex and suggest multiple subregions of this area. S2 is also thought to represent the sensory discriminative aspects of pain.

Neuroimaging studies have found that bilateral activation within the secondary somatosensory cortex relates to the perceived intensity of the pain (Coghill, 2009).

Finally, the secondary somatosensory cortex connects to the hippocampus and the amygdala . This allows it to receive information from the environment and make decisions on how to deal with this information through using past experiences and how we feel about the information.

Homunculus Map

Within the somatosensory cortex, parts of the body are represented on a sensory homunculus map. This means that there are areas within the somatosensory cortex that are arranged such that a particular location receives information from a particular part of the body.

Thus, the surface area of the cortex dedicated to a part of the body correlates with the amount of sensory information from that area.

Some areas of the body are more sensitive than others and are therefore represented in the homunculus map in a distorted way, so those areas of the body take up a disproportionate amount of space.

For instance, the hands and the lips are very sensitive to sensations. So there is a large area of the somatosensory cortex dedicated to sensation in these areas.

In contrast, body parts such as the back are less sensitive to sensations and would therefore have a much smaller area represented in the cortex.

Typically, the medial portions of the sensory homunculus represent body parts such as the hips and, below, those less sensitive to sensation.

At the same time, the lateral sides have a larger surface area which would be where the areas for fingers, lips, eyes, and face would be, those which would be more sensitive to sensations.

Somatosensory Cortex Dysfunction

Damage to the somatosensory cortex can result in mostly mild deficits, and damage symptoms depend on which area was damaged.

Damage to this could result from lesions to one or more areas, sometimes due to a stroke. Another type of lesion is multiple sclerosis (MS) which results in loss of proprioception or exteroception (sensations from stimuli outside the body).

Below are descriptions of some of the symptoms that may be experienced as a result of damage:

Damage to the somatosensory cortex can produce numbness or sometimes paraesthesia, which is a tingling sensation in certain parts of the body. Numbness can result due to damage in the cortex which then affects the receptors in the body for certain areas.

As more sensitive areas such as the hands and face have the most receptors and take up the largest amount of surface area on the cortex, these are most susceptible to numbness.

This numbness as a result of damage can also result in difficulties with being able to detect the temperature of something, which could be a safety issue if an individual is unable to recognize when a surface may be scolding hot, for instance.

Inability to Localize Sensations

Damage could result in individuals being unable to pinpoint where on their body a sensation has taken place. They can localize to an extent by identifying the general region a sensation occurred, such as stating the back or a certain leg.

This identification is possible due to other brain regions in the cerebral cortex being able to localize as well.

Similarly, someone with damage to this area would have difficulty being able to recognize things being traced onto their skin, such as being unable to identify what letter has been traced on a hand.

This inability is called tactile agnosia, and people with this condition may also have difficulties identifying objects by touch alone.

Therefore, if they close their eyes and are asked to identify an object, they may find it difficult to identify whether they are holding a book or a cup, as these may feel the same to them.

Inability to Judge Weight and Pressure

Another possible symptom of damage is the inability to judge the weight of objects. These individuals would not be able to identify whether an object was heavy or light after carrying it.

Likewise, people with this damage would find difficulty in judging physical pressure.

These individuals may be able to know that pressure has been applied to their bodies but would not be able to identify the degree or severity of the pressure applied.

Phantom Limb Pain

It is relatively common for people who have had a limb amputated to experience sensations in their amputated limb. This is called phantom limb and can cause some pain to individuals suffering from it.

Studies have found that this pain shows correlations to changes in the primary somatosensory cortex, which is no longer receiving expected input from the amputated limb (Flor, 2003).

Coghill, R. R. (2009). Pain: Neuroimaging . Encyclopedia of Neuroscience, 409-414.

Flor, H. (2003). Remapping somatosensory cortex after injury. Advances in neurology, 93 , 195-204.

Neuroscientifically Challenged. (2016, March 10). Know your brain: Primary somatosensory cortex . https://www.neuroscientificallychallenged.com/blog/know-your-brain-primary-somatosensory-cortex

Purves, D., Augustine, G., Fitzpatrick, D., Katz, L., LaMantia, A., McNamara, J., & Williams, S. (2001). Neuroscience 2nd edition. sunderland (ma) sinauer associates. Types of Eye Movements and Their Functions .

Raju, H., & Tadi, P. (2020). Neuroanatomy, Somatosensory Cortex. StatPearls [Internet].

The Human Memory. (2020, November, 25). Somatosensory Cortex . https://human-memory.net/somatosensory-cortex/

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Primary somatosensory cortex

Medically Reviewed by Anatomy Team

The primary somatosensory cortex (S1) is a critical region of the brain responsible for processing somatosensory information, such as touch, pressure, pain, and temperature from the body. It is involved in detecting and interpreting a wide range of physical sensations, contributing to our perception of the physical world.

The primary somatosensory cortex is located in the postcentral gyrus, which is the area of the cerebral cortex lying immediately posterior to the central sulcus, a prominent fold in the surface of the brain. This region is situated in the parietal lobe of the brain. Each hemisphere of the brain contains its own S1 area, which specifically processes information from the opposite side of the body.

The primary somatosensory cortex (S1) has a complex and organized anatomy and structure, crucial for its role in processing sensory information from the body:

S1 is located in the postcentral gyrus of the parietal lobe, which lies directly posterior to the central sulcus. This region is situated between the frontal lobe (anteriorly) and the occipital lobe (posteriorly). The primary somatosensory cortex extends across the lateral surface of the hemisphere and dips into the medial surface along the paracentral lobule.

Cytoarchitecture

The cytoarchitecture of the primary somatosensory cortex is defined by the presence of Brodmann areas 3, 1, and 2. These areas have distinct types of cells and arrangements:

• Brodmann area 3 : This area is further subdivided into 3a and 3b and is densely packed with sensory receptors. It is primarily responsible for receiving tactile information from the thalamus.
• Brodmann area 1 : Contains a high density of mechanoreceptors and is involved in processing texture and proprioceptive information.
• Brodmann area 2 : Integrates tactile information with proprioceptive inputs to provide a sense of position and motion of the body parts.

Somatotopic Organization

The primary somatosensory cortex exhibits a somatotopic organization, often illustrated as a sensory homunculus. This is a distorted representation of the human body, based on the neurological “map” of the areas and proportions of the brain dedicated to processing motor functions or sensory input for different parts of the body. In S1, different regions correspond to sensations from specific body parts, with the arrangement reflecting the contralateral (opposite side) sensory input. The size of each cortical area reflects the sensitivity of the corresponding body part.

Cortical Layers

S1 is composed of six distinct cortical layers, each with different types of neurons and connections:

• Layer I (Molecular Layer) : Contains few neurons and serves mainly as a synaptic space for apical dendrites and horizontally oriented axons.
• Layer II (External Granular Layer) : Consists of small granular cells and participates in intracortical connections.
• Layer III (External Pyramidal Layer) : Contains pyramidal neurons and is involved in communicating with other cortical areas.
• Layer IV (Internal Granular Layer) : Receives thalamic inputs and is densely packed with stellate cells.
• Layer V (Internal Pyramidal Layer) : Contains large pyramidal neurons, projecting to subcortical structures.
• Layer VI (Multiform Layer) : Comprises various types of neurons and projects mainly to the thalamus, completing the corticothalamic loop.

Connections

The primary somatosensory cortex receives significant input from the thalamus, specifically from the ventral posterolateral nucleus (VPL) and the ventral posteromedial nucleus (VPM), which relay sensory information from the body and face, respectively. S1 sends information to secondary somatosensory areas and integrates with other cortical areas to contribute to the perception and interpretation of sensory information.

The primary somatosensory cortex (S1) plays a pivotal role in processing and interpreting sensory information from the body.

Sensory Processing and Perception

The fundamental role of S1 is to process somatosensory information received from different parts of the body, such as touch, pressure, pain, temperature, and proprioception (sense of body position). This involves:

• Tactile Sensation : Interpreting various types of touch, including fine touch, vibration, pressure, and texture.
• Pain and Temperature : Discriminating between different intensities and types of pain (sharp, dull, aching) and distinguishing between variations in temperature.
• Proprioception : Understanding the position and movement of body parts, even without visual cues, which is crucial for coordination and movement.

Somatotopic Organization (Homunculus)

S1 is characterized by a somatotopic organization, meaning it has a mapped representation of the entire body:

• Body Representation : Different areas of S1 correspond to specific parts of the body, with the amount of cortical area devoted to each part being proportional to its sensory sensitivity rather than its physical size.
• Contralateral Processing : Each hemisphere of S1 processes sensory information from the opposite side of the body.

Integration of Sensory Information

S1 integrates sensory data from various sources to create a comprehensive perception of the physical world:

• Integration within S1 : Different subregions of S1 (such as Brodmann areas 3, 1, and 2) integrate various types of sensory information from the same body part.
• Cross-modal Integration : S1 works with other cortical areas to integrate somatosensory information with visual, auditory, and motor information, enhancing our understanding and response to the environment.

Cortical Plasticity

S1 exhibits a high degree of neural plasticity:

• Adaptability : The somatotopic organization of S1 can change in response to injury, experience, or training. For example, if a limb is amputated or if one becomes proficient in using a particular body part, such as a musician’s fingers, the corresponding area of S1 can shrink or expand.
• Learning and Memory : Changes in S1 can reflect the acquisition of new skills or adaptation to new sensations.

Role in Movement

Though primarily concerned with sensory processing, S1 also contributes indirectly to movement:

• Feedback for Motor Actions : By providing detailed sensory feedback, S1 helps fine-tune motor actions and movements.
• Interaction with Motor Cortex : S1 communicates closely with the primary motor cortex (M1), assisting in the planning and execution of precise movements.

Clinical Significance

Neurological Disorders : Damage or dysfunction in S1 can result from various causes, including stroke, trauma, or infection, leading to sensory deficits such as numbness, tingling, or loss of proprioception. Understanding S1’s role helps in diagnosing these conditions and assessing the extent of neurological damage.

Pain Management : Chronic pain conditions, such as neuropathic pain, can be associated with changes in the somatosensory cortex. Treatments that target the brain, including neurostimulation and cognitive therapies, may provide relief by altering the processing of pain signals within S1.

Rehabilitation : Following injury or surgery, patients may experience sensory deficits or alterations. Rehabilitation strategies, including sensory re-education and motor-sensory integration exercises, are designed to retrain the brain and S1, helping to restore normal sensation and function.

Plasticity and Recovery : The plasticity of S1 is a double-edged sword; while it allows for adaptation and learning, it can also lead to maladaptive changes following injury, such as phantom limb pain in amputees. Therapies aimed at reshaping the cortical representation can help in recovery and pain management.

Functional Imaging : Techniques such as fMRI and PET scans are used to study S1’s function and structure in both healthy individuals and those with neurological conditions. This has implications for understanding disease mechanisms and developing new therapeutic approaches.

Surgical Planning : Before neurosurgery, particularly in cases involving the brain near S1, functional mapping can be critical to avoid impairing sensory functions. Understanding the exact location and role of S1 aids surgeons in minimizing damage to sensory areas during procedures.

• Trigeminal ganglion
• Olfactory nerve
• Occipital bone
• Cribriform plate

In this Article:

• Last updated on March 22, 2024

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71 The Somatosensory System

General organization of the somatosensory system.

The somatosensory system is composed of the neurons that make sensing touch, temperature, and position in space possible.

Learning Objectives

Describe how the somatosensory system is composed of neurons that make sensing touch, temperature, and position in space possible

Key Takeaways

• The somatosensory system consists of primary, secondary, and tertiary neurons.
• Sensory receptors housed in the dorsal root ganglia project to secondary neurons of the spinal cord that decussate and project to the thalamus or cerebellum.
• Tertiary neurons project to the postcentral gyrus of the parietal lobe, forming a sensory homunculus.
• A sensory homunculus maps sub-regions of the cortical postcentral gyrus to certain parts of the body.
• decussate : Where nerve fibers obliquely cross from one lateral part of the body to the other.
• postcentral gyrus : A prominent structure in the parietal lobe of the human brain and an important landmark that is the location of the primary somatosensory cortex, the main sensory receptive area for the sense of touch.
• organization : The quality of being constituted of parts, each having a special function, act, office, or relation; to systematize.
• thalamus : Either of two large, ovoid structures of gray matter within the forebrain that relay sensory impulses to the cerebral cortex.

The somatosensory system is distributed throughout all major parts of our body. It is responsible for sensing touch, temperature, posture, limb position, and more. It includes both sensory receptor neurons in the periphery (eg., skin, muscle, and organs) and deeper neurons within the central nervous system.

A somatosensory pathway will typically consist of three neurons: primary, secondary, and tertiary.

• In the periphery, the primary neuron is the sensory receptor that detects sensory stimuli like touch or temperature. The cell body of the primary neuron is housed in the dorsal root ganglion of a spinal nerve or, if sensation is in the head or neck, the ganglia of the trigeminal or cranial nerves.
• The secondary neuron acts as a relay and is located in either the spinal cord or the brainstem. This neuron’s ascending axons will cross, or decussate, to the opposite side of the spinal cord or brainstem and travel up the spinal cord to the brain, where most will terminate in either the thalamus or the cerebellum.
• Tertiary neurons have cell bodies in the thalamus and project to the postcentral gyrus of the parietal lobe, forming a sensory homunculus in the case of touch. Regarding posture, the tertiary neuron is located in the cerebellum.

The primary somatosensory area of the human cortex is located in the postcentral gyrus of the parietal lobe. The postcentral gyrus is the location of the primary somatosensory area, the area of the cortex dedicated to the processing of touch information. At this location there is a map of sensory space referred to as a sensory homunculus.

A cortical homunculus is the brain’s physical representation of the human body; it is a neurological map of the anatomical divisions of the body. The surface area of cortex dedicated to a body part correlates with the amount of somatosensory input from that area.

For example, there is a large area of cortex devoted to sensation in the hands, while the back requires a much smaller area. Somatosensory information involved with proprioception and posture is processed in the cerebellum.

Homunculus : Image representing the cortical sensory homunculus. It shows how the anatomical portions of the body, such as the tongue, elbow, and hip, are mapped out on the homonculus. The surface area of cortex dedicated to a body part correlates with the amount of somatosensory input from that area.

The somatosensory system functions in the body’s periphery, spinal cord, and the brain.

• Periphery: Sensory receptors (i.e., thermoreceptors, mechanoreceptors, etc.) detect the various stimuli.
• Spinal cord: Afferent pathways in the spinal cord serve to pass information from the periphery and the rest of the body to the brain.
• Brain: The postcentral gyrus contains Brodmann areas (BA) 3a, 3b, 1, and 2 that make up the somatosensory cortex. BA3a is involved with the sense of relative position of neighboring body parts and the amount of effort being used during movement. BA3b is responsible for distributing somatosensory information to BA1 and shape and size information to BA2.

Tactile Sensation

Touch is sensed by mechanoreceptive neurons that respond to pressure in various ways.

Describe how touch is sensed by mechanoreceptive neurons responding to pressure

• Our sense of touch, or tactile sensation, is mediated by cutaneous mechanoreceptors located in our skin.
• There are four main types of cutaneous mechanoreceptors: Pacinian corpuscles, Meissner’s corpuscles, Merkel’s discs, and Ruffini endings.
• Cutaneous mechanoreceptors are categorized by morphology, by the type of sensation they perceive, and by the rate of adaptation. Furthermore, each has a different receptive field.
• receptive field : The particular region of the sensory space (e.g., the body surface, space inside the ear) in which a stimulus will trigger the firing of that neuron.
• adaptation : A change over time in the responsiveness of the sensory system to a constant stimulus.
• Aβ fiber : A type of sensory nerve fiber that carries cold, pressure, and some pain signals.
• Aδ fiber : Carries sensory information related to muscle spindle secondary endings, touch, and kinesthesia.

A mechanoreceptor is a sensory receptor that responds to mechanical pressure or distortion. For instance, in the periodontal ligament, there are mechanoreceptors that allow the jaw to relax when biting down on hard objects; the mesencephalic nucleus is responsible for this reflex.

In the skin, there are four main types in glabrous (hairless) skin:

• Ruffini endings.
• Meissner’s corpuscles.
• Pacinian corpuscles.
• Merkel’s discs.

There are also mechanoreceptors in hairy skin. The hair cells in the cochlea are the most sensitive mechanoreceptors, transducing air pressure waves into nerve signals sent to the brain.

Cutaneous Mechanoreceptors

Cutaneous mechanoreceptors are located in the skin, like other cutaneous receptors. They provide the senses of touch, pressure, vibration, proprioception, and others. They are all innervated by Aβ fibers, except the mechanoreceiving free nerve endings, which are innervated by Aδ fibers.

They can be categorized by morphology, by the type of sensation they perceive, and by the rate of adaptation. Furthermore, each has a different receptive field:

• Ruffini’s end organs detect tension deep in the skin.
• Meissner’s corpuscles detect changes in texture (vibrations around 50 Hz) and adapt rapidly.
• Pacinian corpuscles detect rapid vibrations (about 200–300 Hz).
• Merkel’s discs detect sustained touch and pressure.
• Mechanoreceiving free nerve endings detect touch, pressure, and stretching.
• Hair follicle receptors are located in hair follicles and sense the position changes of hair strands.

Ruffini Ending

The Ruffini ending (Ruffini corpuscle or bulbous corpuscle) is a class of slowly adapting mechanoreceptors thought to exist only in the glabrous dermis and subcutaneous tissue of humans. It is named after Angelo Ruffini.

This spindle-shaped receptor is sensitive to skin stretch, and contributes to the kinesthetic sense of and control of finger position and movement. It is believed to be useful for monitoring the slippage of objects along the surface of the skin, allowing the modulation of grip on an object.

Ruffini endings are located in the deep layers of the skin. They register mechanical information within joints, more specifically angle change, with a specificity of up to two degrees, as well as continuous pressure states. They also act as thermoreceptors that respond for a long time, such as holding hands with someone during a walk. In a case of a deep burn to the body, there will be no pain as these receptors will be burned off.

Meissner’s Corpuscles

Meissner’s corpuscles (or tactile corpuscles) are responsible for sensitivity to light touch. In particular, they have the highest sensitivity (lowest threshold) when sensing vibrations lower than 50 hertz. They are rapidly adaptive receptors.

Pacinian Corpuscles

Pacinian corpuscles (or lamellar corpuscles) are responsible for sensitivity to vibration and pressure. The vibrational role may be used to detect surface texture, e.g., rough versus smooth.

Merkel Nerve

Merkel nerve endings are mechanoreceptors found in the skin and mucosa of vertebrates that provide touch information to the brain. The information they provide are those regarding pressure and texture. Each ending consists of a Merkel cell in close apposition with an enlarged nerve terminal.

This is sometimes referred to as a Merkel cell–neurite complex, or a Merkel disk receptor. A single afferent nerve fiber branches to innervate up to 90 such endings. They are classified as slowly adapting type I mechanoreceptors.

Proprioceptive Sensations

Proprioception refers to the sense of knowing how one’s body is positioned in three-dimensional space.

Describe how propioception is the sense of the position of parts of our body in a three dimensional space

• Proprioception is the sense of the position of parts of our body and force being generated during movement.
• Proprioception relies on two, primary stretch receptors: Golgi tendon organs and muscle spindles.
• Muscle spindles are sensory receptors within the belly of a muscle that primarily detect changes in the length of this muscle. They convey length information to the central nervous system via sensory neurons. This information can be processed by the brain to determine the position of body parts.
• The Golgi organ (also called Golgi tendon organ, tendon organ, neurotendinous organ, or neurotendinous spindle) is a proprioceptive sensory receptor organ that is located at the insertion of skeletal muscle fibers into the tendons of skeletal muscle.
• alpha motor neuron : Large, multipolar lower motor neurons of the brainstem and spinal cord that are directly responsible for initiating muscle contraction.
• proprioreceptor : A sensory receptor that responds to position and movement and that receives internal bodily stimuli.
• posterior (dorsal) column-medial lemniscus pathway : A sensory pathway of the central nervous system that conveys localized sensations of fine touch, vibration, two-point discrimination, and proprioception from the skin and joints.
• Law of Righting : A reflex rather than a law, this refers to the sudden movement of the head to level the eyes with the horizon in the event of an accidental tilting or imbalance of the body.
• Golgi tendon organ : A proprioceptive sensory receptor organ that is located at the insertion of skeletal muscle fibers into the tendons of skeletal muscle.
• muscle spindle : Sensory receptors within the belly of a muscle that primarily detect changes in the length of this muscle.
• proprioception : The sense of the position of parts of the body, relative to other neighboring parts of the body.

Proprioception is the sense of the relative position of neighboring parts of the body and the strength of effort being employed in movement. It is distinguished from exteroception, perception of the outside world, and interoception, perception of pain, hunger, and the movement of internal organs, etc.

The initiation of proprioception is the activation of a proprioreceptor in the periphery. The proprioceptive sense is believed to be composed of information from sensory neurons located in the inner ear (motion and orientation) and in the stretch receptors located in the muscles and the joint-supporting ligaments (stance).

Conscious proprioception is communicated by the posterior ( dorsal ) column–medial lemniscus pathway to the cerebrum. Unconscious proprioception is communicated primarily via the dorsal and ventral spinocerebellar tracts to the cerebellum.

An unconscious reaction is seen in the human proprioceptive reflex, or Law of Righting. In the event that the body tilts in any direction, the person will cock their head back to level the eyes against the horizon. This is seen even in infants as soon as they gain control of their neck muscles. This control comes from the cerebellum, the part of the brain that affects balance.

Muscle spindles are sensory receptors within the belly of a muscle that primarily detect changes in the length of a muscle. They convey length information to the central nervous system via sensory neurons. This information can be processed by the brain to determine the position of body parts. The responses of muscle spindles to changes in length also play an important role in regulating the contraction of muscles.

Muscle spindle : Mammalian muscle spindle showing typical position in a muscle (left), neuronal connections in spinal cord (middle), and expanded schematic (right). The spindle is a stretch receptor with its own motor supply consisting of several intrafusal muscle fibers. The sensory endings of a primary (group Ia) afferent and a secondary (group II) afferent coil around the non-contractile central portions of the intrafusal fibers.

The Golgi organ (also called Golgi tendon organ, tendon organ, neurotendinous organ or neurotendinous spindle) is a proprioceptive sensory receptor organ that is located at the insertion of skeletal muscle fibers onto the tendons of skeletal muscle. It provides the sensory component of the Golgi tendon reflex.

The Golgi organ should not be confused with the Golgi apparatus—an organelle in the eukaryotic cell —or the Golgi stain, which is a histologic stain for neuron cell bodies.

Golgi tendon organ : The Golgi tendon organ contributes to the Golgi tendon reflex and provides proprioceptive information about joint position.

The Golgi tendon reflex is a normal component of the reflex arc of the peripheral nervous system. In a Golgi tendon reflex, skeletal muscle contraction causes the agonist muscle to simultaneously lengthen and relax. This reflex is also called the inverse myotatic reflex, because it is the inverse of the stretch reflex.

