lateralization of function hypothesis

Lateralization of Brain Function & Hemispheric Specialization

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

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

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Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

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

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

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Lateralization of brain function is the view that distinct brain regions perform certain functions.

For instance, it is believed that different brain areas are responsible for controlling language, formulating memories, and making movements.

If a certain area of the brain becomes damaged, the function associated with that area will also be affected.

It contrasts with the holistic theory of the brain that all parts of the brain are involved in the processing of thought and action.

Left brain vs. Right brain

The human brain is split into two hemispheres, right and left. They are joined together by the corpus callosum, a bundle of nerve fibers located in the middle of the brain.

Hemispheric lateralization is the idea that each hemisphere is responsible for different functions . Each of these functions is localized to either the right or left side.

The left hemisphere is associated with language functions, such as formulating grammar and vocabulary and containing different language centers (Broca’s and Wernicke’s area).

The right hemisphere is associated with more visuospatial functions such as visualization, depth perception, and spatial navigation. These left and right functions are the cases in most people, especially right-handed people.

The brain contains cortices such as the visual, motor, and somatosensory cortices . These cortices are all contralateral, meaning that each hemisphere controls the opposite side of the body.

For example, the motor cortex in the left hemisphere controls the muscle movements of the right arm and leg. Likewise, damage to the right occipital lobe (responsible for vision) can result in loss of sight in the left field of vision.

Language Lateralization

Hemispheric lateralization is the idea that both hemispheres are functionally different and that certain mental processes and behaviors are mainly controlled by one hemisphere rather than the other.

There is evidence of some specialization of function, mainly regarding differences in language ability. Beyond that, however, the differences found have been minor. We know that the left hemisphere controls the right half of the body, and the right hemisphere controls the left half of the body.

Broca’s Area

Paul Broca was a French physician and one of the earlier advocators for lateralization of brain function. In 1861, Broca met a patient who he would refer to as ‘Tan.’

At the time, there was a lot of debate as to whether there was the localization of function within the brain or if the whole brain was utilized in performing every function.

Broca described the patient ‘Tan,’ who was named this due to this being the only word they could say. Often this patient would repeat the word twice, saying ‘Tan Tan.’

When ‘Tan’ died, a post-mortem of his brain revealed damage to a part of his left frontal cortex. Broca found that other patients with similar problems to Tan had damage to the same region.

It was concluded that the damage to this region, then given the name ‘Broca’s area,’ was the reason for Tan’s language problems. Broca’s area is believed to be located in a part of the inferior frontal gyrus in the frontal lobe , on the left side of the majority of people.

This research largely supports the view that the role of language function is localized to the brain’s left hemisphere.

Broca’s area is associated with multiple language functions, including language comprehension and being able to articulate words.

This region is also associated with listening, as understanding words requires articulating them in your head. It has also been suggested to be active during planning, initiating, and understanding another’s movement.

Broca’s area may also contain mirror neurons as this area appears to be involved in observing people and imitating them (Amunts & Hari, 2005).

The term Broca’s Aphasia was used to describe the condition of Tan and Broca’s other patients. People who have damage to Broca’s area tend to have suffered a brain injury (e.g., through a stroke) which then affects this language area.

The main symptom of Broca’s aphasia is a deficit in spoken and written language production. A person with damage to this area would likely be unable to articulate words or be able to string a coherent sentence together.

Speaking in an abnormal tone or rhythm can also be a symptom of this damage, as well as speech being repetitive, disordered grammar, and a disordered structure of individual words.

Finally, damage can also result in transcortical motor aphasia, meaning the speech is non-fluent and often limited to two words at a time.

Wernicke’s Area

A few years after Broca’s discoveries, in 1876, German neurologist Carl Wernicke identified another region of the brain associated with language.

Wernicke identified that some of his patients were able to speak but were not able to actually comprehend language. When examining the brains of these patients, it was revealed that there were lesions at a junction of the upper temporal lobe in the left hemisphere.

This region was named Wernicke’s area and was described as an area where heard and seen words are understood and words selected for articulation.

This area also works together with Broca’s area. Wernicke’s area comprehends the language and chooses words, which are then sent to Broca’s area to be articulated.

Wernicke’s area contains motor neurons involved in speech comprehension and is surrounded by an area called Geschwind’s territory.

When a person hears words, Wernicke’s area associates the sounds with their meaning, to which neurons in Geschwind’s territory are thought to help by combining the many different properties of words (such as the sound and meaning) to provide fuller comprehension.

When a person speaks, however, this process happens in reverse as Wernicke’s area will find the right words to correspond to the thoughts to be expressed.

Wernicke’s Aphasia was coined to describe damage to Wernicke’s area. This is often thought to be damaged via head trauma or disease.

People who experience Wernicke’s aphasia may experience symptoms such as an inability to understand spoken language and speaking using inappropriate words.

Their sentences may not make sense. They may repeat words, make up meaningless words, or have sentences lacking meaning.

Most of the time, people with Wernicke’s aphasia often speak fluently, compared to Broca’s aphasia, where language is non-fluent or broken up.

Some patients may not even be aware that they have an issue with their speech and will believe they are speaking normally.

Research Studies

Split-brains.

Split-brain research demonstrates that the brain’s two hemispheres can operate independently when the corpus callosum, which connects them, is severed. This reveals lateralization of brain functions, with certain tasks predominantly managed by one hemisphere or another.

For instance, the left hemisphere typically handles language and logical processing, while the right is more involved in spatial and holistic processing.

As an outdated treatment for severe epilepsy, the corpus callosum was sliced, meaning the connections between the two hemispheres were halted.

People who undergo this procedure are known as split-brain patients. In the 1960s, neurobiologist Roger Sperry conducted experiments on these split-brain patients to test whether there was a localization of function in the hemispheres.

Sperry conducted many split-brain experiments, one being the ‘divided field experiment.’ An example of this experiment would be to project words on the right and left fields of vision while one eye is covered to test whether the patients can say the word.

They found that the patients could say the word presented on the right visual field, controlled by the left hemisphere and containing the language centers. The words presented on the left side, controlled by the right hemisphere, could not be spoken.

However, the patients would instead be able to draw the word that was shown on the left side or pick up the object of the word shown due to the right hemisphere being able to control motor movements of the left hand.

When asked why the patients chose or drew the objects, they were unable to say, suggesting that the right hemisphere (in most people) is unconscious, although the information it holds can affect behavior.

Another study by Gazzaniga (1983) conducted a similar experiment but used faces projected to both visual fields. It was found that faces on the left visual field, thus projecting to the right hemisphere, were recognized, but not through the right visual field to the left hemisphere.

This demonstrates that the right hemisphere may better recognize faces in general.

Although it is known that the lateralization of language functions is in the left hemisphere in most people, this lateralization may depend on personal handedness.

Szaflarski et al. (2002) used functional magnetic resonance imaging (fMRI) on individuals who were left-hand dominant while they completed language acquisition and non-linguistic tasks.

It was found through the fMRI that there was more activation in the right hemisphere of the participants, concluding that they had typical language dominance.

There is a question of whether or not lateralization of language function occurs from birth, or if this lateralization develops over time.

Olulade et al. (2020) aimed to study the lateralization of language development by using fMRI on children and adults completing language-based tasks.

The researchers found that in the youngest children (aged 4-6 years old), there was left and right hemispheric activation, so language was not lateralized to one hemisphere.

They also found that right-side activation significantly decreased with age, with over 60% of adults lacking any considerable right activation.

This study suggests that lateralization of language, predominately the left hemisphere, develops over time during childhood.

Emotion lateralization

A review of the literature investigating the lateralization of emotion in the brain found that the left and right hemispheres have different functions regarding emotions (Silberman & Weingartner, 1986).

It was suggested that the right hemisphere is better at controlling emotional expressions and recognizing emotions and is associated with feelings of negative emotions.

Meanwhile, the left hemisphere specialized in dealing with positive emotions. This implied that different functions of emotion lateralized to each hemisphere.

In support of this view, another study found that patients who had suffered trauma to their left frontal lobe, particularly their prefrontal cortex, experienced depression as a result (Paradiso et al., 1999).

Similarly, patients who had suffered damage to their right frontal lobes were found to be more likely to show signs of inappropriate cheerfulness and mania (Starkstein et al., 1989).

This supports the idea that the left hemisphere is lateralized to positive emotions while the right is lateralized to negative ones.

Gender Differences

There are several studies that support the notion that there are differences in the lateralization of function in the brains of males and females.

Tomasi and Volkow (2012) found that males had increased right lateralization of connectivity in areas of the temporal, frontal, and occipital cortices. In contrast, females had increased left lateralization of connectivity in the left frontal cortex.

It is suggested that differences in the lateralization of males’ and females’ brains may underlie some of the typical gender differences in cognitive styles.

For instance, females’ typical linguistic advantage over males may reflect increased left lateralization of language areas. In contrast, males’ typical advantage of visuospatial skills may reflect increased lateralization of right-side visuospatial areas (Clements et al., 2006).

Reber and Tranel (2017) reviewed studies of brain differences in males and females. They found a lot of evidence of a sex-related difference in an area of the brain called the ventral-medial prefrontal cortex (vmPFC), an area associated with decision-making and emotion.

Tranel et al. (2002) found that male patients with damage to their right vmPFC showed deficits in social, emotional, and decision-making skills than those with left-side damage.

However, the only female patient with right vmPFC damage displayed fewer deficits in all behavioral categories. This evidence implies that lateralization of higher cognitive functions depends on the sex of the individual.

Phineas Gage (1848)

The theory of brain localization is supported by the famous case study of Phineas Gage (1848) , an American railway construction foreman. During an accident, a large iron rod was driven completely through his head, destroying much of his brain’s left frontal lobe.

He survived the accident, but his personality changed; he became unstable and is reported not to have been able to hold down a job.

This supports the localization of functions theory as it shows that control of social behavior is located in the frontal cortex .

Clements, A. M., Rimrodt, S. L., Abel, J. R., Blankner, J. G., Mostofsky, S. H., Pekar, J. J., Denckla, M. B. & Cutting, L. E. (2006). Sex differences in cerebral laterality of language and visuospatial processing. Brain and Language, 98 (2), 150-158.

Gazzaniga, M. S., & Smylie, C. S. (1983). Facial recognition and brain asymmetries: Clues to underlying mechanisms. Annals of Neurology: Official Journal of the American Neurological Association and the Child Neurology Society, 13 (5), 536-540.

Olulade, O. A., Seydell-Greenwald, A., Chambers, C. E., Turkeltaub, P. E., Dromerick, A. W., Berl, M. M., Gaillard, W. D. & Newport, E. L. (2020). The neural basis of language development: Changes in lateralization over age. Proceedings of the National Academy of Sciences, 117 (38), 23477-23483.

Paradiso, S., Johnson, D. L., Andreasen, N. C., O’Leary, D. S., Watkins, G. L., Boles Ponto, L. L., & Hichwa, R. D. (1999). Cerebral blood flow changes associated with attribution of emotional valence to pleasant, unpleasant, and neutral visual stimuli in a PET study of normal subjects. American Journal of Psychiatry, 156 (10), 1618-1629.

Reber, J., & Tranel, D. (2017). Sex differences in the functional lateralization of emotion and decision making in the human brain. Journal of Neuroscience Research, 95 (1-2), 270-278.

Silberman, E. K., & Weingartner, H. (1986). Hemispheric lateralization of functions related to emotion. Brain and Cognition, 5 (3), 322-353.

Sperry, R. W. (1967). Split-brain approach to learning problems . The neu.

Starkstein, S. E., Robinson, R. G., Honig, M. A., Parikh, R. M., Joselyn, J., & Price, T. R. (1989). Mood changes after right-hemisphere lesions. The British Journal of Psychiatry, 155 (1), 79-85.

Szaflarski, J. P., Binder, J. R., Possing, E. T., McKiernan, K. A., Ward, B. D., & Hammeke, T. A. (2002). Language lateralization in left-handed and ambidextrous people: fMRI data. Neurology, 59 (2), 238-244.

Tomasi, D., & Volkow, N. D. (2012). Laterality patterns of brain functional connectivity: gender effects. Cerebral Cortex, 22 (6), 1455-1462.

Tranel, D., Bechara, A., & Denburg, N. L. (2002). Asymmetric functional roles of right and left ventromedial prefrontal cortices in social conduct, decision-making, and emotional processing. Cortex, 38 (4), 589-612.

Further Reading

• Gainotti, G. (2014). Why are the right and left hemisphere conceptual representations different?. Behavioral neurology, 2014.
• Macdonald, K., Germine, L., Anderson, A., Christodoulou, J., & McGrath, L. M. (2017). Dispelling the myth: Training in education or neuroscience decreases but does not eliminate beliefs in neuromyths. Frontiers in psychology , 8, 1314.
• Corballis, M. C. (2014). Left brain, right brain: facts and fantasies. PLoS Biol, 12 (1), e1001767.
• Nielsen, J. A., Zielinski, B. A., Ferguson, M. A., Lainhart, J. E., & Anderson, J. S. (2013). An evaluation of the left-brain vs. right-brain hypothesis with resting state functional connectivity magnetic resonance imaging. PloS one, 8 (8), e71275.

