name a major goal of neuroscience research

Fundamental Neuroscience Research

For well over a century, discoveries in basic neuroscience research have been the basis for our understanding of the nervous system and the foundation for developing treatments for neurological disorders. Insights into fundamental neuroscience (FN) have advanced at an ever-faster pace in the 21 st  century, with remarkable novel discoveries in areas ranging from subcellular mechanisms of action to whole brain activities. FN generates key insights, drives innovation, and underlies many therapeutic breakthroughs that benefit humanity.

Background:

Neurology’s Stake in Foundational Neuroscience Research

Back to Basics: A call for fundamental neuroscience research

Discussion of Present and Future Plans For NINDS Support of Fundamental Neuroscience

The National Institute of Neurological Disorders and Stroke ( NINDS ) is planning a critical effort focused on advancing research in Fundamental Neuroscience (FN). To address this important and foundational aspect of neuroscience research, NINDS convened a  Fundamental Neuroscience Working Group (FNWG) . FNWG held a series of meetings to discuss key issues and prepared a report to the NANDS Council with recommendations to inform NINDS approaches and plans to support and foster FN research. The report will be presented to NANDS Council on Wednesday September 6, 2023: NIH VideoCast - NANDS Council - September 2023 .

FNWG activities and materials can be found here: Fundamental Neuroscience Working Group (FNWG) . The FNWG's report on Advancing Fundamental Neuroscience Research (pdf, 915 KB) can be found here.  Members of the public are encouraged to submit comments on the report, council presentation or any points related to promoting FN research to  [email protected] .

Do you have a cool image related to FN that you would like share?

If so, please include your image with the following information and submit to: [email protected]

• Description of image
• Credit of image

NOTE: Image should be in a jpg, jpeg, gif, png or pdf format.

Resources and Tools Animal Models Resources available to neuroscience researchers interested in utilizing animals as models for nervous system function. Gene Expression The resources listed below are for gene expression-related information relevant to neuroscience research. Strategic Plan NINDS supports and performs a broad array of rigorous and important neuroscience research from fundamental studies of basic nervous system function to studies to improve treatments and prevent neurological disorders. NINDS Funding Strategy Current NINDS funding guidelines and payline. NIH BRAIN Initiative The  Brain Research Through Advancing Innovative Neurotechnologies®  ( BRAIN ) Initiative is aimed at revolutionizing our understanding of the human brain. Building Up The Nerve Neuroscience trainees are taken through the life cycle of a grant from idea to award at NINDS with the people who make it happen. Find an NINDS Program Director Please reach out to individual Program Directors or Program Managers for more information about specific opportunities or visit  Find Your Program Director  to learn more. Contact Us We would love to hear from you! Related FN Articles Inviting your input: fostering research in fundamental neuroscience Request for Information (RFI) on Advancing Research in Fundamental Neuroscience   (Expired)

Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.

1 What is Neuroscience?

What is neuroscience.

• Scientist links to learn more
• Glossary Terms

Key Takeaways

• Test Yourself

Neuroscience is the study of the nervous system, the collection of nerve cells that interpret all sorts of information which allows the body to coordinate activity in response to the environment.

The study of neuroscience has taught us that the brain is a complicated organ with several connection routes, both between different bodily organs and within itself. Some of those connections communicate information down towards the body, such as signals that allow us to control the movements of our muscles or to change the activity of our internal organs. Other connections ascend into the brain, conveying all sorts of information from the world around us into a representation of our surroundings. Still, other routes communicate between brain areas, such as when the sudden detection of a threat passes through our visual system and turns into a “get ready” signal that then prepares the rest of our body for conflict. Because of this complex system of communication, the nervous system can be thought of as a series of highways and roads that connect different cities (organs).

The nervous system conveys all of these different types of information using a combination of electrical and chemical signals. The main active cellular units of the nervous system, the neurons , are highly sensitive to changes in their environment. A wide variety of chemicals called neurotransmitters are responsible for passing information between neurons.

Neuroscience is an integrative field of study

Realistically, our modern understanding of “neuroscience” is a combination of several academic disciplines, all using their strengths to understand some aspect of the nervous system. Because of this integrative nature, it is possible to study neuroscience from many different perspectives, each of them more fitting for answering different types of questions. These “angles” of analysis are described below.

At the root of the study is biology. Whenever you are studying living processes, such as learning, visual perception, or consciousness, you dip into the realm of biology. The broad field of biology can be subdivided into smaller, more precise categories. Molecular neurobiologists study proteins and gene regulation, cellular neurobiologists examine how networks of neurons communicate with one another, and cognitive neuroscientists study the underlying causes of behaviors. Understanding neuroscience involves genetics, such as the autosomal dominant neurodegenerative condition Huntington’s disease. Other biological sub-disciplines, such as ecology and evolution, are also considered in neuroscience as well, such as the parasite Toxoplasma, which changes an animal’s response to fearful stimuli, allowing the organism to reproduce as it moves through different species in the food web.

Psychology provided the earliest explanations about the brain and ideas about the origin of the mind. Some questions in this field branched off from philosophy as people began thinking about the “mind–body problem”, the discussion that centered around the question of whether a function as complex as consciousness could result from the activity of a clump of cells. Psychologists also wondered whether parts of the brain in isolation have different properties than when those parts are working together. This property, called emergence, is the idea that the whole is greater than the sum of its parts. Psychologists examine neuroscience from a top-down view, aiming questions at understanding the whole organism before looking at smaller components of the organism (compare this with biological approaches, often a bottom-up view that starts at the level of cells or molecules).

Chemistry is a strong influencer of nervous system function—just ask anyone who forgot their morning cup of coffee! We use a variety of endogenous (originating from within the body) chemicals that act as signaling molecules, allowing communication between cells. These chemicals exist in many different structures, which determine their function; some are acidic while others are basic, some are polar, others are fat-soluble, and some are even gases. The nervous system is also highly sensitive to influence by exogenous chemicals (meaning they originate from outside the body), such as caffeine and cocaine.

Many principles of physics can be observed through the functioning of neurons. For example, neurons maintain a negative electrical charge, usually measured on the scale of tens of millivolts (a millivolt is a thousandth of a volt.) The main way for neurons to send signals depends on a temporary change in this voltage; this signal is called an action potential . This change in voltage is brought on by the movement of charged ions across the cell membrane, and they closely follow the rules of magnetism: opposite charges attract while like charges repel.

Mathematical Modeling

The field of computational neuroscience has grown from the use of mathematical modeling to describe or predict some aspect of the nervous system. If our current estimates are correct, we have around 86 billion neurons in the brain, a number so large that it is difficult to conceptualize. It would be nearly impossible to understand that many components of a system without taking advantage of the sheer mathematical strength of a computer.

Healthcare Providers

Healthcare providers, like neurologists and psychiatrists, work from a different angle. They coordinate closely with researchers to apply scientific knowledge from the field or laboratory to treat patients, thus using biological principles as therapies. For example, neurologist Dr. Oliver Sacks used his knowledge of the dopamine neurotransmitter system to treat patients with a paralysis-like condition in the 1960s, leading to the development of levadopa treatment for Parkinson’s disease . Other healthcare providers use imaging strategies like a CT scan to assess the extent of a head injury or the location of a brain tumor, while an EEG can be helpful for the diagnosis of epilepsy .

Engineers help develop the tools needed to understand questions in neuroscience, such as the patch clamp rig or electron microscope, highly specialized pieces of lab equipment. They also work closely with healthcare providers to translate science into therapy, such as the deep brain stimulator devices for the treatment of conditions such as Parkinson’s disease . Collectively, all the people who participate in neuroscience in some way are united by their interest in the workings of the body. Because of the overwhelming complexity of the nervous system, there are many questions still unanswered. The continual appearance of new questions in neuroscience keeps us wondering, inspires curiosity, and promises a multitude of fascinating career paths for centuries to come.

How do we learn about neuroscience?

Experimental design.

The gold standard in science is the use of experimental design . In an experiment, the scientist uses a stepwise process of developing a research question and hypothesis, then answering that question by performing tests. The main goal of an experiment is to establish a causal relationship between one factor that is being changed, the independent variable, and the factor that is influenced, the dependent variable. A well-designed experiment has variables that are carefully controlled, which minimizes the influence of extraneous variables, often called confounding variables. The influence of confounding variables can be eliminated by comparing the experimental group with a control group, a group that is as similar as possible in every way except for the manipulation of the independent variable. Importantly, subjects or patients are generally assigned to the experimental or control group at random.

Case Studies

Another strategy is the case study , a highly detailed description of a single patient and their condition. A case study documents the details regarding a specific deficit or enhancement and is an opportunity to examine individuals with very rare conditions, which are useful for informing about the functions of different brain structures. Like a quasi-experimental study, case studies only show correlation, not causation. It is difficult to generalize the findings from a case study to the population at large.

Perhaps the most famous case study in all of neuroscience is the 1848 story of the railroad worker Phineas Gage . Gage was a construction foreman working on the railroad when an unfortunate explosive workplace accident caused a iron rod to be driven through his left frontal lobe, largely destroying it.  Remarkably, Gage survived this accident and lived another 12 years. However, Gage’s acquaintances described subsequent changes in his personality, teaching us that one of the functions of this area of the brain is regulating our inhibitions.

Case studies can be helpful for the development of hypotheses that can later be tested experimentally. For example, consider another famous case study of Patient HM , the man who had his left and right hippocampus surgically removed and couldn’t create certain types of memory. A research question based on this case study might be: “Is the hippocampus needed for the creation of navigational memory?” Then, an experimental study could be performed in rodents, where we surgically remove the hippocampus (experimental group) or a different part of the brain (control group) and see how well the rodents perform on a memory task.

The Use of Animals in Research

Though there are many ways that we can directly study humans through experimentation or case studies,  it is often impossible to test every question in humans. Instead of always studying humans, scientists often use nonhuman model organisms, the most common organisms being the worm C. elegans , fruit flies ( Drosophila melanogaster ), zebrafish ( Danio rerio ), song birds, mice, rats, and macaque monkeys.

The closer we move towards the human, the more similarities the model organism shares with us. Of the commonly used model organisms, macaque monkeys are the non-humans that are most similar to humans. We share 93% of our genetic material with macaques, but we still have different metabolic and physiological processes, and our behaviors are much different from theirs. Ethical constraints prevent us from performing experiments that may cause physical or psychological harm if performed in humans. We would never conduct a test on humans to assess what concentration of neurotoxin leads to brain damage (these experiments aren’t done very frequently in nonhumans anyway). Invertebrates, such as worms and fruit flies are not as heavily regulated by ethics oversight committees, allowing scientists to conduct a wider set of experiments on these animals.

Our moral responsibilities toward animal subjects are that:

• Animals should only be used in worthwhile experiments.
• All steps are taken to minimize pain and distress.
• All possible alternatives to animal research are considered.

Research facilities at colleges and universities are monitored by an Institutional Care and Use Committee (IACUC). The IACUC consists of fulltime veterinarians, scientists, and community members. They must follow federal laws when approving animal research.