Although muscle tension is increasing during the contraction, alpha motor neurons in the spinal cord that supply the muscle are inhibited. However, antagonistic muscles are activated.

Somatic Sensory Pathways

The somatosensory pathway is composed of three neurons located in the dorsal root ganglion, the spinal cord, and the thalamus.

Describe the somatosensory area in the human cortex

• A somatosensory pathway will typically have three neurons: primary, secondary, and tertiary.
• The cell bodies of the three neurons in a typical somatosensory pathway are located in the dorsal root ganglion, the spinal cord, and the thalamus.
• A major target of somatosensory pathways is the postcentral gyrus in the parietal lobe of the cerebral cortex.
• A major somatosensory pathway is the dorsal column–medial lemniscal pathway.
• The postcentral gyrus is the location of the primary somatosensory area that takes the form of a map called the sensory homunculus.
• parietal lobe : A part of the brain positioned superior to the occipital lobe and posterior to the frontal lobe that integrates sensory information from different modalities and is particularly important for determining spatial sense and navigation.
• reticular activating system : A set of connected nuclei in the brain responsible for regulating wakefulness and sleep-to-wake transitions.
• postcentral gyrus : A prominent structure in the parietal lobe of the human brain that is the location of the primary somatosensory cortex, the main sensory receptive area for the sense of touch.

A somatosensory pathway will typically have three long neurons: primary, secondary, and tertiary. The first always has its cell body in the dorsal root ganglion of the spinal nerve.

Dorsal root ganglion : Sensory nerves of a dorsal root ganglion are depicted entering the spinal cord.

The second has its cell body either in the spinal cord or in the brainstem; this neuron’s ascending axons will cross to the opposite side either in the spinal cord or in the brainstem. The axons of many of these neurons terminate in the thalamus, and others terminate in the reticular activating system or the cerebellum.

In the case of touch and certain types of pain, the third neuron has its cell body in the ventral posterior nucleus of the thalamus and ends in the postcentral gyrus of the parietal lobe.

In the periphery, the somatosensory system detects various stimuli by sensory receptors, such as by mechanoreceptors for tactile sensation and nociceptors for pain sensation. The sensory information (touch, pain, temperature, etc.,) is then conveyed to the central nervous system by afferent neurons, of which there are a number of different types with varying size, structure, and properties.

Generally, there is a correlation between the type of sensory modality detected and the type of afferent neuron involved. For example, slow, thin, unmyelinated neurons conduct pain whereas faster, thicker, myelinated neurons conduct casual touch.

Ascending Pathways

In the spinal cord, the somatosensory system includes ascending pathways from the body to the brain. One major target within the brain is the postcentral gyrus in the cerebral cortex. This is the target for neurons of the dorsal column–medial lemniscal pathway and the ventral spinothalamic pathway.

Note that many ascending somatosensory pathways include synapses in either the thalamus or the reticular formation before they reach the cortex. Other ascending pathways, particularly those involved with control of posture, are projected to the cerebellum, including the ventral and dorsal spinocerebellar tracts.

Another important target for afferent somatosensory neurons that enter the spinal cord are those neurons involved with local segmental reflexes.

Spinal nerve : The formation of the spinal nerve from the dorsal and ventral roots.

Parietal Love: Primary Somatosensory Area

The primary somatosensory area in the human cortex is located in the postcentral gyrus of the parietal lobe. This is the main sensory receptive area for the sense of touch.

Like other sensory areas, there is a map of sensory space called a homunculus at this location. Areas of this part of the human brain map to certain areas of the body, dependent on the amount or importance of somatosensory input from that area.

For example, there is a large area of cortex devoted to sensation in the hands, while the back has a much smaller area. Somatosensory information involved with proprioception and posture also target an entirely different part of the brain, the cerebellum.

Cortical Homunculus

This is a pictorial representation of the anatomical divisions of the primary motor cortex and the primary somatosensory cortex; namely, the portion of the human brain directly responsible for the movement and exchange of sensory and motor information of the body.

Homunculus : Image representing the cortical sensory homunculus.

The thalamus is a midline symmetrical structure within the brain of vertebrates including humans; it is situated between the cerebral cortex and midbrain, and surrounds the third ventricle.

Its function includes relaying sensory and motor signals to the cerebral cortex, along with the regulation of consciousness, sleep, and alertness.

Thalamic nuclei : The ventral posterolateral nucleus receives sensory information from the body.

Mapping the Primary Somatosensory Area

The cortical sensory homunculus is located in the postcentral gyrus and provides a representation of the body to the brain.

Describe how primary somatosensory areas can be mapped

• A sensory homunculus is a pictorial representation of the primary somatosensory cortex.
• Somatotopy is the correspondence of an area of the body to a specific point in the brain.
• Wilder Penfield was a researcher and surgeon who created maps of the somatosensory cortex.
• somesthetic cortex : The primary mechanism of cortical processing for sensory information originating at body surfaces and other tissues (eg., muscles, joints).
• precentral gyrus : The precentral gyrus lies in front of the postcentral gyrus and is the site of the primary motor cortex (Brodmann area 4).

A cortical homunculus is a pictorial representation of the anatomical divisions of the primary motor cortex and the primary somatosensory cortex; it is the portion of the human brain directly responsible for the movement and exchange of sensory and motor information of the body.

It is a visual representation of the concept of the body within the brain—that one’s hand or face exists as much as a series of nerve structures or a neuron concept as it does in a physical form. There are two types of homunculus: sensory and motor. Each one shows a representation of how much of its respective cortex innervates certain body parts.

The primary somesthetic cortex (sensory) pertains to the signals within the postcentral gyrus coming from the thalamus, and the primary motor cortex pertains to signals within the precentral gyrus coming from the premotor area of the frontal lobes.

These are then transmitted from the gyri to the brain stem and spinal cord via corresponding sensory or motor nerves. The reason for the distorted appearance of the homunculus is that the amount of cerebral tissue or cortex devoted to a given body region is proportional to how richly innervated that region is, not to its size.

The homunculus is like an upside-down sensory or motor map of the contralateral side of the body. The upper extremities such as the facial body parts and hands are closer to the lateral sulcus than lower extremities such as the leg and toes.

Homunculus : The idea of the cortical homunculus was created by Wilder Penfield and serves as a rough map of the receptive fields for regions of primary somatosensory cortex.

The resulting image is a grotesquely disfigured human with disproportionately huge hands, lips, and face in comparison to the rest of the body. Because of the fine motor skills and sense nerves found in these particular parts of the body, they are represented as being larger on the homunculus. A part of the body with fewer sensory and/or motor connections to the brain is represented to appear smaller.

This is the point-for-point correspondence of an area of the body to a specific point on the central nervous system. Typically, the area of the body corresponds to a point on the primary somatosensory cortex (postcentral gyrus).

This cortex is typically represented as a sensory homunculus which orients the specific body parts and their respective locations upon the homunculus. Areas such as the appendages, digits, and face can draw their sensory locations upon the somatosensory cortex.

Areas that are finely controlled, such as the digits, have larger portions of the somatosensory cortex, whereas areas that are coarsely controlled, such as the trunk, have smaller portions. Areas such as the viscera do not have sensory locations on the postcentral gyrus.

Montreal Procedure

Wilder Penfield was a groundbreaking researcher and highly original surgeon. With his colleague, Herbert Jasper, he invented the Montreal procedure, in which he treated patients with severe epilepsy by destroying nerve cells in the brain where the seizures originated.

Before operating, he stimulated the brain with electrical probes while the patients were conscious on the operating table (under only local anesthesia), and observed their responses. In this way he could more accurately target the areas of the brain responsible, reducing the side-effects of the surgery.

This technique also allowed him to create maps of the sensory and motor cortices of the brain,  showing their connections to the various limbs and organs of the body. These maps are still used today, practically unaltered.

Along with Herbert Jasper, he published this landmark work in 1951 as Epilepsy and the Functional Anatomy of the Human Brain. This work contributed a great deal to understanding the lateralization of brain function.

Penfield’s maps showed considerable overlap between regions (for instance, the motor region controlling muscles in the hand sometimes also controlled muscles in the upper arm and shoulder), a feature that he put down to individual variation in brain size and localization; we now know that this is due to the fractured somatotropy of the motor cortex.

Somatic Sensory Pathways to the Cerebellum

The ventral and dorsal spinocerebellar tracts convey proprioceptive information from the body to the cerebellum.

Describe the somatic sensory pathways to the cerebellum

• The main somatosensory pathways that communicate with the cerebellum are the ventral (or anterior) and dorsal (or posterior ) spinocerebellar tracts.
• The ventral spinocerebellar tract will cross to the opposite side of the body then cross again to end in the cerebellum (referred to as a double cross). The dorsal spinocerebellar tract does not decussate or cross sides at all through its path.
• The dorsal spinocerebellar tract (also called the posterior spinocerebellar tract, Flechsig’s fasciculus, or Flechsig’s tract) conveys inconscient proprioceptive information from the body to the cerebellum.
• Clarke’s nucleus : A group of interneurons important in proprioception that is found in the intermediate zone of the spinal cord.
• first order neuron : Conducts impulses from proprioceptors and skin receptors to the spinal cord or brain stem.

A sensory system is a part of the nervous system responsible for processing sensory information. A sensory system consists of sensory receptors, neural pathways, and the parts of the brain involved in sensory perception. Commonly recognized sensory systems are those for vision, hearing, somatic sensation (touch), taste, and olfaction (smell).

In short, senses are transducers from the physical world to the realm of the mind where we interpret the information, creating our perception of the world around us.

The ventral spinocerebellar tract conveys proprioceptive information from the body to the cerebellum. It is part of the somatosensory system and runs in parallel with the dorsal spinocerebellar tract.

Both tracts involve two neurons. The ventral spinocerebellar tract will cross to the opposite side of the body then cross again to end in the cerebellum (referred to as a double cross). The dorsal spinocerebellar tract does not decussate, or cross sides, at all through its path.

The major tracts of the spinal cord : The anterior and posterior spinocerebellar tracts are the major somatosensory pathways communicating with the cerebellum.

The ventral tract (under L2/L3) gets its proprioceptive/fine touch/vibration information from a first order neuron, with its cell body in a dorsal ganglion. The axon runs via the fila radicularia (nerve rootlets) to the dorsal horn of the gray matter. There it makes a synapse with the dendrites of two neurons that send their axons bilaterally to the ventral border of the lateral funiculi (transmit the contralateral corticospinal and spinothalamic tracts). The ventral spinocerebellar tract then enters the cerebellum via the superior cerebellar peduncle (connects the cerebellum to the midbrain).

This is in contrast with the dorsal spinocerebellar tract (C8 – L2/L3), which only has one unilateral axon that has its cell body in Clarke’s nucleus (only at the level of C8 – L2/L3). The fibers of the ventral spinocerebellar tract then eventually enter the cerebellum via the superior cerebellar peduncle.

This is one of the few afferent tracts through the superior cerebellar peduncle. Axons first cross midline in the spinal cord and run in the ventral border of the lateral funiculi. These axons ascend to the pons where they join the superior cerebellar peduncle to enter the cerebellum.

Once in the deep, white matter of the cerebellum, the axons recross the midline, give off collaterals to the globose and emboliform nuclei (deep cerebellar nuclei), and terminate in the cortex of the anterior lobe and vermis of the posterior lobe.

The dorsal spinocerebellar tract (also called the posterior spinocerebellar tract, Flechsig’s fasciculus, or Flechsig’s tract) conveys inconscient proprioceptive information from the body to the cerebellum. It is part of the somatosensory system and runs in parallel with the ventral spinocerebellar tract.

Proprioceptive information is taken to the spinal cord via central processes of the dorsal root ganglia (where first order neurons reside). These central processes travel through the dorsal horn where they synapse with second order neurons of Clarke’s nucleus.

Axon fibers from Clarke’s nucleus convey this proprioceptive information in the spinal cord to the peripheral region of the posterolateral funiculus ipsilaterally until it reaches the cerebellum, where unconscious proprioceptive information is processed. This tract involves two neurons and ends up on the same side of the body.

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Somatosensation

14 Somatosensory Representations in the Brain

Learning Objectives

Know what the somatosensory homunculus is.

Know where you can find primary sensory cortex, and what is different about the neural responses in primary sensory cortex and nearby regions in parietal cortex that also respond when you touch an object.

Be able to describe the effect of attention on neural responses.

The somatosensory homunculus is a representation of the human body in the brain’s somatosensory cortex. The term homunculus comes from the Latin word for “little man” and refers to a map of the human body that is laid across a portion of the cerebral cortex. Our brains are maps. This mapping results from the way connections in the brain are ordered and arranged. The ordering of neural pathways between different parts of the brain and those going to and from our muscles and sensory organs produces specific patterns on the brain surface.

The patterns on the brain surface can be seen at various levels of organization. At the most general level, areas that control motor functions (muscle movement) map to the front-most areas of the cerebral cortex while areas that receive and process sensory information are more towards the back of the brain. In the somatosensory homunculus, body parts with high sensory receptor density, such as the hands, lips, and tongue, are represented by larger areas of the homunculus, indicating a greater cortical area devoted to processing sensory input from these regions. Conversely, body parts with lower sensory receptor density, such as the trunk or limbs, are represented by smaller areas on the homunculus. The somatosensory homunculus serves as a model for understanding how the brain organizes and processes sensory information from different parts of the body.

Primary somatotopic representation (S1) is on the postcentral gyrus. It is a distorted map (body parts with high receptor density get more territory). Some senses that are controlled by the primary sensory cortex are touch, thermal information, orientation and direction. Regions in the parietal cortex outside S1 respond to more complex features such as object-selective responses.

Unattended stimuli can fail to elicit neural response, even in primary somatosensory cortex. But the effects of attention are stronger outside S1.

Watch the video linked here and included below to learn more about somatosensory representations in the brain!

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Cheryl Olman PSY 3031 Detailed Outline Provided by: University of Minnesota Download for free at http://vision.psych.umn.edu/users/caolman/courses/PSY3031/ License of original source: CC Attribution 4.0 Adapted by: Hanna Hoyt and Eman Mohamed

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Purves D, Augustine GJ, Fitzpatrick D, et al., editors. Neuroscience. 2nd edition. Sunderland (MA): Sinauer Associates; 2001.

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Neuroscience. 2nd edition.

The somatic sensory cortex.

The axons arising from neurons in the ventral posterior complex of the thalamus project to cortical neurons located primarily in layer IV of the somatic sensory cortex (see Figure 9.7 ; also see Box A in Chapter 26 for a more detailed description of cortical lamination). The somatic sensory cortex in humans, which is located in the parietal lobe , comprises four distinct regions, or fields, known as Brodmann's areas 3a, 3b, 1 , and 2 . Although area 3b is generally known as the primary somatic sensory cortex (also called SI), all four areas are involved in processing tactile information. Experiments carried out in nonhuman primates indicate that neurons in areas 3b and 1 respond primarily to cutaneous stimuli, whereas neurons in 3a respond mainly to stimulation of proprioceptors ; area 2 neurons process both tactile and proprioceptive stimuli. Mapping studies in humans and other primates show further that each of these four cortical areas contains a separate and complete representation of the body. In these somatotopic maps , the foot, leg, trunk, forelimbs, and face are represented in a medial to lateral arrangement, as shown in Figures 9.8A , B and 9.9 .

Somatotopic order in the human primary somatic sensory cortex. (A) Diagram showing the region of the human cortex from which electrical activity is recorded following mechanosensory stimulation of different parts of the body. The patients in the study (more...)

The primary somatic sensory map in the owl monkey based, as in Figure 9.8, on the electrical responsiveness of the cortex to peripheral stimulation. Much more detailed mapping is possible in experimental animals than in neurosurgical patients. The enlargement (more...)

Although the topographic organization of the several somatic sensory areas is similar, the functional properties of the neurons in each region and their organization are distinct ( Box D ). For instance, the neuronal receptive fields are relatively simple in area 3b; the responses elicited in this region are generally to stimulation of a single finger. In areas 1 and 2, however, the majority of the receptive fields respond to stimulation of multiple fingers. Furthermore, neurons in area 1 respond preferentially to particular directions of skin stimulation, whereas many area 2 neurons require complex stimuli to activate them (such as a particular shape). Lesions restricted to area 3b produce a severe deficit in both texture and shape discrimination. In contrast , damage confined to area 1 affects the ability of monkeys to perform accurate texture discrimination. Area 2 lesions tend to produce deficits in finger coordination, and in shape and size discrimination.

Patterns of Organization within the Sensory Cortices: Brain Modules.

A salient feature of cortical maps, recognized soon after their discovery, is their failure to represent the body in actual proportion. When neurosurgeons determined the representation of the human body in the primary sensory (and motor ) cortex , the homunculus (literally, “little man”) defined by such mapping procedures had a grossly enlarged face and hands compared to the torso and proximal limbs ( Figure 9.8C ). These anomalies arise because manipulation, facial expression, and speaking are extraordinarily important for humans, requiring more central (and peripheral) circuitry to govern them. Thus, in humans, the cervical spinal cord is enlarged to accommodate the extra circuitry related to the hand and upper limb, and as stated earlier, the density of receptors is greater in regions such as the hands and lips. Such distortions are also apparent when topographical maps are compared across species . In the rat brain, for example, an inordinate amount of the somatic sensory cortex is devoted to representing the large facial whiskers that provide a key component of the somatic sensory input for rats and mice (see Boxes B and D ), while raccoons overrepresent their paws and the platypus its bill. In short, the sensory input (or motor output) that is particularly significant to a given species gets relatively more cortical representation.

• Cite this Page Purves D, Augustine GJ, Fitzpatrick D, et al., editors. Neuroscience. 2nd edition. Sunderland (MA): Sinauer Associates; 2001. The Somatic Sensory Cortex.
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14.5 Sensory and Motor Pathways

Learning objectives.

By the end of this section, you will be able to:

• Describe the pathways that sensory systems follow into the central nervous system
• Differentiate between the two major ascending pathways in the spinal cord
• Describe the pathway of somatosensory input from the face and compare it to the ascending pathways in the spinal cord
• Explain topographical representations of sensory information in at least two systems
• List the components of the basic processing stream for the motor system
• Describe the pathway of descending motor commands from the cortex to the skeletal muscles
• Compare different descending pathways, both by structure and function
• Explain the initiation of movement from the neurological connections
• Describe several reflex arcs and their functional roles

Spinal Nerves

Generally, spinal nerves contain afferent axons from sensory receptors in the periphery, such as from the skin, mixed with efferent axons travelling to the muscles or other effector organs. As the spinal nerve nears the spinal cord, it splits into dorsal and ventral roots. The dorsal root contains only the axons of sensory neurons, whereas the ventral roots contain only the axons of the motor neurons. Some of the branches will synapse with local neurons in the dorsal root ganglion, posterior (dorsal) horn, or even the anterior (ventral) horn, at the level of the spinal cord where they enter. Other branches will travel a short distance up or down the spine to interact with neurons at other levels of the spinal cord. A branch may also turn into the posterior (dorsal) column of the white matter to connect with the brain. For the sake of convenience, we will use the terms ventral and dorsal in reference to structures within the spinal cord that are part of these pathways. This will help to underscore the relationships between the different components. Typically, spinal nerve systems that connect to the brain are contralateral , in that the right side of the body is connected to the left side of the brain and the left side of the body to the right side of the brain.

Cranial Nerves

Cranial nerves convey specific sensory information from the head and neck directly to the brain. For sensations below the neck, the right side of the body is connected to the left side of the brain and the left side of the body to the right side of the brain. Whereas spinal information is contralateral, cranial nerve systems are mostly ipsilateral , meaning that a cranial nerve on the right side of the head is connected to the right side of the brain. Some cranial nerves contain only sensory axons, such as the olfactory, optic, and vestibulocochlear nerves. Other cranial nerves contain both sensory and motor axons, including the trigeminal, facial, glossopharyngeal, and vagus nerves (however, the vagus nerve is not associated with the somatic nervous system). The general senses of somatosensation for the face travel through the trigeminal system.

Sensory Pathways

Specific regions of the CNS coordinate different somatic processes using sensory inputs and motor outputs of peripheral nerves. A simple case is a reflex caused by a synapse between a dorsal sensory neuron axon and a motor neuron in the ventral horn. More complex arrangements are possible to integrate peripheral sensory information with higher processes. The important regions of the CNS that play a role in somatic processes can be separated into the spinal cord brain stem, diencephalon, cerebral cortex, and subcortical structures.

Spinal Cord and Brain Stem

A sensory pathway that carries peripheral sensations to the brain is referred to as an ascending pathway , or ascending tract. The various sensory modalities each follow specific pathways through the CNS. Tactile and other somatosensory stimuli activate receptors in the skin, muscles, tendons, and joints throughout the entire body. However, the somatosensory pathways are divided into two separate systems on the basis of the location of the receptor neurons. Somatosensory stimuli from below the neck pass along the sensory pathways of the spinal cord, whereas somatosensory stimuli from the head and neck travel through the cranial nerves—specifically, the trigeminal system.

The dorsal column system (sometimes referred to as the dorsal column–medial lemniscus) and the spinothalamic tract are two major pathways that bring sensory information to the brain ( Figure 14.5.1 ). The sensory pathways in each of these systems are composed of three successive neurons.

The dorsal column system begins with the axon of a dorsal root ganglion neuron entering the dorsal root and joining the dorsal column white matter in the spinal cord. As axons of this pathway enter the dorsal column, they take on a positional arrangement so that axons from lower levels of the body position themselves medially, whereas axons from upper levels of the body position themselves laterally. The dorsal column is separated into two component tracts, the fasciculus gracilis that contains axons from the legs and lower body, and the fasciculus cuneatus that contains axons from the upper body and arms.

The axons in the dorsal column terminate in the nuclei of the medulla, where each synapses with the second neuron in their respective pathway. The nucleus gracilis is the target of fibers in the fasciculus gracilis, whereas the nucleus cuneatus is the target of fibers in the fasciculus cuneatus. The second neuron in the system projects from one of the two nuclei and then decussates , or crosses the midline of the medulla. These axons then continue to ascend the brain stem as a bundle called the medial lemniscus . These axons terminate in the thalamus, where each synapses with the third neuron in their respective pathway. The third neuron in the system projects its axons to the postcentral gyrus of the cerebral cortex, where somatosensory stimuli are initially processed and the conscious perception of the stimulus occurs.

The spinothalamic tract also begins with neurons in a dorsal root ganglion. These neurons extend their axons to the dorsal horn, where they synapse with the second neuron in their respective pathway. The name “spinothalamic” comes from this second neuron, which has its cell body in the spinal cord gray matter and connects to the thalamus. Axons from these second neurons then decussate within the spinal cord and ascend to the brain and enter the thalamus, where each synapses with the third neuron in its respective pathway. The neurons in the thalamus then project their axons to the spinothalamic tract, which synapses in the postcentral gyrus of the cerebral cortex.