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Open Access

Peer-reviewed

Research Article

Dynamic changes in brain lateralization correlate with human cognitive performance

Contributed equally to this work with: Xinran Wu, Xiangzhen Kong, Deniz Vatansever

Roles Conceptualization, Data curation, Formal analysis, Methodology, Validation, Visualization, Writing – original draft, Writing – review & editing

Affiliation Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China

Roles Methodology, Supervision, Writing – review & editing

Affiliation Department of Psychology and Behavioral Sciences, Zhejiang University, Zhejiang, China

Roles Methodology, Writing – review & editing

Affiliation Psychiatric & Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America

Roles Writing – review & editing

Affiliation School of Computer Science and Technology, East China Normal University, Shanghai, China

Affiliations Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China, Department of the Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, United Kingdom, Department of Psychology, University of Cambridge, Cambridge, United Kingdom

Roles Supervision, Writing – review & editing

Affiliations Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China, Department of the Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, United Kingdom, Department of Psychiatry, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom

Roles Funding acquisition, Project administration, Supervision

Affiliations Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China, Department of Computer Science, University of Warwick, Coventry, United Kingdom, Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China, Shanghai Center for Mathematical Sciences, Shanghai, China

Affiliation Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & Informatics, Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America

Roles Conceptualization, Funding acquisition, Methodology, Project administration, Resources, Supervision, Writing – review & editing

* E-mail: [email protected]

• Xinran Wu, 
• Xiangzhen Kong, 
• Deniz Vatansever, 
• Zhaowen Liu, 
• Kai Zhang, 
• Barbara J. Sahakian, 
• Trevor W. Robbins, 
• Jianfeng Feng, 
• Paul Thompson, 

• Published: March 17, 2022
• https://doi.org/10.1371/journal.pbio.3001560
• Peer Review
• Reader Comments

Hemispheric lateralization constitutes a core architectural principle of human brain organization underlying cognition, often argued to represent a stable, trait-like feature. However, emerging evidence underlines the inherently dynamic nature of brain networks, in which time-resolved alterations in functional lateralization remain uncharted. Integrating dynamic network approaches with the concept of hemispheric laterality, we map the spatiotemporal architecture of whole-brain lateralization in a large sample of high-quality resting-state fMRI data ( N = 991, Human Connectome Project). We reveal distinct laterality dynamics across lower-order sensorimotor systems and higher-order associative networks. Specifically, we expose 2 aspects of the laterality dynamics: laterality fluctuations (LF), defined as the standard deviation of laterality time series, and laterality reversal (LR), referring to the number of zero crossings in laterality time series. These 2 measures are associated with moderate and extreme changes in laterality over time, respectively. While LF depict positive association with language function and cognitive flexibility, LR shows a negative association with the same cognitive abilities. These opposing interactions indicate a dynamic balance between intra and interhemispheric communication, i.e., segregation and integration of information across hemispheres. Furthermore, in their time-resolved laterality index, the default mode and language networks correlate negatively with visual/sensorimotor and attention networks, which are linked to better cognitive abilities. Finally, the laterality dynamics are associated with functional connectivity changes of higher-order brain networks and correlate with regional metabolism and structural connectivity. Our results provide insights into the adaptive nature of the lateralized brain and new perspectives for future studies of human cognition, genetics, and brain disorders.

Citation: Wu X, Kong X, Vatansever D, Liu Z, Zhang K, Sahakian BJ, et al. (2022) Dynamic changes in brain lateralization correlate with human cognitive performance. PLoS Biol 20(3): e3001560. https://doi.org/10.1371/journal.pbio.3001560

Academic Editor: Matthew F. S. Rushworth, Oxford University, UNITED KINGDOM

Received: November 5, 2021; Accepted: January 31, 2022; Published: March 17, 2022

Copyright: © 2022 Wu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: Raw fMRI data of Human Connectome Project and behavioral measurements are publicly available and can be downloaded from the Human Connectome Project Access ( http://www.humanconnectome.org/data ), while subjects’ personal information (or “restricted data”, like information about zygosity and parents, et al.) is restricted for researchers who meet the criteria for access to confidential data ( https://www.humanconnectome.org/study/hcp-young-adult/document/1200-subjects-data-release ). Researchers want to get the restricted data can contact HCP Project Manager according to the application process provided by HCP ( https://www.humanconnectome.org/study/hcp-young-adult/document/restricted-data-usage ) to obtain the data. Subject identification numbers in the database and all other relevant data are within its Supporting Information files ( S1 – S5 Data).

Funding: Data used in this work were provided by Human Connectome Project ( https://www.humanconnectome.org/ ). JZ was supported by Science and Technology Innovation 2030 - Brain Science and Brain-Inspired Intelligence Project (Grant No. 2021ZD0200204), Shanghai Municipal Science and Technology Major Project (No.2018SHZDZX01) and NSFC 61973086, and ZJLab. XK is supported by the Fundamental Research Funds for the Central Universities (2021XZZX006), the National Natural Science Foundation of China (32171031), and Information Technology Center of Zhejiang University. DV was funded by the National Natural Science Foundation of China (No. 31950410541), the Shanghai Municipal Science and Technology Major Project (No. 2018SHZDZX01). PMT was supported, in part, by NIH grant U54 EB020403. JF was supported by the 111 Project (No. B18015), the key project of Shanghai Science and Technology (No. 16JC1420402), National Key R&D Program of China (No. 2018YFC1312900), National Natural Science Foundation of China (NSFC 91630314). KZ was supported by the Shanghai Pujiang Program. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors declared that there are no competing interests.

Abbreviations: AFG, Arenas-Fernandez-Gomez; AI, autonomy index; ALFF, amplitude of low-frequency fluctuation; BCT, Brain Connectivity Toolbox; BMI, body mass index; CAB-NP, Cole-Anticevic Brain-wide Network Partition; DFC, dynamic functional connectivity; DLI, dynamic laterality index; DTI, diffusion tensor imaging; DZ, dizygotic; FA, fractional anisotropy; FD, frame distance; FDR, false discovery rate; FN, fiber number; FSL, FMRIB Software Library; GS, global signal; HCP, Human Connectome Project; LF, laterality fluctuations; LR, laterality reversal; MLI, mean laterality index; MZ, monozygotic; PALM, Permutation Analysis of Linear Models; ROI, region of interest; SC, structural connectivity

Introduction

Hemispheric lateralization is a prominent feature of human brain organization [ 1 ], with interhemispheric differences repeatedly observed in both structure and function [ 2 – 8 ]. For example, the left planum temporale, commonly referred to as the Wernicke’s area, shows reliable activity in cognitive paradigms that probe auditory processing and receptive language [ 9 , 10 ]. In addition to such reports from “task-induced activation” studies on functional lateralization [ 7 , 11 ], emerging evidence also indicates lateralization within the human brain’s intrinsic connectivity architecture at rest. For example, recent neuroimaging studies suggest that hemispheric lateralization, estimated using network-based approaches on resting-state fMRI data, can accurately predict activity-based lateralization during cognitive task performance [ 12 , 13 ]. Together, existing evidence alludes to the vital contribution of hemispheric lateralization to healthy and adaptive mentation.

Functional lateralization is traditionally considered as a static and trait-level characteristic of individuals [ 8 , 12 , 14 , 15 ] that is hypothesized to enhance neural capacity [ 16 , 17 ]. For example, laterality of language and attention networks have been previously associated with individual differences in linguistic and visuospatial abilities [ 8 , 12 , 14 , 15 ]. Beyond such trait-level characteristics, however, recent evidence also suggests the “dynamic” nature of intrinsic brain networks over time [ 18 – 20 ]. Dynamic functional connectivity (DFC) can track the time-varying alterations in cognitive states, task demands, and performance [ 21 – 23 ], providing insights into how brain network reconfiguration relates to cognition, consciousness, and psychiatric disorders [ 19 , 22 , 24 , 25 ]. In parallel, emerging findings now indicate that the degree of lateralization is instantaneously modulated by various external factors such as attention, task contexts, and cognitive demands [ 26 – 29 ], which may arise from time-varying interactions between bottom-up and top-down neural processing [ 7 , 30 ]. Therefore, it is possible that hemispheric lateralization also changes across time to accommodate changing demands of the environment, which may be evaluated by dynamic brain network approaches.

However, these 2 vital aspects of brain network interactions, in other words, the dynamic changes of brain lateralization and their relationship to higher-order cognition, have not been explored to date. Therefore, in the present study, we developed a measure of “dynamic lateralization” and tested its significance in explaining individual variability in cognitive performance. Specifically, we investigated the laterality dynamics by 2 complementary measures, i.e., laterality fluctuations (LF) and laterality reversal (LR), which reflect moderate and extreme changes in laterality, respectively, on intrinsic brain networks constructed from high-quality resting-state fMRI data from the Human Connectome Project (HCP) [ 31 ]. Here, we show that LF are positively associated with language function and cognitive flexibility, while LR shows an opposite effect, suggesting a balance between intra and interhemispheric information communication. Furthermore, negative correlations in time-varying laterality between default mode network and visual/sensorimotor and attention networks and their relationship with cognitive performance were revealed, suggesting parallel information processing capacity, which may facilitate adaptive cognition. Additionally, we also investigated the neural and anatomical factors that may affect dynamic laterality of the human brains and established the heritability of the dynamic laterality measures.

Dynamic laterality index

We analyzed resting-state fMRI data from 991 participants in the HCP cohort and extracted BOLD time series of all 360 cortical regions using a group-level parcellation scheme (HCP MMP1.0) [ 32 ]. To map the time-varying lateralization architecture, we developed a measure termed dynamic laterality index (DLI) by calculating the laterality index in each sliding window for each region of interest (ROI) ( Fig 1A ). Specifically, we adopted a global signal (GS)-based laterality index that can effectively capture brain lateralization characteristics underlying higher-order cognition [ 14 ], defined as the difference between an ROI’s BOLD correlation with the GS of left brain and its correlation with the right brain at each time window (see Methods ). GS-based laterality index of an ROI reflects whether the activity of the ROI is more synchronized with the left or the right hemisphere. The correlation between BOLD signal of an ROI and the left/right hemispheric global signal (GS L/ GS R ) represents its synchronization with the left/right hemisphere, respectively. Higher correlation of an ROI with GS L compared to GS R indicates left-hemispheric lateralization and vice versa. DLI of an ROI (a time series of laterality index) is then obtained by calculating the laterality index for each time window; see Fig 1A . Positive DLI of a region indicates stronger interaction with the left hemisphere (i.e., leftward laterality), while a negative one indicates rightward laterality.

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(a) Definition of the DLI. The DLI of ROI i within time window t is defined as the correlation coefficient (z-transformed) between GS L and the ROI minus the correlation coefficient between GS R and the ROI. Using a sliding window approach, we obtained a time series of DLI for each ROI. (b) Laterality correlation matrix, which is obtained by correlating laterality time series across all ROIs. Spatial clustering is then performed to identify spatial clusters of brain regions demonstrating covariation in laterality time series. (c) Temporal clustering of whole-brain laterality patterns, which identifies potential recurring laterality patterns. (d) Illustration of DLI and relevant dynamic laterality measures using an ROI that is left-lateralized. The red curve represents the time series of DLI. The green dotted line is the MLI (0.26), and the blue double arrow denotes the standard deviation of the laterality time series, which measures the level of LFs. The black arrow represents the LR (the change of the sign of lateralization across 2 consecutive time windows). Large magnitude of laterality index indicates segregation at the hemispheric level, while small magnitude of laterality index (near 0) indicates integration across 2 hemispheres. DLI, dynamic laterality index; GS, global signal; GS L , global signal of the left hemisphere; GS R , global signal of the right hemisphere; LF, laterality fluctuation; LR, laterality reversal; MLI, mean laterality index; ROI, region of interest.

https://doi.org/10.1371/journal.pbio.3001560.g001

We demonstrate that the GS-based laterality index we adopted is highly similar to a conventional laterality measure autonomy index (AI) [ 12 , 33 ] that is widely used. Using resting-state fMRI data from HCP, we found high correlation coefficient between the whole-brain laterality profile obtained by the mean laterality index (MLI, average across all time windows) and that obtained by AI (Pearson r = 0.64 ± 0.12, all participants have p < 0.05). The MLI has good replicability over 4 sessions involving left and right scans (correlation across sessions: 0.66 ± 0.018); see S1 Fig . Furthermore, GS-based laterality index is efficient in that it only takes 2*n time, compared to the traditional ROI-based method that takes n 2 time (n being the number of regions), therefore is economic for multiple time windows.

Using this DLI, we characterized the time-averaged laterality of large-scale brain networks in resting state. The left and right hemispheres largely illustrated positive and negative mean laterality, respectively ( Fig 2A ). In the left hemisphere, regions in the language (MLI = 0.17 ± 0.08) and default mode (MLI = 0.14 ± 0.07) networks showed strong leftward laterality (networks defined by Cole-Anticevic Brain-wide Network Partition (CAB-NP) [ 34 ]). In the right hemisphere, the cingulo-opercular (MLI = −0.11 ± 0.07), dorsal attention (MLI = −0.10 ± 0.06), and visual networks (MLI = −0.08 ± 0.08; Fig 2A ) showed strong rightward laterality. Frontoparietal network illustrated strong laterality within both hemispheres (left: MLI = 0.14 ± 0.07, right: MLI = −0.15 ± 0.07). Comparatively, bilateral sensorimotor regions (left: MLI = 0.036 ± 0.04, right: MLI = −0.022 ± 0.05) and left visual areas (MLI = −0.025 ± 0.06) depicted relatively weak mean laterality.

(a) MLI, i.e., time average of DLI. (b) LFs, i.e., standard deviation of laterality time series. (c) LR, i.e., the number of switches in the sign of laterality (from positive to negative or vice versa) of all 360 brain regions (averaged across 991 participants), which are rearranged into 12 subnetworks by the CAB-NP. Vis1, Visual1; vis2, Visual2; smn, Somatomotor; con, Cingulo-Opercular network; dan, Dorsal-Attention network; lan, Language network; fpn, Frontoparietal network; aud, Auditory network; dmn, Default Mode network; pmm, Posterior-Multimodal; vmm, Ventral-Multimodal; ora, Orbito-Affective. L, left hemisphere; R, right hemisphere. The underlying data for Fig 2A can be found in S1 Data . The underlying data for Fig 2B can be found in S2 Data. The underlying data for Fig 2C can be found in S3 Data. CAB-NP, Cole-Anticevic Brain-wide Network Partition; DLI, dynamic laterality index; LF, laterality fluctuation; LR, laterality reversal; MLI, mean laterality index.

https://doi.org/10.1371/journal.pbio.3001560.g002

We further characterized the dynamic changes in laterality of a region from 2 different perspectives: the magnitude and the sign of laterality, by LF and LR, respectively; see Fig 1D . LF is defined as the standard deviation of laterality time series. Standard deviation of a time-varying measure is commonly used in dynamic brain network analysis (e.g., DFC), which reflects its variability [ 24 , 35 – 37 ]. LR specifically refers to the number of zero crossings of laterality (switch between left and right laterality) in 2 consecutive windows. It is also inspired by the dynamic brain network analysis, i.e., a region may change the module it belongs to (named “nodal flexibility” proposed by Bassett and colleagues), which correlated closely with cognitive ability [ 38 , 39 ].