Experimental Preparations

Performing an experiment in an intact, living organism, whether human or nonhuman, is described as an in vivo (Latin meaning “within life”) preparation. The main strength of this strategy is that the data collected here are more predictive of the human condition, which is one of the main goals of biomedical research. However, the in vivo preparation has challenges, because thousands of variables within a living system are uncontrolled or still unknown. There are also very strict ethical limitations on the nature of experiments that can be done in vivo.

On the other hand, an in vitro (Latin meaning “within glass”) preparation is an experiment performed on cultured cells or isolated molecules of DNA, RNA, or protein. These preparations have the opposite strengths and weaknesses of in vivo preparations. They allow for extremely good control over variables, but the results are less reliable in translating to a therapy. The regulations on these experiments are much more lax compared to in vivo experiments; most of the regulatory guidelines are to protect the experimenter rather than the patient or the experimental subject.

Falling in between these two preparations is an ex vivo experiment. In this kind of experiment, a section of the living organism is taken, such as a slice of brain, a tissue biopsy, or a detached frog leg. The strengths and limitations of these experiments are somewhere in between that of the other two preparations.

What neuroscience is not

As complex as the brain is, naturally misconceptions make their way into popular culture. It’s valuable to address these myths about neuroscience and explain the evidence that refutes these statements .

Myth 1: “We only use 10% of our brain.”

This wildly inaccurate statistic has been the foundation for several fictional movies, TV shows, and books. The truth is that we use every part of the brain, and most of our brain is active most of the time—just not at the same time. Neurologist V.S. Ramachandran uses a great analogy to describe the fallacy of this myth: does a traffic light only use 33% of its lights? A properly functioning traffic light will use all three lights at very precise times. The activity of the brain is closely regulated by multiple mechanisms which prevent unusual electrical activity. In fact, if too many cells were active at the wrong times, just like a traffic light showing both green and red, chaos ensues—one cause of seizures is excessive neural activity.

Myth 2: “Forming memories causes new neurons to be born.”

Another misconception is the idea that each new cell in our brain represents a new memory. While we are far from understanding the process of exactly how memories are formed in the brain, we do have a few clues. Most likely, memories are stored at the sites of close contact between neurons, called synapses. Changes in ways neurons connect and communicate with one another is likely the mechanism behind how memories are formed and stored, rather than the creation of new neurons. Even though the process of cell reproduction is halted in the majority of adult neurons, we are still capable of new neuronal growth, a process called neurogenesis . A few brain areas in particular, like the hippocampus (used in learning and memory functions and the olfactory epithelium (used for smelling), do exhibit frequent birth and death of new neurons.

Myth 3: “The brain cannot repair itself.”

If neurons aren’t being replaced in adulthood, then how do people spontaneously recover from neurological injuries like a stroke ? One of the most amazing features of the brain is the phenomenon of plasticity , the ability to change over time. Even if critical brain areas are damaged, it is theorized that the brain learns how to “rewire itself”, essentially figuring out how to carry out these functions without using the damaged connections. Unfortunately, there are some conditions that are neurodegenerative, meaning that their symptoms get progressively worse over time. Many of these disorders, like Parkinson’s disease and Alzheimer’s disease , currently do not have any simple cures or treatments that don’t carry risks and side effects. For people with these conditions, there is not strong evidence that the brain can recover from the destruction caused by these diseases.

Myth 4: “If you are analytical, you are left brain dominant, but if you are creative, you are right brain dominant.”

A common misconception is that the two hemispheres of the brain are responsible for wildly different functions. The truth is that nearly every function that the left half of the brain can do, the right half can do just as well, and vice versa. Sensory information, voluntary control of the muscles, memories, and many other behaviors can be performed equally well by both the left and right halves of the brain. A major exception to the “left vs. right” component is the processing and production of language. For some reason unknown to scientists, these functions are heavily lateralized in the left hemisphere for most people.

Fascinatingly, we do have one strange quirk about signaling between the brain and the rest of the body: signaling pathways from the left brain crosses over to communicate with the right half of the body, and vice versa. This contralateral organization is an unintended consequence of evolution, and is one of the major distinguishing features of the vertebrate brain.

Neuroscience is ever-changing

One of the most exciting and satisfying aspects of modern science is the rapidity of new discoveries in the field. New findings are often communicated by publishing academic studies in scientific journals. More neuroscience studies were published between 2015 and 2020 than in the previous seventy years! But, advancements in neuroscience haven’t always moved so quickly.

Trepanation was a surgical intervention that involved drilling a hole into an individual’s skull. It is believed to be one of the oldest surgical procedures according to archaeological evidence. Interestingly, skulls that show evidence of trepanation have been dated to 6500 BCE and show evidence of healing, indicating that the patient survived the surgery.

Localizationism

For hundreds of years, physicians attempted to correlate behaviors with changes in the brain. In the mid 1800s, the physician Paul Broca contributed to localization theory by concluding that specific areas of the brain were responsible for carrying out specific functions. This idea was supported by ablation studies that demonstrated that when different brain structures were ablated, or lesioned, there were specific associated functional losses. Further, electrically exciting specific brain structures resulted in eliciting specific behaviors.

Most likely, some behaviors are more localized than others, but still rely on signals from across many other brain areas. As with most fields of biology, absolutes are rare in neuroscience.

The real strength of our brain is its flexibility: brains are capable of changing and adapting to a wide variety of circumstances. Blind people use their visual areas of the brain while echolocating, stroke survivors can regain lost motor functions using the unaffected brain circuits, and babies can effortlessly learn two languages simultaneously in a bilingual household.

Plasticity is based on the idea that not only is the brain capable change, but that our experiences change the structure and function of our nervous system.

• Neuroscience is the study of the nervous system and is an integrative field of study that incorporates biology, psychology, chemistry, physics, mathematical modeling, and health care providers.
• The study of neuroscience is accomplished through experimental studies, case studies, and the use of experimental animal models.
• There are many popular myths concerning neuroscience and it is important to analyze data that refutes these myths.
• Though the field of neuroscience is relatively young and ever-changing, humans have been interested in the brain and its function for centuries.

Test Yourself!

Attributions.

Portions of this chapter were remixed and revised from the following sources:

• Open Neuroscience Initiative by Austin Lim. The original work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License .

Media Attributions

• Comparison of Brain Size Across Species © OpenStax adapted by Valerie Hedges is licensed under a CC BY-SA (Attribution ShareAlike) license
• Neuroscience is an integrative field of study © T. Wesley Mills adapted by Valerie Hedges is licensed under a Public Domain license
• Scientific method © Efbrazil adapted by Valerie Hedges is licensed under a CC BY-SA (Attribution ShareAlike) license
• Phineas Gage and his injury adapted by Valerie Hedges is licensed under a Public Domain license
• Commonly used animal models in neuroscience adapted by Valerie Hedges
• Preparations © Valerie Hedges is licensed under a CC BY-SA (Attribution ShareAlike) license
• Trepanated skull © Rama adapted by Valerie Hedges is licensed under a CC BY-SA (Attribution ShareAlike) license
• Paul Broca © Unknown is licensed under a Public Domain license
• Plasticity © Alan Thistle adapted by Valerie Hedges is licensed under a CC BY-SA (Attribution ShareAlike) license

The study of the nervous system

Electrically excitable cells of the nervous system that receive and transmit signals to different areas of the body.

Chemicals that are released by neurons or other cells that bind to receptors on other neurons or cells to elicit change in the target cell.

The electrical signal transmitted by the axon of a neuron.

Neurological disorder of motor function resulting from the loss of dopamine-producing cells in the substantia nigra

Electroencephalogram. A noninvasive method to record activity within the brain by using electrodes that are placed on the scalp.

Neurological disorder that causes seizures or unusual sensory experiences

a highly detailed description of a single patient and their condition

Performing an experiment in an intact, living organism, whether human or nonhuman

an experiment performed on cultured cells or isolated molecules of DNA, RNA, or protein

an experiment that takes a section of the living organism, such as a slice of brain, a tissue biopsy, or a detached frog leg

The process where new neurons are generated

A neural injury caused by either blockage of a blood vessel or bursting of a blood vessel within the brain

The ability to change

Neurodegenerative disease that causes cell death and dementia

specific areas of the brain were responsible for carrying out specific functions

A procedure that removes a structure and/or its function

Introduction to Neuroscience Copyright © 2022 by Valerie Hedges is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

Share This Book

Georgetown University Medical Center

About Neuroscience

What is neuroscience.

neu·ro·sci·ence ˌn(y)o͝orōˈsīəns/ noun

any or all of the sciences, such as neurochemistry and experimental psychology, which deal with the structure or function of the nervous system and brain.

Neuroscience , also known as Neural Science, is the study of how the nervous system develops, its structure, and what it does.

Neuroscientists focus on the brain and its impact on behavior and cognitive functions. Not only is neuroscience concerned with the normal functioning of the nervous system, but also what happens to the nervous system when people have neurological, psychiatric and neurodevelopmental disorders.

Neuroscience is often referred to in the plural, as neurosciences.

Neuroscience has traditionally been classed as a subdivision of biology. These days, it is an interdisciplinary science which liaises closely with other disciplines, such as mathematics, linguistics, engineering, computer science, chemistry, philosophy, psychology, and medicine.

Many researchers say that neuroscience means the same as neurobiology. However, neurobiology looks at the biology of the nervous system, while neuroscience refers to anything to do with the nervous system.

Neuroscientists are involved in a much wider scope of fields today than before. They study the cellular, functional, evolutionary, computational, molecular, cellular and medical aspects of the nervous system.

The major branches of modern neuroscience

The following branches of neuroscience, based on research areas and subjects of study can be broadly categorized in the following disciplines (neuroscientists usually cover several branches at the same time):

Affective neuroscience  – in most cases, research is carried out on laboratory animals and looks at how neurons behave in relation to emotions.

Behavioral neuroscience  – the study of the biological bases of behavior. Looking at how the brain affects behavior.

Cellular neuroscience  – the study of neurons, including their form and physiological properties at cellular level.

Clinical neuroscience  – looks at the disorders of the nervous system, while psychiatry, for example, looks at the disorders of the mind.

Cognitive neuroscience  – the study of higher cognitive functions that exist in humans, and their underlying neural bases. Cognitive neuroscience draws from linguistics, neuroscience, psychology and cognitive science. Cognitive neuroscientists can take two broad directions; behavioral/experimental or computational/modeling, the aim being to understand the nature of cognition from a neural point of view.

Computational neuroscience  – attempting to understand how brains compute, using computers to simulate and model brain functions, and applying techniques from mathematics, physics and other computational fields to study brain function.

Cultural neuroscience  – looks at how beliefs, practices and cultural values are shaped by and shape the brain, minds and genes over different periods.

Developmental neuroscience  – looks at how the nervous system develops on a cellular basis; what underlying mechanisms exist in neural development.

Molecular neuroscience  – the study of the role of individual molecules in the nervous system.