These two systems are similar in that they both begin with dorsal root ganglion cells, as with most general sensory information. The dorsal column system is primarily responsible for touch sensations and proprioception, whereas the spinothalamic tract pathway is primarily responsible for pain and temperature sensations. Another similarity is that the second neurons in both of these pathways are contralateral, because they project across the midline to the other side of the brain or spinal cord. In the dorsal column system, this decussation takes place in the brain stem; in the spinothalamic pathway, it takes place in the spinal cord at the same spinal cord level at which the information entered. The third neurons in the two pathways are essentially the same. In both, the second neuron synapses in the thalamus, and the thalamic neuron projects to the somatosensory cortex.

The trigeminal pathway carries somatosensory information from the face, head, mouth, and nasal cavity. As with the previously discussed nerve tracts, the sensory pathways of the trigeminal pathway each involve three successive neurons. First, axons from the trigeminal ganglion enter the brain stem at the level of the pons. These axons project to one of three locations. The spinal trigeminal nucleus of the medulla receives information similar to that carried by spinothalamic tract, such as pain and temperature sensations. Other axons go to either the chief sensory nucleus in the pons or the mesencephalic nuclei in the midbrain. These nuclei receive information like that carried by the dorsal column system, such as touch, pressure, vibration, and proprioception. Axons from the second neuron decussate and ascend to the thalamus along the trigeminothalamic tract. In the thalamus, each axon synapses with the third neuron in its respective pathway. Axons from the third neuron then project from the thalamus to the primary somatosensory cortex of the cerebrum.

Diencephalon

The diencephalon is beneath the cerebrum and includes the thalamus and hypothalamus. In the somatic nervous system, the thalamus is an important relay for communication between the cerebrum and the rest of the nervous system. The hypothalamus has both somatic and autonomic functions. In addition, the hypothalamus communicates with the limbic system, which controls emotions and memory functions.

Sensory input to the thalamus comes from most of the special senses and ascending somatosensory tracts. Each sensory system is relayed through a particular nucleus in the thalamus. The thalamus is a required transfer point for most sensory tracts that reach the cerebral cortex, where conscious sensory perception begins. The one exception to this rule is the olfactory system. The olfactory tract axons from the olfactory bulb project directly to the cerebral cortex, along with the limbic system and hypothalamus.

The thalamus is a collection of several nuclei that can be categorized into three anatomical groups. White matter running through the thalamus defines the three major regions of the thalamus, which are an anterior nucleus, a medial nucleus, and a lateral group of nuclei. The anterior nucleus serves as a relay between the hypothalamus and the emotion and memory-producing limbic system. The medial nuclei serve as a relay for information from the limbic system and basal ganglia to the cerebral cortex. This allows memory creation during learning, but also determines alertness. The special and somatic senses connect to the lateral nuclei, where their information is relayed to the appropriate sensory cortex of the cerebrum.

Cortical Processing

As described earlier, many of the sensory axons are positioned in the same way as their corresponding receptor cells in the body. This allows identification of the position of a stimulus on the basis of which receptor cells are sending information. The cerebral cortex also maintains this sensory topography in the particular areas of the cortex that correspond to the position of the receptor cells. The somatosensory cortex provides an example in which, in essence, the locations of the somatosensory receptors in the body are mapped onto the somatosensory cortex. This mapping is often depicted using a sensory homunculus ( Figure 14.5.2 ).

The term homunculus comes from the Latin word for “little man” and refers to a map of the human body that is laid across a portion of the cerebral cortex. In the somatosensory cortex, the external genitals, feet, and lower legs are represented on the medial face of the gyrus within the longitudinal fissure. As the gyrus curves out of the fissure and along the surface of the parietal lobe, the body map continues through the thighs, hips, trunk, shoulders, arms, and hands. The head and face are just lateral to the fingers as the gyrus approaches the lateral sulcus. The representation of the body in this topographical map is medial to lateral from the lower to upper body. It is a continuation of the topographical arrangement seen in the dorsal column system, where axons from the lower body are carried in the fasciculus gracilis, whereas axons from the upper body are carried in the fasciculus cuneatus. As the dorsal column system continues into the medial lemniscus, these relationships are maintained. Also, the head and neck axons running from the trigeminal nuclei to the thalamus run adjacent to the upper body fibers. The connections through the thalamus maintain topography such that the anatomic information is preserved. Note that this correspondence does not result in a perfectly miniature scale version of the body, but rather exaggerates the more sensitive areas of the body, such as the fingers and lower face. Less sensitive areas of the body, such as the shoulders and back, are mapped to smaller areas on the cortex.

The cortex has been described as having specific regions that are responsible for processing specific information; there is the visual cortex, somatosensory cortex, gustatory cortex, etc. However, our experience of these senses is not divided. Instead, we experience what can be referred to as a seamless percept. Our perceptions of the various sensory modalities—though distinct in their content—are integrated by the brain so that we experience the world as a continuous whole.

In the cerebral cortex, sensory processing begins at the primary sensory cortex , then proceeds to an association area , and finally, into a multimodal integration area . For example, somatosensory information inputs directly into the primary somatosensory cortex in the post-central gyrus of the parietal lobe where general awareness of sensation (location and type of sensation) begins. In the somatosensory association cortex details are integrated into a whole. In the highest level of association cortex details are integrated from entirely different modalities to form complete representations as we experience them.

Motor Responses

The defining characteristic of the somatic nervous system is that it controls skeletal muscles. Somatic senses inform the nervous system about the external environment, but the response to that is through voluntary muscle movement. The term “voluntary” suggests that there is a conscious decision to make a movement. However, some aspects of the somatic system use voluntary muscles without conscious control. One example is the ability of our breathing to switch to unconscious control while we are focused on another task. However, the muscles that are responsible for the basic process of breathing are also utilized for speech, which is entirely voluntary.

Cortical Responses

Let’s start with sensory stimuli that have been registered through receptor cells and the information relayed to the CNS along ascending pathways. In the cerebral cortex, the initial processing of sensory perception progresses to associative processing and then integration in multimodal areas of cortex. These levels of processing can lead to the incorporation of sensory perceptions into memory, but more importantly, they lead to a response. The completion of cortical processing through the primary, associative, and integrative sensory areas initiates a similar progression of motor processing, usually in different cortical areas.

Whereas the sensory cortical areas are located in the occipital, temporal, and parietal lobes, motor functions are largely controlled by the frontal lobe. The most anterior regions of the frontal lobe—the prefrontal areas—are important for executive functions , which are those cognitive functions that lead to goal-directed behaviors. These higher cognitive processes include working memory , which has been called a “mental scratch pad,” that can help organize and represent information that is not in the immediate environment. The prefrontal lobe is responsible for aspects of attention, such as inhibiting distracting thoughts and actions so that a person can focus on a goal and direct behavior toward achieving that goal.

The functions of the prefrontal cortex are integral to the personality of an individual, because it is largely responsible for what a person intends to do and how they accomplish those plans. A famous case of damage to the prefrontal cortex is that of Phineas Gage, dating back to 1848. He was a railroad worker who had a metal spike impale his prefrontal cortex ( Figure 14.5.3 ). He survived the accident, but according to second-hand accounts, his personality changed drastically. Friends described him as no longer acting like himself. Whereas he was a hardworking, amiable man before the accident, he turned into an irritable, temperamental, and lazy man after the accident. Many of the accounts of his change may have been inflated in the retelling, and some behavior was likely attributable to alcohol used as a pain medication. However, the accounts suggest that some aspects of his personality did change. Also, there is new evidence that though his life changed dramatically, he was able to become a functioning stagecoach driver, suggesting that the brain has the ability to recover even from major trauma such as this.

Secondary Motor Cortices

In generating motor responses, the executive functions of the prefrontal cortex will need to initiate actual movements. One way to define the prefrontal area is any region of the frontal lobe that does not elicit movement when electrically stimulated. These are primarily in the anterior part of the frontal lobe. The regions of the frontal lobe that remain are the regions of the cortex that produce movement. The prefrontal areas project into the secondary motor cortices, which include the premotor cortex and the supplemental motor area .

Two important regions that assist in planning and coordinating movements are located adjacent to the primary motor cortex. The premotor cortex is more lateral, whereas the supplemental motor area is more medial and superior. The premotor area aids in controlling movements of the core muscles to maintain posture during movement, whereas the supplemental motor area is hypothesized to be responsible for planning and coordinating movement. The supplemental motor area also manages sequential movements that are based on prior experience (that is, learned movements). Neurons in these areas are most active leading up to the initiation of movement. For example, these areas might prepare the body for the movements necessary to drive a car in anticipation of a traffic light changing.

Adjacent to these two regions are two specialized motor planning centers. The frontal eye fields are responsible for moving the eyes in response to visual stimuli. There are direct connections between the frontal eye fields and the superior colliculus. Also, anterior to the premotor cortex and primary motor cortex is Broca’s area . This area is responsible for controlling movements of the structures of speech production. The area is named after a French surgeon and anatomist who studied patients who could not produce speech. They did not have impairments to understanding speech, only to producing speech sounds, suggesting a damaged or underdeveloped Broca’s area.

Primary Motor Cortex

The primary motor cortex is located in the precentral gyrus of the frontal lobe. A neurosurgeon, Walter Penfield, described much of the basic understanding of the primary motor cortex by electrically stimulating the surface of the cerebrum. Penfield would probe the surface of the cortex while the patient was only under local anesthesia so that he could observe responses to the stimulation. This led to the belief that the precentral gyrus directly stimulated muscle movement. We now know that the primary motor cortex receives input from several areas that aid in planning movement, and its principle output stimulates spinal cord neurons to stimulate skeletal muscle contraction.

The primary motor cortex is arranged in a similar fashion to the primary somatosensory cortex, in that it has a topographical map of the body, creating a motor homunculus (see Chapter 14.2 Figure 14.2.5 ). The neurons responsible for musculature in the feet and lower legs are in the medial wall of the precentral gyrus, with the thighs, trunk, and shoulder at the crest of the longitudinal fissure. The hand and face are in the lateral face of the gyrus. Also, the relative space allotted for the different regions is exaggerated in muscles that have greater enervation. The greatest amount of cortical space is given to muscles that perform fine, agile movements, such as the muscles of the fingers and the lower face that are parts of small motor units. The “power muscles” that perform coarser movements, such as the buttock and back muscles, occupy much less space on the motor cortex.

Descending Pathways

The motor output from the cortex descends into the brain stem and to the spinal cord to control the musculature through motor neurons. Neurons located in the primary motor cortex, named Betz cells , are large cortical neurons that synapse with lower motor neurons in the spinal cord or the brain stem. The two descending pathways travelled by the axons of Betz cells are the corticospinal tract and the corticobulbar tract . Both tracts are named for their origin in the cortex and their targets—either the spinal cord or the brain stem (the term “bulbar” refers to the brain stem as the bulb, or enlargement, at the top of the spinal cord).

These two descending pathways are responsible for the conscious or voluntary movements of skeletal muscles. Any motor command from the primary motor cortex is sent down the axons of the Betz cells to activate upper motor neurons in either the cranial motor nuclei or in the ventral horn of the spinal cord. The axons of the corticobulbar tract are ipsilateral, meaning they project from the cortex to the motor nucleus on the same side of the nervous system. Conversely, the axons of the corticospinal tract are largely contralateral, meaning that they cross the midline of the brain stem or spinal cord and synapse on the opposite side of the body. Therefore, the right motor cortex of the cerebrum controls muscles on the left side of the body, and vice versa.

The corticospinal tract descends from the cortex through the deep white matter of the cerebrum. It then passes between the caudate nucleus and putamen of the basal nuclei as a bundle called the internal capsule . The tract then passes through the midbrain as the cerebral peduncles , after which it burrows through the pons. Upon entering the medulla, the tracts make up the large white matter tract referred to as the pyramids ( Figure 14.5.4 ). The defining landmark of the medullary-spinal border is the pyramidal decussation , which is where most of the fibers in the corticospinal tract cross over to the opposite side of the brain. At this point, the tract separates into two parts, which have control over different domains of the musculature.

Appendicular Control

The lateral corticospinal tract is composed of the fibers that cross the midline at the pyramidal decussation (see Figure 14.5.4 ). The axons cross over from the anterior position of the pyramids in the medulla to the lateral column of the spinal cord. These axons are responsible for controlling appendicular muscles.

This influence over the appendicular muscles means that the lateral corticospinal tract is responsible for moving the muscles of the arms and legs. The ventral horn in both the lower cervical spinal cord and the lumbar spinal cord both have wider ventral horns, representing the greater number of muscles controlled by these motor neurons. The cervical enlargement is particularly large because there is greater control over the fine musculature of the upper limbs, particularly of the fingers. The lumbar enlargement is not as significant in appearance because there is less fine motor control of the lower limbs.

Axial Control

The anterior corticospinal tract is responsible for controlling the muscles of the body trunk (see Figure 14.5.4 ). These axons do not decussate in the medulla. Instead, they remain in an anterior position as they descend the brain stem and enter the spinal cord. These axons then travel to the spinal cord level at which they synapse with a lower motor neuron. Upon reaching the appropriate level, the axons decussate, entering the ventral horn on the opposite side of the spinal cord from which they entered. In the ventral horn, these axons synapse with their corresponding lower motor neurons. The lower motor neurons are located in the medial regions of the ventral horn, because they control the axial muscles of the trunk.

Because movements of the body trunk involve both sides of the body, the anterior corticospinal tract is not entirely contralateral. Some collateral branches of the tract will project into the ipsilateral ventral horn to control synergistic muscles on that side of the body, or to inhibit antagonistic muscles through interneurons within the ventral horn. Through the influence of both sides of the body, the anterior corticospinal tract can coordinate postural muscles in broad movements of the body. These coordinating axons in the anterior corticospinal tract are often considered bilateral, as they are both ipsilateral and contralateral.

External Website

Watch this video to learn more about the descending motor pathway for the somatic nervous system. The autonomic connections are mentioned, which are covered in another chapter. From this brief video, only some of the descending motor pathway of the somatic nervous system is described. Which division of the pathway is described and which division is left out?

Extrapyramidal Controls

Other descending connections between the brain and the spinal cord are called the extrapyramidal system . The name comes from the fact that this system is outside the corticospinal pathway, which includes the pyramids in the medulla. A few pathways originating from the brain stem contribute to this system.

The tectospinal tract projects from the midbrain to the spinal cord and is important for postural movements that are driven by the superior colliculus. The name of the tract comes from an alternate name for the superior colliculus, which is the tectum. The reticulospinal tract connects the reticular system, a diffuse region of gray matter in the brain stem, with the spinal cord. This tract influences trunk and proximal limb muscles related to posture and locomotion. The reticulospinal tract also contributes to muscle tone and influences autonomic functions. The vestibulospinal tract connects the brain stem nuclei of the vestibular system with the spinal cord. This allows posture, movement, and balance to be modulated on the basis of equilibrium information provided by the vestibular system.

The pathways of the extrapyramidal system are influenced by subcortical structures. For example, connections between the secondary motor cortices and the extrapyramidal system modulate spine and cranium movements. The basal nuclei, which are important for regulating movement initiated by the CNS, influence the extrapyramidal system as well as its thalamic feedback to the motor cortex.

The conscious movement of our muscles is more complicated than simply sending a single command from the precentral gyrus down to the proper motor neurons. During the movement of any body part, our muscles relay information back to the brain, and the brain is constantly sending “revised” instructions back to the muscles. The cerebellum is important in contributing to the motor system because it compares cerebral motor commands with proprioceptive feedback. The corticospinal fibers that project to the ventral horn of the spinal cord have branches that also synapse in the pons, which project to the cerebellum. Also, the proprioceptive sensations of the dorsal column system have a collateral projection to the medulla that projects to the cerebellum. These two streams of information are compared in the cerebellar cortex. Conflicts between the motor commands sent by the cerebrum and body position information provided by the proprioceptors cause the cerebellum to stimulate the red nucleus of the midbrain. The red nucleus then sends corrective commands to the spinal cord along the rubrospinal tract . The name of this tract comes from the word for red that is seen in the English word “ruby.”

A good example of how the cerebellum corrects cerebral motor commands can be illustrated by walking in water. An original motor command from the cerebrum to walk will result in a highly coordinated set of learned movements. However, in water, the body cannot actually perform a typical walking movement as instructed. The cerebellum can alter the motor command, stimulating the leg muscles to take larger steps to overcome the water resistance. The cerebellum can make the necessary changes through the rubrospinal tract. Modulating the basic command to walk also relies on spinal reflexes, but the cerebellum is responsible for calculating the appropriate response. When the cerebellum does not work properly, coordination and balance are severely affected. The most dramatic example of this is during the overconsumption of alcohol. Alcohol inhibits the ability of the cerebellum to interpret proprioceptive feedback, making it more difficult to coordinate body movements, such as walking a straight line, or guide the movement of the hand to touch the tip of the nose.

Visit this site to read about an elderly woman who starts to lose the ability to control fine movements, such as speech and the movement of limbs. Many of the usual causes were ruled out. It was not a stroke, Parkinson’s disease, diabetes, or thyroid dysfunction. The next most obvious cause was medication, so her pharmacist had to be consulted. The side effect of a drug meant to help her sleep had resulted in changes in motor control. What regions of the nervous system are likely to be the focus of haloperidol side effects?

The Sensory and Motor Exams

Connections between the body and the CNS occur through the spinal cord. The cranial nerves connect the head and neck directly to the brain, but the spinal cord receives sensory input and sends motor commands out to the body through the spinal nerves. Whereas the brain develops into a complex series of nuclei and fiber tracts, the spinal cord remains relatively simple in its configuration ( Figure 14.5.5 ). From the initial neural tube early in embryonic development, the spinal cord retains a tube-like structure with gray matter surrounding the small central canal and white matter on the surface in three columns. The dorsal, or posterior, horns of the gray matter are mainly devoted to sensory functions whereas the ventral, or anterior, and lateral horns are associated with motor functions. In the white matter, the dorsal column relays sensory information to the brain, and the anterior column is almost exclusively relaying motor commands to the ventral horn motor neurons. The lateral column, however, conveys both sensory and motor information between the spinal cord and brain.

Sensory Modalities and Location

The general senses are distributed throughout the body, relying on nervous tissue incorporated into various organs. Somatic senses are incorporated mostly into the skin, muscles, or tendons, whereas the visceral senses come from nervous tissue incorporated into the majority of organs such as the heart or stomach. The somatic senses are those that usually make up the conscious perception of the how the body interacts with the environment. The visceral senses are most often below the limit of conscious perception because they are involved in homeostatic regulation through the autonomic nervous system.

The sensory exam tests the somatic senses, meaning those that are consciously perceived. Testing of the senses begins with examining the regions known as dermatomes that connect to the cortical region where somatosensation is perceived in the postcentral gyrus. To test the sensory fields, a simple stimulus of the light touch of the soft end of a cotton-tipped applicator is applied at various locations on the skin. The spinal nerves, which contain sensory fibers with dendritic endings in the skin, connect with the skin in a topographically organized manner, illustrated as dermatomes ( Figure 14.5.6 ). For example, the fibers of eighth cervical nerve innervate the medial surface of the forearm and extend out to the fingers. In addition to testing perception at different positions on the skin, it is necessary to test sensory perception within the dermatome from distal to proximal locations in the appendages, or lateral to medial locations in the trunk. In testing the eighth cervical nerve, the patient would be asked if the touch of the cotton to the fingers or the medial forearm was perceptible, and whether there were any differences in the sensations.

Other modalities of somatosensation can be tested using a few simple tools. The perception of pain can be tested using the broken end of the cotton-tipped applicator. The perception of vibratory stimuli can be testing using an oscillating tuning fork placed against prominent bone features such as the distal head of the ulna on the medial aspect of the elbow. When the tuning fork is still, the metal against the skin can be perceived as a cold stimulus. Using the cotton tip of the applicator, or even just a fingertip, the perception of tactile movement can be assessed as the stimulus is drawn across the skin for approximately 2–3 cm. The patient would be asked in what direction the stimulus is moving. All of these tests are repeated in distal and proximal locations and for different dermatomes to assess the spatial specificity of perception. The sense of position and motion, proprioception, is tested by moving the fingers or toes and asking the patient if they sense the movement. If the distal locations are not perceived, the test is repeated at increasingly proximal joints.

The various stimuli used to test sensory input assess the function of the major ascending tracts of the spinal cord. The dorsal column pathway conveys fine touch, vibration, and proprioceptive information, whereas the spinothalamic pathway primarily conveys pain and temperature. Testing these stimuli provides information about whether these two major ascending pathways are functioning properly. Within the spinal cord, the two systems are segregated. The dorsal column information ascends ipsilateral to the source of the stimulus and decussates in the medulla, whereas the spinothalamic pathway decussates at the level of entry and ascends contralaterally. The differing sensory stimuli are segregated in the spinal cord so that the various subtests for these stimuli can distinguish which ascending pathway may be damaged in certain situations.

Whereas the basic sensory stimuli are assessed in the subtests directed at each submodality of somatosensation, testing the ability to discriminate sensations is important. Pairing the light touch and pain subtests together makes it possible to compare the two submodalities at the same time, and therefore the two major ascending tracts at the same time. Mistaking painful stimuli for light touch, or vice versa, may point to errors in ascending projections, such as in a hemisection of the spinal cord that might come from a motor vehicle accident.

Another issue of sensory discrimination is not distinguishing between different submodalities, but rather location. The two-point discrimination subtest highlights the density of sensory endings, and therefore receptive fields in the skin. The sensitivity to fine touch, which can give indications of the texture and detailed shape of objects, is highest in the fingertips. To assess the limit of this sensitivity, two-point discrimination is measured by simultaneously touching the skin in two locations, such as could be accomplished with a pair of forceps. Specialized calipers for precisely measuring the distance between points are also available. The patient is asked to indicate whether one or two stimuli are present while keeping their eyes closed. The examiner will switch between using the two points and a single point as the stimulus. Failure to recognize two points may be an indication of a dorsal column pathway deficit.

Similar to two-point discrimination, but assessing laterality of perception, is double simultaneous stimulation. Two stimuli, such as the cotton tips of two applicators, are touched to the same position on both sides of the body. If one side is not perceived, this may indicate damage to the contralateral posterior parietal lobe. Because there is one of each pathway on either side of the spinal cord, they are not likely to interact. If none of the other subtests suggest particular deficits with the pathways, the deficit is likely to be in the cortex where conscious perception is based. The mental status exam contains subtests that assess other functions that are primarily localized to the parietal cortex, such as stereognosis and graphesthesia.

A final subtest of sensory perception that concentrates on the sense of proprioception is known as the Romberg test . The patient is asked to stand straight with feet together. Once the patient has achieved their balance in that position, they are asked to close their eyes. Without visual feedback that the body is in a vertical orientation relative to the surrounding environment, the patient must rely on the proprioceptive stimuli of joint and muscle position, as well as information from the inner ear, to maintain balance. This test can indicate deficits in dorsal column pathway proprioception, as well as problems with proprioceptive projections to the cerebellum through the spinocerebellar tract .