We illustrate laterality time series and these 2 measures using a brain region with high level of mean laterality (0.26, left laterality; Fig 1D ). The standard deviation of time-varying laterality index corresponds to moderate changes, or deviations, with respect to the mean laterality. By contrast, LR reflects larger changes in laterality, corresponding to sufficiently extreme deviations from the mean, which results in sign changes in laterality. Moreover, a large magnitude of the laterality index corresponds to segregation at the level of hemispheres, and a small magnitude (near 0) indicates integration across 2 hemispheres; see Fig 1D and “Network and structural basis of laterality dynamics” (Results) for explanation.

Based on the above 2 dynamic laterality characteristics, we found that the laterality of most brain regions varied considerably over time. Specifically, primary visual (LF = 0.46 ± 0.09; LR = 46.8 ± 1.7) and sensorimotor regions (LF = 0.44 ± 0.09; LR = 46.6 ± 1.9) generally illustrated stronger levels of variation in laterality (both LF and LR) than higher-order association regions—including the frontoparietal (LF = 0.43 ± 0.11; LR = 43.4 ± 1.97), language (LF = 0.43 ± 0.10; LR = 43.5 ± 2.1), and default mode networks (LF = 0.41 ± 0.10; LR = 44.7 ± 1.7); see Fig 2B and 2C .

Spatial clustering of the laterality dynamics

After characterizing the temporal LF across the whole brain, we investigated how spatially distributed regions that support different functional specializations correlate with each other in laterality ( Fig 1B ). Through spatial clustering of the laterality time series across all brain regions, we identified 4 major clusters ( Fig 3A ): Cluster 1 consisted mainly of regions from the bilateral visual network; Cluster 2 consisted of regions from the bilateral sensorimotor and cingulo-opercular networks; Cluster 3 mainly covered the frontoparietal network (more from the right hemisphere) and attention network; and Cluster 4 mainly consisted of regions from bilateral default mode network, part of the frontoparietal network (mainly the left hemisphere) and regions from the language network ( Fig 3A ). Clusters 1 and 2 showed higher levels of LF (reflected by higher standard deviation of DLI and LR) when compared to Clusters 3 and 4 (repeated-measures ANOVA, p < 0.001, pairwise t test with p < 0.01; see S3 Fig ).

(a) Four spatial clusters revealed by clustering of the laterality time series of all 360 brain regions. The word cloud for each cluster indicates the functional networks being involved (based on the CAB-NP), with the font size representing the proportion of each network in the cluster. L, left-lateralized; R, right-lateralized; MLI, mean laterality index; LF, laterality fluctuations; LR, laterality reversal. The mean value of the 3 dynamic laterality measures of the 4 spatial clusters are shown in the inset. (b) The laterality correlation matrix across 360 brain regions (averaged over all 991 participants). (c) The correlation between the 3 dynamic laterality measures (MLI, LF, LR) of the 4 spatial clusters and cognitive performance of 3 tasks. CardSort, the performance of Dimensional Change Card Sort Test; ProcSpeed, the performance of Pattern Comparison Processing Speed Test; LanDiff, mean difficulty of stories for each participant in HCP language task. (d) The association between the laterality correlation within/between clusters and the processing speed (Pattern Comparison Processing Speed Test). Only significant results (Pearson correlation, FDR corrected, FDR q < 0.05, marked by one asterisk *) are shown. The underlying data for this figure can be found in S4 Data . CAB-NP, Cole-Anticevic Brain-wide Network Partition; FDR, false discovery rate; HCP, Human Connectome Project.

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We then explored the association between laterality dynamics and out-of-scanner cognitive performance. Specifically, we used multiple tasks adopted by HCP that are generally associated with either lateralized (e.g., language: story comprehension task, and attention: Flanker task) or bilateral (e.g., cognitive flexibility: Card Sort task, and working memory: List Sorting task) brain function. First, we found that the LF and LR of the identified spatial clusters correlated in a positive and negative manner, respectively, with cognitive performance. Second, significant correlation was only found for language function (story comprehension tasks), cognitive flexibility (Card Sort Test and pattern comparison task), and processing speed (Pattern Comparison Test) out of all 13 cognitive measures; see S1 Table . Correction for multiple comparisons were performed using the false discovery rate (FDR) method for all 29 laterality measures employed in our analyses (see Methods section for details of these laterality measures). Furthermore, we also adopted a more conservative multiple-comparisons correction scheme for the 13 behavioral measures. That is, based on the FDR correction of the 29 dynamic laterality measures (FDR q < 0.05), we further employed Bonferroni correction for the 13 behavioral measures, i.e., FDR q < 0.05/13 = 0.0038. All the following results were based on the FDR correction for all 29 laterality measures (FDR q < 0.05). Those correlations that passed the stricter correction method (FDR q < 0.05/13) were also marked.

Specifically, LF of Clusters 3 (FPN) and 4 (DMN and language network) correlated positively with the difficulty of stories a participant could understand (language task difficulty, LanDiff, Cluster 4: r = 0.11, p = 0.018; Cluster 3: t = 0.11, p = 0.014; Fig 3C ). A cognitive flexibility measure (Dimensional Change Card Sort Test, CardSort) also correlated positively with LF across all clusters (Cluster 1, r = 0.12, p = 0.001; Cluster 2, r = 0.14, p < 0.001; Cluster 3, r = 0.14, p < 0.001; Cluster 4, r = 0.14, p < 0.001; all FDR q < 0.05/13). In contrast, LR of these clusters correlated negatively with cognitive performance: for language task difficulty, r = −0.2 ( p < 0.001, FDR q < 0.05/13) for Clusters 3, and r = −0.14 ( p = 0.002) for Clusters 4. For Card Sort task, r = −0.15 ( p < 0.001, FDR q < 0.05/13) for Cluster 3 and r = −0.15 ( p < 0.001, FDR q < 0.05/13) for Cluster 4. In addition, the performance of Pattern Comparison Processing Speed Test (ProcSpeed, the speed of completing the task) also showed positive association with LF of Cluster 3 ( r = 0.09, p = 0.009) and negative association with LR of Clusters 3 ( r = −0.14, p = 0.001) and 4 ( r = −0.12, p = 0.003).

We furthermore explored how time-varying laterality time series of different clusters correlate with each other. Importantly, Cluster 4 showed negative laterality correlations with all other 3 clusters (Cluster 1: t = −81.7; Cluster 2: t = −84.37; Cluster 3: t = −62.1, all p < 0.001; Fig 3B ), while Clusters 1, 2, and 3 showed positive correlation among themselves (Cluster 1 and 2, t = 20.4; p < 0.001; Clusters 2 and 3, t = 15.2; p < 0.001), indicating that Cluster 4 shows a tendency to lateralize in the hemisphere opposite to those of the other 3 clusters over time. The negative laterality correlation between Cluster 4 and Clusters 1, 2, and 3 also bear functional significance. We found that individuals with higher Card Sort score showed stronger negative correlation in laterality between Cluster 4 and Cluster 2 ( r = −0.09, p = 0.012), and Cluster 4 between Cluster 3 ( r = −0.12, p = 0.001, FDR q < 0.05/13); see Fig 3D and S2 Table .

Temporal clustering of laterality dynamics

We further explored the temporal organization of laterality dynamics by clustering whole-brain laterality state of multiple time windows (see Fig 1C and Methods ). Three recurring laterality states ( Fig 4A ) were identified, each showing distinct laterality patterns, different dwelling times, and transition probabilities ( Fig 4B ). Specifically, State 1 showed a typical leftward laterality in Cluster 4 ( t = 77.9, p < 1e-20) and a rightward laterality in Cluster 2 ( t = −109.3, p < 1e-20) ( Fig 4A and 4B ). State 1 is the primary laterality state with the largest fraction of 41% and a mean dwelling time of 49.9 ± 3.58 windows (about 35 s). State 2 showed a typical rightward laterality in Cluster 1 ( t = −108, p < 1e-20), and a leftward laterality in Cluster 2 ( t = 60.1, p < 1e-20) and Cluster 4 ( t = 53.2, p < 1e-20) ( Fig 4A and 4B ). State 3 showed a leftward laterality in Cluster 1 and 2, and rightward laterality of Cluster 4 ( t = −64.7, p < 1e-20) and Cluster 3 ( t = −41.4, p < 1e-20). These 2 states showed similar fractions (State 2: 28%; State 3: 31%) and dwelling time (State 2: 10.5 ± 2 s; State 3: 12.1 ± 2.3 s).

(a) The 3 recurring laterality states obtained by temporal clustering of the time-varying, whole-brain laterality states (360 regions) with colors indicating the network arrangement of the CAB-NP. L, left hemisphere; R, right hemisphere. (b) The mean fraction of 3 states and the mean probability of switching between them. The bar plot next to each state represents the averaged laterality of the 4 spatial clusters in each state. L, left-lateralized; R, right-lateralized. (c) The correlation between the fraction/mean dwelling time of 3 states and memory ability (mem)/language ability. (d) The correlation between the probability of switching between/within the 3 states and language ability. Only significant results (Pearson correlation, FDR corrected, FDR q < 0.05, marked by one asterisk *) are shown. The underlying data for this figure can be found in S4 Data . CAB-NP, Cole-Anticevic Brain-wide Network Partition; FDR, false discovery rate.

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We further investigated the behavioral correlates of individual variations in the state transition properties. Results showed a link between State 2 and cognitive functions: Individuals with less State 2 showed higher language task difficulty (fraction, r = −0.1, p = 0.007; dwelling time, r = −0.09, p = 0.015), higher CardSort performance (fraction, r = −0.08, p = 0.014) and higher ProcSpeed score (fraction, r = −0.09, p = 0.01). In addition, CardSort was positively correlated with the fraction ( r = 0.1, p = 0.006) and dwelling time ( r = 0.1, p = 0.014) of State 1. See Fig 4C and 4D and S3 Table . Multiple comparisons correction was performed using the FDR method for all laterality measures used in the analyses; see Methods section.

Network and structural basis of laterality dynamics

Next, we investigated how dynamic laterality is related to functional and structural brain network properties. First, we investigated the functional correlates of dynamic laterality, i.e., we investigated how dynamic laterality of a cluster is related to the time-varying functional connectivity of the brain. We then calculated the correlation between time-varying laterality index and time-varying network features (including degree, participation coefficient, and modularity, reflecting brain integration/segregation) and BOLD activity (amplitude of low-frequency fluctuation (ALFF)).

We found that the functional connectivity of the high-order brain networks, especially those in the left hemisphere (e.g., FPN, DMN, and language network, mainly covered by Cluster 4) play an essential role in dynamic changes in lateralization. For the 3 right-lateralized clusters (Clusters 1/2/3), higher level of right lateralization was associated mainly with weaker functional connectivity of these clusters with the higher-order networks on the left hemisphere ( Fig 5B ). For Cluster 4 that is intrinsically left-lateralized, its increased left lateralization was accompanied by reduced functional connectivity with multiple lower-order networks at the right hemisphere, including many of the networks covered by Clusters 1/2/3 (the right subfigure of Fig 5B ). Stronger functional connectivity of a cluster with its ipsilateral higher-order networks also plays a role in the increased lateralization of the 4 clusters but has less contribution ( Fig 5B ; details can be found in S4 Fig ).

(a) Analysis pipeline. We established linear regression models of cluster-level DLI time series on subnetwork-level DFC and performed one-sample t test on the regression coefficient (β) to investigate whether the influence of DFC on DLI was significantly greater than 0 (positive coupling) or less than 0 (negative coupling). (b) The association between dynamic laterality time series of Clusters 1/2/3/4 and the DFC within/between 24 subnetworks (according to CAB-NP). Only functional connectivity with very significant t -value of regression coefficient (with p < 1e-200 or |t| > 40, one-sample t test) is being shown since there are too many significant connections. Here, a positive laterality index indicates left lateralization. Colors of points indicate the network arrangement of the CAB-NP. L, left hemisphere; R, right hemisphere. The underlying data for this figure can be found in S4 Data . CAB-NP, Cole-Anticevic Brain-wide Network Partition; DFC, dynamic functional connectivity; DLI, dynamic laterality index; aud, Auditory network; con, Cingulo-Opercular network; dan, Dorsal-Attention network; dmn, Default Mode network; fpn, Frontoparietal network; lan, Language network; ora, Orbito-Affective; pmm, Posterior-Multimodal; smn, Somatomotor; Vis1, Visual1; vis2, Visual2; vmm, Ventral-Multimodal.

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For functional network features and ALFF, we found that the whole-brain lateralization (the mean absolute value of DLI across all 360 regions) correlated negatively with the averaged degree ( r = −0.43 ± 0.1, 91% p < 0.05), participation coefficient ( r = −0.2 ± 0.12, 81% p < 0.05), interhemispheric connection ( r = −0.52 ± 0.11, 92% p < 0.05), and ALFF ( r = −0.22 ± 0.12, 85% p < 0.05), while showing positive correlations with modularity of the brain network ( r = 0.47 ± 0.06, 91% p <0 .05) across time windows; see S5 Fig . This indicates that when the whole brain is highly segregated (modular), it also demonstrates high laterality, while an integrated state of the whole brain (low modularity) are accompanied by low laterality. At the local level, we found that these correlation patterns are most prominent for the high-order brain regions (most significantly in default mode network, the language network, and the frontoparietal network; S5 Fig ).