Neuroengineering  – using engineering techniques to better understand, replace, repair, or improve neural systems.

Neuroimaging  – a branch of medical imaging that concentrates on the brain. Neuroimaging is used to diagnose disease and assess the health of the brain. It can also be useful in the study of the brain, how it works, and how different activities affect the brain.

Neuroinformatics  – integrates data across all areas of neuroscience, to help understand the brain and treat diseases. Neuroinformatics involves acquiring data, sharing, publishing and storing information, analysis, modeling, and simulation.

Neurolinguistics  – studying what neural mechanisms in the brain control the acquisition, comprehension and utterance of language.

Neurophysiology – looks at the relationship of the brain and its functions, and the sum of the body’s parts and how they interrelate. The study of how the nervous system functions, typically using physiological techniques, such as stimulation with electrodes, light-sensitive channels, or ion- or voltage-sensitive dyes.

Paleoneurology  – the study of the brain using fossils.

Social neuroscience  – this is an interdisciplinary field dedicated to understanding how biological systems implement social processes and behavior. Social neuroscience gathers biological concepts and methods to inform and refine theories of social behavior. It uses social and behavioral concepts and data to refine neural organization and function theories.

Systems neuroscience  – follows the pathways of data flow within the CNS (central nervous system) and tries to define the kinds of processing going on there. It uses that information to explain behavioral functions.

Written by: Christian Nordqvist This article can be viewed in full at Medical News Today

Resources to Learn More About Neuroscience:

• Animal Facility
• Core Facilities
• NIH (new window)
• Pubmed (new window)
• Brain & Language Lab
• Center for Neural Injury and Recovery (CNIR)

Harvard University COVID-19 updates

Undergraduate Neuroscience Research and Thesis Neuro Research Guide

Neuro research guide, guide to choosing a neuroscience research lab, part 1: what factors to consider when joining a lab..

There are hundreds of Harvard affiliated labs, and all of them are different. Below, we have distilled those differences into several important categories to consider as you make your choice. The overarching goal is to help you choose a lab that will foster your development as a scientist.

Research Topic. Students often feel that the research topic is the decisive factor. For example, you may have a topic that you are already passionate about ( e.g., Alzheimer’s Disease, Traumatic Brain Injury, or Free Will). While this pre-existing interest can be a great motivator, consider the following: over three semesters and one summer, you will spend > 750 hours in the lab! For that much time, we think the priority should be finding a great lab environment with supportive mentors.

• False! A topic isn’t usually exciting until you learn more about it. We find that once students realize what questions are being asked, what the debates are in this area, and what real-world implications the topic has, what methods are being used/developed, they quickly become passionate about their work.
• False! Medical and graduate schools are evaluating your lab experiences in two major ways: First, they want evidence of your scientific development in the lab, including your independence in designing experiments/analyzing data and your understanding of your research topic. Second, they want to see an authentic, personalized, positive letter from your faculty mentor detailing your drive, independence, attention to detail, collegiality, etc .

Size of the lab. The size of the lab is not always, but very often, a predictor of student success. Higher visibility labs tend to be big (>20 people). Although they are publishing great papers, the environment may not be ideal for undergrads. Why? In bigger labs, it is harder to get face time with the faculty mentor. Moreover, each daily supervisor (post doc or grad student) may have several undergraduates working with them or be consumed with their own work. Students can feel lost when they do not get enough mentoring and attention. Another peril of working for ‘rockstar’ faculty is that projects may be aiming for publication in Nature, Science, or Cell, which can take more than 5 years of full-time teamwork … when things go right. As such, students are often a small part of a bigger, longer project (a ‘cog in the machine’). Their role is often more of a technical one with less control, thought, or creativity in the experimental design and therefore less scientific development and growth.

In spite of the drawbacks, some students prefer to work on high visibility projects in big labs. That is fine but be aware that these projects can lead to lower thesis evaluation grades because faculty evaluators look for evidence that the student has put independent thought and individual work into the thesis.

Independence and Project Type. It is daunting to be responsible for your own research project’s success or failure, especially when you are just starting out in science. Yet, this really is the best way to learn how to do science. Having to make decisions about what experiment to do or how to analyze your work requires a deep understanding of your research area. Large, team projects can be fun, but students often grow and learn more from small projects where they can make decisions.

Typically, projects that are small in scope (short term) work best, so you can learn from your mistakes and get feedback on your results within weeks to reconfigure if need be. Working more independently on a project also gives you more control of your data, rather than being handed data from someone else and not having any influence on how or why the experiments were done.

Mentoring. Arguably the most important aspect of your lab experience is your direct mentor. Try to meet your direct mentor before signing up with a lab. You want to make sure that they are invested in your success: meaning, 1) they has time to meet with your regularly, they have reasonable demands on your time (15 hours or less per week during the term), and they can communicate clearly with you.

Whatever lab you’re in, be sure to schedule face time with the faculty mentor (alone or with your daily supervisor) at least once per month. This will help you forge a connection to the lab head and be part of conversations that can influence your study design and color the interpretation of your results. You should also make an effort to attend lab meeting to learn about other projects and develop your critical thinking/questioning skills.

Commute/ Location. It might seem harrowing, but commuting to a lab is very possible. The free M2 shuttle can get you to the Longwood/hospital area in about 30 minutes (outside of rush hour times and extreme weather). The MBTA can get you to MGH, MIT, Broad Institute just as quickly. As long as you can arrange your schedule to have big blocks of consecutive lab time (3 hours), the commute will only be a fraction of your dedicated lab time.

The good news, if you don’t want to commute, is that labs closer to Cambridge typically have more experience working with undergrads. This often translates into a better mentoring culture for students. All things being equal, we recommend you start looking for a lab on campus (Biolabs, NW Building, William James). If you don’t find a good fit there, consider labs at the Medical School that are affiliated with the Program for Neuroscience . If you still aren’t satisfied, you can extend your search to other Harvard-affiliated hospitals or centers (MGH, Children’s Hospital, Beth Israel, Brigham and Women’s, McLean, etc .)

Part 2: Questions to ask before joining a lab.

Here is a list of potential questions to ask the lab director when you meet to talk about joining a lab:

• Typically, students meet with the faculty mentor two or three times per semester. Its great if it is more frequently, but it should not be less.
• Typically, students work with a grad student or a post-doc. They often meet every time the student comes to lab (at least at the beginning) and communicate informally by email. Since they play a big role in mentoring you, it is always a good idea to meet them before joining.
• Student projects are most rewarding when students are involved in experimental design and all aspects of data analysis. It gives the student more ownership and control of the project, which very often creates a better environment to learn to do science.
• Longer term experiments (more than a year) are usually team-projects where students don’t have much influence or control of the project.
• While every student is different, other undergrads in the lab can usually tell you what kind of experience to expect. (Laura and Ryan can give you feedback on labs students have worked in as well in case you want an additional opinion.)
• Lab meetings can be a great way to assess the group dynamics and lab culture to make sure it feels like a comfortable and stimulating environment for you.
• Typically, students should expect to spend 5-10 hrs/week if they are volunteering in the lab during the semester, or 10-15 hrs/week if they are enrolled in research for credit (Neuro 91). Most labs expect students spend one summer working full-time in the lab (often before senior year) if they are serious about a thesis or a career in research after graduation.
• This varies by lab: sometimes students will choose among several options. Sometimes there might only be one project that needs additional help (or has an available mentor). Occasionally, faculty mentors want students to develop their own project idea! You just don’t know until you ask.

Fields of Study in Neuroscience

Reviewed by Psychology Today Staff

Neuroscience is a vast field of study containing a range of narrower subfields. Each involves a spotlight on the brain and other parts of the nervous system, connecting them to one or more zones of psychology and behavior—from thought processes to social interactions to mental illness.

Given how enmeshed the different aspects of mental life are, there is plenty of overlap between the different domains of neuroscience. Different branches can blend and feed into one another: Scientific research on cognition or emotions can be of value to neuroscientists who study psychiatric disorders, for instance.

Commonly recognized categories such as the ones below offer a sense of the breadth and diversity of neuroscience as an endeavor. Among the other fields of neuroscience are neuroanatomy, cellular and molecular neuroscience, and neurogenetics (the study of the nervous system’s genetic basis). Neuroscientists in each field are typically researchers with a doctoral-level degree (such as a Ph.D. or MD).

On This Page

• Cognitive Neuroscience
• Social Neuroscience
• Clinical Neuroscience
• Developmental Neuroscience
• Affective Neuroscience
• Behavioral Neuroscience
• Computational Neuroscience

Cognitive neuroscience investigates the neural mechanisms that underlie thinking and perception. It explores how information processing, which includes learning, remembering, deciding, and problem-solving, is made possible by the brain.

The scope of cognitive neuroscience includes how thought processes unfold at the cellular level—in specific neurons and the connections between them—as well as in the links between mental processes and larger brain regions and systems.

Cognitive neuroscientists explore how the brain gives rise to mental processes and abilities. To do so, they analyze measures of cognition and aspects of individual brains—from structural variation and differences in the function of certain brain areas down to the activity of specific neurons (as they encode, for example, the location of an object in space). Such research provides insights into which parts of the brain, for example, are especially active when someone is engaged in a cognitive function such as remembering or reading.

Examples of topics in cognitive neuroscience include the formation of memories at the level of neurons, how different brain areas collaborate to produce language ability , and how the brain’s perception of the world can be biased by factors such as motivation . Cognitive neuroscientists also focus on areas such as attention, learning, decision-making, and consciousness.

Social neuroscience examines the brain in the context of its connections to other people and the broader social world. It recognizes that humans are a highly social species and that the complexity of social interaction could help explain the evolution of the highly developed human brain.

Research in social neuroscience explores how a physical system gives rise to the kinds of relational processes that have long been observed in social psychology. It also seeks to describe the ways in which the brain and body themselves are affected by aspects of social life, such as social isolation or integration.

Social neuroscientists study the relationships between the nervous system and various aspects of social cognition and behavior. These include mental processes such as identifying someone as a member of a social group and trying to understand someone else’s perspective. Researchers use measurements of biological activity (such as functional activation of particular brain areas) as well as measures of thinking and behavior to investigate the associations between them and how they influence each other.

Social neuroscience has highlighted the existence of instantaneous brain activity related to social categorization and prejudice ; loneliness-related differences in how the brain represents the self and other people; and differences in the amygdalae of altruists and psychopaths . Other phenomena explored in social neuroscience include empathy, learning in social contexts, and group hierarchies.

Clinical neuroscience is an area of study that focuses on mental and nervous system disorders. These include disorders studied in psychiatry and clinical psychology, such as depression and anxiety disorders, as well as neurodegenerative conditions such as Alzheimer’s disease and multiple sclerosis. A major aim of neuroscientific research on clinical conditions is to contribute to improved methods of diagnosis, treatment, and prevention of disorder.