Watch this video to see a quick demonstration of two-point discrimination. Touching a specialized caliper to the surface of the skin will measure the distance between two points that are perceived as distinct stimuli versus a single stimulus. The patient keeps their eyes closed while the examiner switches between using both points of the caliper or just one. The patient then must indicate whether one or two stimuli are in contact with the skin. Why is the distance between the caliper points closer on the fingertips as opposed to the palm of the hand? And what do you think the distance would be on the arm, or the shoulder?

Muscle Strength and Voluntary Movement

The skeletomotor system is largely based on the simple, two-cell projection from the precentral gyrus of the frontal lobe to the skeletal muscles. The corticospinal tract represents the neurons that send output from the primary motor cortex. These fibers travel through the deep white matter of the cerebrum, then through the midbrain and pons, into the medulla where most of them decussate, and finally through the spinal cord white matter in the lateral (crossed fibers) or anterior (uncrossed fibers) columns. These fibers synapse on motor neurons in the ventral horn. The ventral horn motor neurons then project to skeletal muscle and cause contraction. These two cells are termed the upper motor neuron (UMN) and the lower motor neuron (LMN). Voluntary movements require these two cells to be active.

The motor exam tests the function of these neurons and the muscles they control. First, the muscles are inspected and palpated for signs of structural irregularities. Movement disorders may be the result of changes to the muscle tissue, such as scarring, and these possibilities need to be ruled out before testing function. Along with this inspection, muscle tone is assessed by moving the muscles through a passive range of motion. The arm is moved at the elbow and wrist, and the leg is moved at the knee and ankle. Skeletal muscle should have a resting tension representing a slight contraction of the fibers. The lack of muscle tone, known as hypotonicity or flaccidity , may indicate that the LMN is not conducting action potentials that will keep a basal level of acetylcholine in the neuromuscular junction.

If muscle tone is present, muscle strength is tested by having the patient contract muscles against resistance. The examiner will ask the patient to lift the arm, for example, while the examiner is pushing down on it. This is done for both limbs, including shrugging the shoulders. Lateral differences in strength—being able to push against resistance with the right arm but not the left—would indicate a deficit in one corticospinal tract versus the other. An overall loss of strength, without laterality, could indicate a global problem with the motor system. Diseases that result in UMN lesions include cerebral palsy or MS, or it may be the result of a stroke. A sign of UMN lesion is a negative result in the subtest for pronator drift . The patient is asked to extend both arms in front of the body with the palms facing up. While keeping the eyes closed, if the patient unconsciously allows one or the other arm to slowly relax, toward the pronated position, this could indicate a failure of the motor system to maintain the supinated position.

Watch this video to see how to test reflexes in the abdomen. Testing reflexes of the trunk is not commonly performed in the neurological exam, but if findings suggest a problem with the thoracic segments of the spinal cord, a series of superficial reflexes of the abdomen can localize function to those segments. If contraction is not observed when the skin lateral to the umbilicus (belly button) is stimulated, what level of the spinal cord may be damaged?

Comparison of Upper and Lower Motor Neuron Damage

Many of the tests of motor function can indicate differences that will address whether damage to the motor system is in the upper or lower motor neurons. Signs that suggest a UMN lesion include muscle weakness, strong deep tendon reflexes, decreased control of movement or slowness, pronator drift, a positive Babinski sign, spasticity , and the clasp-knife response . Spasticity is an excess contraction in resistance to stretch. It can result in hyperflexia , which is when joints are overly flexed. The clasp-knife response occurs when the patient initially resists movement, but then releases, and the joint will quickly flex like a pocket knife closing.

A lesion on the LMN would result in paralysis, or at least partial loss of voluntary muscle control, which is known as paresis . The paralysis observed in LMN diseases is referred to as flaccid paralysis , referring to a complete or partial loss of muscle tone, in contrast to the loss of control in UMN lesions in which tone is retained and spasticity is exhibited. Other signs of an LMN lesion are fibrillation , fasciculation , and compromised or lost reflexes resulting from the denervation of the muscle fibers.

Disorders of the…Spinal Cord

In certain situations, such as a motorcycle accident, only half of the spinal cord may be damaged in what is known as a hemisection. Forceful trauma to the trunk may cause ribs or vertebrae to fracture, and debris can crush or section through part of the spinal cord. The full section of a spinal cord would result in paraplegia, or loss of voluntary motor control of the lower body, as well as loss of sensations from that point down. A hemisection, however, will leave spinal cord tracts intact on one side. The resulting condition would be hemiplegia on the side of the trauma—one leg would be paralyzed. The sensory results are more complicated.

The ascending tracts in the spinal cord are segregated between the dorsal column and spinothalamic pathways. This means that the sensory deficits will be based on the particular sensory information each pathway conveys. Sensory discrimination between touch and painful stimuli will illustrate the difference in how these pathways divide these functions.

On the paralyzed leg, a patient will acknowledge painful stimuli, but not fine touch or proprioceptive sensations. On the functional leg, the opposite is true. The reason for this is that the dorsal column pathway ascends ipsilateral to the sensation, so it would be damaged the same way as the lateral corticospinal tract. The spinothalamic pathway decussates immediately upon entering the spinal cord and ascends contralateral to the source; it would therefore bypass the hemisection.

The motor system can indicate the loss of input to the ventral horn in the lumbar enlargement where motor neurons to the leg are found, but motor function in the trunk is less clear. The left and right anterior corticospinal tracts are directly adjacent to each other. The likelihood of trauma to the spinal cord resulting in a hemisection that affects one anterior column, but not the other, is very unlikely. Either the axial musculature will not be affected at all, or there will be bilateral losses in the trunk.

Sensory discrimination can pinpoint the level of damage in the spinal cord. Below the hemisection, pain stimuli will be perceived in the damaged side, but not fine touch. The opposite is true on the other side. The pain fibers on the side with motor function cross the midline in the spinal cord and ascend in the contralateral lateral column as far as the hemisection. The dorsal column will be intact ipsilateral to the source on the intact side and reach the brain for conscious perception. The trauma would be at the level just before sensory discrimination returns to normal, helping to pinpoint the trauma. Whereas imaging technology, like magnetic resonance imaging (MRI) or computed tomography (CT) scanning, could localize the injury as well, nothing more complicated than a cotton-tipped applicator can localize the damage. That may be all that is available on the scene when moving the victim requires crucial decisions be made.

The Coordination and Gait Exams

Location and connections of the cerebellum.

The cerebellum is located in apposition to the dorsal surface of the brain stem, centered on the pons. The name of the pons is derived from its connection to the cerebellum. The word means “bridge” and refers to the thick bundle of myelinated axons that form a bulge on its ventral surface. Those fibers are axons that project from the gray matter of the pons into the contralateral cerebellar cortex. These fibers make up the middle cerebellar peduncle (MCP) and are the major physical connection of the cerebellum to the brain stem ( Figure 14.5.7 ). Two other white matter bundles connect the cerebellum to the other regions of the brain stem. The superior cerebellar peduncle (SCP) is the connection of the cerebellum to the midbrain and forebrain. The inferior cerebellar peduncle (ICP) is the connection to the medulla.

These connections can also be broadly described by their functions. The ICP conveys sensory input to the cerebellum, partially from the spinocerebellar tract, but also through fibers of the inferior olive . The MCP is part of the cortico-ponto-cerebellar pathway that connects the cerebral cortex with the cerebellum and preferentially targets the lateral regions of the cerebellum. It includes a copy of the motor commands sent from the precentral gyrus through the corticospinal tract, arising from collateral branches that synapse in the gray matter of the pons, along with input from other regions such as the visual cortex. The SCP is the major output of the cerebellum, divided between the red nucleus in the midbrain and the thalamus, which will return cerebellar processing to the motor cortex. These connections describe a circuit that compares motor commands and sensory feedback to generate a new output. These comparisons make it possible to coordinate movements. If the cerebral cortex sends a motor command to initiate walking, that command is copied by the pons and sent into the cerebellum through the MCP. Sensory feedback in the form of proprioception from the spinal cord, as well as vestibular sensations from the inner ear, enters through the ICP. If you take a step and begin to slip on the floor because it is wet, the output from the cerebellum—through the SCP—can correct for that and keep you balanced and moving. The red nucleus sends new motor commands to the spinal cord through the rubrospinal tract .

The cerebellum is divided into regions that are based on the particular functions and connections involved. The midline regions of the cerebellum, the vermis and flocculonodular lobe , are involved in comparing visual information, equilibrium, and proprioceptive feedback to maintain balance and coordinate movements such as walking, or gait , through the descending output of the red nucleus ( Figure 15.5.8 ). The lateral hemispheres are primarily concerned with planning motor functions through frontal lobe inputs that are returned through the thalamic projections back to the premotor and motor cortices. Processing in the midline regions targets movements of the axial musculature, whereas the lateral regions target movements of the appendicular musculature. The vermis is referred to as the spinocerebellum because it primarily receives input from the dorsal columns and spinocerebellar pathways. The flocculonodular lobe is referred to as the vestibulocerebellum because of the vestibular projection into that region. Finally, the lateral cerebellum is referred to as the cerebrocerebellum , reflecting the significant input from the cerebral cortex through the cortico-ponto-cerebellar pathway.

Coordination and Alternating Movement

Testing for cerebellar function is the basis of the coordination exam. The subtests target appendicular musculature, controlling the limbs, and axial musculature for posture and gait. The assessment of cerebellar function will depend on the normal functioning of other systems addressed in previous sections of the neurological exam. Motor control from the cerebrum, as well as sensory input from somatic, visual, and vestibular senses, are important to cerebellar function.

The subtests that address appendicular musculature, and therefore the lateral regions of the cerebellum, begin with a check for tremor. The patient extends their arms in front of them and holds the position. The examiner watches for the presence of tremors that would not be present if the muscles are relaxed. By pushing down on the arms in this position, the examiner can check for the rebound response, which is when the arms are automatically brought back to the extended position. The extension of the arms is an ongoing motor process, and the tap or push on the arms presents a change in the proprioceptive feedback. The cerebellum compares the cerebral motor command with the proprioceptive feedback and adjusts the descending input to correct. The red nucleus would send an additional signal to the LMN for the arm to increase contraction momentarily to overcome the change and regain the original position.

The check reflex depends on cerebellar input to keep increased contraction from continuing after the removal of resistance. The patient flexes the elbow against resistance from the examiner to extend the elbow. When the examiner releases the arm, the patient should be able to stop the increased contraction and keep the arm from moving. A similar response would be seen if you try to pick up a coffee mug that you believe to be full but turns out to be empty. Without checking the contraction, the mug would be thrown from the overexertion of the muscles expecting to lift a heavier object.

Several subtests of the cerebellum assess the ability to alternate movements, or switch between muscle groups that may be antagonistic to each other. In the finger-to-nose test, the patient touches their finger to the examiner’s finger and then to their nose, and then back to the examiner’s finger, and back to the nose. The examiner moves the target finger to assess a range of movements. A similar test for the lower extremities has the patient touch their toe to a moving target, such as the examiner’s finger. Both of these tests involve flexion and extension around a joint—the elbow or the knee and the shoulder or hip—as well as movements of the wrist and ankle. The patient must switch between the opposing muscles, like the biceps and triceps brachii, to move their finger from the target to their nose. Coordinating these movements involves the motor cortex communicating with the cerebellum through the pons and feedback through the thalamus to plan the movements. Visual cortex information is also part of the processing that occurs in the cerebrocerebellum while it is involved in guiding movements of the finger or toe.

Rapid, alternating movements are tested for the upper and lower extremities. The patient is asked to touch each finger to their thumb, or to pat the palm of one hand on the back of the other, and then flip that hand over and alternate back-and-forth. To test similar function in the lower extremities, the patient touches their heel to their shin near the knee and slides it down toward the ankle, and then back again, repetitively. Rapid, alternating movements are part of speech as well. A patient is asked to repeat the nonsense consonants “lah-kah-pah” to alternate movements of the tongue, lips, and palate. All of these rapid alternations require planning from the cerebrocerebellum to coordinate movement commands that control the coordination.

Posture and Gait

Gait can either be considered a separate part of the neurological exam or a subtest of the coordination exam that addresses walking and balance. Testing posture and gait addresses functions of the spinocerebellum and the vestibulocerebellum because both are part of these activities. A subtest called station begins with the patient standing in a normal position to check for the placement of the feet and balance. The patient is asked to hop on one foot to assess the ability to maintain balance and posture during movement. Though the station subtest appears to be similar to the Romberg test, the difference is that the patient’s eyes are open during station. The Romberg test has the patient stand still with the eyes closed. Any changes in posture would be the result of proprioceptive deficits, and the patient is able to recover when they open their eyes.

Subtests of walking begin with having the patient walk normally for a distance away from the examiner, and then turn and return to the starting position. The examiner watches for abnormal placement of the feet and the movement of the arms relative to the movement. The patient is then asked to walk with a few different variations. Tandem gait is when the patient places the heel of one foot against the toe of the other foot and walks in a straight line in that manner. Walking only on the heels or only on the toes will test additional aspects of balance.

A movement disorder of the cerebellum is referred to as ataxia . It presents as a loss of coordination in voluntary movements. Ataxia can also refer to sensory deficits that cause balance problems, primarily in proprioception and equilibrium. When the problem is observed in movement, it is ascribed to cerebellar damage. Sensory and vestibular ataxia would likely also present with problems in gait and station.

Ataxia is often the result of exposure to exogenous substances, focal lesions, or a genetic disorder. Focal lesions include strokes affecting the cerebellar arteries, tumors that may impinge on the cerebellum, trauma to the back of the head and neck, or MS. Alcohol intoxication or drugs such as ketamine cause ataxia, but it is often reversible. Mercury in fish can cause ataxia as well. Hereditary conditions can lead to degeneration of the cerebellum or spinal cord, as well as malformation of the brain, or the abnormal accumulation of copper seen in Wilson’s disease.

Watch this short video to see a test for station. Station refers to the position a person adopts when they are standing still. The examiner would look for issues with balance, which coordinates proprioceptive, vestibular, and visual information in the cerebellum. To test the ability of a subject to maintain balance, asking them to stand or hop on one foot can be more demanding. The examiner may also push the subject to see if they can maintain balance. An abnormal finding in the test of station is if the feet are placed far apart. Why would a wide stance suggest problems with cerebellar function?

Everyday Connections –  The Field Sobriety Test

The neurological exam has been described as a clinical tool throughout this chapter. It is also useful in other ways. A variation of the coordination exam is the Field Sobriety Test (FST) used to assess whether drivers are under the influence of alcohol. The cerebellum is crucial for coordinated movements such as keeping balance while walking, or moving appendicular musculature on the basis of proprioceptive feedback. The cerebellum is also very sensitive to ethanol, the particular type of alcohol found in beer, wine, and liquor.

Walking in a straight line involves comparing the motor command from the primary motor cortex to the proprioceptive and vestibular sensory feedback, as well as following the visual guide of the white line on the side of the road. When the cerebellum is compromised by alcohol, the cerebellum cannot coordinate these movements effectively, and maintaining balance becomes difficult.

Another common aspect of the FST is to have the driver extend their arms out wide and touch their fingertip to their nose, usually with their eyes closed. The point of this is to remove the visual feedback for the movement and force the driver to rely just on proprioceptive information about the movement and position of their fingertip relative to their nose. With eyes open, the corrections to the movement of the arm might be so small as to be hard to see, but proprioceptive feedback is not as immediate and broader movements of the arm will probably be needed, particularly if the cerebellum is affected by alcohol.

Reciting the alphabet backwards is not always a component of the FST, but its relationship to neurological function is interesting. There is a cognitive aspect to remembering how the alphabet goes and how to recite it backwards. That is actually a variation of the mental status subtest of repeating the months backwards. However, the cerebellum is important because speech production is a coordinated activity. The speech rapid alternating movement subtest is specifically using the consonant changes of “lah-kah-pah” to assess coordinated movements of the lips, tongue, pharynx, and palate. But the entire alphabet, especially in the nonrehearsed backwards order, pushes this type of coordinated movement quite far. It is related to the reason that speech becomes slurred when a person is intoxicated.

Chapter Review

Sensory input to the brain enters through pathways that travel through either the spinal cord (for somatosensory input from the body) or the brain stem (for everything else, except the visual and olfactory systems) to reach the diencephalon. In the diencephalon, sensory pathways reach the thalamus. This is necessary for all sensory systems to reach the cerebral cortex, except for the olfactory system that is directly connected to the frontal and temporal lobes.

The two major tracts in the spinal cord, originating from sensory neurons in the dorsal root ganglia, are the dorsal column system and the spinothalamic tract. The major differences between the two are in the type of information that is relayed to the brain and where the tracts decussate. The dorsal column system primarily carries information about touch and proprioception and crosses the midline in the medulla. The spinothalamic tract is primarily responsible for pain and temperature sensation and crosses the midline in the spinal cord at the level at which it enters. The trigeminal nerve adds similar sensation information from the head to these pathways.

The motor components of the somatic nervous system begin with the frontal lobe of the brain, where the prefrontal cortex is responsible for higher functions such as working memory. The integrative and associate functions of the prefrontal lobe feed into the secondary motor areas, which help plan movements. The premotor cortex and supplemental motor area then feed into the primary motor cortex that initiates movements. Large Betz cells project through the corticobulbar and corticospinal tracts to synapse on lower motor neurons in the brain stem and ventral horn of the spinal cord, respectively. These connections are responsible for generating movements of skeletal muscles.

The extrapyramidal system includes projections from the brain stem and higher centers that influence movement, mostly to maintain balance and posture, as well as to maintain muscle tone. The superior colliculus and red nucleus in the midbrain, the vestibular nuclei in the medulla, and the reticular formation throughout the brain stem each have tracts projecting to the spinal cord in this system. Descending input from the secondary motor cortices, basal nuclei, and cerebellum connect to the origins of these tracts in the brain stem.

All of these motor pathways project to the spinal cord to synapse with motor neurons in the ventral horn of the spinal cord. These lower motor neurons are the cells that connect to skeletal muscle and cause contractions. These neurons project through the spinal nerves to connect to the muscles at neuromuscular junctions. One motor neuron connects to multiple muscle fibers within a target muscle. The number of fibers that are innervated by a single motor neuron varies on the basis of the precision necessary for that muscle and the amount of force necessary for that motor unit. The quadriceps, for example, have many fibers controlled by single motor neurons for powerful contractions that do not need to be precise. The extraocular muscles have only a small number of fibers controlled by each motor neuron because moving the eyes does not require much force, but needs to be very precise.

Reflexes are the simplest circuits within the somatic nervous system. A withdrawal reflex from a painful stimulus only requires the sensory fiber that enters the spinal cord and the motor neuron that projects to a muscle. Antagonist and postural muscles can be coordinated with the withdrawal, making the connections more complex. The simple, single neuronal connection is the basis of somatic reflexes. The corneal reflex is contraction of the orbicularis oculi muscle to blink the eyelid when something touches the surface of the eye. Stretch reflexes maintain a constant length of muscles by causing a contraction of a muscle to compensate for a stretch that can be sensed by a specialized receptor called a muscle spindle.

Interactive Link Questions

The video only describes the lateral division of the corticospinal tract. The anterior division is omitted.

The movement disorders were similar to those seen in movement disorders of the extrapyramidal system, which would mean the basal nuclei are the most likely source of haloperidol side effects. In fact, haloperidol affects dopamine activity, which is a prominent part of the chemistry of the basal nuclei.

Watch this video to learn more about the reflex arc of the corneal reflex. When the right cornea senses a tactile stimulus, what happens to the left eye? Explain your answer.

The left eye also blinks. The sensory input from one eye activates the motor response of both eyes so that they both blink.

Watch this video to learn more about newborn reflexes. Newborns have a set of reflexes that are expected to have been crucial to survival before the modern age. These reflexes disappear as the baby grows, as some of them may be unnecessary as they age. The video demonstrates a reflex called the Babinski reflex, in which the foot flexes dorsally and the toes splay out when the sole of the foot is lightly scratched. This is normal for newborns, but it is a sign of reduced myelination of the spinal tract in adults. Why would this reflex be a problem for an adult?

While walking, the sole of the foot may be scraped or scratched by many things. If the foot still reacted as in the Babinski reflex, an adult might lose their balance while walking.

Glossary (sensory)

Glossary (motor).

This work, Anatomy & Physiology, is adapted from Anatomy & Physiology by OpenStax , licensed under CC BY . This edition, with revised content and artwork, is licensed under CC BY-SA except where otherwise noted.

Images, from Anatomy & Physiology by OpenStax , are licensed under CC BY except where otherwise noted.

Access the original for free at https://openstax.org/books/anatomy-and-physiology/pages/1-introduction .

Anatomy & Physiology Copyright © 2019 by Lindsay M. Biga, Staci Bronson, Sierra Dawson, Amy Harwell, Robin Hopkins, Joel Kaufmann, Mike LeMaster, Philip Matern, Katie Morrison-Graham, Kristen Oja, Devon Quick, Jon Runyeon, OSU OERU, and OpenStax is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License , except where otherwise noted.

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10.5E: Mapping the Primary Somatosensory Area

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The cortical sensory homunculus is located in the postcentral gyrus and provides a representation of the body to the brain.

Learning Objectives

• Describe how primary somatosensory areas can be mapped
• A sensory homunculus is a pictorial representation of the primary somatosensory cortex.
• Somatotopy is the correspondence of an area of the body to a specific point in the brain.
• Wilder Penfield was a researcher and surgeon who created maps of the somatosensory cortex.
• somesthetic cortex : The primary mechanism of cortical processing for sensory information originating at body surfaces and other tissues (eg., muscles, joints).
• postcentral gyrus : A prominent structure in the parietal lobe of the human brain that is the location of the primary somatosensory cortex, the main sensory receptive area for the sense of touch.
• precentral gyrus : The precentral gyrus lies in front of the postcentral gyrus and is the site of the primary motor cortex (Brodmann area 4).

Cortical Homunculus

A cortical homunculus is a pictorial representation of the anatomical divisions of the primary motor cortex and the primary somatosensory cortex; it is the portion of the human brain directly responsible for the movement and exchange of sensory and motor information of the body.

It is a visual representation of the concept of the body within the brain—that one’s hand or face exists as much as a series of nerve structures or a neuron concept as it does in a physical form. There are two types of homunculus: sensory and motor. Each one shows a representation of how much of its respective cortex innervates certain body parts.

The primary somesthetic cortex (sensory) pertains to the signals within the postcentral gyrus coming from the thalamus, and the primary motor cortex pertains to signals within the precentral gyrus coming from the premotor area of the frontal lobes.

These are then transmitted from the gyri to the brain stem and spinal cord via corresponding sensory or motor nerves. The reason for the distorted appearance of the homunculus is that the amount of cerebral tissue or cortex devoted to a given body region is proportional to how richly innervated that region is, not to its size.

The homunculus is like an upside-down sensory or motor map of the contralateral side of the body. The upper extremities such as the facial body parts and hands are closer to the lateral sulcus than lower extremities such as the leg and toes.

Homunculus : The idea of the cortical homunculus was created by Wilder Penfield and serves as a rough map of the receptive fields for regions of primary somatosensory cortex.