Second, we explored the structural correlates of laterality dynamics using diffusion MRI data. We used diffusion imaging data from a subset of HCP S1200 (HCP Unrelated 100) to constructed 2 kinds of structural connectivity matrices, fractional anisotropy (FA) matrix and fiber number (FN) matrix for each participant. We found significant positive correlations between the laterality correlation matrix and structural connectivity matrix, including FA matrix (Spearman’s rho = 0.14 ± 0.02, significant in 99% participants) and FN matrix (Spearman’s rho = 0.14 ± 0.02, significant in 99% participants); see Fig 6A . In line with this, homotopic regions generally showed positive correlations in their laterality time series (mean r = 0.39 to 0.79; S2 Fig ) due to the critical role of corpus callosum [ 40 ]. These results suggested that brain regions with similar laterality dynamics tend to have stronger structural connection. Furthermore, LF of a region correlated positively with its FA/FN-degree, i.e., the sum of FA/FN of fibers between this region and all other regions (LF-FN: rho = 0.32 ± 0.09, significant in 99% participants; LF-FA: rho = 0.28 ± 0.08, significant in 99% participants; see Fig 6B ).

(a) Laterality correlation matrix (left half-matrix) and structural connection matrix (right half-matrix) demonstrates high level of correlation. These 2 matrices are obtained by averaging across all 99 participants. L, left hemisphere; R, right hemisphere. The color ribbons indicate the network arrangement of CAB-NP detailed in Fig 2 . (b) Upper: the relationship between a structural measure (FN-degree) and the LF across all brain regions. Lower: the relationship between another structural measure (FA-degree) and the LF. The large scatter maps show the association between the averaged map of dynamic laterality measures and the averaged FA-/FN-degree map (both across all 99 participants), and the inset shows the distribution of the individual correlation coefficients between the FA-/FN-degree map and laterality fluctuation map of each participant. The underlying data for this figure can be found in S4 Data . CAB-NP, Cole-Anticevic Brain-wide Network Partition; FA, fractional anisotropy; FN, fiber number; LF, laterality fluctuations.

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Heritability of laterality dynamics

Finally, we investigated heritability of lateralization dynamics by twin analyses (using the kinship information). First, we estimated heritability through the ACE model [additive heritability (A), common (C), and specific (E) environmental factors model]. We found that LF showed the highest heritability ( h 2 ) among all dynamic laterality measures ( h 2 = 0.09 to 0.48 for LF; h 2 = 0.002 to 0.41 for MLI; h 2 = 0.001 to 0.31 for LR; see Fig 7A–7C ). Of all 4 spatial clusters, Cluster 4 (e.g., the default mode network, part of the right frontoparietal network, and the language network) showed the highest heritability (MLI h 2 = 0.19; LF h 2 = 0.4; LR h 2 = 0.06), followed by Cluster 3 (MLI h 2 = 0.19; LF h 2 = 0.36; LR h 2 = 0.06. The heritability of Cluster 2 (MLI h 2 = 0.13; LF h 2 = 0.35; LR h 2 = 0.03) and Cluster 1 (MLI h 2 = 0.12; LF h 2 = 0.33; LR h 2 = 0.03) were relatively low.

(a-c) Heritability analysis of MLI, LF, and LR. Upper: Heritability ( h 2 ) of each brain region. Only the regions with h 2 of p < 0.05 are retained. Lower left: The h 2 and common environmental factors ( c 2 ) for each cluster, and each data point represents a brain region. Lower right: The cosine distance between whole brain map for each pair of MZ twins, DZ twins, SI, and UN. Asterisk indicates the significant difference between the groups (one-way ANOVA). (d) Heritability of lateralization correlation matrix. Left: Average h 2 and c 2 in 4 clusters. Upper right: Differences in heritability between the positive and the negative links, i.e., links with positive or negative correlation in laterality. Lower right: The cosine distance of laterality correlation matrix of each pair of MZ, DZ, SI, and UN. The error bar represents the mean value ±1 SD. One asterisk (*), p < 0.05; 2 asterisks (**), p < 0.01; 3 asterisks (***), p < 0.001; ns, nonsignificant. Pos, positive correlation; neg, negative correlation. The underlying data for this figure can be found in S4 Data . DZ, dizygotic; LF, laterality fluctuations; LR, laterality reversal; MLI, mean laterality index; MZ, monozygotic; SI, sibling; UN, unrelated group.

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We then calculated the cosine distance between each pair of monozygotic (MZ) twins, dizygotic (DZ) twins, sibling (SI), and unrelated group participants using various dynamic laterality measures across the whole brain. As expected, the laterality correlation matrix showed greater similarity within MZ twins than those within DZ or non-twins (one-way ANOVA, F = 152.7, p < 0.0001; Fig 7D ). Similarly, the similarity of MLI and LF (of all regions) in twins, SIs, and unrelated participants showed a decreasing trend, although the difference between MZ and DZ was not significant (for MLI, F = 23.8, p < 0.0001, MZ versus DZ, p > 0.99; for LF, F = 10.8, p < 0.0001, MZ versus DZ, p = 0.99; Fig 7A and 7B ). There was no significant difference in LR between the 4 groups ( F = 1.48, p = 0.22; Fig 7C ). All one-way ANOVA were done using GraphPad Prism 9.3.1 ( http://www.graphpad.com ). In summary, dynamic laterality measures were generally heritable.

Validation analysis

To rule out the possible influence of potential nuisance variables on our dynamic laterality results, we performed a series of validation analyses: (1) the effect of sample size: all 991 participants were split into two halves, and the main analyses were repeated separately on both halves of the data ( S10 Fig ); (2) head movements: we repeated the main analyses with multiple head movement parameters (Friston 24 parameters [ 41 ]) being regressed out from the BOLD signal ( S12 Fig ). We also used more stringent exclusion criteria (mean frame distance (FD) < 0.15 mm and FD < 0.1 mm) to repeat our analyses ( S12 Fig ); (3) GS: we repeated the main analyses, while regressing out the GS of the whole brain ( S11 Fig ). This resulted in the sum of the GS from the left and right hemispheres being 0, i.e., when the GS L is positive, the GS R is equal to its negative number (rather than a GS of 0 for one hemisphere). Therefore, the correlation coefficients could still be calculated between hemispheric GS and individual ROIs; (4) time window lengths: another 2 window lengths (60 TRs and 90 TRs) were used to investigate the reproducibility of DLI ( S13 and S14 Figs); and (5) the potential contaminating effect of an ROI to its ipsilateral GS: when we calculated the correlation coefficient between an ROI and the GS of its ipsilateral hemisphere, the ROI was included in this calculation, which may have “contaminated” its ipsilateral GS. We therefore removed the ROI when extracting the ipsilateral hemisphere GS and repeated our analyses to examine whether our results are affected ( S15 Fig ). In all the above cases, our main results, including the distribution of dynamic laterality indices across the whole brain, and their correlation with cognitive performance, were largely replicated, which indicated the reliability of the DLIs. See more details in S1 Text .

Laterality is traditionally argued to represent a stable, trait-like feature of the human brain. However, considerable amount of work has recently been devoted to characterizing DFC that has been shown to be related to mental flexibility [ 19 ] and to predict ongoing mental states or task performance [ 21 ], which inspires the question of whether laterality of the brain is also fluctuating over time. This study proposed a framework for dynamic laterality analysis. The time-averaged DLI replicated well previous findings in terms of the static spatial patterns of functional lateralization [ 12 – 14 ], while the time-varying laterality measures provided new insights into the dynamic changes of lateralization in resting state. Specifically, different levels of temporal variation of laterality were found across the brain: Regions within the visual and sensorimotor networks generally showed higher variations, while regions from the default mode, frontoparietal, and language network showed relatively weaker variations. This may suggest that laterality in lower-order brain regions is more flexible, consistent with previous research showing that lower-order regions have shorter intrinsic time scale than higher-order regions [ 42 ]. That is, the bilateral sensory areas need to process constantly changing sensory inputs and integrate them with information from the contralateral hemisphere in real time to form accurate and coherent percepts.

To systematically characterize dynamic laterality changes, we used 2 measures, i.e., LF and LR, which showed positive and negative correlations with cognitive performance, respectively. These opposing associations suggest that these 2 measures capture different aspects of dynamic laterality. Generally, brain regions in the left and right hemisphere illustrated positive and negative mean laterality, respectively (indicating that they have more ipsilateral connections), with their laterality fluctuating around the mean value ( Fig 1 ). Previous literature has suggested that comprehending literal meanings was associated with left language areas (high left laterality), while dealing with difficult metaphors were shown to involve the right homotopic regions [ 43 ]. Greater fluctuations of laterality therefore suggested larger number of states of intra and interhemisphere interactions, which may involve both left hemisphere language areas and the right homotopic regions that may be beneficial for difficult language tasks. For cognitive flexibility (Card Sort task) that generally involves both hemispheres [ 44 ] and larger fluctuations of laterality suggested flexible recruitment of both hemispheres that is potentially beneficial.

Although larger fluctuations in laterality correlated with better cognitive performance, extreme changes in laterality, i.e., frequent LR correlated negatively with task performance. LR suggests that laterality of a region fluctuate too much and deviates remarkably from the mean value (the dominant laterality regime; Fig 1D ), i.e., a region’s functional connectivity switches from being more ipsilateral to highly contralateral. Frequent reversal thus indicates that a brain region constantly leaves its dominant functional regime (i.e., more ipsilateral connectivity). Collectively, these results suggest that moderate changes in laterality may enhance cognitive performance, while extreme laterality changes may hamper cognition. In the Results part, we have shown that low laterality of the whole brain corresponds to high level of interhemispheric communication (or less intrahemispheric information processing), while high laterality of the whole brain corresponds to the opposite case; these results therefore indicate optimal cognitive performance may be related to a dynamic balance between interhemispheric information exchange and intrahemispheric information processing. As we also show that low laterality of the whole brain corresponds to low modularity (integration) while high laterality of the whole brain corresponds to high modularity (segregation/functional specialization), our results also echo the recent findings that cognitive function depends on a dynamic, context-sensitive balance between functional integration and segregation [ 45 – 49 ].

In addition to LF and LR, we furthermore resolved the temporal structures of the whole-brain laterality dynamics. We revealed 3 recurring states (or “meta-states”) with distinct laterality profiles, dwelling time, and transition probabilities. These states generally correspond to the multiple lateralization “axes” identified by Karolis and colleagues through dimensionality reduction of 590 meta-analysis maps: Our State 1 is characterized by strong left laterality of the left default mode and language networks, corresponding to the “symbolic communication” axis that involves Broca’s and Wernicke’s areas. Our State 3 demonstrates strong right laterality of frontoparietal and default mode networks in the right hemisphere consistent with the “active/perception” axis identified in [ 40 ].

Laterality of the human brain is hypothesized to be controlled by multiple factors [ 12 ], which is supported by the distinct spatial clusters identified by the clustering of laterality time series of all brain regions. Three of the 4 clusters (Clusters 1, 3, and 4) we identified roughly correspond to the top 3 factors identified in [ 12 ], which include the visual, attention, and the default mode network, respectively. It should be noted that our clustering analysis was conducted based on laterality dynamics of each individual, rather than on variations across individuals [ 12 ].

The 4 spatial clusters also showed distinct patterns of intercorrelations in their time-varying laterality index ( Fig 3B ). Of particular interest is that the time-varying laterality index of Cluster 4 (the default mode and language network) correlated negatively with those of the other 3 clusters (Cluster 1, visual network; Cluster 2, sensorimotor network; Cluster 3, attention and FPN network) in most participants (Clusters 4 and 1, 99.7% participants with significant negative correlation; Clusters 4 and 2, 99.1%; Clusters 4 and 3, 95.4%). This negative temporal correlation suggests opposite lateralization patterns between Cluster 4 and Clusters 1 to 3 over different time windows. The association analysis between dynamic lateralization and time-varying functional connectivity also suggested the essential role of Cluster 4: The increased level of right lateralization of Clusters 1/2/3 was associated with their decreased functional connectivity with Cluster 4 on the left hemisphere. Similarly, the increased level of left lateralization of Cluster 4 was associated with its decreased functional connectivity with Clusters 1/2/3 at the right hemisphere, suggesting that Cluster 4 might be critical for lateralization state of the whole brain. Furthermore, the more negative the correlation in laterality between Cluster 4 (default mode and language network) and the other 3 clusters (especially visual and sensorimotor network), the better the cognitive performance. This suggested that opposite lateralization pattern between Cluster 4 and Clusters 1 to 3 optimizes parallel processing in the 2 hemispheres [ 17 ] and enhances neural capacity. These results could be explained by the causal hypothesis of hemispheric specialization, which states that lateralization of one function forces the other function to the opposing hemisphere, which optimize parallel processing in complex tasks and increases processing efficiency [ 5 , 50 , 51 ].

Laterality dynamics of the brain may be related to multiple factors. Functionally, greater lateralization of higher-level brain regions was associated with disconnections with the contralateral hemisphere, lower BOLD activity of the whole brain, and greater network segregation, suggesting less exchange of information across hemispheres, which may be related to less energy (glucose) consumption [ 16 , 52 ]. Structurally, LF of a region correlated positively with its degree of structural connections. A brain region with wider structural connections may be modulated by multiple systems that likely demonstrate rich patterns of interhemisphere interaction, thus larger fluctuations in laterality [ 53 , 54 ]. Furthermore, we found that pairs of regions with stronger structural connections are more positively correlated in their time-varying laterality index, indicating the role of structural connection in synchronization of functional lateralization of spatially distributed brain regions. In comparison, brain regions with negative intercorrelation of laterality showed less structural connections, possibly affected by more global factors, such as the regulatory effect of neurotransmitters on large-scale brain networks [ 39 , 47 , 55 ]. In view of the influence of GS and head movements on the dynamic brain network statistics [ 56 , 57 ], these 2 factors may also affect DLI. To exclude potential influence from these 2 factors, we repeated the above analyses by regressing GS and head movements parameters from the BOLD signal, respectively, and we found that our main results such as the distribution of DLI across the brain and their correlation with cognitive performance remain largely unchanged (see S11 and S12 Figs for details).