Researchers in clinical neuroscience analyze data on brain activation and other aspects of nervous system function and how they relate to various forms of mental illness or dysfunction. This may involve comparing patterns of brain activation in subjects who have clinical levels of dysfunction with those of healthy subjects. A clinical neuroscientist conducts scientific research but does not necessarily treat patients, whereas specialists in related clinical fields, such as neurology and neuropsychology, are involved in assessment and treatment.

Neuroscientific methods such as brain imaging have been applied to better understand various psychiatric, neurodevelopmental, and neurological conditions. Neuroscientists have observed, for instance, differences in how autistic children’s brains respond to incoming social information and in the activity of brain areas related to cognitive control in people with mood and anxiety disorders. Such findings could help inform theorizing about what causes symptoms in these conditions and how to target treatments.

Developmental neuroscience seeks to describe how the brain and nervous system form and change. The study of how age-related changes in the brain correspond with the development of individuals’ thinking and perception is called developmental cognitive neuroscience. Developmental neuroscientists explore both typical and atypical trajectories of nervous system development to better understand the bases of normal function and dysfunction.

Broadly speaking, developmental neuroscientists research how the anatomical form and functions of nervous systems develop within species, including both invertebrates and vertebrates. More specifically, developmental cognitive neuroscientists are interested in how cognitive processes that change as humans grow up—such as judgment and learning—correspond to aspects of the developing brain. They may, for example, present individuals of different ages, from young children to adults, with cognitive tasks and analyze how the results relate to differences in the activation of certain brain areas during the tasks.

Developmental neuroscientists have connected aspects of brain structure and function to various kinds of thinking and behavior over the lifespan. Neuroscientists have uncovered evidence that, for example, parts of the brain are more responsive to peer monitoring in adolescence than in adulthood; that less sleep in children is associated with lower volume in various brain areas ; and that the brain’s structure at age 6 is predictive of brain function and reasoning years later.

Affective neuroscience explores how the brain produces emotions. It identifies how particular structures, chemicals, and networks in the brain relate to affective states such as anger, fear, pleasure, and desire and how they give rise to complex emotional experiences. Affective neuroscientists use findings from humans as well as non-human animals with analogous emotional responses to better understand the nature of these responses.

Affective neuroscience involves observing how the activation of certain areas and networks of structures in the brain (as well as differences in brain structure) correspond to various emotional states, which researchers may evoke in the lab. Based on their findings, affective neuroscientists not only identify what is happening in the brain when individuals have particular sorts of emotional experiences, but also theorize and debate about what an emotion actually is —whether, for instance, there are deeply rooted “basic emotion” categories or whether emotions are more complex and consciously constructed.

Emotional experiences of all shades, and how the brain produces them, are the territory of affective neuroscientists. They have explored how specific brain chemicals and structures help produce emotional responses—such as the neurotransmitter dopamine and desire or the amygdala and automatic responses to threats (and less directly, feelings of fear). They have also illustrated that different parts of the brain work together to produce any particular emotional state.

Behavioral neuroscience, also called biological psychology, is the study of how the brain and the rest of the nervous system provide the foundation for behavior. It examines the neural basis of capacities such as thinking and perception, learning, emotion, and motivation. As such, behavioral neuroscience overlaps with a number of contemporary fields in neuroscience, such as cognitive neuroscience (focused on thinking and perception) and affective neuroscience (focused on emotions).

Behavioral neuroscientists investigate the links between the body and behavior in humans as well as non-human animals. They use a wide array of neuroscientific methods, from brain imaging in humans to the artificial stimulation of specific brain regions in rats. They also explore the role of factors that interact with the nervous system (such as hormones and drug consumption) in explaining behavior.

Studies of the neural mechanisms underlying craving and addiction, the formation of memories, fear conditioning and behavioral responses to threats, and impairments in cognitive abilities are all examples of research that falls under the category of behavioral neuroscience. Many experiments in behavioral neuroscience use models of nonhuman animal behavior as a way to better understand the neural basis of human behavior.

Computational neuroscience is an area of brain research that makes use of the power of computer modeling and mathematical tools to unpack how the brain works in all of its complexity. The approaches of computational neuroscience, which enable researchers to grapple with vast quantities of data about the nervous system, help them to develop explanations for how events unfold from the levels of chemicals and individual neurons up through the levels of neuronal networks and ultimately behavior and cognition.

Computational neuroscientists develop mathematical models and theories of neural function—accounts of the brain’s workings that can be tested based on data from studies of humans and nonhuman animals. Some use computer-based artificial neural networks, which use simplified representations of interconnected neurons, to help understand how information is handled by real networks in the brain. Computational neuroscientists also create models that directly incorporate real-world experimental data.

Neuroscientists have used computational methods to explore the processes underlying memory, attention, object recognition, and decision-making, among other cognitive capacities. Models of brain activity have been employed to study, for example, how the prefrontal cortex learns about new situations and how damage to it might impair this ability.

Your aging brain knows how to match the function of the other bits of your body. We just have to alter how we do things to match the conditions across the lifespan.

Dog intelligence has a problem-solving factor, a learning factor, and a G factor that ties them together.

Discover how brainspotting can complement therapy, aiding in trauma resolution.

AI can detect emotions on par with human performance from audio clips as short as 1.5 seconds, new research shows.

Are you frustrated with the speed at which your child completes tasks? You might be able to improve their processing speed with video games and other technologies.

An AI deep learning model predicts behavior at nearly real-time speed with 95% accuracy.

A one-size-fits-all noninvasive brain-computer interface (BCI) enables users to play computer games through mind-reading.

Social neuroscience provides important new information that helps explain narcissism, distinguishes the two types, and offers insight into how to treat this devastating problem.

Are teenagers immune to their parents' influence? Find out how to break through.

Our daily behaviors and choices send unconscious signals about who we are. Recognizing these unintended messages allows for greater self-awareness.

• Find a Therapist
• Find a Treatment Center
• Find a Support Group
• International
• New Zealand
• South Africa
• Switzerland
• Asperger's
• Bipolar Disorder
• Chronic Pain
• Eating Disorders
• Passive Aggression
• Personality
• Goal Setting
• Positive Psychology
• Stopping Smoking
• Low Sexual Desire
• Relationships
• Child Development
• Therapy Center NEW
• Diagnosis Dictionary
• Types of Therapy

Understanding what emotional intelligence looks like and the steps needed to improve it could light a path to a more emotionally adept world.

• Coronavirus Disease 2019
• Affective Forecasting
• Neuroscience

COVID-19: Vaccine Program | Testing |  Visitor Guidelines | Information for Employees MONKEYPOX: UConn Health is NOT currently offering the monkeypox vaccine. Please visit the CT DPH website for more information or contact your health provider directly. -->

School of Medicine

Neuroscience Department

Research goals.

The Neuroscience Department is an interdisciplinary and interdepartmental program, with 32 program faculty members. The goal of research in this program is to understand the development, organization, function, and dysfunction of the nervous system at the molecular, cellular, systems, and whole animal levels. Molecular, electrophysiological, behavioral, genetic, confocal imaging, and stem or virtual cell approaches are employed, as well as cellular, animal, transgenic, and mathematical models. The breadth of this program is depicted in a survey of the numerous topics covered by faculty research, which include: stem and precursor cell biology as it pertains to gliogenesis and neurogenesis in the developing nervous system; biochemistry and regulation of gene expression, signal transduction, and intracellular trafficking in neurons and glia; structure and function of voltage-sensitive ion channels; synthesis, storage and secretion of neuropeptides; neurotransmission and plasticity; synaptic organization and stimulus coding; sensory perception, behavioral neuroscience and human psychophysics; neuroinflammation, autoimmunity, and neurodegeneration; the biology of substance abuse and biomarker imaging. Research pertaining to specific diseases or disorders include: substance abuse, stroke, epilepsy, multiple sclerosis, blindness, and deafness.

• Graduate Programs
• Prospective Students
• Current Students
• Faculty & Staff
• Degree Programs >

PHD, Neuroscience

UGA’s Interdisciplinary Neuroscience Graduate Program trains doctoral students using novel, multidisciplinary approaches to study the brain and behavior preparing them to address major neurologic and neurodegenerative diseases of the 21st century.

Degree Type: Doctoral

Degree Program Code: PHD_NEUR_BT

Degree Program Summary:

The Interdisciplinary Neuroscience Ph.D. Program at the University of Georgia (UGA) was approved by the University System of Georgia Board of Regents in 2005 and instituted in 2006. The Neuroscience Program was established in conjunction with the Biomedical and Health Science Institute (BHSI), and the BHSI serves as its administrative home. The purpose of the BHSI is to facilitate and promote interdisciplinary research and instructional efforts at UGA in the fields of biomedical and health sciences. The BHSI is comprised of three divisions: Neuroscience, One Health, and Basic and Translational Biomedical Sciences. The Neuroscience Ph.D. Program at UGA involves more than 30 faculty representing 14 departments. The Program awards doctoral degrees in Neuroscience. The Neuroscience Ph.D. Program provides graduate students with the broad academic background, technical expertise, clinical exposure, and scientific scholarship necessary to continue their careers in neuroscience research. A major goal of this research is to provide society with the basic information about nervous system function that is critical for developing treatments for neurological and behavioral disorders. Scientific study of the nervous system is thus essential for overall health and well-being. Damage or disorders in this system may result in severe impairment to the patient and costs billions of dollars to diagnose and treat each year. Examples of brain disorders that exact a devastating toll on the nation’s health include traumatic brain injury, Alzheimer’s disease, Parkinson’s disease, epilepsy, stroke, depression, schizophrenia, and drug abuse, to name but a few. Furthermore, as biomedical research progresses, it has become increasingly clear that the nervous system is critically involved in all diseases, not just behavioral and neurological disorders. Brain function influences the onset and progression of illnesses ranging from infectious disease to cancer to diabetes. Behavior, which is the manifestation of brain functions, is probably the most important factor in determining disease onset. Examples of behaviors directly influencing health include exercise, diet, smoking, and illicit drug use. Understanding how the brain regulates these behaviors may yield the most important information about how to prevent major diseases.

Degree Awarded: PhD

Degree Code: PHD_NEUR

The goals of the BHSI are to: offer a number of broadly based, interdisciplinary graduate degrees which will facilitate the recruitment of graduate students in the areas of interest to the BHSI; provide a “seamless” application and administration process for interdisciplinary grant applications; serve as a focal point at UGA for future biomedical and/or health science related initiatives both on campus and with outside partners establish a prominent public profile for UGA biomedical and health sciences research; enhance UGA public service and outreach in the biomedical and health fields.

Based on the range of current activity, existing strengths, and opportunities for expansion, the Institute is structured with three program areas, each led by a Divisional Chair: the Division of Basic and Translational Biomedical Sciences – Chair, Kojo Mesa Wilmot, Ph.D.; the Division of Neuroscience – Chair, Phil Holmes, Ph.D; the Division of One Health – Chair, Susan Sanchez, Ph.D.

Admissions to the graduate program in Neuroscience will follow the guidelines of the University of Georgia Graduate School. However, competitive candidates will score at least 1000 combined verbal and quantitative on the GRE and have a GPA of at least 3.0 on a 4.0 scale.