The resulting image is a grotesquely disfigured human with disproportionately huge hands, lips, and face in comparison to the rest of the body. Because of the fine motor skills and sense nerves found in these particular parts of the body, they are represented as being larger on the homunculus. A part of the body with fewer sensory and/or motor connections to the brain is represented to appear smaller.

Postcentral gyrus : The postcentral gyrus is located in the parietal lobe of the human cortex and is the primary somatosensory region of the human brain.

This is the point-for-point correspondence of an area of the body to a specific point on the central nervous system. Typically, the area of the body corresponds to a point on the primary somatosensory cortex (postcentral gyrus).

This cortex is typically represented as a sensory homunculus which orients the specific body parts and their respective locations upon the homunculus. Areas such as the appendages, digits, and face can draw their sensory locations upon the somatosensory cortex.

Areas that are finely controlled, such as the digits, have larger portions of the somatosensory cortex, whereas areas that are coarsely controlled, such as the trunk, have smaller portions. Areas such as the viscera do not have sensory locations on the postcentral gyrus.

Montreal Procedure

Wilder Penfield was a groundbreaking researcher and highly original surgeon. With his colleague, Herbert Jasper, he invented the Montreal procedure, in which he treated patients with severe epilepsy by destroying nerve cells in the brain where the seizures originated.

Before operating, he stimulated the brain with electrical probes while the patients were conscious on the operating table (under only local anesthesia), and observed their responses. In this way he could more accurately target the areas of the brain responsible, reducing the side-effects of the surgery.

This technique also allowed him to create maps of the sensory and motor cortices of the brain, showing their connections to the various limbs and organs of the body. These maps are still used today, practically unaltered.

Along with Herbert Jasper, he published this landmark work in 1951 as Epilepsy and the Functional Anatomy of the Human Brain. This work contributed a great deal to understanding the lateralization of brain function.

Penfield’s maps showed considerable overlap between regions (for instance, the motor region controlling muscles in the hand sometimes also controlled muscles in the upper arm and shoulder), a feature that he put down to individual variation in brain size and localization; we now know that this is due to the fractured somatotropy of the motor cortex.

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• Published: 13 May 2024

Representation of internal speech by single neurons in human supramarginal gyrus

• Sarah K. Wandelt   ORCID: orcid.org/0000-0001-9551-8491 1 , 2 ,
• David A. Bjånes 1 , 2 , 3 ,
• Kelsie Pejsa 1 , 2 ,
• Brian Lee 1 , 4 , 5 ,
• Charles Liu   ORCID: orcid.org/0000-0001-6423-8577 1 , 3 , 4 , 5 &
• Richard A. Andersen 1 , 2  

Nature Human Behaviour ( 2024 ) Cite this article

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• Brain–machine interface
• Neural decoding

Speech brain–machine interfaces (BMIs) translate brain signals into words or audio outputs, enabling communication for people having lost their speech abilities due to diseases or injury. While important advances in vocalized, attempted and mimed speech decoding have been achieved, results for internal speech decoding are sparse and have yet to achieve high functionality. Notably, it is still unclear from which brain areas internal speech can be decoded. Here two participants with tetraplegia with implanted microelectrode arrays located in the supramarginal gyrus (SMG) and primary somatosensory cortex (S1) performed internal and vocalized speech of six words and two pseudowords. In both participants, we found significant neural representation of internal and vocalized speech, at the single neuron and population level in the SMG. From recorded population activity in the SMG, the internally spoken and vocalized words were significantly decodable. In an offline analysis, we achieved average decoding accuracies of 55% and 24% for each participant, respectively (chance level 12.5%), and during an online internal speech BMI task, we averaged 79% and 23% accuracy, respectively. Evidence of shared neural representations between internal speech, word reading and vocalized speech processes was found in participant 1. SMG represented words as well as pseudowords, providing evidence for phonetic encoding. Furthermore, our decoder achieved high classification with multiple internal speech strategies (auditory imagination/visual imagination). Activity in S1 was modulated by vocalized but not internal speech in both participants, suggesting no articulator movements of the vocal tract occurred during internal speech production. This work represents a proof-of-concept for a high-performance internal speech BMI.

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Online speech synthesis using a chronically implanted brain–computer interface in an individual with ALS

A high-performance speech neuroprosthesis

The speech neuroprosthesis

Speech is one of the most basic forms of human communication, a natural and intuitive way for humans to express their thoughts and desires. Neurological diseases like amyotrophic lateral sclerosis (ALS) and brain lesions can lead to the loss of this ability. In the most severe cases, patients who experience full-body paralysis might be left without any means of communication. Patients with ALS self-report loss of speech as their most serious concern 1 . Brain–machine interfaces (BMIs) are devices offering a promising technological path to bypass neurological impairment by recording neural activity directly from the cortex. Cognitive BMIs have demonstrated potential to restore independence to participants with tetraplegia by reading out movement intent directly from the brain 2 , 3 , 4 , 5 . Similarly, reading out internal (also reported as inner, imagined or covert) speech signals could allow the restoration of communication to people who have lost it.

Decoding speech signals directly from the brain presents its own unique challenges. While non-invasive recording methods such as functional magnetic resonance imaging (fMRI), electroencephalography (EEG) or magnetoencephalography 6 are important tools to locate speech and internal speech production, they lack the necessary temporal and spatial resolution, adequate signal-to-noise ratio or portability for building an online speech BMI 7 , 8 , 9 . For example, state-of-the-art EEG-based imagined speech decoding performances in 2022 ranged from approximately 60% to 80% binary classification 10 . Intracortical electrophysiological recordings have higher signal-to-noise ratios and excellent temporal resolution 11 and are a more suitable choice for an internal speech decoding device.

Invasive speech decoding has predominantly been attempted with electrocorticography (ECoG) 9 or stereo-electroencephalographic depth arrays 12 , as they allow sampling neural activity from different parts of the brain simultaneously. Impressive results in vocalized and attempted speech decoding and reconstruction have been achieved using these techniques 13 , 14 , 15 , 16 , 17 , 18 . However, vocalized speech has also been decoded from localized regions of the cortex. In 2009, the use of a neurotrophic electrode 19 demonstrated real-time speech synthesis from the motor cortex. More recently, speech neuroprosthetics were built from small-scale microelectrode arrays located in the motor cortex 20 , 21 , premotor cortex 22 and supramarginal gyrus (SMG) 23 , demonstrating that vocalized speech BMIs can be built using neural signals from localized regions of cortex.

While important advances in vocalized speech 16 , attempted speech 18 and mimed speech 17 , 22 , 24 , 25 , 26 decoding have been made, highly accurate internal speech decoding has not been achieved. Lack of behavioural output, lower signal-to-noise ratio and differences in cortical activations compared with vocalized speech are speculated to contribute to lower classification accuracies of internal speech 7 , 8 , 13 , 27 , 28 . In ref. 29 , patients implanted with ECoG grids over frontal, parietal and temporal regions silently read or vocalized written words from a screen. They significantly decoded vowels (37.5%) and consonants (36.3%) from internal speech (chance level 25%). Ikeda et al. 30 decoded three internally spoken vowels using ECoG arrays using frequencies in the beta band, with up to 55.6% accuracy from the Broca area (chance level 33%). Using the same recording technology, ref. 31 investigated the decoding of six words during internal speech. The authors demonstrated an average pair-wise classification accuracy of 58%, reaching 88% for the highest pair (chance level 50%). These studies were so-called open-loop experiments, in which the data were analysed offline after acquisition. A recent paper demonstrated real-time (closed-loop) speech decoding using stereotactic depth electrodes 32 . The results were encouraging as internal speech could be detected; however, the reconstructed audio was not discernable and required audible speech to train the decoding model.

While, to our knowledge, internal speech has not previously been decoded from SMG, evidence for internal speech representation in the SMG exists. A review of 100 fMRI studies 33 not only described SMG activity during speech production but also suggested its involvement in subvocal speech 34 , 35 . Similarly, an ECoG study identified high-frequency SMG modulation during vocalized and internal speech 36 . Additionally, fMRI studies have demonstrated SMG involvement in phonologic processing, for instance, during tasks while participants reported whether two words rhyme 37 . Performing such tasks requires the participant to internally ‘hear’ the word, indicating potential internal speech representation 38 . Furthermore, a study performed in people suffering from aphasia found that lesions in the SMG and its adjacent white matter affected inner speech rhyming tasks 39 . Recently, ref. 16 showed that electrode grids over SMG contributed to vocalized speech decoding. Finally, vocalized grasps and colour words were decodable from SMG from one of the same participants involved in this work 23 . These studies provide evidence for the possibility of an internal speech decoder from neural activity in the SMG.

The relationship between inner speech and vocalized speech is still debated. The general consensus posits similarities between internal and vocalized speech processes 36 , but the degree of overlap is not well understood 8 , 35 , 40 , 41 , 42 . Characterizing similarities between vocalized and internal speech could provide evidence that results found with vocalized speech could translate to internal speech. However, such a relationship may not be guaranteed. For instance, some brain areas involved in vocalized speech might be poor candidates for internal speech decoding.

In this Article, two participants with tetraplegia performed internal and vocalized speech of eight words while neurophysiological responses were captured from two implant sites. To investigate neural semantic and phonetic representation, the words were composed of six lexical words and two pseudowords (words that mimic real words without semantic meaning). We examined representations of various language processes at the single-neuron level using recording microelectrode arrays from the SMG located in the posterior parietal cortex (PPC) and the arm and/or hand regions of the primary somatosensory cortex (S1). S1 served as a control for movement, due to emerging evidence of its activation beyond defined regions of interest 43 , 44 . Words were presented with an auditory or a written cue and were produced internally as well as orally. We hypothesized that SMG and S1 activity would modulate during vocalized speech and that SMG activity would modulate during internal speech. Shared representation between internal speech, vocalized speech, auditory comprehension and word reading processes was investigated.

Task design

We characterized neural representations of four different language processes within a population of SMG and S1 neurons: auditory comprehension, word reading, internal speech and vocalized speech production. In this manuscript, internal speech refers to engaging a prompted word internally (‘inner monologue’), without correlated motor output, while vocalized speech refers to audibly vocalizing a prompted word. Participants were implanted in the SMG and S1 on the basis of grasp localization fMRI tasks (Fig. 1 ).

a , b , SMG implant locations in participant 1 (1 × 96 multielectrode array) ( a ) and participant 2 (1 × 64 multielectrode array) ( b ). c , d , S1 implant locations in participant 1 (2 × 96 multielectrode arrays) ( c ) and participant 2 (2 × 64 multielectrode arrays) ( d ).

The task contained six phases: an inter-trial interval (ITI), a cue phase (cue), a first delay (D1), an internal speech phase (internal), a second delay (D2) and a vocalized speech phase (speech). Words were cued with either an auditory or a written version of the word (Fig. 2a ). Six of the words were informed by ref. 31 (battlefield, cowboy, python, spoon, swimming and telephone). Two pseudowords (nifzig and bindip) were added to explore phonetic representation in the SMG. The first participant completed ten session days, composed of both the auditory and the written cue tasks. The second participant completed nine sessions, focusing only on the written cue task. The participants were instructed to internally say the cued word during the internal speech phase and to vocalize the same word during the speech phase.

a , Written words and sounds were used to cue six words and two pseudowords in a participant with tetraplegia. The ‘audio cue’ task was composed of an ITI, a cue phase during which the sound of one of the words was emitted from a speaker (between 842 and 1,130 ms), a first delay (D1), an internal speech phase, a second delay (D2) and a vocalized speech phase. The ‘written cue’ task was identical to the ‘audio cue’ task, except that written words appeared on the screen for 1.5 s. Eight repetitions of eight words were performed per session day and per task for the first participant. For the second participant, 16 repetitions of eight words were performed for the written cue task. b – e , Example smoothed firing rates of neurons tuned to four words in the SMG for participant 1 (auditory cue, python ( b ), and written cue, telephone ( c )) and participant 2 (written cue, nifzig ( d ), and written cue, spoon ( e )). Top: the average firing rate over 8 or 16 trials (solid line, mean; shaded area, 95% bootstrapped confidence interval). Bottom: one example trial with associated audio amplitude (grey). Vertically dashed lines indicate the beginning of each phase. Single neurons modulate firing rate during internal speech in the SMG.

For each of the four language processes, we observed selective modulation of individual neurons’ firing rates (Fig. 2b–e ). In general, the firing rates of neurons increased during the active phases (cue, internal and speech) and decreased during the rest phases (ITI, D1 and D2). A variety of activation patterns were present in the neural population. Example neurons were selected to demonstrate increases in firing rates during internal speech, cue and vocalized speech. Both the auditory (Fig. 2b ) and the written cue (Fig. 2c–e ) evoked highly modulated firing rates of individual neurons during internal speech.

These stereotypical activation patterns were evident at the single-trial level (Fig. 2b–e , bottom). When the auditory recording was overlaid with firing rates from a single trial, a heterogeneous neural response was observed (Supplementary Fig. 1a ), with some SMG neurons preceding or lagging peak auditory levels during vocalized speech. In contrast, neural activity from primary sensory cortex (S1) only modulated during vocalized speech and produced similar firing patterns regardless of the vocalized word (Supplementary Fig. 1b ).

Population activity represented selective tuning for individual words

Population analysis in the SMG mirrored single-neuron patterns of activation, showing increases in tuning during the active task phases (Fig. 3a,d ). Tuning of a neuron to a word was determined by fitting a linear regression model to the firing rate in 50-ms time bins ( Methods ). Distinctions between participant 1 and participant 2 were observed. Specifically, participant 1 exhibited strong tuning, whereas the number of tuned units was notably lower in participant 2. Based on these findings, we exclusively ran the written cue task with participant number 2. In participant 1, representation of the auditory cue was lower compared with the written cue (Fig. 3b , cue). However, this difference was not observed for other task phases. In both participants, the tuned population activity in S1 increased during vocalized speech but not during the cue and internal speech phases (Supplementary Fig. 3a,b ).

a , The average percentage of tuned neurons to words in 50-ms time bins in the SMG over the trial duration for ‘auditory cue’ (blue) and ‘written cue’ (green) tasks for participant 1 (solid line, mean over ten sessions; shaded area, 95% confidence interval of the mean). During the cue phase of auditory trials, neural data were aligned to audio onset, which occurred within 200–650 ms following initiation of the cue phase. b , The average percentage of tuned neurons computed on firing rates per task phase, with 95% confidence interval over ten sessions. Tuning during action phases (cue, internal and speech) following rest phases (ITI, D1 and D2) was significantly higher (paired two-tailed t -test, d.f. 9, P ITI_CueWritten  < 0.001, Cohen’s d  = 2.31; P ITI_CueAuditory  = 0.003, Cohen’s d  = 1.25; P D1_InternalWritten  = 0.008, Cohen’s d  = 1.08; P D1_InternalAuditory  < 0.001, Cohen’s d  = 1.71; P D2_SpeechWritten  < 0.001, Cohen’s d  = 2.34; P D2_SpeechAuditory  < 0.001, Cohen’s d  = 3.23). c , The number of neurons tuned to each individual word in each phase for the ‘auditory cue’ and ‘written cue’ tasks. d , The average percentage of tuned neurons to words in 50-ms time bins in the SMG over the trial duration for ‘written cue’ (green) tasks for participant 2 (solid line, mean over nine sessions; shaded area, 95% confidence interval of the mean). Due to a reduced number of tuned units, only the ‘written cue’ task variation was performed. e , The average percentage of tuned neurons computed on firing rates per task phase, with 95% confidence interval over nine sessions. Tuning during cue and internal phases following rest phases ITI and D1 was significantly higher (paired two-tailed t -test, d.f. 8, P ITI_CueWritten  = 0.003, Cohen’s d  = 1.38; P D1_Internal  = 0.001, Cohen’s d  = 1.67). f , The number of neurons tuned to each individual word in each phase for the ‘written cue’ task.

Source data

To quantitatively compare activity between phases, we assessed the differential response patterns for individual words by examining the variations in average firing rate across different task phases (Fig. 3b,e ). In both participants, tuning during the cue and internal speech phases was significantly higher compared with their preceding rest phases ITI and D1 (paired t -test between phases. Participant 1: d.f. 9, P ITI_CueWritten  < 0.001, Cohen’s d  = 2.31; P ITI_CueAuditory  = 0.003, Cohen’s d  = 1.25; P D1_InternalWritten  = 0.008, Cohen’s d  = 1.08; P D1_InternalAuditory  < 0.001, Cohen’s d  = 1.71. Participant 2: d.f. 8, P ITI_CueWritten  = 0.003, Cohen’s d  = 1.38; P D1_Internal  = 0.001, Cohen’s d  = 1.67). For participant 1, we also observed significantly higher tuning to vocalized speech than to tuning in D2 (d.f. 9, P D2_SpeechWritten  < 0.001, Cohen’s d  = 2.34; P D2_SpeechAuditory  < 0.001, Cohen’s d  = 3.23). Representation for all words was observed in each phase, including pseudowords (bindip and nifzig) (Fig. 3c,f ). To identify neurons with selective activity for unique words, we performed a Kruskal–Wallis test (Supplementary Fig. 3c,d ). The results mirrored findings of the regression analysis in both participants, albeit weaker in participant 2. These findings suggest that, while neural activity during active phases differed from activity during the ITI phase, neural responses of only a few neurons varied across different words for participant 2.

The neural population in the SMG simultaneously represented several distinct aspects of language processing: temporal changes, input modality (auditory, written for participant 1) and unique words from our vocabulary list. We used demixed principal component analysis (dPCA) to decompose and analyse contributions of each individual component: timing, cue modality and word. In Fig. 4 , demixed principal components (PCs) explaining the highest amount of variance were plotted by projecting data onto their respective dPCA decoder axis.

a – e , dPCA was performed to investigate variance within three marginalizations: ‘timing’, ‘cue modality’ and ‘word’ for participant 1 ( a – c ) and ‘timing’ and ‘word’ for participant 2 ( d and e ). Demixed PCs explaining the highest variance within each marginalization were plotted over time, by projecting the data onto their respective dPCA decoder axis. In a , the ‘timing’ marginalization demonstrates SMG modulation during cue, internal speech and vocalized speech, while S1 only represents vocalized speech. The solid blue lines (8) represent the auditory cue trials, and dashed green lines (8) represent written cue trials. In b , the ‘cue modality’ marginalization suggests that internal and vocalized speech representation in the SMG are not affected by the cue modality. The solid blue lines (8) represent the auditory cue trials, and dashed green lines (8) represent written cue trials. In c , the ‘word’ marginalization shows high variability for different words in the SMG, but near zero for S1. The colours (8) represent individual words. For each colour, solid lines represent auditory trials and dashed lines represent written cue trials. d is the same as a , but for participant 2. The dashed green lines (8) represent written cue trials. e is the same as c , but for participant 2. The colours (8) represent individual words during written cue trials. The variance for different words in the SMG (left) was higher than in S1 (right), but lower in comparison with SMG in participant 1 ( c ).

For participant 1, the ‘timing’ component revealed that temporal dynamics in the SMG peaked during all active phases (Fig. 4a ). In contrast, temporal S1 modulation peaked only during vocalized speech production, indicating a lack of synchronized lip and face movement of the participant during the other task phases. While ‘cue modality’ components were separable during the cue phase (Fig. 4b ), they overlapped during subsequent phases. Thus, internal and vocalized speech representation may not be influenced by the cue modality. Pseudowords had similar separability to lexical words (Fig. 4c ). The explained variance between words was high in the SMG and was close to zero in S1. In participant 2, temporal dynamics of the task were preserved (‘timing’ component). However, variance to words was reduced, suggesting lower neuronal ability to represent individual words in participant 2. In S1, the results mirrored findings from S1 in participant 1 (Fig. 4d,e , right).

Internal speech is decodable in the SMG

Separable neural representations of both internal and vocalized speech processes implicate SMG as a rich source of neural activity for real-time speech BMI devices. The decodability of words correlated with the percentage of tuned neurons (Fig. 3a–f ) as well as the explained dPCA variance (Fig. 4c,e ) observed in the participants. In participant 1, all words in our vocabulary list were highly decodable, averaging 55% offline decoding and 79% (16–20 training trials) online decoding from neurons during internal speech (Fig. 5a,b ). Words spoken during the vocalized phase were also highly discriminable, averaging 74% offline (Fig. 5a ). In participant 2, offline internal speech decoding averaged 24% (Supplementary Fig. 4b ) and online decoding averaged 23% (Fig. 5a ), with preferential representation of words ‘spoon’ and ‘swimming’.

a , Offline decoding accuracies: ‘audio cue’ and ‘written cue’ task data were combined for each individual session day, and leave-one-out CV was performed (black dots). PCA was performed on the training data, an LDA model was constructed, and classification accuracies were plotted with 95% confidence intervals, over the session means. The significance of classification accuracies were evaluated by comparing results with a shuffled distribution (averaged shuffle results over 100 repetitions indicated by red dots; P  < 0.01 indicates that the average mean is >99.5th percentile of shuffle distribution, n  = 10). In participant 1, classification accuracies during action phases (cue, internal and speech) following rest phases (ITI, D1 and D2) were significantly higher (paired two-tailed t -test: n  = 10, d.f. 9, for all P  < 0.001, Cohen’s d  = 6.81, 2.29 and 5.75). b , Online decoding accuracies: classification accuracies for internal speech were evaluated in a closed-loop internal speech BMI application on three different session days for both participants. In participant 1, decoding accuracies were significantly above chance (averaged shuffle results over 1,000 repetitions indicated by red dots; P  < 0.001 indicates that the average mean is >99.95th percentile of shuffle distribution) and improved when 16–20 trials per words were used to train the model (two-sample two-tailed t -test, n (8–14)  = 8, d.f. 11, n (16–20)  = 5, P  = 0.029), averaging 79% classification accuracy. In participant 2, online decoding accuracies were significant (averaged shuffle results over 1,000 repetitions indicated by red dots; P  < 0.05 indicates that average mean is >97.5th percentile of shuffle distribution, n  = 7) and averaged 23%. c , An offline confusion matrix for participant 1: confusion matrices for each of the different task phases were computed on the tested data and averaged over all session days. d , An online confusion matrix: a confusion matrix was computed combining all online runs, leading to a total of 304 trials (38 trials per word) for participant 1 and 448 online trials for participant 2. Participant 1 displayed comparable online decoding accuracies for all words, while participant 2 had preferential decoding for the words ‘swimming’ and ‘spoon’.

In participant 1, trial data from both types of cue (auditory and written) were concatenated for offline analysis, since SMG activity was only differentiable between the types of cue during the cue phase (Figs. 3a and 4b ). This resulted in 16 trials per condition. Features were selected via principal component analysis (PCA) on the training dataset, and PCs that explained 95% of the variance were kept. A linear discriminant analysis (LDA) model was evaluated with leave-one-out cross-validation (CV). Significance was computed by comparing results with a null distribution ( Methods ).