At the genetic level, structural brain lateralization is known to be heritable in large population samples [ 2 ]. Our research showed that the dynamic characteristics of lateralization were also heritable, especially in those high-order networks such as default mode and frontal-parietal networks, suggesting that laterality dynamics are stable properties of lateralization. From an evolutionary perspective, lateralization arises as a solution to minimize wiring costs while maximizing information processing efficiency in the rapid expansion of the cortex in evolution [ 58 ]. Higher-order systems such as default mode and frontal-parietal networks were among the most expanded regions in evolution [ 59 ]. Considering the dynamic laterality of these networks correlates significantly with cognitive performance, the heritability of dynamic lateralization in these networks, therefore, may confer evolutionary advantages.

Limitations

In this study, resting-state data were analyzed, so it was not possible to directly relate the results to specific cognitive processes. Future studies should investigate dynamic changes in brain lateralization under task modulation, particularly with multiple task states or various task loads, to better understand the specific cognitive advantages of dynamic brain lateralization. In addition, while the GS-based laterality index has the advantages of being computationally convenient and highly correlated with the traditional ROI-based laterality index, it has the problem of averaging and being too coarse to detect network interactions. Therefore, we further analyzed which specific functional connectivity or network drives the temporal changes of laterality index of the 4 clusters. Finally, age and handedness are important factors potentially affecting lateralization, and handedness is also partly controlled by genetic factors [ 60 ]. Future studies are warranted that explore how cognitive deterioration with aging may affect lateralization dynamics.

Conclusions

To conclude, our study demonstrates the dynamic nature of laterality in the human brain at resting state. We characterized comprehensively the spatiotemporal laterality dynamics by identifying 4 spatial clusters and 3 recurring temporal states and demonstrate that the temporal fluctuations of laterality as well as the negative correlation in laterality among different clusters associate with better language task and intellectual performance. We further explored the functional and structural basis underlying such laterality dynamics, and heritability of laterality dynamics. Our study not only contributes to the understanding of the adaptive nature of human brain laterality in healthy population but may also provide a new perspective for future studies of the genetics of brain laterality and potentially abnormal laterality dynamics in various brain diseases.

Materials and methods

Ethics statement.

This paper utilized data collected for the HCP. The scanning protocol, participant recruitment procedures, and informed written consent forms, including consent to share deidentified data, were approved by the Washington University institutional review board [ 31 ]. Data acquisition for the HCP was approved by the Institutional Review Board of The Washington University in St. Louis (IRB # 201204036), and all open access data were deidentified. The data collected by the HCP adhered to the principles of the Declaration of Helsinki. No experimental activity with any involvement of human participants took place at the author’s institutions. Our data analysis was performed in accordance with ethical guidelines of the Fudan University ethics committee.

We analyzed multimodal brain imaging data and behavioral measures from the HCP 1200 Subjects Release (S1200) [ 31 ]. All brain imaging data were acquired using a multiband sequence on a 3-Tesla Siemens Skyra scanner. For each participant, 4 resting-state fMRI scans were acquired: 2 with right-to-left phase encoding and 2 with left-to-right phase encoding direction (1,200 volumes for each scanning session, TR = 0.72 s, voxel size = 2 × 2 × 2 mm). High-resolution T1-weighted MRI (voxel size = 0.7 × 0.7 × 0.7 mm) and diffusion MRI (voxel size = 1.25 mm, 3 shells: b -values = 1,000, 2,000, and 3,000 s/mm 2 , 90 diffusion directions per shell) were also acquired for each participant. A total of 991 participants (28.7 ± 3.7 years old, 528 females) were included in the final cohort utilized in this study according to the following criteria: (1) completed all 4 resting-state fMRI scans; (2) limited in-scanner head motion (mean FD < 0.2 mm); (3) same number of sampling points (i.e., 1,200 volumes per scanning session); (4) without any missing data in regions included in the employed parcellation scheme.

Resting-state fMRI preprocessing

Resting-state fMRI data were preprocessed using the HCP minimal preprocessing pipeline ( fMRIVolume ) [ 61 ] and were denoised using the ICA-FIX method [ 62 ]. Then, a spatial smoothing (FHWM = 4 mm) and a low-pass filtering (0.01 Hz to 0.1 Hz) were performed. We did not employ GS regression, as the mean hemispheric time series were used for calculating the lateralization index [ 14 ]. The whole brain was parcellated into 360 regions (180 for each hemisphere), using the HCP’s multimodal parcellation (HCP MMP1.0) [ 32 ]. These regions were grouped into 12 functional networks based on the CAB-NP [ 34 ].

Spatial clustering of laterality time series across the whole brain

To explore the spatial organization of dynamic lateralization across the whole brain, we performed spatial clustering on the laterality correlation matrix (obtained by calculating Pearson correlation between laterality time series of all pairs of brain regions). Specifically, we applied the Louvain community detection algorithm on the laterality correlation matrix averaged over all 991 participants and 4 runs. We set the γ parameter to 1, ran the algorithm for 100 times, and reported the cluster partitioning with the maximum modularity parameter ( Q ).

Temporal clustering of whole-brain laterality state

To identify the recurring laterality states of the whole brain from 991 participants and 1,171 time windows, we adopted a 3-stage temporal clustering approach. Firstly, the 1,171 × 4 time windows of the 4 runs of each participant were clustered into 10 states by k -means clustering. Second, all 9,910 states (991 participants × 10 states) were clustered into 1,000 groups by a second level k-means clustering. Finally, we used Arenas-Fernandez-Gomez (AFG) community detection to cluster the 1,000 groups obtained in the second step. AFG allows for multiple resolution screening of the modular structure and a data-driving selection of clustering number. We determined the optimal number of clusters using the modularity coefficients obtained by changing the resolution parameter from 0.1 to 1.5 in steps of 0.1 [ 64 ]. The code of K-means and AFG clustering were from MATLAB function kmeans (with the cosine distance metric) and MATLAB Community Detection Toolbox (CDTB v. 0.9, https://www.mathworks.com/matlabcentral/fileexchange/45867-community-detection-toolbox ) [ 65 ], respectively. Based on the final clustering, we took the average pattern of each category as centroids and reclassified all the windows of each participant according to the cosine distance between each window and each centroid.

Cognitive performance in behavioral tasks

To understand the cognitive significance of the dynamic brain laterality, we utilized individual performance of a wide variety of cognitive tasks from the NIH Toolbox for Assessment of Neurological and Behavioral function and Penn computerized neurocognitive battery [ 66 ]. These involve story comprehension task that is more left lateralized, Flanker Task (inhibitory control and attention) that maybe more right lateralized, and Card Sort (cognitive flexibility) and List Sorting task (working memory) that tend to more bilateral.

The story comprehension task [ 67 ] includes a story condition that presents brief auditory stories adapted from Aesop’s fables followed by a binomial forced-choice question to check the participants’ understanding of the story topic (For example, after a story about an eagle that saves a man who had done him a favor, participants were asked, “Was that about revenge or reciprocity?”), and a math condition to answer addition or subtraction problems. To ensure similar level of difficulty across participants, math trials automatically adapted to the participants’ responses. The story and math trials were matched in terms of auditory, duration, attention demand, and phonological input. We used 3 relevant measures: “LanAcc” (“Language_Task_Story_Acc”, the accuracy in answering questions about the story), “LanRT” (“Language_Task_Story_Median_RT”, median response time to answer the questions), and “LanDiff” (“Language_Task_Story_Avg_Difficulty_Level”, the average difficulty of all stories a participant can understand).

Correlation between dynamic laterality index and cognitive performance

In calculating the correlation between dynamic laterality measures and cognitive performance (unadjusted scores), we used Permutation Analysis of Linear Models (PALM; http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/PALM ) to correct for the bias of significance estimation due to the kinship among participants. PALM used exchangeability blocks that is widely adopted to control the family structure–related bias in HCP data [ 68 , 69 ]. We calculated the Pearson correlation coefficients between dynamic lateralization attributes [MLI, LF, LR of 4 clusters; laterality correlation within and between 4 clusters; fraction and dwelling time of 3 states, state transitions] and cognitive performances, regressing out sex, age, education years, race, body mass index (BMI), handedness, gray matter volume, white matter volume, and head motion (mean FD). We obtained p -values for all correlation coefficients using 5,000 times permutation tests. FDR (q = 0.05) was used for multiple comparison correction (multiply comparison times = 4 MLI + 4 LF + 4 LR + 10 DLI correlation + 3 dwelling time +3 fraction + transition = 29). We show all the results of FDR q < 0.05. Moreover, we also highlight results that survive a more conservative multiple comparisons correction (FDR q < 0.05/13 = 0.0038, where 13 is the number or behavioral measurements used in this study).

Association between dynamic laterality and dynamic functional connectivity

To identify which functional interactions may play important roles in the dynamic change of laterality, we adopted a regression-based approach to investigate which subnetworks (12 pairs of symmetric networks in CAB-NP) may affect the dynamic laterality of the 4 identified clusters. We averaged the functional connectivity within/between the 24 subnetworks; therefore, there are 24 * 23 / 2 = 276 subnetwork-level functional connectivity. Specifically, we built linear regression models of cluster-level DLI time series on subnetwork-level DFC and performed one-sample t test on the regression coefficient (β), to investigate whether the influence of DFC on DLI was significantly greater than 0 (positive coupling) or less than 0 (negative coupling) ( Fig 5A ).

Association between dynamic laterality and dynamic network properties and amplitude of low-frequency fluctuation

To understand the neural and network basis of dynamic brain lateralization, we correlated the laterality time series of each brain region with time-varying brain network measures (which reflect the organization of the brain like modular, or integration property) and the ALFF averaged over the whole brain for each participant. We constructed functional brain network using Pearson correlation within each sliding time window and computed the following topological measures:

• Interhemispheric connection [ 74 ], measuring the averaged interhemispheric connection strength.

Among these indicators, the averaged DC is measured at the global network level, B T and Q are measured at the module level, and interhemispheric connection is measured at the hemispheric level DC , B T and Q were calculated using the Brain Connectivity Toolbox (BCT; http://www.brain-connectivity-toolbox.net/ ) [ 75 ]. We removed all negative connections in line with the general assumptions of graph theory [ 71 ].

Structural network analysis

Because the probabilistic fiber tracking is very time-consuming, diffusion tensor imaging (DTI) data of a subset of HCP S1200 release, the “100 Unrelated Subjects” ( n = 100, 54 females, mean age = 29 years) was used for the structural analysis (1 participant was removed due to head motion). Data underwent motion, susceptibility distortion, and eddy current distortion correction [ 61 , 77 ]. We used FMRIB Software Library v6.0 (FSL; https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/ ) and MRtrix3 , a toolkit for diffusion-weighted MRI analysis ( https://mrtrix.readthedocs.io/en/dev/ ) [ 78 ], to construct the SC matrix. The processing steps were as follows: (1) tissue segmentation based on T1-weighted structural image; (2) calculation of 4D images with gray matter, white matter, and cerebrospinal fluid using multi-shell, multi-tissue constrained spherical deconvolution [ 79 ]; (3) generation of white matter–constrained tractography using second-order integration over fiber orientation distributions (iFOD2), a probabilistic tracking algorithm (1,000,000 streamlines for each participant, max length = 250 mm, cutoff = 0.06) [ 80 ]; (4) use of FSL’s FNIRT to reverse-register the MMP parcellation to individual space; (5) calculation of FA maps using dtifit of FSL; and (6) measurement of the average FA on all the streamlines connecting any 2 parcels. This process finally resulted in 360 × 360 FA matrices and FN matrices for 99 participants. We calculated the FA-degree and FN-degree based on this structural connectivity (SC) matrix, i.e., the sum of FA and FN of each row of the SC matrix. The Spearman correlation between laterality correlation matrix and SC matrix and the correlation between MLI/LF/LR and FN-/FA-degree were calculated for each participant.

Heritability analysis

The ratio of the variance explained by A, C, and E to the total variance was used as estimates of heritability ( h 2 ), environmental factors ( c 2 ), and error ( e 2 ), all of which sum to 1. All 4 models (for MLI, LF, LR, and laterality correlation matrix) were fitted and estimated using APACE (the Advanced Permutation inference for ACE models; www.warwick.ac.uk/tenichols/apace ) [ 82 ]. Before model fitting, sex, age, education years, race, BMI, handedness, gray matter volume, white matter volume, and mean FD were regressed out from phenotype as covariables. The significance of heritability for each brain region was obtained using permutation test (1,000 permutations per region).

Code availability

The code used in this study to calculate DLI is available on https://github.com/XinRanWu/Dynamic_Laterality .

Supporting information

S1 fig. repeatability of mli and ai across different sessions..

Notice that the spatial distribution of MLI and AI is very similar. (A) Left: the average DLI map of 4 sessions of HCP data. Right: correlation coefficient among the 4 sessions (average among all the participants). (B) Left: the AI map calculated by 4 sessions of HCP data. Right: correlation coefficient between the 4 sessions (average among all the participants). The underlying data for this figure can be found in S5 Data . AI, autonomy index; DLI, dynamic laterality index; HCP, Human Connectome Project; MLI, mean laterality index.

https://doi.org/10.1371/journal.pbio.3001560.s001

S2 Fig. Laterality correlation between homotopic regions (average of 991 participants).

https://doi.org/10.1371/journal.pbio.3001560.s002

(A) Dynamic laterality measures (MLI, LF, and LR) across 4 clusters. The statistical test was repeated-measure ANOVA, with the asterisk representing significance (***, p < 0.01, ****, p <0.001) (B) The averaged laterality correlation matrix over all 991 participants (arranged by 12 functional networks of the CAB-NP). The underlying data for this figure can be found in S5 Data . CAB-NP, Cole-Anticevic Brain-wide Network Partition; LF, laterality fluctuations; LR, laterality reversal; MLI, mean laterality index.

https://doi.org/10.1371/journal.pbio.3001560.s003

S4 Fig. The association between dynamic FC and the DLI time series of the 4 clusters.