Given the biological and molecular nature of neuroscience, it is also recommended that students have a background in upper division biology, as well as chemistry, biochemistry and/or cell biology.

Multiple departments participate in the Neuroscience Ph.D. program at the University of Georgia. This interdisciplinary nature of neuroscience requires study across a range of topics. Thus, students will be interacting with faculty in multiple departments.

Departments with faculty represented in the Neuroscience Ph.D. program include:

• Animal and Dairy Science
• Biological and Agricultural Engineering
• Cellular Biology
• Communication Sciences and Special Education
• Educational Psychology and Instructional Technology
• Foods and Nutrition
• Kinesiology
• Physiology and Pharmacology
• Pharmaceutical and Biomedical Sciences
• Small Animal Medicine

The Ph.D. program in Neuroscience at the University of Georgia has three areas of content focus. These include the following:

• Cellular and Molecular Neurobiology
• Cognitive Neuroscience and Neuroimaging
• Systems Neuroscience

Locations Offered:

Athens (Main Campus)

College / School:

Interdisciplinary

Department:

Neuroscience

Graduate Coordinator(s):

Jesse Schank

Search for another degree

Find your graduate program.

Offering 200+ degrees, certificates and programs of study, we’ll help you get started on your graduate journey.

 or 

Search by keyword, program of study, department or area of interest

Interested in earning both a bachelor’s & master’s degree in five years or less?

Learn more about Double Dawgs .

Unlocking potential. Building futures.

Apply Today

The Graduate School Brooks Hall 310 Herty Drive Athens, GA 30602 706.542.1739

• Administration
• Graduate Bulletin
• Strategic Plan
• Virtual Tour
• Request Information
• Requirements
• Application Fee
• Check Status
• UGA Main Campus
• UGA Gwinnett
• UGA Griffin
• UGA Atlanta-Buckhead

Research Areas

Neuroscience spans from the microscopic to the macroscopic, from genes and proteins all the way to behavior, thought, and computation. Our faculty laboratories study nervous system function at multiple levels, using approaches from biology, chemistry, physics, physiology, psychology, computation, engineering, and other fields.

The department is organized into four research areas. Most faculty members are associated with several areas, reflecting the interdisciplinary nature of neuroscience. The areas serve as intellectual focus groups, to promote research collaborations, to develop undergraduate and graduate courses, and to foster graduate and postdoctoral scientific training.

Circuit, Systems & Behavioral Neuroscience

Circuit, systems and behavioral neuroscience seeks to understand how networks of neurons process information and mediate behavior; how neural activity mediates sensation, learning, movement, sleep, mood, social interaction, and many complex behaviors; and how new technologies can be used to study large-scale brain function and its role in disease.

Molecular and Cellular Neuroscience

Molecular and cellular neuroscience seeks to understand how brain cells (neurons) function at the cellular, genetic, and molecular level; how neurons develop and age; how they communicate with each other; how they are disrupted in disease; and how molecular tools can be used to study brain function and treat disease.

Circuit, Systems & Behavioral Neuroscience Faculty

• Hillel Adesnik
• Annaliese Beery
• Dan Feldman
• Yvette Fisher
• David Foster
• Daniela Kaufer
• Preeya Khanna
• Lance Kriegsfeld
• Stephan Lammel
• Frédéric Theunissen
• Joni Wallis
• Linda Wilbrecht
• Michael Yartsev
• Helen Bateup
• Michael DeWeese
• David Feinberg
• Marla Feller
• Jack Gallant
• Ehud Isacoff
• Bill Jagust
• Richard Kramer
• Bruno Olshausen
• Michael Silver
• Kevin Weiner

Molecular & Cellular Neuroscience Faculty

• Steve Brohawn
• Markita Landry
• David Presti

Cognitive Neuroscience

Cognitive neuroscience aims to understand the neurobiological basis for human cognition and human behavior, including sensory perception, attention, language, emotion, learning and cognitive flexibility, and many of the things that make us human. Cognitive neuroscientists use and develop brain imaging, EEG, and other methods that allow brain function to be studied in people.

Computational Neuroscience

Computational neuroscience aims to elucidate the principles of how, and what, the brain computes. It uses mathematical and computer science approaches to build models of brain function, to analyze complex multidimensional neural data, and to develop new computational methods and devices inspired by brain function.

Cognitive Neuroscience Faculty

• Frédéric  Theunissen

Computational Neuroscience Faculty

• Departments /
• Neurology /
• Cognitive Neuroscience Research

Medical College of Wisconsin Cognitive Neuroscience Research Program

Cognitive neuroscience focuses on understanding human brain systems underlying higher cognitive processes such as language, reasoning, decision-making, social behavior, and memory. Breakdown of these functions is a central feature of normal aging and of many common neurological conditions, including stroke, Alzheimer’s disease, epilepsy, traumatic brain injury, and Parkinson’s disease. The resulting cognitive impairments, which can include loss of language abilities, impaired memory, impaired concentration and problem solving, impaired judgment, and many other specific processing disorders, are often devastating to patients and caregivers, and are the main cause of disability in these conditions. Cognitive neuroscience attempts to understand these processes, how they break down in pathological states, and how they can be restored using pharmacological, behavioral, and physiological methods. As demonstrated by recent funding of the NIH BRAIN Initiative, the Human Connectome Project, and multiple large disease-related brain connectome projects (including the Epilepsy Connectome Project and the Alzheimer Disease Connectome Project, both headed by MCW PIs), there are large gaps in our understanding of cognitive brain processes, many of which arise from activity in large, complex neural networks, and a great need to fill these gaps. Understanding the architectural organization of these networks and the specific roles played by the hundreds of distinct information processing nodes so far identified in the human brain is a major goal of neuroscience.

Program Goal

Our overall goal is to improve the health of Wisconsin citizens by enhancing research on the diagnosis and treatment of cognitive disorders. The Cognitive Neuroscience Research Program (CNRP) brings together researchers and clinical providers at MCW working on diagnosis and treatment of these conditions, providing powerful state-of-the-art tools and infrastructure support for enabling coordinated research projects.

Funding Support Advancing a Healthier Wisconsin Endowment Project #5520462: Cognitive Neuroscience Research Program B.-F. Fitzsimmons, Principal Investigator

Program Aims

Aim 1. Provide core support for clinical and basic researchers at MCW in the field of cognitive neuroscience . This infrastructure includes expertise and personnel for computational analysis of imaging (MRI and MEG) and electrophysiological (MEG and EEG) data, management of clinical research databanks, and administrative support.

Aim 2. Maintain patient databanks to support research in memory disorders, epilepsy, and aphasia . These are disease-specific, REDCap databanks based on prospective recruitment under IRB approved protocols. The databases complement existing large clinical programs in these areas, leveraging these data to enable novel research programs.

Aim 3. Assist the We Energies Center for Aphasia Research (WE-CARE) in conducting cutting-edge research on communication disorders and novel treatment protocols for aphasia . The aphasia research program is a natural extension of longstanding NIH-supported Neurology Department research in language neuroscience. CARE leverages this expertise in neuroimaging, neuropsychology, noninvasive stimulation, and other investigative approaches to develop novel therapies for aphasia, with the aim of creating a new Midwest regional referral center for aphasia research, evaluation, and treatment.

Aim 4. Administer a competitive pilot grant program to support MRI and MEG imaging studies in cognitive neuroscience . Up to 3 projects will be funded per year, and approximately 10-12 over the span of the current AHW award. These grants will enable scientists to acquire pilot data in preparation for larger extramural grant applications.

Meet Our Team

Program faculty & staff.

Jeffrey R. Binder, MD

Sara B. Pillay, PhD, ABPP

Assistant professor.

Manoj Raghavan, MD, PhD

Priyanka Shah-Basak, PhD

Samantha Drane, MS

Program manager.

Joe Heffernan, MS

Staff scientist, monica keith, phd, engineer ii, affiliated faculty.

Piero G. Antuono, MD

Joseph L. Amaral, PhD

Diane S. Book, MD

Associate professor.

Benjamin Brett, PhD

Alissa M. Butts, PhD, ABPP

Chad Carlson, MD

Leonardo Fernandino, PhD

Malgorzata Franczak, MD

Elias David Granadillo Deluque, MD

Adam Greenberg, PhD

Associate dean of postdoctoral education, school of graduate studies; associate professor of biomedical engineering; associate professor of ophthalmology & visual sciences; director of the sensory neuroscience, attention, and perception laboratory, william gross, md, phd.

Amy Heffelfinger, PhD, MPE, ABPP

Professor, chief.

Julie K. Janecek, PhD, ABPP

Jennifer I. Koop, PhD, ABPP

Michelle Loman Moudry, PhD, ABPP-CN

Michael McCrea, PhD, ABPP

Timothy B. Meier, PhD

Lindsay Nelson, PhD, ABPP

Sara J. Swanson, PhD, ABPP

Chief, professor.

Laura Glass Umfleet, PsyD, ABPP

Harry T. Whelan, MD

Cnrp pilot grants awarded.

Sara Pillay, PhD, Principal Investigator

Abstract Speech therapy treatments for post-stroke aphasia are of limited effectiveness, and patients are often left with residual deficits. There is a need for new inventions that have the potential to improve treatment outcomes. One such new approach is transcranial direct current stimulation (tDCS), a non-invasive brain stimulation method that uses a weak electrical current to modulate neuronal resting membrane potentials. Despite promising clinical trial results, it remains unclear how tDCS affects different language processes during recovery. The objectives of this study are to (1) study the behavioral effects of tDCS on language processes during recovery after stroke, and (2) examine how tDCS affects neural connectivity using resting-state fmri (rs- fMRI). Patients identified as having a phonologic (word-sound) impairment but intact semantics (word- meaning) will receive high-density array anodal or sham tDCS concurrent with either standard speech therapy or semantic feature analysis (SFA), a targeted speech therapy intervention aimed at recruiting the undamaged semantic system. Patients will be randomly assigned to receive standard speech therapy or SFA, and will receive either anodal- or sham-tDCS for 10 sessions (Treatment A) before crossing-over to receive a second 10 sessions (Treatment B) with whatever tDCS invention they did not receive initially. Patients and therapists will be blind to tDCS status. We propose to stimulate the left angular gyrus (AG), an area implicated in several language processes including semantic access. Patients will complete a language assessment plus rs-fMRI to at 3 time-points (pre-therapy, post-Treatment A, post-Treatment B). We have previously shown that BOLD activation in this region is associated specifically with successful responses during word reading in chronic aphasia patients with phonologic retrieval deficits but intact semantics. We hypothesize that the left AG has the potential to 'boost' phonological retrieval through activation of associated semantic information.