Significant word decoding was observed during all phases, except during the ITI (Fig. 5a , n  = 10, mean decoding value above 99.5th percentile of shuffle distribution is P  < 0.01, per phase, Cohen’s d  = 0.64, 6.17, 3.04, 6.59, 3.93 and 8.26, confidence interval of the mean ± 1.73, 4.46, 5.21, 5.67, 4.63 and 6.49). Decoding accuracies were significantly higher in the cue, internal speech and speech condition, compared with rest phases ITI, D1 and D2 (Fig. 5a , paired t -test, n  = 10, d.f. 9, for all P  < 0.001, Cohen’s d  = 6.81, 2.29 and 5.75). Significant cue phase decoding suggested that modality-independent linguistic representations were present early within the task 45 . Internal speech decoding averaged 55% offline, with the highest session at 72% and a chance level of ~12.5% (Fig. 5a , red line). Vocalized speech averaged even higher, at 74%. All words were highly decodable (Fig. 5c ). As suggested from our dPCA results, individual words were not significantly decodable from neural activity in S1 (Supplementary Fig. 4a ), indicating generalized activity for vocalized speech in the S1 arm region (Fig. 4c ).

For participant 2, SMG significant word decoding was observed during the cue, internal and vocalized speech phases (Supplementary Fig. 4b , n  = 9, mean decoding value above 97.5th/99.5th percentile of shuffle distribution is P  < 0.05/ P  < 0.01, per phase Cohen’s d  = 0.35, 1.15, 1.09, 1.44, 0.99 and 1.49, confidence interval of the mean ± 3.09, 5.02, 6.91, 8.14, 5.45 and 4.15). Decoding accuracies were significantly higher in the cue and internal speech condition, compared with rest phases ITI and D1 (Supplementary Fig. 4b , paired t -test, n  = 9, d.f. 8, P ITI_Cue  = 0.013, Cohen’s d  = 1.07, P D1_Internal  = 0.01, Cohen’s d  = 1.11). S1 decoding mirrored results in participant 1, suggesting that no synchronized face movements occurred during the cue phase or internal speech phase (Supplementary Fig. 4c ).

High-accuracy online speech decoder

We developed an online, closed-loop internal speech BMI using an eight-word vocabulary (Fig. 5b ). On three separate session days, training datasets were generated using the written cue task, with eight repetitions of each word for each participant. An LDA model was trained on the internal speech data of the training set, corresponding to only 1.5 s of neural data per repetition for each class. The trained decoder predicted internal speech during the online task. During the online task, the vocalized speech phase was replaced with a feedback phase. The decoded word was shown in green if correctly decoded, and in red if wrongly decoded (Supplementary Video 1 ). The classifier was retrained after each run of the online task, adding the newly recorded data. Several online runs were performed on each session day, corresponding to different datapoints on Fig. 5b . When using between 8 and 14 repetitions per words to train the decoding model, an average of 59% classification accuracy was obtained for participant 1. Accuracies were significantly higher (two-sample two-tailed t -test, n (8–14)  = 8, n (16–20)  = 5, d.f. 11, P  = 0.029) the more data were added to train the model, obtaining an average of 79% classification accuracy with 16–20 repetitions per word. The highest single run accuracy was 91%. All words were well represented, illustrated by a confusion matrix of 304 trials (Fig. 5d ). In participant 2, decoding was statistically significant, but lower compared with participant 1. The lower number of tuned units (Fig. 3a–f ) and reduced explained variance between words (Fig. 4e , left) could account for these findings. Additionally, preferential representation of words ‘spoon’ and ‘swimming’ was observed.

Shared representations between internal speech, written words and vocalized speech

Different language processes are engaged during the task: auditory comprehension or visual word recognition during the cue phase, and internal speech and vocalized speech production during the speech phases. It has been widely assumed that each of these processes is part of a highly distributed network, involving multiple cortical areas 46 . In this work, we observed significant representation of different language processes in a common cortical region, SMG, in our participants. To explore the relationships between each of these processes, for participant 1 we used cross-phase classification to identify the distinct and common neural codes separately in the auditory and written cue datasets. By training our classifier on the representation found in one phase (for example, the cue phase) and testing the classifier on another phase (for example, internal speech), we quantified generalizability of our models across neural activity of different language processes (Fig. 6 ). The generalizability of a model to different task phases was evaluated through paired t -tests. No significant difference between classification accuracies indicates good generalization of the model, while significantly lower classification accuracies suggest poor generalization of the model.

a , Evaluating the overlap of shared information between different task phases in the ‘auditory cue’ task. For each of the ten session days, cross-phase classification was performed. It consisted in training a model on a subset of data from one phase (for example, cue) and applying it on a subset of data from ITI, cue, internal and speech phases. This analysis was performed separately for each task phase. PCA was performed on the training data, an LDA model was constructed and classification accuracies were plotted with a 95% confidence interval over session means. Significant differences in performance between phases were evaluated between the ten sessions (paired two-tailed t -test, FDR corrected, d.f. 9, P  < 0.001 for all, Cohen’s d  ≥ 1.89). For easier visibility, significant differences between ITI and other phases were not plotted. b , Same as a for the ‘written cue’ task (paired two-tailed t -test, FDR corrected, d.f. 9, P Cue_Internal  = 0.028, Cohen’s d  > 0.86; P Cue_Speech  = 0.022, Cohen’s d  = 0.95; all others P  < 0.001 and Cohen’s d  ≥ 1.65). c , The percentage of neurons tuned during the internal speech phase that are also tuned during the vocalized speech phase. Neurons tuned during the internal speech phase were computed as in Fig. 3b separately for each session day. From these, the percentage of neurons that were also tuned during vocalized speech was calculated. More than 80% of neurons during internal speech were also tuned during vocalized speech (82% in the ‘auditory cue’ task, 85% in the ‘written cue’ task). In total, 71% of ‘auditory cue’ and 79% ‘written cue’ neurons also preserved tuning to at least one identical word during internal speech and vocalized speech phases. d , The percentage of neurons tuned during the internal speech phase that were also tuned during the cue phase. Right: 78% of neurons tuned during internal speech were also tuned during the written cue phase. Left: a smaller 47% of neurons tuned during the internal speech phase were also tuned during the auditory cue phase. In total, 71% of neurons preserved tuning between the written cue phase and the internal speech phase, while 42% of neurons preserved tuning between the auditory cue and the internal speech phase.

The strongest shared neural representations were found between visual word recognition, internal speech and vocalized speech (Fig. 6b ). A model trained on internal speech was highly generalizable to both vocalized speech and written cued words, evidence for a possible shared neural code (Fig. 6b , internal). In contrast, the model’s performance was significantly lower when tested on data recorded in the auditory cue phase (Fig. 6a , training phase internal: paired t -test, d.f. 9, P Cue_Internal  < 0.001, Cohen’s d  = 2.16; P Cue_Speech  < 0.001, Cohen’s d  = 3.34). These differences could stem from the inherent challenges in comparing visual and auditory language stimuli, which differ in processing time: instantaneous for text versus several hundred milliseconds for auditory stimuli.

We evaluated the capability of a classification model, initially trained to distinguish words during vocalized speech, in its ability to generalize to internal and cue phases (Fig. 6a,b , training phase speech). The model demonstrated similar levels of generalization during internal speech and in response to written cues, as indicated by the lack of significance in decoding accuracy between the internal and written cue phase (Fig. 6b , training phase speech, cue–internal). However, the model generalized significantly better to internal speech than to representations observed during the auditory cue phase (Fig. 6a , training phase speech, d.f. 9, P Cue_Internal  < 0.001, Cohen’s d  = 2.85).

Neuronal representation of words at the single-neuron level was highly consistent between internal speech, vocalized speech and written cue phases. A high percentage of neurons were not only active during the same task phases but also preserved identical tuning to at least one word (Fig. 6c,d ). In total, 82–85% of neurons active during internal speech were also active during vocalized speech. In 71–79% of neurons, tuning was preserved between the internal speech and vocalized speech phases (Fig. 6c ). During the cue phase, 78% of neurons active during internal speech were also active during the written cue (Fig. 6d , right). However, a lower percentage of neurons (47%) were active during the auditory cue phase (Fig. 6d , left). Similarly, 71% of neurons preserved tuning between the written cue phase and the internal speech phase, while 42% of neurons preserved tuning between the auditory cue phase and the internal speech phase.

Together with the cross-phase analysis, these results suggest strong shared neural representations between internal speech, vocalized speech and the written cue, both at the single-neuron and at the population level.

Robust decoding of multiple internal speech strategies within the SMG

Strong shared neural representations in participant 1 between written, inner and vocalized speech suggest that all three partly represent the same cognitive process or all cognitive processes share common neural features. While internal and vocalized speech have been shown to share common neural features 36 , similarities between internal speech and the written cue could have occurred through several different cognitive processes. For instance, the participant’s observation of the written cue could have activated silent reading. This process has been self-reported as activating internal speech, which can involve ‘hearing’ a voice, thus having an auditory component 42 , 47 . However, the participant could also have mentally pictured an image of the written word while performing internal speech, involving visual imagination in addition to language processes. Both hypotheses could explain the high amount of shared neural representation between the written cue and the internal speech phases (Fig. 6b ).

We therefore compared two possible internal sensory strategies in participant 1: a ‘sound imagination’ strategy in which the participant imagined hearing the word, and a ‘visual imagination’ strategy in which the participant visualized the word’s image (Supplementary Fig. 5a ). Each strategy was cued by the modalities we had previously tested (auditory and written words) (Table 1 ). To assess the similarity of these internal speech processes to other task phases, we conducted a cross-phase decoding analysis (as performed in Fig. 6 ). We hypothesized that, if the high cross-decoding results between internal and written cue phases primarily stemmed from the participant engaging in visual word imagination, we would observe lower decoding accuracies during the auditory imagination phase.

Both strategies demonstrated high representation of the four-word dataset (Supplementary Fig. 5b , highest 94%, chance level 25%). These results suggest our speech BMI decoder is robust to multiple types of internal speech strategy.

The participant described the ‘sound imagination’ strategy as being easier and more similar to the internal speech condition of the first experiment. The participant’s self-reported strategy suggests that no visual imagination was performed during internal speech. Correspondingly, similarities between written cue and internal speech phases may stem from internal speech activation during the silent reading of the cue.

In this work, we demonstrated a decoder for internal and vocalized speech, using single-neuron activity from the SMG. Two chronically implanted, speech-abled participants with tetraplegia were able to use an online, closed-loop internal speech BMI to achieve on average 79% and 23% classification accuracy with 16–32 training trials for an eight-word vocabulary. Furthermore, high decoding was achievable with only 24 s of training data per word, corresponding to 16 trials each with 1.5 s of data. Firing rates recorded from S1 showed generalized activation only during vocalized speech activity, but individual words were not classifiable. In the SMG, shared neural representations between internal speech, the written cue and vocalized speech suggest the occurrence of common processes. Robust control could be achieved using visual and auditory internal speech strategies. Representation of pseudowords provided evidence for a phonetic word encoding component in the SMG.

Single neurons in the SMG encode internal speech

We demonstrated internal speech decoding of six different words and two pseudowords in the SMG. Single neurons increased their firing rates during internal speech (Fig. 2 , S1 and S2), which was also reflected at the population level (Fig. 3a,b,d,e ). Each word was represented in the neuronal population (Fig. 3c,f ). Classification accuracy and tuning during the internal speech phase were significantly higher than during the previous delay phase (Figs. 3b,e and 5a , and Supplementary Figs. 3c,d and 4b ). This evidence suggests that we did not simply decode sustained activity from the cue phase but activity generated by the participant performing internal speech. We obtained significant offline and online internal speech decoding results in two participants (Fig. 5a and Supplementary Fig. 4b ). These findings provide strong evidence for internal speech processing at the single-neuron level in the SMG.

Neurons in S1 are modulated by vocalized but not internal speech

Neural activity recorded from S1 served as a control for synchronized face and lip movements during internal speech. While vocalized speech robustly activated sensory neurons, no increase of baseline activity was observed during the internal speech phase or the auditory and written cue phases in both participants (Fig. 4 , S1). These results underline no synchronized movement inflated our decoding accuracy of internal speech (Supplementary Fig. 4a,c ).

A previous imaging study achieved significant offline decoding of several different internal speech sentences performed by patients with mild ALS 6 . Together with our findings, these results suggest that a BMI speech decoder that does not rely on any movement may translate to communication opportunities for patients suffering from ALS and locked-in syndrome.

Different face activities are observable but not decodable in arm area of S1

The topographic representation of body parts in S1 has recently been found to be less rigid than previously thought. Generalized finger representation was found in a presumably S1 arm region of interest (ROI) 44 . Furthermore, an fMRI paper found observable face and lip activity in S1 leg and hand ROIs. However, differentiation between two lip actions was restricted to the face ROI 43 . Correspondingly, we observed generalized face and lip activity in a predominantly S1 arm region for participant 1 (see ref. 48 for implant location) and a predominantly S1 hand region for participant 2 during vocalized speech (Fig. 4a,d and Supplementary Figs. 1 and 4a,b ). Recorded neural activity contained similar representations for different spoke words (Fig. 4c,e ) and was not significantly decodable (Supplementary Fig. 4a,c ).

Shared neural representations between internal and vocalized speech

The extent to which internal and vocalized speech generalize is still debated 35 , 42 , 49 and depends on the investigated brain area 36 , 50 . In this work, we found on average stronger representation for vocalized (74%) than internal speech (Fig. 5a , 55%) in participant 1 but the opposite effect in participant 2 (Supplementary Fig. 4b , 24% internal, 21% vocalized speech). Additionally, cross-phase decoding of vocalized speech from models trained on data during internal speech resulted in comparable classification accuracies to those of internal speech (Fig. 6a,b , internal). Most neurons tuned during internal speech were also tuned to at least one of the same words during vocalized speech (71–79%; Fig. 6c ). However, some neurons were only tuned during internal speech, or to different words. These observations also applied to firing rates of individual neurons. Here, we observed neurons that had higher peak rates during the internal speech phase than the vocalized speech phase (Supplementary Fig. 1 : swimming and cowboy). Together, these results further suggest neural signatures during internal and vocalized speech are similar but distinct from one another, emphasizing the need for developing speech models from data recorded directly on internal speech production 51 .

Similar observations were made when comparing internal speech processes with visual word processes. In total, 79% of neurons were active both in the internal speech phase and the written cue phase, and 79% preserved the same tuning (Fig. 6d , written cue). Additionally, high cross-decoding between both phases was observed (Fig. 6b , internal).

Shared representation between speech and written cue presentation

Observation of a written cue may engage a variety of cognitive processes, such as visual feature recognition, semantic understanding and/or related language processes, many of which modulate similar cortical regions as speech 45 . Studies have found that silent reading can evoke internal speech; it can be modulated by a presumed author’s speaking speed, voice familiarity or regional accents 35 , 42 , 47 , 52 , 53 . During silent reading of a cued sentence with a neutral versus increased prosody (madeleine brought me versus MADELEINE brought me), one study in particular found that increased left SMG activation correlated with the intensity of the produced inner speech 54 .

Our data demonstrated high cross-phase decoding accuracies between both written cue and speech phases in our first participant (Fig. 6b ). Due to substantial shared neural representation, we hypothesize that the participant’s silent reading during the presentation of the written cue may have engaged internal speech processes. However, this same shared representation could have occurred if visual processes were activated in the internal speech phase. For instance, the participant could have performed mental visualization of the written word instead of generating an internal monologue, as the subjective perception of internal speech may vary between individuals.

Investigating internal speech strategies

In a separate experiment, participant 1 was prompted to execute different mental strategies during the internal speech phase, consisting of ‘sound imagination’ or ‘visual word imagination’ (Supplementary Fig. 5a ). We found robust decoding during the internal strategy phase, regardless of which mental strategy was performed (Supplementary Fig. 5b ). This participant reported the sound strategy was easier to execute than the visual strategy. Furthermore, this participant reported that the sound strategy was more similar to the internal speech strategy employed in prior experiments. This self-report suggests that the patient did not perform visual imagination during the internal speech task. Therefore, shared neural representation between internal and written word phases during the internal speech task may stem from silent reading of the written cue. Since multiple internal mental strategies are decodable from SMG, future patients could have flexibility with their preferred strategy. For instance, people with a strong visual imagination may prefer performing visual word imagination.

Audio contamination in decoding result

Prior studies examining neural representation of attempted or vocalized speech must potentially mitigate acoustic contamination of electrophysiological brain signals during speech production 55 . During internal speech production, no detectable audio was captured by the audio equipment or noticed by the researchers in the room. In the rare cases the participant spoke during internal speech (three trials), the trials were removed. Furthermore, if audio had contaminated the neural data during the auditory cue or vocalized speech, we would have probably observed significant decoding in all channels. However, no significant classification was detected in S1 channels during the auditory cue phase nor the vocalized speech phase (Supplementary Fig. 2b ). We therefore conclude that acoustic contamination did not artificially inflate observed classification accuracies during vocalized speech in the SMG.

Single-neuron modulation during internal speech with a second participant

We found single-neuron modulation to speech processes in a second participant (Figs. 2d,e and 3f , and Supplementary Fig. 2d ), as well as significant offline and online classification accuracies (Fig. 5a and Supplementary Fig. 4b ), confirming neural representation of language processes in the SMG. The number of neurons distinctly active for different words was lower compared with the first participant (Fig. 2e and Supplementary Fig. 3d ), limiting our ability to decode with high accuracy between words in the different task phases (Fig. 5a and Supplementary Fig. 4b ).

Previous work found that single neurons in the PPC exhibited a common neural substrate for written action verbs and observed actions 56 . Another study found that single neurons in the PPC also encoded spoken numbers 57 . These recordings were made in the superior parietal lobule whereas the SMG is in the inferior parietal lobule. Thus, it would appear that language-related activity is highly distributed across the PPC. However, the difference in strength of language representation between each participant in the SMG suggests that there is a degree of functional segregation within the SMG 37 .

Different anatomical geometries of the SMG between participants mean that precise comparisons of implanted array locations become difficult (Fig. 1 ). Implant locations for both participants were informed from pre-surgical anatomical/vasculature scans and fMRI tasks designed to evoke activity related to grasp and dexterous hand movements 48 . Furthermore, the number of electrodes of the implanted array was higher in the first participant (96) than in the second participant (64). A pre-surgical assessment of functional activity related to language and speech may be required to determine the best candidate implant locations within the SMG for online speech decoding applications.

Impact on BMI applications

In this work, an online internal speech BMI achieved significant decoding from single-neuron activity in the SMG in two participants with tetraplegia. The online decoders were trained on as few as eight repetitions of 1.5 s per word, demonstrating that meaningful classification accuracies can be obtained with only a few minutes’ worth of training data per day. This proof-of-concept suggests that the SMG may be able to represent a much larger internal vocabulary. By building models on internal speech directly, our results may translate to people who cannot vocalize speech or are completely locked in. Recently, ref. 26 demonstrated a BMI speller that decoded attempted speech of the letters of the NATO alphabet and used those to construct sentences. Scaling our vocabulary to that size could allow for an unrestricted internal speech speller.

To summarize, we demonstrate the SMG as a promising candidate to build an internal brain–machine speech device. Different internal speech strategies were decodable from the SMG, allowing patients to use the methods and languages with which they are most comfortable. We found evidence for a phonetic component during internal and vocalized speech. Adding to previous findings indicating grasp decoding in the SMG 23 , we propose the SMG as a multipurpose BMI area.

Experimental model and participant details

Two male participants with tetraplegia (33 and 39 years) were recruited for an institutional review board- and Food and Drug Administration-approved clinical trial of a BMI and gave informed consent to participate (Institutional Review Board of Rancho Los Amigos National Rehabilitation Center, Institutional Review Board of California Institute of Technology, clinical trial registration NCT01964261 ). This clinical trial evaluated BMIs in the PPC and the somatosensory cortex for grasp rehabilitation. One of the primary effectiveness objectives of the study is to evaluate the effectiveness of the neuroport in controlling virtual or physical end effectors. Signals from the PPC will allow the subjects to control the end effector with accuracy greater than chance. Participants were compensated for their participation in the study and reimbursed for any travel expenses related to participation in study activities. The authors affirm that the human research participant provided written informed consent for publication of Supplementary Video 1 . The first participant suffered a spinal cord injury at cervical level C5 1.5 years before participating in the study. The second participant suffered a C5–C6 spinal cord injury 3 years before implantation.

Method details

Data were collected from implants located in the left SMG and the left S1 (for anatomical locations, see Fig. 1 ). For description of pre-surgical planning, localization fMRI tasks, surgical techniques and methodologies, see ref. 48 . Placement of electrodes was based on fMRI tasks involving grasp and dexterous hand movements.

The first participant underwent surgery in November 2016 to implant two 96-channel platinum-tipped multi-electrode arrays (NeuroPort Array, Blackrock Microsystems) in the SMG and in the ventral premotor cortex and two 7 × 7 sputtered iridium oxide film (SIROF)-tipped microelectrode arrays with 48 channels each in the hand and arm area of S1. Data were collected between July 2021 and August 2022. The second participant underwent surgery in October 2022 and was implanted with SIROF-tipped 64-channel microelectrode arrays in S1 (two arrays), SMG, ventral premotor cortex and primary motor cortex. Data were collected in January 2023.

Data collection

Recording began 2 weeks after surgery and continued one to three times per week. Data for this work were collected between 2021 and 2023. Broadband electrical activity was recorded from the NeuroPort Arrays using Neural Signal Processors (Blackrock Microsystems). Analogue signals were amplified, bandpass filtered (0.3–7,500 Hz) and digitized at 30,000 samples s −1 . To identify putative action potentials, these broadband data were bandpass filtered (250–5,000 Hz) and thresholded at −4.5 the estimated root-mean-square voltage of the noise. For some of the analyses, waveforms captured at these threshold crossings were then spike sorted by manually assigning each observation to a putative single neuron; for others, multiunit activity was considered. For participant 1, an average of 33 sorted SMG units (between 22 and 56) and 83 sorted S1 units (between 59 and 96) were recorded per session. For participant 2, an average of 80 sorted SMG units (between 69 and 92) and 81 sorted S1 units (between 61 and 101) were recorded per session. Auditory data were recorded at 30,000 Hz simultaneously to the neural data. Background noise was reduced post-recording by using the noise reduction function of the program ‘Audible’.

Experimental tasks

We implemented different tasks to study language processes in the SMG. The tasks cued six words informed by ref. 31 (spoon, python, battlefield, cowboy, swimming and telephone) as well as two pseudowords (bindip and nifzig). The participants were situated 1 m in front of a light-emitting diode screen (1,190 mm screen diagonal), where the task was visualized. The task was implemented using the Psychophysics Toolbox 58 , 59 , 60 extension for MATLAB. Only the written cue task was used for participant 2.

Auditory cue task

Each trial consisted of six phases, referred to in this paper as ITI, cue, D1, internal, D2 and speech. The trial began with a brief ITI (2 s), followed by a 1.5-s-long cue phase. During the cue phase, a speaker emitted the sound of one of the eight words (for example, python). Word duration varied between 842 and 1,130 ms. Then, after a delay period (grey circle on screen; 0.5 s), the participant was instructed to internally say the cued word (orange circle on screen; 1.5 s). After a second delay (grey circle on screen; 0.5 s), the participant vocalized the word (green circle on screen, 1.5 s).