We adopted the CAB-NP that contains 12 pairs of networks in bilateral hemispheres. We calculated the dynamic FC within/between the 12 pairs of networks, and then calculated their regression coefficients of DLI on FC for each participant in each run. We averaged the regression coefficients of the 4 runs of each participant and tested whether they were significantly greater than or less than 0 with single-sample t test. The figure shows the t -values of each functional connectivity. Vis1, Visual1; vis2, Visual2; smn, Somatomotor; con, Cingulo-Opercular network; dan, Dorsal-Attention network; lan, Language network; fpn, Frontoparietal network; aud, Auditory network; dmn, Default Mode network; pmm, Posterior-Multimodal; vmm, Ventral-Multimodal; ora, Orbito-Affective. L, left hemisphere; R, right hemisphere. Note: This figure is the same as Fig 5 in the text, but it shows the regression coefficients of all FC (without thresholds) with more detail. The underlying data for this figure can be found in S4 Data . CAB-NP, Cole-Anticevic Brain-wide Network Partition; DLI, dynamic laterality index; FC, functional connectivity.

https://doi.org/10.1371/journal.pbio.3001560.s004

(A-E) The association between the DLI and the average time series of network indicators and ALFFs of each brain region and each subnetwork. The values in the brain maps represent the t -value of the Pearson correlation coefficient r (one-sample t test). The underlying data for this figure can be found in S5 Data . ALFF, amplitude of low-frequency fluctuation; DLI, dynamic laterality index.

https://doi.org/10.1371/journal.pbio.3001560.s005

S6 Fig. The statistical differences of lateralization correlation matrix, ALFF, and network index between every pair of the 3 temporal states.

The paired t test was used, and all values shown in the figure were t statistics. Only regions with t -value exceeded the significant threshold (±2.33, p < 0.001) were shown in brain maps. The underlying data for this figure can be found in S5 Data . ALFF, amplitude of low-frequency fluctuation.

https://doi.org/10.1371/journal.pbio.3001560.s006

S7 Fig. The statistical differences of Q between every pair of the 3 temporal states.

The one-way ANOVA and post hoc test were used. One asterisk (*), p < 0.05; 2 asterisks (**), p < 0.01; 3 asterisks (***), p < 0.001; 4 asterisks (****), p < 0.0001; ns, nonsignificant. The underlying data for this figure can be found in S5 Data .

https://doi.org/10.1371/journal.pbio.3001560.s007

S8 Fig. The association between the absolute value of averaged DLI time series of whole brain and the time-varying network indicators and ALFFs.

Higher absolute value represents higher left/right lateralization. We selected data from 3 participants, sub-100206, sub-100307, and sub-100408, to show the relationship between global lateralization (quantified by the averaged absolute value of laterality) and dynamic ALFF/graph theory indicators. The results show that higher global lateralization is associated with lower ALFF and stronger whole-brain dissociation (higher modularization Q, lower participant coefficient, centrality degree, and interhemispheric connectivity). One asterisk (*), p < 0.05; 2 asterisks (**), p < 0.01; 3 asterisks (***), p < 0.001; 4 asterisks (****), p < 0.0001; ns, nonsignificant. The underlying data for this figure can be found in S5 Data . ALFF, amplitude of low-frequency fluctuation; DLI, dynamic laterality index.

https://doi.org/10.1371/journal.pbio.3001560.s008

S9 Fig. The results of structural analysis.

(A) Mean structural connection matrix. (B) Mean FN degree. (C) FA degree. (D) The distribution of Spearman correlation coefficients between structural connection attributes [FN-degree, FA-degree, and SCM (based on FN or FA)] and dynamic laterality measures (LF, LR, and LCM). FA, fractional anisotropy; FN, fiber number; LCM, laterality correlation matrix; LF, laterality fluctuations; LR, laterality reversal; SCM, structural connection matrix.

https://doi.org/10.1371/journal.pbio.3001560.s009

S10 Fig. The result of spilt-half validation.

(A-E) Results of part 1 (odd indexed participants in participant list, N = 446). (F-G) Results of part 2 (even indexed participants in participant list, N = 445). (A/F) MLI. (B/G) LF. (C/H) LR. (D/I) Result of spatial clustering. (E/J) Correlation between cognitive measures and indicator of dynamic lateralization. The cross indicates that P is less than 0.05 (FDR). C1-C4: correlation between DLI of Cluster 1 and Cluster 4, and so on. DLI, dynamic laterality index; FDR, false discovery rate; LF, laterality fluctuations; LR, laterality reversal; MLI, mean laterality index.

https://doi.org/10.1371/journal.pbio.3001560.s010

S11 Fig. The results with GS of the whole brain being regressed out.

(A) The t -value of regression coefficient of DLI on GS. (B) MLI. (C) LF. (D) LR. (E) Result of spatial clustering. (F) The results of temporal clustering. (G) Correlation between cognitive measures and indicator of dynamic lateralization. The cross indicates FDR q < 0.05. C1-C4: correlation between DLI of Cluster 1 and Cluster 4, and so on. DLI, dynamic laterality index; FDR, false discovery rate; GS, global signal; LF, laterality fluctuations; LR, laterality reversal; MLI, mean laterality index.

https://doi.org/10.1371/journal.pbio.3001560.s011

S12 Fig. The result using stricter head motion controlling procedure.

(A-F) Results using BOLD residual (with FD and Friston 24 parameters being regressed out). (A) MLI. (B) LF. (C) LR. (D) Result of spatial clustering. (E) Results of temporal clustering. (G-H) Correlation between cognitive measures and DLI. The cross indicates FDR q < 0.05. C1-C4: correlation between DLI of Cluster 1 and Cluster 4, and so on. (G) Correlation results using participants of mean FD < 0.15 mm. (H) Correlation results using participants of mean FD < 0.1 mm. DLI, dynamic laterality index; FD, frame distance; FDR, false discovery rate; LF, laterality fluctuations; LR, laterality reversal; MLI, mean laterality index.

https://doi.org/10.1371/journal.pbio.3001560.s012

S13 Fig. The main result using different window size.

(A-E) Results of window size = 60 TRs. (F-G) Results of window size = 90 TRs. (A/F) MLI. (B/G) LF. (C/H) LR. (D/I) Result of spatial clustering. (E/J) Correlation between cognitive measures and DLI. The cross indicates that P is less than 0.05 (FDR corrected). C1-C4: correlation between DLI of Cluster 1 and Cluster 4, and so on. DLI, dynamic laterality index; FDR, false discovery rate; LF, laterality fluctuations; LR, laterality reversal; MLI, mean laterality index.

https://doi.org/10.1371/journal.pbio.3001560.s013

S14 Fig. Repeatability of various dynamic laterality measures across different window lengths.

The r on vertical axis represents Pearson correlation coefficient between dynamic laterality indictor patterns using window length of 60 TR/90 TR and indictor patterns of 30 TR (used in the text) among all participants. Each dot in the graph represents one of the 991 participants. As can be seen, MLI has the highest reproducibility (close to 1), followed by laterality correlation, LF, and LR. With the increase of the window length, the correlation between the dynamic laterality indicators and the results of the window length of 30 TR decreased gradually. REST1/REST2 indicates the scan time (in first or second day). LR/RL indicates the scanning direction. LR, left to right. RL, right to left. LF, laterality fluctuations; LR, laterality reversal; MLI, mean laterality index.

https://doi.org/10.1371/journal.pbio.3001560.s014

(A-F) The influence of removing the ROI from its ipsilateral GS in calculating the laterality index. That is, we reconstructed GS by removing the ROI A from its ipsilateral GS. (A) MLI. (B) LF. (C) LR. (D) Result of spatial clustering. (E) The results of temporal clustering. (F) Correlation between cognitive measures and indicator of dynamic lateralization. The cross indicates FDR q < 0.05. C1-C4: correlation between DLI of Cluster 1 and Cluster 4, and so on. DLI, dynamic laterality index; FDR, false discovery rate; GS, global signal; LF, laterality fluctuations; LR, laterality reversal; MLI, mean laterality index; ROI, region of interest.

https://doi.org/10.1371/journal.pbio.3001560.s015

S1 Table. The association between dynamic lateralization characteristic of 4 clusters and cognitive abilities.

Each row of the table corresponds to a cognitive test score in the HCP. PicVocab, Picture Vocabulary Test; ReadEng, Reading Test (reading decoding skill); Cardsort, Dimensional Change Card Sort Test (executive function, specifically tapping cognitive flexibility); Flanker, Flanker task (executive function, specifically tapping inhibitory control and attention); ProcSpeed, Pattern Comparison Processing Test (speed of processing); PicSeq, Picture Sequence Memory Test (episodic memory); VSPLOT_TC, Total Number Correct of Penn Line Orientation. PMAT24_A_CR, Number of Correct Responses of Penn Matrix Test (nonverbal reasoning); ListSort, List Sorting Working Memory Test; IWRD_TOT, Total Number of Correct Responses of Penn Word Memory; LanAcc, the accuracy of answering questions about the story of language task; LanRT, median response time to answer the questions of language task; LanDiff, the average difficulty of all stories of language task for each participant, which represents the overall language comprehension ability. r, Pearson correlation coefficient; p, the permutation test significance obtained by PALM. The bold text represents the association remains significant after FDR correction (q = 0.05, comparison number = 29). The asterisk (*) represents the association remains significant after FDR correction (q = 0.05/13 = 0.0038). See HCP Data Dictionary for more details. FDR, false discovery rate; HCP, Human Connectome Project; PALM, Permutation Analysis of Linear Models.

https://doi.org/10.1371/journal.pbio.3001560.s016

S2 Table. The association between laterality correlation among 4 clusters and cognitive abilities.

Each row of the table corresponds to a cognitive test score in the HCP. r, Pearson correlation coefficient; p, the permutation test significance obtained by PALM. The bold text represents the association remains significant after FDR correction (q = 0.05). The asterisk (*) represents the association remains significant after FDR correction (q = 0.05/13 = 0.0038). FDR, false discovery rate; HCP, Human Connectome Project; PALM, Permutation Analysis of Linear Models.

https://doi.org/10.1371/journal.pbio.3001560.s017

S3 Table. The association between characteristics of 3 laterality states and cognitive abilities.

https://doi.org/10.1371/journal.pbio.3001560.s018

S1 Data. “S1_Data.xlsx” includes all individual observations used in Fig 2A .

https://doi.org/10.1371/journal.pbio.3001560.s019

S2 Data. “S2_Data.xlsx” includes all individual observations used in Fig 2B .

https://doi.org/10.1371/journal.pbio.3001560.s020

S3 Data. “S3_Data.xlsx” includes all individual observations used in Fig 2C .

https://doi.org/10.1371/journal.pbio.3001560.s021

S4 Data. “S4_Data.xlsx” includes all statistic results (except for the statistics listed in S1 – S3 Tables) and individual observations used in Figs 3 – 7 .

https://doi.org/10.1371/journal.pbio.3001560.s022

S5 Data. “S5_Data.xlsx” includes all statistic results and individual observations used in S1 – S8 Figs.

https://doi.org/10.1371/journal.pbio.3001560.s023

S1 Text. Supplemental text.

https://doi.org/10.1371/journal.pbio.3001560.s024

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Chapter 7: Physiological Measures of Emotion

3 Theories of Hemispheric Lateralization

Currently, three theories of hemispheric lateralization (also called frontal asymmetry) exist.  These theories hypothesize how the hemispheres of the brain are related to our emotional experiences.  Hemispheric lateralization was described earlier in the EEG section.

Right Hemisphere Hypothesis

The right hemisphere hypothesis suggests that the right hemisphere of the brain processes emotions, while the left hemisphere is not involved. The right hemisphere is thought to control the behavior, cognitive appraisal, and subjective feelings components of emotion.  Brain lesion studies support the right hemisphere hypothesis, while fMRI and EEG studies do not provide strong support (Schirmer & Kotz, 2006).  Damage to the right hemisphere of the brain or left side of the body has been linked to volatile emotions and inability to identify emotions in others’ facial expressions (Adophs et al., 2000, 1996).  But, keep in mind these findings only tell us what happens when the right side is damaged, but does not tell us anything about if the same thing happens when the left side of the brain is damaged.  The Wada test is a methodology that can help us to test the functions of the right and left hemispheres separately.  The Wada test occurs when a sedative sedates only one side of the brain and body and effectively puts one side of the brain to sleep.  But, the other side of the brain and body continues to function normally.  Researchers used the Wada test to evaluate how the right and left side of the contribute to emotional experiences.  When the Wada test sedated the right side of the brain and participants were shown a photo on the left side of their visual field, participants reported less intense emotions  than when the right side was functioning normally (Ahern et al., 1991).  When the Wada test sedated the left side of the brain and participants were shown the same face in the right visual field, participants reported no change in emotion.  Thus, these findings do suggest the right hemisphere might control arousal or intensity of emotions, but not necessarily valence or specific emotions.

Valence Hypothesis

The valence hypothesis theorizes that the right hemisphere of the brain controls negative emotions and the left hemisphere controls positive emotions.  There is some evidence to support this hypothesis.  A Wada test to sedate the left hemisphere caused an increase in sadness – because now the right hemisphere is dominant (Demarree et al., 2005).  Further, damage to the right hemisphere makes it harder to recognize negative emotions in facial expressions, but not positive emotions (Adolphs et al., 1996).  Finally, when people perceive negative emotions in others’ faces and when they show negative emotions on their own faces, an EEG shows more activity in the right hemisphere than the left hemisphere (Davison & Irwin, 1999).

What about the left side of the hemisphere?  A Wada test that sedated the right hemisphere caused joy-like emotions, because the left hemisphere is now dominant (Demarree et al., 2005).  When people perceived or showed positive emotions, the EEG similarly showed more activity in the left (vs. right) hemisphere (Davison & Irwin, 1999).  Although an interesting hypothesis, some recent research suggests that difference between the left and right side of the brain is not valence, but whether people are motivated to approach or avoid.