Lisa Conant, PhD, Principal Investigator

Abstract Verbs play a fundamental role in the interface of semantics and syntax, and there is evidence that they may be particularly vulnerable to disruption in both developmental and acquired language disorders due to their semantic complexity. Verb meanings are bipartite in structure, with each verb defined by both idiosyncratic semantic content and a structural component shared across verbs of a specific class. However, the nature of their conceptual representation and its neural instantiation remain little understood. There is now significant evidence that concepts are learned through a process of generalization from real-world experiences and are subsequently partially 'embodied' in the modal systems involved in these experiences, with more abstract representation of conceptual knowledge in heteromodal 'hubs'. We previously developed a model of experiential attributes including not only sensory and motor domains but also affective, social, and other more abstract attributes, and have used it successfully to predict behavioral and brain data; however, this model does not adequately capture the structural component of verb meaning, known as event structure. In the proposed project, we plan to decompose event structure categories into meaningful dimensions based on how they are experienced, and incorporate these into our conceptual model. We will use representational similarity analysis applied to fMRI data collected during verb processing to identify brain regions sensitive to variations in these dimensions and to identify differences in the neural representation of aspectuality and argument structure. We hypothesize that the full model will be associated with activity in widely distributed regions comprising the semantic network, and that information about event structure will be associated with activity in left inferior frontal, posterior temporal, and inferior parietal regions. A better understanding of how verbs are represented could have clinical significance with regard to the development of more sensitive assessment tools and targeted interventions for language disorders.

Leonardo Fernandino, PhD, Principal Investigator

Abstract Acquired language impairments resulting from brain injury are known as aphasias. According to the National Institutes of Health, 180,000 new cases of aphasia are diagnosed each year in the United States. The pattern of language impairments varies depending on the location and extent of the lesion, but many patients present with specifically semantic deficits. A major unexplained finding in aphasia research is that selective semantic deficits for animal concepts are substantially more common than selective deficits for human-made artifacts. The present project will address this issue by investigating the nature of the information encoded in the neural activity patterns underlying these concepts in healthy participants. Specifically, we will evaluate the extent to which different types of information (e.g., categorical, experiential, distributional) and different neurocognitive systems contribute to the representation of animal and artifact concepts, with the aim of elucidating the mechanisms that drive their differential rate of impairment. First, we will use functional MRI to evaluate the overall discriminability of the neural representations of animals and artifacts. We predict that, compared to animals, artifacts will be associated with more discriminable neural patterns. We will then use representational similarity analysis to characterize the information content of these patterns. We hypothesize that both animal and artifact concepts rely substantially more on experiential features than on other types of information. Finally, we will evaluate the relative contributions of different experiential features to the neural representation of these concepts. We expect that animal concepts will be associated with a more restricted set of experiential features, particularly visual features. These analyses will be conducted on the same data set, acquired from healthy participants while performing a semantic judgment task on individual nouns. The results will help advance our understanding of semantic language disorders and provide answers to a long-standing question in the field.

Laura Glass Umfleet, PhD, Principal Investigator; Yang Wang, MD, PhD, Co-PI

Abstract Cerebrovascular injury can lead to executive dysfunction, which is a top predictor of decline in independent functioning. Reactivity of glial cells (crucial for CNS homeostasis) plays a critical role in cerebrovascular disease (CVD) seen in neurodegenerative conditions. Examining the neurovascular unit (NVU) provides an opportunity for understanding the intersection between cerebrovascular and glial pathology. NVU dysfunction and reductions in cerebral blood flow (CBF) can occur in dementia, and NVU dysfunction can precede cerebral atrophy. A promising biomarker of CVD is measurement of regional CBF responses, which are normally controlled by activity within the NVU, a process known as neurovascular coupling . Interrogation of this process will yield new insights into cerebrovascular correlates of cognition and neurodegeneration and improving neurovascular coupling could be neuroprotective. The main objectives of this proposal are to examine neurovascular coupling/uncoupling in prodromal dementia (mild cognitive impairment/MCI) and neurovascular coupling/uncoupling predictors of executive functioning. Executive dysfunction is common in non-amnestic MCI (naMCI), and, in particular, the dysexecutive naMCI phenotype is at greater risk for frontosubcortical network dysfunction. Establishing cerebrovascular biomarkers for naMCI phenotypes is crucial for determining outcome, etiological diagnosis/es, and neuroprotective treatments. In this pilot study, we will apply an innovative multiband and multiecho ASL/BOLD sequence to study neurovascular uncoupling in a naMCI cohort with executive dysfunction. This will allow us to achieve our first aim to evaluate MCI group differences in neurovascular coupling. We hypothesize that neurovascular uncoupling will emerge in mesial-temporal circuitry in aMCI and extra-temporal circuitry in naMCI. Our second aim is to examine possible physiological mechanisms that predict executive dysfunction in naMCI. We hypothesize that the neurovascular uncoupling in executive networks will be associated with fMRI executive functioning task performance. Our efforts will result in NIH funding proposals to elucidate mechanisms underlying cerebrovascular pathophysiology to aid in identifying therapeutic targets for neurodegenerative diseases.

Priyanka Shah-Basak, PhD, Principal Investigator

Abstract The overarching objective is a systematic investigation of large-scale oscillatory synchronization underlying lexical-semantic and phonological language processing using magnetoencephalography (MEG) and transcranial alternating current stimulation (tACS). The long-term goal is to provide a critical foundation of evidence to support future studies examining the efficacy of tACS for the treatment of aphasia. Neuronal oscillations are ubiquitous in the human brain. Coherent or synchronous neuronal oscillations that emerge between brain areas are thought to facilitate configuration of functional networks to promote effective and selective transfer of information across regions in a task-dependent manner. Long-range synchronization (LRS) patterns between anterior and posterior perisylvian language regions, underlying component language processes, are poorly understood. Most prior studies have examined oscillatory correlates using spectral power analyses, which quantify local intra-regional synchronization but do not elucidate coupling between regions in the language network. The use of brain stimulation techniques such as tACS to examine the functional significance of LRS in language processing is a fledgling but promising research field. TACS involves delivering sinusoidal currents that can entrain endogenous neuronal oscillatory activity and influence LRS between stimulated regions. TACS thus provides a novel method for examining the physiology of the language network and the causal role of LRS in language processing, but little is known about how parameters such as phase (of alternating currents) affect network function. Additionally, prior tACS language studies have fallen short in their evaluations of LRS patterns in response to tACS phase conditions. To address these gaps, a double-blind, within-subject, sham-controlled experiment is proposed, using MEG to evaluate tACS effects on phase synchronization of oscillatory activity between anterior-posterior language areas during category-exemplar (semantic) and nonword rhyme matching (phonological) tasks, and effects of in-phase and anti-phase 10-Hz tACS on phonological processing and LRS. Our findings will provide a foundation for understanding dynamic functional coupling related to lexical-semantic and phonological processing in healthy language networks.

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Published: May 2008

Choices in neuroscience careers

• Tamas Bartfai ,
• Tom Insel ,
• Gord Fishell &
• Nancy Rothwell  

Nature Reviews Neuroscience volume  9 ,  pages 401–405 ( 2008 ) Cite this article

2468 Accesses

6 Altmetric

Metrics details

How do I choose a mentor? How do I decide what field of neuroscience to work in? Should I consider doing research in industry? Most students and postdoctoral researchers aiming for a successful career in neuroscience ask themselves these questions. In this article, Nature Reviews Neuroscience asks four successful neuroscientists for their thoughts on the factors one should consider when making these decisions. We hope that this Viewpoint will serve as a useful resource for junior neuroscientists who have to make important and sometimes difficult decisions that might have long-lasting consequences for their careers.

This is a preview of subscription content, access via your institution

Access options

Subscribe to this journal

Receive 12 print issues and online access

176,64 € per year

only 14,72 € per issue

Rent or buy this article

Prices vary by article type

Prices may be subject to local taxes which are calculated during checkout

Similar content being viewed by others

Science’s greatest discoverers: a shift towards greater interdisciplinarity, top universities and older age

Alexander Krauss

Early-career factors largely determine the future impact of prominent researchers: evidence across eight scientific fields

Alexander Krauss, Lluís Danús & Marta Sales-Pardo

A European perspective on structural barriers to women’s career progression in neuroscience

Ashley M. Bourke, Teresa Spanò & Erin M. Schuman

Dyson, F. J. Imagined Worlds . (Harvard Univ. Press, Cambridge, Massachusetts, 1997).

Google Scholar  

Download references

You can also search for this author in PubMed   Google Scholar

Related links

Further information.

Tamas Bartfai's homepage

Gord Fishell's homepage

National Institute of Mental Health

Nancy Rothwell's homepage

NIH Blueprint for Neuroscience Research

Faculty of 1000

Rights and permissions

Reprints and permissions

About this article

Cite this article.

Bartfai, T., Insel, T., Fishell, G. et al. Choices in neuroscience careers. Nat Rev Neurosci 9 , 401–405 (2008). https://doi.org/10.1038/nrn2386

Download citation

Issue Date : May 2008

DOI : https://doi.org/10.1038/nrn2386

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Search St. Lawrence

• Neuroscience Major Learning Goals
• Office & Department Directory
• Neuroscience

1.  To develop a broad understanding of the structure and function of the nervous system with a depth of knowledge in cellular/molecular or behavioral/cognitive perspectives.

2.  To use neuroscience research techniques to conduct research.

3.  To integrate content, skills and critical thinking to design feasible independent research projects employing the scientific method.

4.  To critique and contextualize the published neuroscience literature, including the ability to critically analyze experimental design and data interpretation.

5.  To demonstrate quantitative literacy, including the application of statistical methods to analyze and interpret data.

6.  To develop oral and written scientific communication skills through presentations and the writing of papers using scientific conventions of format, succinctness, objectivity and accuracy.

7.  To develop an understanding of the ethical issues surrounding the use of human participants and animal subjects in neuroscience research.

• Request Info Discover why St. Lawrence is the right choice
• Visit & Tour Plan a tour in person or virtually
• Apply Begin your application process

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Funct Neurol
• v.28(3); Jul-Sep 2013

Seven challenges for neuroscience

Although twenty-first century neuroscience is a major scientific enterprise, advances in basic research have not yet translated into benefits for society. In this paper, I outline seven fundamental challenges that need to be overcome. First, neuro-science has to become “big science” – we need big teams with the resources and competences to tackle the big problems. Second, we need to create interlinked sets of data providing a complete picture of single areas of the brain at their different levels of organization with “rungs” linking the descriptions for humans and other species. Such “data ladders” will help us to meet the third challenge – the development of efficient predictive tools, enabling us to drastically increase the information we can extract from expensive experiments. The fourth challenge goes one step further: we have to develop novel hardware and software sufficiently powerful to simulate the brain. In the future, supercomputer-based brain simulation will enable us to make in silico manipulations and recordings, which are currently completely impossible in the lab. The fifth and sixth challenges are translational. On the one hand we need to develop new ways of classifying and simulating brain disease, leading to better diagnosis and more effective drug discovery. On the other, we have to exploit our knowledge to build new brain-inspired technologies, with potentially huge benefits for industry and for society. This leads to the seventh challenge. Neuroscience can indeed deliver huge benefits but we have to be aware of widespread social concern about our work. We need to recognize the fears that exist, lay them to rest, and actively build public support for neuroscience research. We have to set goals for ourselves that the public can recognize and share. And then we have to deliver on our promises. Only in this way, will we receive the support and funding we need.