Written cue task

The task was identical to the auditory cue task, except words were cued in writing instead of sound. The written word appeared on the screen for 1.5 s during the cue phase. The auditory cue was played between 200 ms and 650 ms later than the written cue appeared on the screen, due to the utilization of varied sound outputs (direct computer audio versus Bluetooth speaker).

One auditory cue task and one written cue task were recorded on ten individual session days in participant 1. The written cue task was recorded on seven individual session days in participant 2.

Control experiments

Three experiments were run to investigate internal strategies and phonetic versus semantic processing.

Internal strategy task

The task was designed to vary the internal strategy employed by the participant during the internal speech phase. Two internal strategies were tested: a sound imagination and a visual imagination. For the ‘sound imagination’ strategy, the participant was instructed to imagine what the sound of the word sounded like. For the ‘visual imagination’ strategy, the participant was instructed to perform mental visualization from the written word. We also tested if the cue modality (auditory or written) influenced the internal strategy. A subset of four words were used for this experiment. This led to four different variations of the task.

The internal strategy task was run on one session day with participant 1.

Online task

The ‘written cue task’ was used for the closed-loop experiments. To obtain training data for the online task, a written cue task was run. Then, a classification model was trained only on the internal speech data of the task (see ‘Classification’ section). The closed-loop task was nearly identical to the ‘written cue task’ but replaced the vocalized speech phase by a feedback phase. Feedback was provided by showing the word on the screen either in green if correctly classified or in red if wrongly classified. See Supplementary Video 1 for an example of the participant performing the online task. The online task was run on three individual session days.

Error trials

Trials in which participants accidentally spoke during the internal speech part (3 trials) or said the wrong word during the vocalized speech part (20 trials) were removed from all analysis.

Total number of recording trials

For participant 1, we collected offline datasets composed of eight trials per word across ten sessions. Trials during which participant errors occurred were excluded. In total, between 156 and 159 trials per word were included, with a total of 1,257 trials for offline analysis. On four non-consecutive session days, the auditory cue task was run first, and on six non-consecutive days, the written cue task was run first. For online analysis, datasets were recorded on three different session days, for a total of 304 trials. Participant 2 underwent a similar data collection process, with offline datasets comprising 16 trials per word using the written cue modality over nine sessions. Error trials were excluded. In total, between 142 and 144 trials per word were kept, with a total of 1,145 trials for offline analysis. For online analysis, datasets were recorded on three session days, leading to a total of 448 online trials.

Quantification and statistical analysis

Analyses were performed using MATLAB R2020b and Python, version 3.8.11.

Neural firing rates

Firing rates of sorted units were computed as the number of spikes occurring in 50-ms bins, divided by the bin width and smoothed using a Gaussian filter with kernel width of 50 ms to form an estimate of the instantaneous firing rates (spikes s −1 ).

Linear regression tuning analysis

To identify units exhibiting selective firing rate patterns (or tuning) for each of the eight words, linear regression analysis was performed in two different ways: (1) step by step in 50-ms time bins to allow assessing changes in neuronal tuning over the entire trial duration; (2) averaging the firing rate in each task phase to compare tuning between phases. The model returns a fit that estimates the firing rate of a unit on the basis of the following variables:

where FR corresponds to the firing rate of the unit, β 0 to the offset term equal to the average ITI firing rate of the unit, X is the vector indicator variable for each word w , and β w corresponds to the estimated regression coefficient for word w . W was equal to 8 (battlefield, cowboy, python, spoon, swimming, telephone, bindip and nifzig) 23 .

In this model, β symbolizes the change of firing rate from baseline for each word. A t -statistic was calculated by dividing each β coefficient by its standard error. Tuning was based on the P value of the t -statistic for each β coefficient. A follow-up analysis was performed to adjust for false discovery rate (FDR) between the P values 61 , 62 . A unit was defined as tuned if the adjusted P value is <0.05 for at least one word. This definition allowed for tuning of a unit to zero, one or multiple words during different timepoints of the trial. Linear regression was performed for each session day individually. A 95% confidence interval of the mean was computed by performing the Student’s t -inverse cumulative distribution function over the ten sessions.

Kruskal–Wallis tuning analysis

As an alternative tuning definition, differences in firing rates between words were tested using the Kruskal–Wallis test, the non-parametric analogue to the one-way analysis of variance (ANOVA). For each neuron, the analysis was performed to evaluate the null hypothesis that data from each word come from the same distribution. A follow-up analysis was performed to adjust for FDR between the P values for each task phase 61 , 62 . A unit was defined as tuned during a phase if the adjusted P value was smaller than α  = 0.05.

Classification

Using the neuronal firing rates recorded during the tasks, a classifier was used to evaluate how well the set of words could be differentiated during each phase. Classifiers were trained using averaged firing rates over each task phase, resulting in six matrices of size n ,  m , where n corresponds to the number of trials and m corresponds to the number of recorded units. A model for each phase was built using LDA, assuming an identical covariance matrix for each word, which resulted in best classification accuracies. Leave-one-out CV was performed to estimate decoding performance, leaving out a different trial across neurons at each loop. PCA was applied on the training data, and PCs explaining more than 95% of the variance were selected as features and applied to the single testing trial. A 95% confidence interval of the mean was computed as described above.

Cross-phase classification

To estimate shared neural representations between different task phases, we performed cross-phase classification. The process consisted in training a classification model (as described above) on one of the task phases (for example, ITI) and to test it on the ITI, cue, imagined speech and vocalized speech phases. The method was repeated for each of the ten sessions individually, and a 95% confidence interval of the mean was computed. Significant differences in classification accuracies between phases decoded with the same model were evaluated using a paired two-tailed t -test. FDR correction of the P values was performed (‘Linear regression tuning analysis’) 61 , 62 .

Classification performance significance testing

To assess the significance of classification performance, a null dataset was created by repeating classification 100 times with shuffled labels. Then, different percentile levels of this null distribution were computed and compared to the mean of the actual data. Mean classification performances higher than the 97.5th percentile were denoted with P < 0.05 and higher than 99.5th percentile were denoted with P < 0.01.

dPCA analysis

dPCA was performed on the session data to study the activity of the neuronal population in relation to the external task parameters: cue modality and word. Kobak et al. 63 introduced dPCA as a refinement of their earlier dimensionality reduction technique (of the same name) that attempts to combine the explanatory strengths of LDA and PCA. By deconstructing neuronal population activity into individual components, each component relates to a single task parameter 64 .

This text follows the methodology outlined by Kobak et al. 63 . Briefly, this involved the following steps for N neurons:

First, unlike in PCA, we focused not on the matrix, X , of the original data, but on the matrices of marginalizations, X ϕ . The marginalizations were computed as neural activity averaged over trials, k , and some task parameters in analogy to the covariance decomposition done in multivariate analysis of variance. Since our dataset has three parameters: timing, t , cue modality, \(c\) (for example, auditory or visual), and word, w (eight different words), we obtained the total activity as the sum of the average activity with the marginalizations and a final noise term

The above notation of Kobak et al. is the same as used in factorial ANOVA, that is, \({X}_{{tcwk}}\) is the matrix of firing rates for all neurons, \(< \bullet { > }_{{ab}}\) is the average over a set of parameters \(a,b,\ldots\) , \(\bar{X}= < {X}_{{tcwk}}{ > }_{{tcwk}}\) , \({\bar{X}}_{t}= < {X}_{{tcwk}}-\bar{X}{ > }_{{cwk}}\) , \({\bar{X}}_{{tc}}= < {X}_{{tcwk}}-\bar{X}-{\bar{X}}_{t}-{\bar{X}}_{c}-{\bar{X}}_{w}{ > }_{{wk}}\) and so on. Finally, \({{{\epsilon }}}_{{tcwk}}={X}_{{tcwk}}- < {X}_{{tcwk}}{ > }_{k}\) .

Participant 1 datasets were composed of N  = 333 (SMG), N  = 828 (S1) and k  = 8. Participant 2 datasets were composed of N  = 547 (SMG), N  = 522 (S1) and k  = 16. To create balanced datasets, error trials were replaced by the average firing rate of k  − 1 trials.

Our second step reduced the number of terms by grouping them as seen by the braces in the equation above, since there is no benefit in demixing a time-independent pure task, \(a\) , term \({\bar{X}}_{a}\) from the time–task interaction terms \({\bar{X}}_{{ta}}\) since all components are expected to change with time. The above grouping reduced the parametrization down to just five marginalization terms and the noise term (reading in order): the mean firing rate, the task-independent term, the cue modality term, the word term, the cue modality–word interaction term and the trial-to-trial noise.

Finally, we gained extra flexibility by having two separate linear mappings \({F}_{\varphi }\) for encoding and \({D}_{\varphi }\) for decoding (unlike in PCA, they are not assumed to be transposes of each other). These matrices were chosen to minimize the loss function (with a quadratic penalty added to avoid overfitting):

Here, \({{\mu }}=(\lambda\Vert X\Vert)^{2}\) , where λ was optimally selected through tenfold CV in each dataset.

We refer the reader to Kobak et al. for a description of the full analytic solution.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

The data supporting the findings of this study are openly available via Zenodo at https://doi.org/10.5281/zenodo.10697024 (ref. 65 ). Source data are provided with this paper.

Code availability

The custom code developed for this study is openly available via Zenodo at https://doi.org/10.5281/zenodo.10697024 (ref. 65 ).

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Acknowledgements

We thank L. Bashford and I. Rosenthal for helpful discussions and data collection. We thank our study participants for their dedication to the study that made this work possible. This research was supported by the NIH National Institute of Neurological Disorders and Stroke Grant U01: U01NS098975 and U01: U01NS123127 (S.K.W., D.A.B., K.P., C.L. and R.A.A.) and by the T&C Chen Brain-Machine Interface Center (S.K.W., D.A.B. and R.A.A.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the paper.

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S.K.W., D.A.B. and R.A.A. designed the study. S.K.W. and D.A.B. developed the experimental tasks and collected the data. S.K.W. analysed the results and generated the figures. S.K.W., D.A.B. and R.A.A. interpreted the results and wrote the paper. K.P. coordinated regulatory requirements of clinical trials. C.L. and B.L. performed the surgery to implant the recording arrays.

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The video shows the participant performing the internal speech task in real time. The participant is cued with a word on the screen. After a delay, an orange dot appears, during which the participant performs internal speech. Then, the decoded word appears on the screen, in green if it is correctly decoded and in red if it is wrongly decoded.

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Wandelt, S.K., Bjånes, D.A., Pejsa, K. et al. Representation of internal speech by single neurons in human supramarginal gyrus. Nat Hum Behav (2024). https://doi.org/10.1038/s41562-024-01867-y

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Brain-reading device is best yet at decoding ‘internal speech’.

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• Published: 10 May 2024

Deciphering the functional role of insular cortex stratification in trigeminal neuropathic pain

• Jaisan Islam 1 ,
• Md Taufiqur Rahman 1 ,
• Elina KC 1 , 3 &
• Young Seok Park 1 , 2  

The Journal of Headache and Pain volume  25 , Article number:  76 ( 2024 ) Cite this article

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Trigeminal neuropathic pain (TNP) is a major concern in both dentistry and medicine. The progression from normal to chronic TNP through activation of the insular cortex (IC) is thought to involve several neuroplastic changes in multiple brain regions, resulting in distorted pain perception and associated comorbidities. While the functional changes in the insula are recognized contributors to TNP, the intricate mechanisms underlying the involvement of the insula in TNP processing remain subjects of ongoing investigation. Here, we have overviewed the most recent advancements regarding the functional role of IC in regulating TNP alongside insights into the IC’s connectivity with other brain regions implicated in trigeminal pain pathways. In addition, the review examines diverse modulation strategies that target the different parts of the IC, thereby suggesting novel diagnostic and therapeutic management of chronic TNP in the future.

Peer Review reports

Trigeminal neuropathic pain (TNP) represents a significant medical challenge, characterized by episodic, intense pain affecting the trigeminal nerve, crucial for facial and head sensation. Triggered by routine activities such as eating, speaking, and facial cleansing, TNP manifests in highly sensitive areas, often unrelated to the actual pain sites. Its etiology includes trigeminal nerve root compression, demyelinating diseases, and alterations in pain-related central neural circuits, leading to abnormal neural firing [ 9 , 22 , 50 ]. Current treatment modalities for TNP encompass pharmacological interventions, surgical procedures, and alternative therapies. Primary pharmacological treatments include sodium channel blockers such as carbamazepine and oxcarbazepine, supplemented by tricyclic antidepressants and anticonvulsants [ 37 ]. The diagnosis of TNP is challenging due to its rarity and potential misidentification as dental issues, migraines, or temporomandibular joint disorders, complicating effective management [ 63 , 91 ]. While local anesthetic or steroid injections offer transient relief, invasive surgical interventions pose risks, including further nerve damage, highlighting the need for advancements in neuromodulation techniques [ 45 ].

The insular cortex (IC), deeply situated within the lateral sulcus of the brain’s cerebral cortex, plays a pivotal role in emotion, cognition, and pain perception. Research has established its critical involvement in the TNP processing pathway, evidenced by observed alterations of gray matter volume (GMV) and neurotransmitters in TNP patients and animal models [ 50 , 61 , 68 , 100 , 101 ]. However, the role and mechanism of IC in TNP and its management, compared to other neuropathies, have yet to be fully explored. The IC integrates trigeminal nociceptive inputs from various brain regions, making it a target for modulating trigeminal pain through lesioning, neurostimulation, and pharmacological strategies [ 3 , 50 , 78 , 98 ]. Therefore, given the growing emphasis on the IC in TNP management research, further investigation into its therapeutic possibilities for TNP is paramount. This review aims to elucidate the IC’s significance in TNP and evaluate the potential of various modulation approaches, alongside discussing current and future diagnostic and therapeutic strategies for chronic TNP management involving the IC.

Procedures of literature search and study selection

In this comprehensive review, we conducted a systematic literature search using PubMed and the Scopus Index as primary resources to explore the modulation of the IC in the context of TNP. Our search strategy involved a computerized examination of journal articles without restricting the publication date, employing a broad spectrum of keywords such as “pain,” “trigeminal neuropathic pain,” “insular cortex,” “orofacial pain,” and “trigeminal neuralgia” to ensure comprehensive coverage of the topic.

To thoroughly investigate the functional role of the IC in TNP, our review included studies encompassing both animal models and human subjects. The inclusion of animal studies allowed to understand foundational biological processes and experimental therapeutics, while human studies provided insights into clinical manifestations, imaging findings, and therapeutic outcomes. This dual approach enabled a holistic understanding of the IC’s role across different experimental and clinical settings.

We selected 164 non-duplicated entries, employing a rigorous criterion that focused on the relevance to our area of interest, and the novelty of findings related to the role of the IC in TNP. An initial screening of titles and abstracts, primarily conducted by the first author, identified 107 studies that significantly contributed to our understanding of the IC’s involvement in TNP. These articles were chosen based on their discussions about alterations to the IC following the condition and the various neuromodulation techniques targeting the IC. Our review synthesizes findings associated with the IC in the context of TNP, which encompasses orofacial pain, trigeminal neuropathic pain, and trigeminal neuralgia, ensuring a scientifically robust selection of literature.

Structure of insular cortex

The IC, integral to the processing of multimodal inputs, is delineated by its anatomical location and cytoarchitecture across species. IC is divided into two parts: AIC and PIC. The AIC is comprised of three short gyri which are involved in processing emotions, empathy and social awareness. On the other hand, PIC is comprised of two long gyri which are implicated in perception, motor control, self-awareness and sensory integration [ 34 , 48 , 92 ]. In rats, it lies over the claustrum, bordered rostrodorsally by the lateral frontal and primary somatosensory cortices (SI), caudodorsally by the secondary somatosensory cortex (SII), ventrally by the piriform cortex, and caudally by the perirhinal cortex [ 60 , 86 ]. In primates, it resides within the lateral sulcus’s fold, comprising anterior and posterior sections with distinct connectivity profiles [ 96 ]. Cytoarchitecturally, the IC in primates, including humans, is divided into granular, dysgranular, and agranular areas. The agranular insular cortex (aIC), situated in the AIC, is characterized by its prominent layers II–III, V, and VI, and is mainly associated with efferent functions. The dysgranular insular cortex (dIC), found between the granular and agranular areas, has fewer granule cells in layer IV and a significant layer V. It plays a role in integrating sensory and emotional information, with both afferent and efferent functions. The granular insular cortex (gIC), primarily located in the PIC, is known for its pronounced layer IV and is more associated with afferent pathways [ 3 , 33 , 61 ].

Functions of insular cortex

Functionally, the insula is implicated in diverse processes. It acts as the primary gustatory cortex [ 14 ], visceral [ 88 ], and thermosensory cortex [ 20 ], embodying the primary interoceptive cortex that reflects the body’s physiological and homeostatic conditions [ 61 ]. The PIC, specifically, is known to receive substantial sensory input from cortical sources, highlighting IC’s integral role in comprehensively processing somatosensory information [ 38 ]. This extensive sensory integration indicates the capability of IC to mediate complex interactions between different sensory modalities and the neural network involved in higher-order processing. Its role also extends to embodying consciousness and self-recognition, evidenced by activation upon viewing one’s images [ 28 ] during awareness of heartbeat, bodily control, and emotions [ 19 ]. Its integration with the limbic system underlines its crucial role in emotional processing, including negative behaviors like fear and anxiety [ 32 , 52 ] and positive emotions such as happiness [ 61 ].

Cognitively, the IC is involved in aversive and affective learning, as shown in rat studies [ 53 , 85 ], aligning with its role in the salience network [ 94 ]. It participates in anticipating future states, prediction error computation, and risk estimation, responding predictively to relevant physiological stimuli [ 6 , 40 , 67 ]. This mediation between physiological states and motivated behaviors underscores its significance in both normal and pathological conditions [ 6 , 21 , 79 ].

Pathologically, variations in insular function and structure are linked to anxiety disorders [ 82 ], major depression [ 5 , 76 ], autism spectrum disorders [ 95 ], schizophrenia, obesity, and addiction [ 30 ], highlighting its pivotal role across a spectrum of mental health and behavioral conditions. In addition, the IC plays a multifaceted role in motor control by integrating sensory, emotional, and cognitive information to influence motor functions, including planning, execution, learning, and adaptation. It coordinates with other motor control areas, processes pain, and modulates autonomic responses, highlighting its integral role in the complex interplay between motor activities and internal states [ 87 ].

Chemoarchitectural features of IC involved in trigeminal pain processing

The IC significantly influence TNP through its complex chemoarchitectural characteristics. The extensive expression of neurotransmitter receptors situated in the IC, including opioid, cannabinoid, dopaminergic, and glutamate receptors, enzymatic activities, and specific neurocytological profiles underpins its critical function in the TNP [ 39 , 62 ]. aIC and dIC, known for their reduced myelination and distinct acetylcholinesterase activity, suggest a unique substrate that may influence signal propagation speed and integration, particularly relevant to the processing of TNP signals [ 36 ]. The presence of µ-, δ-, and κ-opioid, along with nicotinic acetylcholine receptors in the IC, highlights its role in modulating pain relief, reward, and addiction. In addition, cannabinoid receptors and serotonergic receptors such as 5-HT1A, 5-HT2A, 5-HT2C, 5-HT3, and potentially 5-HT4, 5, 6, 7, within the IC influence the perception of TNP [ 62 , 93 ].

In TNP, alteration in dopaminergic neurotransmission circuitry affects multiple brain regions such as IC and nucleus accumbens core (NAcc). Since IC has output projections towards NAcc and NAcc modulation have been found to be involved in TNP, IC dopaminergic neurotransmission can influence TNP [ 15 , 38 , 39 , 49 ].

Studies have further elucidated that glutamatergic mechanisms within the IC contribute to central sensitization and the modulation of TNP. Alterations in glutamate receptor expression, such as NMDAR and AMPAR, have been associated with TNP, suggesting the pivotal role of excitatory neurotransmission and synaptic plasticity within the IC [ 50 , 59 ]. GABAergic mechanisms within the IC have also been increasingly recognized for their contribution to neuroplasticity and neuromodulation, particularly in the context of emotional regulation and interoceptive awareness [ 39 ].

In addition, increased Phospho-Extracellular Signal-Regulated Kinase (pERK) activation in the IC is associated with central sensitization of TNP [ 3 , 98 ].

Relationship between insular cortex activity and trigeminal neuropathic pain in human studies

Recent evidence demonstrated the critical involvement of both AIC and PIC in the TNP processing pathway, with alterations in GMV commonly observed in TNP cases [ 26 , 27 , 42 , 47 , 64 , 72 ]. Functional imaging techniques, such as Positron Emission Tomography (PET) and functional Magnetic Resonance Imaging (fMRI), have confirmed the activation of IC in response to nociceptive orofacial stimuli, with the AIC implicated in higher-level pain interpretation and the PIC in basic sensory pain processing [ 10 , 11 , 31 , 71 , 89 ]. Furthermore, individuals with trigeminal neuralgia (TN) showed significant functional connectivity changes and microstructural integrity alterations in the white matter volume (WMV) of the IC [ 100 , 107 ]. The role of IC extends beyond processing orofacial sensations to integrating sensory inputs from both primary (SI) and secondary (SII) somatosensory cortices [ 57 ]. In the IC, there is a distinct pattern of intra-insular outputs, with a greater number from the PIC than the AIC, suggesting a directional, caudal-to-rostral flow of information within the IC [ 39 ]. Therefore, interruptions in connectivity between the AIC and PIC, whether due to lesions or neuromodulation, have been associated with impairments in trigeminal pain and temperature sensations [ 31 ].

The AIC not only processes pain intensity and the emotional dimensions of pain experiences but also plays a crucial role in anticipating pain and facilitating human awareness [ 7 , 31 ]. Intriguingly, after effective treatment for TN, the ventral AIC often shows a normalization of GMV and cortical thickness, suggesting neuroplastic adjustments that correlate with clinical improvement [ 26 , 27 ].

On the other hand, PIC is essential for processing pain and tactile sensations, serving as a primary cortical hub for integrating internal and external bodily signals [ 10 , 38 ]. Its role in somesthesis is underlined by its connections with the spinothalamic tract and the reception of nociceptive and thermoceptive information via the lamina-I-spinothalamocortical pathway from the posterior thalamic nuclei, which are crucial for the sensory discriminative aspects of trigeminal pain [ 71 , 83 ].

Relationship between insular cortex activity and trigeminal neuropathic pain in preclinical studies

Recent preclinical studies have been pivotal in elucidating the insula’s role in trigeminal pain perception, building on the findings from human research. Rodent and monkey studies confirm the IC’s response to trigeminal pain through its activation to oralfacial nociceptive stimulation [ 3 , 57 , 71 , 89 ]. Furthermore, animal studies reveal that IC lesions can mitigate neuropathic and inflammatory pain [ 44 ].