Approach-Withdrawal Hypothesis

The approach-withdrawal hypothesis (see Figure 11) focuses on the right and left frontal lobes, instead of the right and left hemispheres as in the past studies.  This hypothesis suggests that the right frontal lobe is activated for emotions that cause avoidance behavior, while the left frontal lobe is activated for emotions that cause approach behavior.  This theory has been supported, in particular because anger, which motivates us to approach the person who angered us, activates the left and not right side of the brain (Harmon-Jones & Allen, 1998; Harmon-Jones & Sigelman, 2001).  Some initial evidence has found that expressing fear and disgust emotions on the face resulted in less left cortical activity compared to participants who expressed no emotion and compared to participants who expressed joy and anger approach emotions (Coan et al., 2001).  Interestingly, joy and anger facial expressions did not result in more left cortical activity than the control.  So, most recent evidence suggests that the left side is less involved in avoidance emotions, but more work is needed to demonstrate that approach and withdrawal emotions differentially activate the left and right hemispheres.

Figure 11  Approach-Withdrawal Hypothesis  

Psychology of Human Emotion: An Open Access Textbook Copyright © by Michelle Yarwood is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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MINI REVIEW article

Functional lateralization of lexical stress representation: a systematic review of patient data.

• 1 Department for Communication Sciences and Disorders, McGill University, Montreal, QC, Canada
• 2 Centre for Research on Brain, Language and Music, McGill University, Montreal, QC, Canada
• 3 Institute of Germanic Linguistics, Philipps-University Marburg, Marburg, Germany

According to the functional lateralization hypothesis (FLH) the lateralization of speech prosody depends both on its function (linguistic = left, emotional = right) and on the size of the units it operates on (small = left, large = right). In consequence, according to the FLH, lexical stress should be processed by the left (language-dominant) hemisphere, given its linguistic function and small unit size. We performed an exhaustive search for case studies of patients with acquired dysprosody due to unilateral brain damage. In contrast to previous reviews we only regarded dysprosody at the lexical level (excluding phrasal stress). Moreover, we focused on the representational stage of lexical stress processing, excluding more peripheral perceptual or motor deficits. Applying these criteria, we included nine studies reporting on 11 patients. All of these patients showed representational deficits in word stress processing following a lesion in their language-dominant hemisphere. In 9 out of 11 patients, it was the left hemisphere which was affected. This is a much more consistent pattern as found in previous reviews, in which less rigorous inclusion criteria may have blurred the pattern of results. We conclude that the representation of lexical stress crucially relies on the functioning of the language-dominant (mostly left) hemisphere.

Introduction

According to the functional lateralization hypothesis (FLH; Van Lancker, 1980 ; Van Lancker Sidtis et al., 2006 ) the lateralization of speech prosody depends both on its function and the size of the linguistic unit it operates on. Processing of prosody with an emotional function is assumed to be accomplished by the right hemisphere, whereas prosody with a linguistic function should be processed by the left or language-dominant hemisphere. Moreover, the right hemisphere is assumed to operate on larger scale linguistic units such as phrases or sentences, while small units such as syllables should be processed by the left (language dominant) hemisphere. In consequence, according to the FLH, lexical stress should be processed by the left (language-dominant) hemisphere, given its linguistic function and small unit size ( Van Lancker, 1980 ; Wong, 2002 ).

One relevant source of evidence for the FLH are neuropsychological case studies. If lexical stress processing is found to be impaired in subjects with unilateral brain damage, this would provide insights into the neural substrates that are necessarily involved in the processing of this aspect of prosody. However, so far such studies have yielded mixed results with respect to the FLH, and different reviews have arrived at conflicting results ( Baum and Pell, 1999 ; Wong, 2002 ). Whereas the authors in one review concluded that there is sufficient evidence in favor of a consistent involvement of left hemisphere substrates in lexical stress processing ( Baum and Pell, 1999 ), another review found the results too inconclusive to fully support the hypothesis of functional lateralization ( Wong, 2002 ). These contradicting conclusions can partly be attributed to diverging methods and interpretations of the results. For example, Wong (2002) stated that since not all reviewed studies consistently include an LHD, RHD, and normal control group, some results are impossible to evaluate against the hypothesis of functional lateralization. Another potential problem is the fact that most previous studies have intermixed tasks involving different stages of lexical stress processing (such as perception, representation, and production), although it seems implausible that these processing stages are accomplished by the same neural regions at all (for a review, see Zatorre and Gandour, 2008 ). This has possible consequences for lateralization according to the FLH and could also explain why previous reviews did not reach a consistent conclusion in this matter. Finally, existing reviews often included clinical case studies conducted in English, some of which have insufficiently distinguished the size of the linguistic units under consideration. In some studies, compound noun phrases (green 'house vs. 'greenhouse) have been investigated on the same level as noun/verb minimal pairs ('convict vs. con'vict). Such an approach is potentially problematic, since noun phrases have greater semantic and syntactic complexity than compound nouns or simplex nouns and verbs ( Wasow, 1997 ). Consequently, 'green house is not minimally distinct from green 'house in regards to word stress alone. Crucially, they also differ in the size of linguistic units involved which has implications for the lateralization of processing according to the FLH. In sum, various reasons ranging from differing methodologies to contrasting interpretations could explain the rather mixed evidence that has been discussed with respect to the FLH so far.

The goal of the present study was to review existing case reports with respect to the functional lateralization of lexical stress. In contrast to previous reviews ( Baum and Pell, 1999 ; Wong, 2002 ), we only considered clinical case studies that investigated prosody at a purely lexical level, thus excluding studies on noun phrase or compound noun stress processing. Moreover, we focused on the representational stage of lexical stress processing, excluding perceptional and articulatory deficits. Our aim was to evaluate evidence informative to the claim that lexical stress is represented in the language-dominant (i.e., mostly left) hemisphere.

We conducted an exhaustive search on the data bases Google Scholar, PubMed, and Entrez, using the search terms lexical stress AND brain damage , lexical stress AND hemisphere , and lexical stress AND aphasia . We discarded all studies that did not focus on individuals with unilateral brain damage and/or did not investigate stress assignment at a purely lexical level (for example, studies on tone or phrase level stress). In addition, we excluded all studies in which prosodic impairments in speech production could also result from more peripheral perceptual or articulatory difficulties (e.g., cases of dysarthric or apraxic impairment). After the application of these exclusion criteria, 12 articles remained for analysis, reporting on 15 patients with representational impairments in lexical stress processing (meaning that these patients displayed impairments in stress assignment). We reviewed all 12 studies for hemispheric site of lesion and language-dominant hemisphere of the patient. If both types of information were missing and could not be inferred based on the information provided by the authors, the study and/or subject was excluded from further analysis, resulting in the exclusion of four studies/subjects (excluded studies: Lloyd, 1999 ; Howard and Smith, 2002 ; Janssen, 2003 . excluded subject: DE in Black and Byng, 1986 ). An overview on all studies and patients included is provided in Table 1 .

Table 1. Table of patients included in the review .

The languages spoken by the patients included in our analyses were English, German, and Italian—all languages with variable stress. This means that although in all three languages, word stress assignment shows some regularities (for an overview, see Van der Hulst, 1999 ), the assignment of stress to individual words cannot be inferred by phonemic or orthographic rules alone and thus requires activation of word-specific (i.e., lexical) phonological representations ( Miceli and Caramazza, 1993 ). The word stress errors reported by the studies included in this review particularly affected words with infrequent or “irregular” stress patterns ( Coltheart et al., 1983 ; Chiacchio et al., 1993 ; Miceli and Caramazza, 1993 ; Cappa et al., 1997 ; Rozzini et al., 1997 ; Galante et al., 2000 ; Laganaro et al., 2002 ; Janssen and Domahs, 2008 ), typically leading to shifts in stress assignment to the most frequent pattern (“over-regularisations”, e.g., Marshall and Newcombe, 1973 ; Black and Byng, 1986 ; Cappa et al., 1997 ; Laganaro et al., 2002 ; Janssen and Domahs, 2008 ).

Classification Based on Handedness

This analysis is based on the fact that handedness is closely related to hemispheric dominance for language (e.g., Knecht et al., 2000 ). Out of the ten studies with 12 patients that remained in the pool (see Table 1 ), data on both the impaired hemisphere and the patient's handedness were available in eight cases. All of these eight patients presented with systematic errors in stress assignment following a lesion in their language-dominant hemisphere (which was the LH in seven patients and the RH in one patient), as inferred from handedness.

Classification Based on Linguistic Impairment

All 12 patients (including the four cases where handedness information was not available) showed major linguistic impairments, suggesting that they suffered from lesions in their language-dominant hemisphere. This yields a total of 12 out of 12 patients who showed representational deficits in word stress processing following a lesion in their language-dominant hemisphere. In 10 out of 12 patients, it was the left hemisphere which was affected.

The goal of this study was to review evidence from acquired language impairment regarding the functional lateralization hypothesis (FLH, Van Lancker, 1980 ; Van Lancker Sidtis et al., 2006 ), which states that function and size of a prosodic unit determine the cortical hemisphere it is processed in. Specifically, we were interested in the representation of lexical stress, which according to the FLH is a property of the left, language-dominant hemisphere. To this end, we reviewed clinical case studies that focused on brain-damaged patients with representational impairments in lexical stress assignment. Ten studies reporting on 12 cases remained for analysis after the application of all exclusion criteria. The results showed that in all of these patients, impairments in lexical stress assignment followed a lesion in the language-dominant hemisphere. In contrast to earlier reviews, which have arrived at mixed results, our data thus fully support the functional lateralization hypothesis.

The sample of studies that met our inclusion criteria was rather small, given that only few studies addressed the representation (rather than perception or articulation) of lexical stress. However, our rigorous and hypothesis-driven approach yielded a very clear pattern of results, in comparison to previous reviews that investigated speech prosody. In fact, a closer look at patients which were excluded from our analyses because they did not fulfill our criterion of a representational impairment (either because of a speech-motor deficit, e.g., dysarthria, or because of a non-specified deficit affecting lexical stress processing) revealed a much less consistent pattern: 82 of those patients had a left-hemisphere lesion, in comparison to 74 patients with right-hemisphere damage. Furthermore, 65 patients were reported to have lesions at the side of their dominant hand. Clearly, allowing for less precision in the nature of lexical dysprosody (as in previous reviews) would have led to a more impressive number of cases but to a blurred pattern of results. After all, it is highly plausible that perceptual and motor stages of lexical stress processing are subserved by bilateral brain areas whereas the more abstract linguistic representation of word prosody may reside in the language-dominant (left) hemisphere. This could explain the mixed evidence that earlier reviews yielded with respect to the FLH ( Baum and Pell, 1999 ; Wong, 2002 ).

Our findings are consistent with evidence from dichotic listening showing that stress typicality effects (indicative of the representational stage of stress processing) only appeared in repetition and noun/verb-classification when stimuli were presented to the right ear/left hemisphere ( Arciuli and Slowiaczek, 2007 ). More generally, our findings are also consistent with previous studies ( Baum and Pell, 1999 ) that have rejected a strict division of labor regarding the hemispheric representation of prosody. Even though our results support the notion that lexical stress is a property of the language-dominant hemisphere, it seems that any global “all-left” or “all-right” account with respect to the hemispheric lateralization of all prosodic functions is an oversimplification and fails to account for the data. In this context, it seems that to date the FLH is the most promising account put forward to describe the neural substrates of prosody, since it does not set up an all-or-none division for prosodic functions but allows for gradedness of prosodic representation, depending on their function and the size of the processing units involved. This claim is also substantiated by findings in neuro-imaging, which have demonstrated bilateral cortical activations for lexical stress processing ( Aleman et al., 2005 ; Wildgruber et al., 2006 ; Klein et al., 2011 ; Domahs et al., 2013 ). Yet, it is the methodological strength of lesion studies to highlight the functional relevance of brain regions for cognitive functions ( Rorden and Karnath, 2004 ).

In sum, based on the data at hand we conclude that the representation of lexical stress crucially relies on the functioning of the language-dominant (mostly left) hemisphere.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

This research was supported by a grant from the LOEWE initiative of excellence of the Hessian Ministry of Research and the Arts (project LingBas).

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Keywords: word stress, representational knowledge, left hemisphere, right hemisphere, acquired disorders of language, dysprosody

Citation: Häuser K and Domahs F (2014) Functional lateralization of lexical stress representation: a systematic review of patient data. Front. Psychol . 5 :317. doi: 10.3389/fpsyg.2014.00317

Received: 28 January 2014; Accepted: 26 March 2014; Published online: 10 April 2014.

Reviewed by:

Copyright © 2014 Häuser and Domahs. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Frank Domahs, Institute of Germanic Linguistics, Philipps-University Marburg, Wilhelm-Röpke-Str., 6a, D-35032 Marburg, Germany e-mail: [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

• Open access
• Published: 08 May 2024

Reduced lateralization of multiple functional brain networks in autistic males

• Madeline Peterson   ORCID: orcid.org/0000-0002-4249-2774 1 ,
• Molly B. D. Prigge   ORCID: orcid.org/0000-0001-5715-4180 2 , 3 ,
• Dorothea L. Floris   ORCID: orcid.org/0000-0001-5838-6821 4 , 5 ,
• Erin D. Bigler   ORCID: orcid.org/0000-0002-3031-3919 1 , 6 , 7 , 8 ,
• Brandon A. Zielinski 2 , 7 , 9 , 10 ,
• Jace B. King   ORCID: orcid.org/0000-0002-0206-0475 2 ,
• Nicholas Lange 11 ,
• Andrew L. Alexander 3 , 12 , 13 ,
• Janet E. Lainhart 3 , 12 &
• Jared A. Nielsen   ORCID: orcid.org/0000-0002-2717-193X 1 , 6  

Journal of Neurodevelopmental Disorders volume  16 , Article number:  23 ( 2024 ) Cite this article

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Autism spectrum disorder has been linked to a variety of organizational and developmental deviations in the brain. One such organizational difference involves hemispheric lateralization, which may be localized to language-relevant regions of the brain or distributed more broadly.