Introduction

Twenty-first century neuroscience is a rapidly growing, large-scale scientific enterprise. According to PubMed, the number of published papers with the word “brain” in the title increased from fewer than 3000 per year in 1960 to more than 60000 in 2010 ( Fig. 1 ). The research going into these papers is supported by public funding amounting to more than $7 billion a year, mainly from the USA (about $5.6 billion) and the rest from EU countries and increasingly from other areas of the world.

Published papers including the word “brain” (Search results from Entrez PubMed - June 28, 2013)

Yet despite this enormous investment, the number of new drug and other treatments is decreasing and seems to be grinding to a halt. It can be argued that “small science” in neuroscience has failed to harvest our exponentially growing knowledge and turn it into a benefit for society. Neuroscience has also not delivered on many basic promises. After decades of effort, we still have only a very limited understanding of the mechanisms linking brain structure and function at the microscopic level to cognition and behavior or to the large-scale patterns of activity we observe in imaging studies. Neuroscience research has done little to halt the rising tide of brain disease, whose costs may soon reach 10% of world GDP. And it has yet to make a real contribution to computing technology. When a young child recognizes and grasps a furry toy, the child’s brain demonstrates image processing and motor control capabilities beyond those of our most powerful computers. In principle, neuroscience could reveal the biological mechanisms underlying these capabilities – allowing the development of a new generation of brain-inspired computing technology. To date we have failed to do this.

Human beings have a powerful urge to understand their own nature, and a strong practical need to cure brain disease and develop new computing technologies. These demands, combined with the new possibilities opened up by modern ICT and high throughput technology, are driving a rapid transformation of neuroscience in the direction of “big science” and big “data”. The last few years have seen the birth of pioneering efforts such as the Allen Brain Institute’s Brain Atlases, the Human Connectome Project, work at Cold Springs Harbor on the human projectome, the ADNI initiative and our own Blue Brain Project at EPFL. 2013 saw the announcement of the American BRAIN project and the EU-funded Human Brain Project, which I have the privilege to coordinate. Other countries like Canada and China are planning their own initiatives. These projects, and others working in the same direction, have the potential to finally realize the enormous scientific, medical and technological potential of modern neuroscience. But to do so there are still fundamental challenges that need to be overcome. In this paper, I will outline what I believe are the seven most important of these challenges.

Challenge 1. Change the way neuroscience is done

Delivering on the promise of neuroscience is not just a question of research methodology or technology – it implies a change in the structure and practices of our discipline. Big science initiatives in other disciplines such as physics or astronomy or genomics involve large multidisciplinary teams, close collaboration between scientists and engineers, and widespread sharing of data and tools, for example through the deposition of data in public repositories and the use of pre-print servers. In neuroscience, by contrast, most laboratories are relatively small and have only limited access to engineering resources. Despite the emergence of large resources, such as the Allen Brain Atlases, data sharing remains the exception rather than the rule. Attempts to bridge the gaps between different levels of brain organization are hampered by the fragmentation of the discipline into sub-disciplines each with its own journals, conferences, conceptual frameworks, vocabulary and experimental methods.

Neuroscience has the potential to make fundamental contributions to medicine, computing and our understanding of the human condition, but to do so it has to adopt forms of organization and modes of operation better adapted to the needs of big science. The first major challenge is thus to change the way neuro-science is done: to move away from small-scale collaborations towards large teams that bring together the huge range of competences and the technical and financial resources necessary to tackle the “big problems”. We have spent too long waiting for a new Einstein to unify our field. We have to unify it ourselves. The way to do so is to forget about our egos and seriously begin working together.

Challenge 2. Data ladders

The way neuroscience is currently organized has many practical implications for research. Groups working on different levels of brain organization work in different areas of the brain, in different animals, at different ages. Geneticists use mice, studies of neural microcircuitry focus on rats, most of our knowledge of the visual cortex comes from cats, and research on higher cognitive functions uses monkeys or human volunteers. Of course, there are good technical and scientific reasons for this diversity: for instance, most of our current genetic technologies have been developed in mice. However, the lack of a unified strategy has two negative consequences. The first is that there is still not a single area of the brain, in any species, for which we have data spanning all its different levels of organization. This means we have no way of identifying or experimentally manipulating the biological mechanisms linking lower and higher levels. The second negative consequence is that we lack the data to correlate observations in one species with observations in another. In particular, we are missing the systematic knowledge we would need to extrapolate results from animal experiments to humans – where many kinds of experiment are technically or ethically impossible. The second major challenge is thus to create “data ladders” – interlinked sets of data providing an increasingly complete picture of a single area of the brain at different levels of organization (molecules, cells, microcircuits, brain areas, etc.) with “rungs” linking the descriptions for homologous areas in humans and other species ( Fig. 2 ). Creating such ladders is an example of what we can achieve if we can transform the organization and practices of neuroscience along the lines outlined in challenge 1.

Data ladders: interlinked sets of data providing an increasingly complete picture of a single area of the brain at different levels of organization (molecules, cells, microcircuits, brain areas etc.) with “rungs” linking the descriptions for homologous areas in humans and experimental animals.

Challenge 3. Predictive neuroscience

“Big neuroscience” faces challenges that are even harder than the challenges addressed by previous “big science” projects. Consider, for instance, the Human Genome Project. The goal was to measure the approximately 3 billion base pairs of the human genome – a huge but not intractable challenge. The problem facing neuroscience is much larger – and harder to define. To completely characterize the human brain, we would need to measure more than 80 billion neurons, and more than 80 trillion synapses each with its own characteristic structure and electrophysiological behaviors, not to mention their subcellular structures and the innumerable molecular interactions that regulate their development and behavior.

Instead of measuring every individual neuron and each individual synapses – an impossible task – we could characterize the pathways between specific types of neuron. But even then the problem remains extremely hard. Our own studies of the cortical column show that a single column contains more than a thousand such pathways. But after decades of research we have detailed characterizations of just twenty – at a cost of about one million dollars per pathway. Given these numbers, it is evident that we will never be able to measure each of these pathways experimentally. The alternative is to predict the value of key parameters from data that is more readily available. For instance, we have recently published a technique that makes it possible to reliably predict the characteristics of synaptic pathways from the composition of a particular area of the brain – the number of cells belonging to different neuron types – and from 3D reconstructions of their morphology ( Hill et al., 2012 ).

This is only an example of what is possible. The literature shows that we can apply predictive strategies to many different levels of brain organization. Examples of work in this area include a recently published algorithm that can synthesize a broad range of neuron dendritic morphologies ( Cuntz et al., 2010 ), algorithms to generate specific motifs in network connectivity ( Song et al., 2005 ), and algorithms to predict synaptic strength based on network architecture ( Perin et al., 2011 ). In another area of research, recent work has demonstrated that biophysical models of neurons’ electrophysiological properties can successfully predict ion channel distributions and densities on the cell surface ( Hay et al., 2011 ).

By combining these predictions with cellular composition data, it is possible to predict protein maps for neural tissue. Finally, predictive methods can help to resolve one of the most important challenges for modern neuro-science, namely the classification and categorization of different types of cortical interneurons ( Ascoli et al., 2008 ). A recent model uses gene expression data to predict type, morphology and layer of origin with over 80% accuracy ( Khazen et al., 2012 ). The same model reveals rules for the combinatorial expression of ion channel genes.

The third challenge facing neuroscience is to develop these strategies further – in the Human Brain Project we are applying them to sixteen different prediction problems. Only in this way can we obtain the data we need to model and simulate the brain.

Challenge 4. Simulating the brain

How does the brain compute? What are the computational principles that allow it to model, predict, perceive and interact with the outside world? How does the brain implement these principles? To answer these fundamental questions, we need data ladders – but data and correlations among data sets are not enough. What we need to identify are causal mechanisms: for example we need to understand the way neurotransmitters and hormones modulate neural activity, synaptic transmission and plasticity, or, at a higher level, the way the brain “binds” information from multiple visual areas to form a unified picture of the world.

The classical way to establish causation is through experimental manipulation of living brains or tissue samples combined with simultaneous measurements of the response. But experiments in humans and animals are technically difficult, expensive and often cannot answer the questions we need to ask. For ethical and technical reasons, most invasive techniques are impossible to use on humans. Non-invasive imaging methods lack the spatial and temporal resolution to probe detailed neuronal circuitry. Working in animals, there are technical limitations on how many experiments we can perform and how much information we can extract from each experiment.

In other words, neuroscience is in a position similar to that of cosmology or climatology – sciences in which opportunities for experiments are strictly limited. In each of these disciplines, researchers investigate causal mechanisms, not by manipulating a physical system (it is hard to manipulate the cosmos!) but by building computer models of the system and manipulating the models in in silico experiments. Obviously, every model needs to be validated. But once it has been demonstrated that it effectively replicates a particular class of experimental observation, it becomes a new class of experimental tool.

Simulation offers huge advantages to neuroscience. There are no limitations on what we can record: so long as a parameter is represented in the model, we can measure it. Potentially, simulation allows us to record from millions or billions of neurons at a time. There are also no limits on the number of manipulations we can perform: with simulation we can perform systematic studies, unthinkable in animals or in tissue samples. Another advantage is that experiments are perfectly replicable – simulation models are not affected by the variability present even in the best-designed biological experiments. Simulation makes it possible, for the first time, to build bridges between different levels of brain organization. The cortical column we are modeling in the Blue Brain Project represents just one pixel in an image coming from an fMRI study. By modeling and manipulating multiple columns, we can begin to understand the low-level mechanisms underlying the higher-level patterns of activity we observe in our imaging studies.

At the time of writing, we can create cellular-level models of a few tens of cortical columns in the brain of a juvenile rat. But in the next ten years, the Human Brain Project will develop first draft cellular-level models of whole rodent brains and eventually of the whole human brain. With the development of human brain models, simulation will begin to show its full potential. If we want to study the low-level biological mechanisms responsible for human cognitive capabilities and the breakdown of these mechanisms in disease, we will not be able to use the same invasive approaches we use in animals. In silico manipulations and recordings will become our main experimental tool.

Realizing the potential of simulation calls for major technological innovation in high-performance computing. Detailed simulations of the brain have huge memory footprints. Thus, simulating the human brain will require new computer memory hardware, and new ways of managing very large volumes of memory. Very large brain simulations will require new numerical techniques making it possible to efficiently solve huge numbers of differential equations. We will need multi-scale simulation techniques making it possible to simulate some “regions” in greater detail than others. We will need to simulate aspects of the brain that are not yet included in our models: plasticity, the rewiring and pruning of neural circuitry, the role of neuromodulation, glia cells and the vasculature. Researchers performing in silico experiments will require virtual instruments equivalent to the physical instruments they use in the lab – virtual microscopes, virtual imaging technology. Implementing such instruments will require new forms of interactive supercomputing and supercomputer visualization.