At the molecular level, trigeminal nerve injuries activate the ERK-CREB pathway in the IC, leading to an upregulation of glutamate receptors (AMPA and NMDA) and a downregulation of inhibitory potassium channels activity, which promotes neuronal long-term potentiation associated with trigeminal pain [ 98 ]. Enhanced excitatory neural responses in the dorsal IC following sensory stimulation have been observed in rats with trigeminal neuropathy [ 35 ]. Furthermore, plastic changes in neuronal circuitries from the IC to the trigeminal nucleus caudalis (TNC) may amplify responses to peripheral noxious stimulation [ 78 ].

The AIC is significantly responsive to alterations in nociceptive inputs from trigeminal afferents. Enhanced phosphorylation of ERK-1/2 in layers II-III, areas known for housing nociceptive-specific neurons, indicates the active involvement of AIC in TNP processing [ 3 ]. Additionally, the AIC influences spinal cord activities through top-down modulation, likely mediated by noradrenergic outputs from the locus coeruleus (LC), which interacts with inhibitory inputs from the lateral parabrachial nucleus (PBN) and raphe magnus nucleus (RMN). This complex interplay indicates the integral role of AIC in both initiating and modulating the pain response, particularly in conditions like TN where neurovascular compression is a significant factor [ 16 , 56 ].

On the contrary, the PIC plays a critical role in TNP by regulating orofacial sensory-motor functions, and serving as a hub for thalamic sensory inputs [ 2 , 81 , 103 ]. Research in monkeys shows the PIC receives specific thermal and pain signals, integrating them with broader autonomic and limbic systems [ 20 ]. Additionally, the PIC processes a diverse array of information, including sensory discrimination, highlighted by increased brain activity in conditions like brush-evoked allodynia. This demonstrates its capacity for neuroplasticity and neuromodulation, as evidenced by studies on somatosensory-evoked potentials [ 10 , 23 ].

Further, the PIC integrates sensory, autonomic, motor, associative, and limbic inputs, maintaining strong internal connections that facilitate its involvement in varied stimuli and emotional states [ 38 , 84 ]. In the chronic constriction injury of the Infraorbital neve (CCI-ION) rat model for TNP, increased activity of dysgranular PIC (dPIC) glutamatergic neurons (dPICg) in response to the nerve injury had been observed, which was associated with enhanced expression of pERK and CREB in the dPIC. This suggests the nociceptive processing role of dPIC involving glutamatergic neural networks during TNP [ 50 ].

Projections of insular cortex to and from other trigeminal neuropathic pain-associated brain regions

The IC is a hub for processing multisensory information, with each of its subdivisions playing distinct roles in handling various types of sensory data. Beyond receiving inputs from several brain areas, the IC has excitatory projections to different brain structures as well, enriching its multisensory processing capabilities with emotional and affective context (Fig.  1 ). This convergence ensures that sensory information processed within the IC is seamlessly integrated with limbic information, highlighting its comprehensive role in sensory perception and emotional regulation. However, it is important to note that most of the studies documenting these functions and projections are based on animal models, which could have implications for direct translational relevance to human anatomy and pathology.

Connections of IC with different brain regions. (A) Projections of IC with other brain regions. (B) Altered projections of IC with other brain regions in TNP. IC = insular cortex, OFC = orbitofrontal cortex, PFC = prefrontal cortex, MC = motor cortex, SSC = somatosensory cortex, NAc = nucleus accumbens, Str = striatum, Thal = thalamus, Hyp = hypothalamus, Amyg = amygdala, PAG = periaqueductal gray, RMN = raphe magnus nucleus, PBN = parabrachial nucleus, LC = locus coeruleas, RVM = rostral ventromedial medulla, TNC = trigeminal nucleus caudalis

Trigeminal nucleus caudalis

In TNP research, significant insights have been gained into the functional anatomy of the IC and its connections to the TNC and the trigeminal subnucleus oralis (Vo) [ 3 , 98 ]. Descending projections from the IC, specifically targeting lamina I of the medullary dorsal horn and Vo, underscore the IC’s pivotal role in conveying orofacial nociceptive information [ 2 , 20 , 104 ]. The gIC and dIC have been shown to project to both the rostral and caudal parts of TNC laminae I/II, as well as to Vo, through the corticotrigeminal pathway [ 2 , 25 , 78 , 89 , 98 , 104 ]. In addition, inhibition of dPICg activity demonstrated reduced TNC activity in CCI-ION rats highlighting a direct modulatory control of IC over TNC [ 50 ].

PAG and RVM

The gIC and the dIC have been identified as significant contributors to the endogenous descending pain-modulatory system, projecting into key structures such as the periaqueductal gray (PAG) and the rostral ventromedial medulla (RVM). These projections regulates orofacial pain processing in neurons within laminae I/II of the TNC and the Vo. The caudal regions of gIC/dIC predominantly project to the lateral PAG, with both caudal and rostral regions showing fewer projections to the ventrolateral PAG. Additionally, axonal fibers from both rostral and caudal gIC/dIC regions extend and terminate in the RVM, further highlighting IC’s indirect connection to TNC via the PAGl and RVM [ 77 , 89 ].

The dIC and gIC have robust connections to the parabrachial nucleus (PBN) and the Kölliker-Fuse nucleus (KF), both of which, in turn, directly project to the TNC and the Vo [ 89 ]. PBN also projects ascending output to these subregions, a commonality across all mammals, mediated through the ventromedial basal nucleus (VMb) [ 20 ]. In addition, the aIC is known to send glutamatergic excitatory outputs to the lateral PBN [ 56 ]. These connections facilitate the integration of sensory and nociceptive information in the IC.

Locus coeruleus

The aIC selectively projects to the inhibitory neurons in the locus coeruleus (LC) and activates inhibitory descending pain projection pathways [ 51 , 77 ]. This is further supported by evidence showing that the rostral part of the aIC sends glutamatergic excitatory outputs to the LC [ 56 ], indicating a complex interplay of excitatory inputs from the insula to the LC. It has been shown that noradrenaline from the LC can enhance TNP [ 29 ].

Raphe magnus nucleus

The rostral part of the aIC modulates pain through excitatory glutamatergic outputs to the inhibitory neurons within the raphe magnus nucleus (RMN), a key component of the brain’s pain management system [ 56 ]; Nakaya et al. [ 77 ]. This pathway between the aIC and RMN significantly contributes to the descending modulatory systems that regulate nociception, illustrating the essential dynamic balance of excitatory and inhibitory interactions within the brain’s endogenous pain management mechanisms [ 93 ].

Within the IC, a significant expression of the 5-HT1A receptor is observed in projection neurons targeting specific sub-nuclei of the amygdala, namely the central or basolateral nuclei, with approximately 75–80% of these insula-amygdala projection neurons containing 5-HT1A [ 52 ]. The amygdala plays a crucial role in the emotional dimensions of trigeminal pain perception by receiving projections from various IC nuclei. These connections are essential for integrating emotional and nociceptive signals in TNP conditions [ 4 , 97 ].

The AIC and PIC are connected to the amygdala’s central, lateral, dorsolateral, and basolateral nuclei, demonstrating a comprehensive network of insular projections that influence the diverse neural functions of the amygdala [ 90 ]. The PIC is noted for its strong subcortical outputs primarily directed towards the central nucleus of the amygdala (CeA) [ 38 , 57 ]. In contrast, the AIC exhibits limited glutamatergic projections to the amygdala, with only sparse projections observed to the extended amygdala [ 38 , 39 , 56 ]. In patients with classical TN, enhanced functional connectivity has been observed between IC and amygdala [ 99 ].

In terms of projections from the amygdala, dIC receives dense projections from various amygdaloid nuclei, including the lateral, basolateral, and central nuclei. These amygdaloid inputs are distributed across all layers of the dIC [ 56 ].

Both the AIC and PIC have significant projections to the NAcc and caudate putamen (CPu), reflecting distinct interaction patterns with the striatum by different insular subdivisions. In contrast, no feedback connections from the striatum to the IC have been identified, emphasizing a one-way flow of information from the IC to the striatum, underlying the complex communication involved in the processing of emotional and sensory information [ 38 , 39 ]. TNP patients showed a decrease in GMV in the NAcc and CPu [ 42 , 46 ]. The striatum plays a crucial role in trigeminal pain perception and modulation by integrating information from the nociceptive spinothalamic tract and descending cortical pathways. Its involvement in dopamine signaling indicates its significant impact on orofacial pain perception [ 65 ]. Moreover, NAcc has been found to influence TNP through its GABAergic medium spiny neurons and their projections to pain-regulating pathways [ 49 ]. While this could hypothetically suggest changes in the projections from the IC to these areas of the striatum, further investigations are required to directly establish this implication.

The aIC is integrally connected to the thalamus, receiving a diverse range of inputs from the mediodorsal and centro-median nuclei for broad sensory integration [ 39 , 90 ]. It also receives specific projections from the thalamus’s sub-medius and central lateral nuclei, as well as the parvicellular part of the ventral posterior nucleus [ 56 ]. Whereas, the gIC and dIC subregions form deep cortical layers reciprocal projections with the thalamus by receiving extensive sensory inputs and sending significant outputs back, particularly through the VPM, VPMpc, and VPL nuclei, which handle somatosensory, gustatory, and visceral signals [ 38 , 39 , 90 ]. Specifically, the dIC is targeted by nociceptive pathways from the ventral medial thalamic nucleus, highlighting a specific pathway for pain signals [ 7 ]. In TN patients, functional connectivity from the left IC to the thalamus was found to be increased [ 100 ]. These connections indicate the intricate relationship of IC with the thalamus.

Hypothalamus

Neurons within the IC project to both the rostral and caudal sections of the lateral hypothalamus (LH), with approximately 75% of these insula-LH projection neurons expressing the 5-HT1A receptor [ 52 ]. Neurons from both the aIC and the dIC extend their axons to the LH area [ 57 ]. The dIC, in particular, maintains reciprocal connections with the LH [ 56 ]. The projections from the aIC to the hypothalamus and brainstem are posited to play a critical role in the modulation of descending pain inhibitory control [ 93 ].

Somatosensory cortex I/II

The somatosensory cortex I (SI) is primarily influenced by dominant afferent projections from the gIC, forming a potential “spinal–gIC–SI–spinal” positive feedback loop that could underpin the persistence of allodynia [ 11 ]. The critical involvement of IC in the somatosensory neural network is indicated by its extensive sensory input from primary and secondary cortical regions, especially targeting the PIC’s excitatory neurons. On the contrary, optical imaging has revealed significant excitatory propagation from the gIC to both SI and SII [ 35 , 39 , 56 ]. This complex network of projections and feedback loops involving the IC, SI, and SII highlights the intricate mechanisms through which the brain processes and modulates somatosensory and nociceptive signals.

Motor cortex

The motor cortex and AIC has reciprocal connections between them, whereas, the aIC predominantly receives inputs from the motor cortex [ 39 , 75 ].

Prefrontal cortex

Connectivity from prefrontal cortex (PFC) regions to the AIC is notably prominent, particularly targeting inhibitory neurons within the aIC. Further supporting this intricate connectivity, studies have revealed strong direct intracortical pathways linking the IC with the medial PFC (mPFC) [ 2 , 54 ]. In contrast, AIC exhibits a bidirectional strong connection with the ventrolateral PFC, highlighting the nuanced interplay between different cortical areas in processing and integrating a wide range of cognitive functions [ 31 ].

Orbitofrontal cortex

IC has excitatory projections to the OFC and in TNP, this projections get enhanced due to increased excitatory inputs from layer IV to layer II/III pyramidal neurons in the insular-orbitofrontal region [ 35 , 70 ].

Periodontal ligament and dental pulp

The dIC and gIC receive orofacial nociceptive signals originating from the periodontal ligament (PDL) and dental pulp [ 78 ].

Neurofunctional dynamics of the insular cortex in trigeminal neuropathic pain processing pathway

The IC is a key component of the pain matrix, which is involved in the multidimensional aspects of pain perception, including the SI and SII, anterior cingulate cortex, PFC, thalamus, and cerebellar cortices. It is consistently activated during TNP and plays a pivotal role in both the sensory-discriminative and affective-motivational dimensions of TNP, mediating bottom-up and top-down modulation [ 3 , 10 , 57 ]. It influences both antinociceptive and pronociceptive pathways through reciprocal glutamatergic projections to essential brain areas such as the somatosensory, motor, and prefrontal cortices, as well as the striatum, amygdala, and thalamus [ 38 , 43 , 69 , 101 ].

After trigeminal nerve injury, nociceptive signals are transmitted from second-order neurons in the TNC to the VPM thalamus and medial nucleus of the posterior complex, which maintain bidirectional connections with the IC. Hence, innocuous mechanical stimuli can activate the nociceptive-specific neurons situated in the layers II-III of IC to influence TNP states [ 3 , 43 ]. On the other hand, direct descending projections from the IC to the TNC suggest its facilitatory role in TNP. Post-injury, trigeminal nerve projections activate nociceptive neurons in the outer (I-II) and inner (V-VI) laminae of the spinal and medullary dorsal horn, and dIC in rodents has been observed to send crucial contralateral projections to these areas and the brainstem, modulating orofacial nociceptive processing [ 2 , 56 , 69 , 84 , 89 ].

In the IC, 73% of neurons are excitatory glutamatergic and 27% are inhibitory GABAergic. The interaction of IC with NMDA receptors influences antinociceptive effects through descending pain modulatory pathways. Post trigeminal nerve injury, changes in AMPA receptor composition in the IC enhance synaptic Ca2 + permeability, which also facilitates long-term potentiation (LTP) through increased glutamatergic transmission [ 52 , 84 , 98 ]. Additionally, the serotoninergic system in the IC significantly influences chronic pain mechanisms, with 5-HT and 5-HIAA levels rising after trigeminal nerve injuries. Over 70% of glutamatergic neurons in the IC express 5-HT1A receptors, indicating a broad serotonergic influence on its neurochemical circuits [ 17 , 52 , 84 ]. Therefore, neurochemical imbalances in the IC facilitated by increased glutamatergic activity, evidenced by elevated c-Fos expression in these neurons post-CCI-ION surgery, highlight its role in TNP and central sensitization. This neurochemical imbalance is evident in both animal models and human studies of TNP [ 50 , 58 , 77 , 102 ].

Neuromodulation approaches involving the insular cortex in trigeminal neuropathic pain management

The IC holds significant therapeutic potential for managing TNP. Although relatively few, several preclinical studies employing chemical interventions and neuromodulation techniques, such as optogenetics, have shown that modulating the activity of IC plays a crucial role in altering TNP processing. These findings suggest that interventions targeting IC could significantly modify pain perception and emotional responses to TNP (Fig.  2 ). Hence, it is imperative to conduct extensive studies in both clinical and preclinical domains for the development of personalized medicine approaches that consider the individual variability in the structure and function of the IC as it could lead to effective and tailored treatments for those suffering from TNP.

Modulation of IC activity alters trigeminal pain processing pathway. (A) Trigeminal nociceptive transmission pathway via IC after trigeminal nerve injury. (B) Altered trigeminal nociceptive transmission pathway after IC neuromodulation to improve TNP condition. U0126 = ERK inhibitor, DNQX = AMPA receptor blocker, AAV-CaMKIIα-NpHR-EYFP = optogenetic virus targeting glutamatergic neurons, gIC = granular insular cortex, dIC = dysgranular insular cortex, aIC = agranular insular cortex, TNC = trigeminal nucleus caudalis

Chemical modulation

Wang et al. [ 98 ], demonstrated that corticotrigeminal projections from the IC to the TNC regulate orofacial pain and negative emotions in CCI-ION rats. This regulation occurs through the activation of the ERK pathway in IC (mainly in gIC/dIC) neurons. Infusion of U0126, an inhibitor of ERK activation, was shown to decrease both the upregulation of p-ERK in the IC and the expression of Fos in the TNC, thereby alleviating nociceptive behaviors and negative emotions in rats with nerve injury. This suggests that deactivating IC neurons by inhibiting ERK phosphorylation could significantly lessen orofacial neuropathic pain caused by CCI-ION [ 98 ].

Administration of the AMPA receptor blocker DNQX within the IC was found to decrease excitatory postsynaptic potential (EPSP) activity in the spinal trigeminal complex (Sp5C), indicating the role of AMPA receptors in the IC in the transmission of nociceptive signals [ 77 ].

Optogenetic modulation

In our recent study, we investigated the effect of optogenetic modulation of dPICg on TNP in a CCI-ION rat model. We found that optogenetic inhibition of dPICg decreased neural firing rates in the TNC and the VPM thalamus, reduced expression of sensory-responsive cell bodies and transcriptional factors in the dPIC, and improved hyperalgesia, allodynia, and anxiety-like responses in CCI-ION animals. This highlights the potential antinociceptive value of precisely inhibiting certain neural populations within the IC for managing TNP [ 50 ].

Potential prospects for insular cotrex modulation in clinical studies

Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation technique that has shown promising results in inducing lasting changes in brain activity. Although tDCS predominantly influences more superficial brain regions, high-definition montages (HD-tDCS) has proven effective in modulating PIC activity. Consequently, cathodal HD-tDCS targeting the PIC could offer new avenues for TNP management [ 41 ]. On the other hand, indirect modulation of IC through tDCS applied to other cortical areas that have direct reciprocal connections with IC may also hold therapeutic potential. For instance, anodal tDCS applied to the motor cortex has demonstrated a reduction in CNP intensity among patients with multiple sclerosis [ 74 ]. In addition, cathodal tDCS applied to SSC could modulate PIC activity [ 55 ]. Future advancements, including focused stimulation techniques and enhanced electrode designs, and continued research on the application of tDCS for IC modulation in TNP are necessary to fully ascertain the potential of tDCS in clinical settings for effective TNP management.

Transcranial Magnetic Stimulation (TMS) also offers a promising approach to modulating the IC for managing TNP. TMS utilizes magnetic fields to induce electrical currents in specific brain areas, and with advancements such as deep TMS (dTMS), it can target deeper structures like the IC [ 65 , 101 ]. Clinical evidence shows that TMS applied to the PIC in patients with central neuropathic pain can increase thermal thresholds, indicating a modulation of pain sensitivity, though without a significant effect on neuropathic pain scores. Conversely, in peripheral neuropathic pain patients, repeated TMS sessions have demonstrated a significant, albeit short-lasting, analgesic effect [ 61 ]. These findings suggest that while the direct modulation of the IC using TMS presents certain challenges due to the depth of the target area, innovative approaches in TMS technology could enhance its efficacy in modulating insular activity, thus offering a non-pharmacological therapeutic option for TNP management. Continued research into TMS application specifics, such as stimulation parameters and session frequency, will be critical in optimizing its use for effective TNP relief in clinical settings.

IC in TNP management warrants specific consideration due to its unique anatomical connections, notably the direct pathways between the trigeminal nerve and the IC. This specialized interaction is critical for understanding the intense and debilitating nature of TNP. During TNP, the IC exhibits distinct activation patterns and neuroplastic changes such as decreased GMV in the AIC, increased GMV in the PIC, increased glutamatergic neurotransmissions, reflecting long-term adaptations to chronic pain [ 101 ]; Wang et al. [ 50 , 61 , 68 , 100 ]. Consequently, IC neuromodulation holds significant potential for managing TNP, offering a targeted approach to modulate these complex pain pathways effectively.

Advanced neuroimaging techniques, including PET and magnetic resonance imaging (MRI), play crucial part for identifying neuroplastic changes within the IC, serving as potential biomarkers for TNP [ 23 , 24 , 101 ]. These tools help differentiate TNP from other facial pains, enabling more precise and timely interventions through the integration of imaging results with clinical evaluations. Pharmacological manipulation with selective serotonin reuptake inhibitors (SSRIs) and serotonin noradrenaline reuptake inhibitors (SNRIs) could have efficacy in modifying the affective components of orofacial pain since the IC serotoninergic system significantly influences the chronicity of pain [ 13 , 17 ]. Recent advancements in neuromodulation, such as transcranial magnetic stimulation (TMS) and deep brain stimulation (DBS), have also shown promise in targeting the IC to alleviate TNP symptoms by reshaping pain processing. In addition, the introduction of optogenetic stimulation provides a precise method to control the neuronal activity in IC, furthering research in trigeminal pain pathways [ 12 , 50 , 106 ].

The existing gaps in research regarding the IC’s role in TNP primarily stem from a scarcity of direct comparative studies between TNP and other neuropathies. Such comparative studies are essential to identify unique neurobiological and pathophysiological features of TNP, especially those mediated by the IC. Understanding these unique features will facilitate developing specific therapeutic strategies that are finely tuned to the nuances of TNP, potentially leading to more effective treatments. Moreover, there is a notable deficiency in longitudinal research that tracks the progression of IC involvement from the acute phase of TNP to its chronic state. Most existing studies provide only cross-sectional data, capturing a single moment in the disease’s progression. Longitudinal studies would allow researchers to observe how the role of the IC evolves over time, offering insights into the development of chronic TNP.

Therefore, looking ahead, the integration of neuromodulation, advanced diagnostics, and pharmacological strategies involving IC is crucial for a holistic approach to TNP management. Longitudinal studies and genetic research are essential to assess the efficacy of IC-targeted therapies and tailor treatments to individual responses. Combining neuromodulation techniques such as TMS, DBS, and optogenetic or chemogenetic methods could reveal new TNP relief mechanisms. Cross-disciplinary collaborations are also essential for increasing awareness among healthcare professionals and patients about the IC’s role in TNP which can enhance disease recognition and encourage exploration of new treatments.

This review compiles research on the critical role of IC subdivisions in TNP. TNP is associated with IC dysfunction, characterized by early changes in glutamatergic receptor plasticity that lead to pain chronification, maladaptive pERK signaling, and disruptions in GABAergic and dopaminergic systems. Understanding the IC’s influence on TNP requires further preclinical studies that build on clinical insights. This review will contribute to the new perspectives on directly targeting the IC for the effective TNP management.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

• Insular cortex

Agranular insular cortex

Dysgranular insular cortex

Granular insular cortex

Anterior insular cortex

Posterior insular cortex

Glutamatergic neurons of dysgranular posterior insular cortex

• Trigeminal neuropathic pain

Trigeminal neuralgia

Chronic constriction of infraorbital nerve

Gray matter volume

White matter volume

Phospho-Extracellular Signal-Regulated Kinase

Functional Magnetic Resonance Imaging

Diffusion-weighted magnetic resonance imaging

Support vector machine classification

Voxel-based morphometry

Cortical thickness analysis

Somatosensory cortex

Primary somatosensory cortices

Secondary somatosensory cortices

Nucleus accumbens

Nucleus accumbens core

Ventral posteromedial

Ventral posterolateral

Central amygdala

Parabrachial nucleus

Rostral ventromedial medulla

Periaqueductal gray

Lateral hypothalamus

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This work was supported by the National Research Foundation of Korea (NRF2023R1A2C1008079).

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Jaisan Islam, Md Taufiqur Rahman, Elina KC & Young Seok Park

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Islam, J., Rahman, M.T., KC, E. et al. Deciphering the functional role of insular cortex stratification in trigeminal neuropathic pain. J Headache Pain 25 , 76 (2024). https://doi.org/10.1186/s10194-024-01784-5

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