Finally, to fulfill the promise of brain simulation we will need the ability to study how the brain gives rise to behavior. In other words, we have to “close the loop”, simulating how a model brain can control a body interacting with the physical world. Thus, we will need to simulate not just the brain, but also the body, the interface between the brain and the body, the physical world the body inhabits, and their interactions.

Each of these tasks will require radically new hardware and software. Thus, the fourth challenge for neuroscience goes beyond neuroscience. To simulate the brain, we first have to develop the hardware and software we need to do so.

Challenge 5. Classifying and simulating diseases of the brain

According to a recent report, nearly one-third of the citizens of the EU will be affected by psychiatric or neurological disease (anxiety, mood disorders, neurodegenerative disease, etc.) at least once in their life. The cost of brain disease to the European economy has been estimated at nearly Eur 800 billion per year, accounting for 25% of the total direct costs of healthcare (costs borne by national health services, insurance companies, and patients’ families) and a very considerable indirect cost (lost working days for patients and their carers) ( Gustavsson et al., 2011 ). Meanwhile, the cost of developing new CNS drugs is rising exponentially, largely due to high failure rates in phase III clinical trials. As a result, pharmaceutical companies are shutting down their neuroscience research labs and shifting their resources to other, more profitable areas of medicine and the rate of drug discovery is falling ( Fig. 3 ) ( Abbott, 2011 ). Many of the drugs we currently use date back to the 1980s and 1990s or even to the 1950s. Almost none are curative.

Number of new drugs registered by the FDA for sale in the USA (calculated from data provided by http://www.centerwatch.com/drug-information/fda-approvals/ ).

Neurological and psychiatric disease begins with an initial change in the brain – sometimes triggered by events in the patient’s external environment – followed by a cascade of knock-on effects. The fundamental reason it is so difficult to diagnose and treat brain disease is that we lack an adequate understanding of these cascades. As a result, most brain diseases are diagnosed not in terms of objective biological markers such as those we use to diagnose cancer or cardiovascular disease but by cognitive and behavioral symptoms, grouped into syndromes. This creates a severe risk of misdiagnosis – autopsy studies suggest that as many as 20% of cases of Alzheimer’s disease are misdiagnosed ( Beach et al., 2012 ). It also means many diseases (including Alzheimer’s) are diagnosed at late stages in which they have already caused irreversible damage. Meanwhile, pharmaceutical companies are forced to invest in drug candidates without fully understanding their mechanism of action or their potential side effects. Critically, there is currently no drug for the central nervous system (CNS) for which we have a full picture of its impact at all relevant levels of biological organization, from genes, ion channels and receptors through to cells, circuits, and the whole brain. Poor diagnostic methods and the lack of reliable biomarkers make it hard for researchers to select drug targets and candidate molecules, hard to choose patients for trials, and hard to measure outcomes. In these conditions, it is not surprising that trials of CNS drugs are long, require large numbers of participants and have higher rates of failure than trials for other indications. Worse, trials often fail to identify potentially valuable drugs that are effective only for a subgroup of patients ( Pangalos et al., 2007 ).

The fifth challenge facing neuroscience is to help resolve this impasse. To do so, we need to characterize the way diseases of the brain modify the structure and function of the brain at its different levels of organization. As in studies of the healthy brain, the first step will be to gather the data we need – in this case, very large sets of genetic data, lab results, imaging, and clinical observations from patients with the broadest possible range of pathologies. The data we need exists: hospital archives store vast volumes of data about patients; clinical trials and long-term longitudinal studies have accumulated enormous databases. However, we still need technical, legal and organizational solutions to federate this data and make it available to researchers, while simultaneously satisfying legitimate concerns about privacy and data protection.

The second step will be to analyze and cluster the data – identifying groups of patients whose biological data show common patterns, e.g. mutations in the same genes, similar patterns of gene expression, similar modifications in the large features of neuroanatomy or brain activity, as detected by imaging. The discovery of common patterns will make it possible to develop objective classifications of diseases, allowing physicians to diagnose diseases of the brain in terms of their unique biological signatures. New diagnostic methods, based on these classifications, will ensure that patients receive the therapies best adapted to their conditions, providing new opportunities for personalized medicine. The new methods will also make it easier for pharmaceutical companies to select participants for clinical trials and to measure the outcomes. In a longer-term perspective, they will make it possible to modify models of the healthy brain to reproduce the biological signatures of disease. In medicine as in neuroscience, brain modeling and simulation will allow in silico experiments that are ethically or technically impossible with any other technique. In particular, simulation will enable researchers to systematically explore alternative intervention strategies before embarking on costly animal studies and clinical trials. Neuroscience has the potential to accelerate drug discovery, reduce failure rates in trials and cut the cost of CNS drug discovery. It is a moral imperative that it realize that potential.

Challenge 6. From the brain to brain-inspired technology

The human brain is the world’s most sophisticated information processing machine, yet it operates on computational principles that seem to be completely different from those of conventional computing technology. These principles – which we have still to properly understand – allow it to solve computational problems that are difficult or intractable with current computing technology, all while consuming about 30W of power. They allow it to learn new skills without explicit programming. They ensure that it can operate reliably even when many of its components fail. These are highly desirable characteristics for future generation computing technologies.

In the coming years, neuroscience will learn more and more about the brain’s unique ability to model and predict the outside world, about the basic computational principles underlying this ability, and about the biological mechanisms implementing these principles at different levels of brain organization. In particular, experimental research combined with modeling and simulation will help us to distinguish aspects of neurobiology that are essential to the brain’s computational and cognitive capabilities from details that are not directly relevant to brain function. The sixth challenge for neuroscience is to translate this fundamental knowledge into brain-inspired technologies, directly inspired by the architecture of the brain.

There are many problems we will need to solve. Some are engineering issues. Future “neuromorphic” computing systems will contain millions and ultimately billions of artificial neurons. Implementing such systems will require new hardware technologies; designing, configuring, testing and using them will require new software. But many of the key issues have a direct tie to neuroscience. Given the limitations of our hardware technology, we will need techniques that allow us to simplify our brain models, while conserving the functionality we wish to replicate in technology. This will require a deep theoretical understanding of the way the brain implements its computational principles. Knowledge of the cognitive architectures underlying capabilities such as visual perception can help us to design computing systems offering functionality completely absent in current systems.

These are the challenges. The prize at stake is a completely new category of computing technology, with potentially huge benefits for industry and society.

Challenge 7. Working with society

Every neuroscientist is aware of widespread social concern about our work. There is concern about our research methods. Animal experimentation is increasingly unpopular – not just among activists and extremists. Proposals to re-use clinical data in research arouses concerns about privacy and consent. Perhaps more critically, many sectors of public opinion are frightened by claims about what we are likely to achieve. For many, understanding the brain is one more step in the “disenchantment of the world”. What would it mean for our perceptions of ourselves as human beings if we finally understood the biological mechanisms underlying human decision-making, human emotions, our perceptions of beauty, our sense of right and wrong? What would it mean for our concepts of free will and moral responsibility? What would it mean for our system of criminal law? We have to recognize that these are deeply rooted concerns.

Other fears focus on possible technological applications of neuroscience results – some real, some imaginary. For instance, media reports of experiments in “mind-reading” and transcranial magnetic stimulation have raised concerns that future technologies could be used to probe or manipulate people’s inmost thoughts. Work on the neural correlates of violent or other forms of deviant behavior raises the specter of preventative legal measures against citizens deemed to be at high risk – before they have committed a crime.

In computing, the idea of brain simulation and of brain-inspired computing technologies has led to speculations about new forms of artificial intelligence, more powerful than human intelligence. More realistically, the possibility of a new category of neuromorphic computing technology with strong disruptive potential raises a broad range of concerns, from worries about military applications, and applications for mass surveillance, to questions concerning the impact on industry and employment.

Some of the issues raised by pundits and the media are due to a misapprehension of the current state of neuroscience and technology. However, the fears to which they give rise are absolutely real.

The seventh, vital challenge for neuroscience is to recognize these fears, lay them to rest, and actively build public support for neuroscience research. For neuroscience to deliver on its promise it is not enough that society tolerates what we are doing – leaving us alone in our laboratories. We need society’s active support. This means we have to take public concerns seriously – even when they seem to be irrational or ill-founded. This means we should not denigrate our opponents. It means we should work hard to educate the public about our goals, methods and results.

However, first and foremost it means we have to set goals for ourselves that the public can recognize and share. And then we have to deliver on our promises. Only if we do this, engaging and informing the public, will we receive the support and funding we need to address the other challenges I have outlined in this paper.

• Abbott A . Novartis to shut brain research facility . Nature . 2011 ; 480 : 161 – 162 . [ PubMed ] [ Google Scholar ]
• Ascoli GA , Alonso-Nanclares L , Anderson SA , et al. Petilla terminology: nomenclature of features of GABAergic interneurons of the cerebral cortex . Nat Rev Neurosci . 2008 ; 9 : 557 – 568 . [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Beach TG , Monsell SE , Phillips LE , et al. Accuracy of the clinical diagnosis of Alzheimer disease at National Institute on Aging Alzheimer Disease Centers, 2005–2010 . J Neuropathol Exp Neurol . 2012 ; 71 : 266 – 273 . [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Cuntz H , Forstner F , Borst A , et al. One rule to grow them all: a general theory of neuronal branching and its practical application . PLoS Comput Biol . 2010 ; 6 : e1000877 . [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Hay E , Hill S , Schürmann F , et al. Models of neocortical layer 5b pyramidal cells capturing a wide range of dendritic and perisomatic active properties . PLoS Comput Biol . 2011 ; 7 : e1002107 . [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Hill SL , Wang Y , Riachi I , et al. Statistical connectivity provides a sufficient foundation for specific functional connectivity in neocortical neural microcircuits . Proc Natl Acad Sci U S A . 2012 ; 109 : E2885 – E2894 . [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Khazen G , Hill SL , Schürmann F , et al. Combinatorial expression rules of ion channel genes in juvenile rat (Rattus norvegicus) neocortical neurons . PLoS One . 2012 ; 7 : e34786 . [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Pangalos MN , Schechter LE , Hurko O . Drug development for CNS disorders: strategies for balancing risk and reducing attrition . Nat Rev Drug Discov . 2007 ; 6 : 521 – 532 . [ PubMed ] [ Google Scholar ]
• Perin R , Berger TK , Markram H . A synaptic organizing principle for cortical neuronal groups . Proc Natl Acad Sci U S A . 2011 ; 108 : 5419 – 5424 . [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Song S , Sjöström PJ , Reigl M , et al. Highly nonrandom features of synaptic connectivity in local cortical circuits . PLoS Biol . 2005 ; 3 : e68 . [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Gustavsson A , Svensson M , Jacobi F , et al. Cost of disorders of the brain in Europe 2010 . Eur Neuropsychopharmacol . 2011 ; 21 : 718 – 779 . [ PubMed ] [ Google Scholar ]