In the present study, we estimated brain hemispheric lateralization in autism based on each participant’s unique functional neuroanatomy rather than relying on group-averaged data. Additionally, we explored potential relationships between the lateralization of the language network and behavioral phenotypes including verbal ability, language delay, and autism symptom severity. We hypothesized that differences in hemispheric asymmetries in autism would be limited to the language network, with the alternative hypothesis of pervasive differences in lateralization. We tested this and other hypotheses by employing a cross-sectional dataset of 118 individuals (48 autistic, 70 neurotypical). Using resting-state fMRI, we generated individual network parcellations and estimated network asymmetries using a surface area-based approach. A series of multiple regressions were then used to compare network asymmetries for eight significantly lateralized networks between groups.

We found significant group differences in lateralization for the left-lateralized Language (d = -0.89), right-lateralized Salience/Ventral Attention-A (d = 0.55), and right-lateralized Control-B (d = 0.51) networks, with the direction of these group differences indicating less asymmetry in autistic males. These differences were robust across different datasets from the same participants. Furthermore, we found that language delay stratified language lateralization, with the greatest group differences in language lateralization occurring between autistic males with language delay and neurotypical individuals.

Conclusions

These findings evidence a complex pattern of functional lateralization differences in autism, extending beyond the Language network to the Salience/Ventral Attention-A and Control-B networks, yet not encompassing all networks, indicating a selective divergence rather than a pervasive one. Moreover, we observed an association between Language network lateralization and language delay in autistic males.

Autism spectrum disorder (ASD) is a heterogenous neurodevelopmental condition characterized by challenges in social communication and the presence of restricted repetitive behaviors (Diagnostic and Statistical Manual-5; [ 1 ]). As a neurodevelopmental condition, ASD is linked to atypical timelines of social, cognitive, and physiological development. A pivotal question in the study of autism revolves around the role that alterations in brain asymmetries may play in its development.

Hemispheric specialization, a key principle of brain organization and design, refers to the phenomenon whereby specific cognitive functions are predominantly localized in one hemisphere over the other. This specialization is akin to a division of labor within the brain, with each hemisphere assuming distinct yet not exclusive cognitive responsibilities. In essence, it is as if the brain has a dominant hand for certain types of cognitive operations, such as emotion responsiveness, visuospatial attention, conscious problem solving, and language processing, among others [ 2 ]. The near universality of these functional asymmetries in the human brain raises intriguing questions regarding their behavioral and cognitive purposes. It has been hypothesized that the emergence of lateralized cognitive functions may have been a crucial adaptation that allowed humans to excel in various aspects of life, including improved mobility, more astute resource-seeking behavior, and more effective defense against predators [ 3 ]. The implications for brain function are also intriguing, and it is thought that functional asymmetries reflect a dynamic trade-off between decreases in redundancy [ 4 ], processing speed [ 5 ], and interhemispheric conflict in function initiation [ 6 , 7 ], and the loss of system redundancies and inter-hemispheric connections.

When considering neurodevelopmental conditions such as ASD, the consequences of atypical hemispheric lateralization or a lack thereof become particularly relevant. Language laterality in particular is of interest to autism researchers, since the diagnosis includes a number of language-related features. Consequently, the field of autism research has a long history of investigating lateralization, employing a variety of research methods. For instance, a dichotic listening task paradigm identified a reversal or reduction of lateralization for speech in ASD [ 8 ]. Subsequent electroencephalography studies arrived at a similar conclusion [ 9 , 10 , 11 , 12 ]. However, despite evidence from these and additional studies, it is unknown if differences in hemispheric lateralization in autism are localized to language-relevant regions, as posited in the left hemisphere dysfunction theory of autism [ 13 ], or if they are more pervasive.

Current evidence surrounding this lateralization debate is inconsistent, with findings for generally increased activity in the right hemisphere in autism [ 14 , 15 , 16 , 17 , 18 ], generally decreased activity in the left hemisphere [ 19 , 20 , 21 ], both increased activity in the right hemisphere and decreased activity in the left hemisphere [ 22 , 23 , 24 ], and generally decreased connectivity across both hemispheres [ 25 ]. Conversely, recent evidence for specific differences in lateralization for regions involved in language processing in autism is compelling. For example, in a functional connectivity-based study, a reduction in left lateralization was observed for several connections involving left-lateralized hubs, particularly those related to language and the default network [ 26 ]. This was examined once more by Jouravlev et al. [ 27 ] with a functional language task on an individual level. Within the language network, autistic participants showed less lateralized responses due to greater right hemisphere activity [ 27 ]. Interestingly, there was no strong difference in lateralization for the theory of mind and multiple demand networks between autistic and neurotypical (NT) participants, suggesting that differences in lateralization are constrained to language regions [ 27 ].

Adding another layer to this debate is the potential role that language delay might play in stratifying differences in lateralization in autism. Using normative modeling, one team found that language delay explained the most variance in extreme rightward deviations of laterality in autism [ 28 ]. This is a promising direction, as it appears language delay is capable of parsing the heterogeneity of atypical lateralization patterns in autism. Furthermore, this result points to the behavioral relevance of atypical lateralization patterns to language development in autism. However, it is unclear as to if atypical lateralization in language regions specifically or global alterations of lateralization are contributing to the observed language deficits [ 29 ].

The aim of the present study is to address this ongoing debate regarding the specificity of atypical lateralization patterns to language-relevant regions in autism. This was undertaken by approaching both brain network parcellations and network lateralization from an individual level. The use of these individualized elements is non-trivial, since functional networks vary more by stable group and individual factors than cognitive or daily variation [ 30 ]. Furthermore, group averaging can obscure individual differences and blur functional and anatomical details—details which are potentially clinically useful [ 31 , 32 ]. Thus, through the use of this individual approach, we are better positioned to capture idiosyncratic functional and anatomical details relevant to network lateralization.

The present study explored the following hypotheses. First, it was hypothesized that male autistic individuals would show reduced hemispheric lateralization only in areas associated with language compared with neurotypical individuals. Second, we examined the relationships between language lateralization and three behavioral phenotypes: verbal ability, autism symptom severity, and language delay. More specifically, we hypothesized a positive relationship between language lateralization and verbal ability (as previously described by [ 33 ]), and a negative relationship between language lateralization and autism symptom severity. Finally, we hypothesized that language delay would stratify language lateralization, with the greatest expected differences in lateralization to occur between male autistic individuals with language delay and neurotypical individuals.

Participants

A previously collected dataset was used, and further information on participant recruitment and diagnosis can be found elsewhere [ 34 , 35 , 36 , 37 ]. All data were obtained with assent and informed consent according to the University of Utah’s Institutional Review Board. This dataset was originally developed to understand ASD from a longitudinal perspective and includes up to six waves of data collection. For the purposes of this analysis, data from collection wave five were exclusively used due to the availability of multi-echo fMRI data, which were solely acquired during this particular wave. Participants underwent two 15-minute resting-state multi-echo functional magnetic resonance imaging (fMRI) scans and were instructed to simply rest with their eyes open while letting their thoughts wander [ 38 ]. A total of n  = 89 ASD and n  = 108 NT participants had fMRI data. Exclusion criteria included participants without age data, participants without handedness data (Edinburgh Handedness Inventory; [ 39 ]), participants older than 50 years, female participants, participants with less than 50% of volumes remaining after motion censoring, and participants with a mean framewise displacement greater than 0.2 mm and mean DVARS greater than 50. The exclusion criterion of age greater than 50 was selected due to the lack of matched controls for participants older than 50. Female participants were excluded from the analyses due to their limited representation ( n  = 3). A total of n  = 48 ASD and n  = 70 NT male participants were included in the final analysis. In summary, ASD mean age was 27.22 years, range 14.67–46.42 years; NT mean age was 27.92 years, range 16.33–46.92 years; overall mean age was 27.63 years. Additional demographic information can be found in Table  1 .

Autistic participants and neurotypical participants did not significantly differ in mean age ( t (96.87) = − 0.49, p  = .62) or handedness ( t (92.38) = -1.69, p  = .09). However, the two groups did differ in data quality (mean framewise displacement; t (93.81) = 2.51, p  = .01) and quantity (percent volumes available; t (85.13) = -2.61, p  = .01). Furthermore, there was a significant difference between groups on available intelligence quotient (IQ) measures ( p  < .001). Details regarding IQ measures in this dataset have been previously reported [ 35 , 37 ]. Using full-scale IQ score of 79 or lower as the criterion for low verbal and cognitive performance [ 40 ], there were three autistic participants who met this criterion. Additionally, 93.33% of the autistic participants had high verbal and cognitive performance and 100% of the NT sample had high verbal and cognitive performance.

Table  1 also presents the Autism Diagnostic Observation Schedule (ADOS) calibrated severity scores (CSS) at entry. The ADOS was administered by trained clinicians or research-reliable senior study staff as detailed previously [ 35 , 37 ]. The ADOS CSS scores were then calculated based on ADOS module and participant age [ 41 ]. A few participants had ADOS CSS scores derived more recently ( n  = 6). The ASD diagnosis of these select participants was confirmed prior to study enrollment, so the ADOS was not administered at study entry to these participants. Autism Diagnostic Interview-Revised (ADI-R) scores are also reported, and these scores act as a summary of autism severity during childhood.

Characteristics of autistic participants with and without language delay can be found in Table  2 . In accordance with prior work [ 28 , 42 ], language delay was operationalized as having the onset of first words later than 24 months and/or having onset of first phrases later than 33 months as assessed via the ADI (not the ADI-R). These ADI items were available for 45/48 autistic participants, of which 29 met the threshold for language delay.

MRI acquisition parameters

MRI data were acquired at the Utah Center for Advanced Imaging Research using a Siemens Prisma 3T MRI scanner (80 mT/m gradients) with the vendor’s 64-channel head coil (see [ 38 ]; Siemens, Erlangen, Germany). Structural images were acquired with a Magnetization Prepare 2 Rapid Acquisition Gradient Echoes (MP2RAGE) sequence with isotropic 1.0 mm resolution (Repetition Time (TR) = 5000 milliseconds, Echo Time (TE) = 2.93 milliseconds, and inversion time = 700 milliseconds). Resting-state functional images were acquired with a multiband, multi-echo, echo-planar sequence (TR = 1553 milliseconds; flip angle = 65°; in-plane acceleration factor = 2; fields of view = 208 mm; 72 axial slices; resolution = 2.0 mm isotropic; multiband acceleration factor = 4; partial Fourier = 6/8; bandwidth = 1850 Hz; 3 echoes with TEs of 12.4 milliseconds, 34.28 milliseconds, and 56.16 milliseconds; and effective TE spacing = 22.0 milliseconds).

fMRI preprocessing

Preprocessing took place on raw Neuroimaging Informatics Technology Initiative (NIfTI) files for the resting-state fMRI runs using a pipeline developed by the Computational Brain Imaging Group (CBIG;) [ 43 , 44 ]. Briefly, preprocessing steps included surface reconstruction (using FreeSurfer 6.0.1;) [ 45 ], removal of the first four frames (using FSL, or FMRIB Software Library;) [ 46 , 47 ], multi-echo integration and denoising (using tedana ; [ 48 ], structural and functional alignment using boundary-based registration (using FsFast;) [ 49 ], linear regression using multiple nuisance regressors (using a combination of CBIG in-house scripts and the FSL MCFLIRT tool;) [ 46 ], projection to FreeSurfer fsaverage6 surface space (using FreeSurfer’s mri_vol2surf function), and smoothing with a 6 mm full-width half-maximum kernel (using FreeSurfer’s mri_surf2surf function;) [ 50 ]. As FreeSurfer reconstructions were primarily used to map functional data, no additional quality-control steps were taken after successful reconstructions were estimated. To take full advantage of the multi-echo echo planar image scans in this dataset, the parameters of the CBIG preprocessing pipeline included tedana [ 48 ]. Multi-echo data are acquired by taking three or more images per volume at echo times spanning tens of milliseconds [ 51 , 52 ]. This provides two specific benefits: (1) Echoes can be integrated into a single time-series with improved blood oxygen level dependent contrast and less susceptibility artifact via weighted averaging, and (2) the way in which signals decay across echoes can be used to inform denoising [ 53 ]. Therefore, to take advantage of these properties, tedana creates a weighted sum of individual echoes and then denoises the data using a multi-echo independent component analysis-based denoising method [ 48 ]. Additionally, as suggested by Kundu et al. [ 54 ], bandpass filtering was not included as a preprocessing step for the multi-echo data.

Individual network parcellation

After the implementation of multi-echo preprocessing, network parcellations were computed using a multi-session hierarchical Bayesian modeling pipeline [ 43 ]. This pipeline was implemented in MATLAB R2018b [ 55 ]. In summary, the pipeline estimates group-level priors from a training dataset (37 Brain Genomics Superstruct Project subjects; [ 43 , 56 ]) and applies those to estimate individual-specific parcellations. A k of 17 networks was selected for all subjects, following the 17-network solution found in Yeo et al. [ 57 ]. A Hungarian matching algorithm was then used to match the clusters with the Yeo et al. [ 57 ] 17-network group parcellation.

Network surface area ratio

Following the generation of individual network parcellations, lateralization was estimated using the network surface area ratio (NSAR) calculated in Connectome Workbench wb_command v1.5.0 [ 58 ]. This measure was previously examined for validity and reliability [ 59 ] and is calculated on an individual basis for each of 17 networks. NSAR values range from − 1.0 to + 1.0, with negative values indicating left hemisphere lateralization for a given network and positive values indicating right hemisphere lateralization. NSAR values closer to zero indicate less lateralization (e.g., hemispheric symmetry).

Statistical analysis

Validation of the neurotypical group.

Before formally testing the hypotheses, the laterality pattern of the NT group was first validated using a series of multiple regressions. Models consisted of NSAR values as the dependent variable with the covariates of mean-centered age, mean-centered mean framewise displacement, and handedness index score [ 39 ].

Group differences in network lateralization

A within-dataset replication was first performed using participants with two available resting-state runs ( N  = 97; ASD = 37, NT = 60). A demographics table for this subset of individuals is available in the Supplementary Materials (see Supplementary Table  1 ), as is a table describing data quality across the two available runs (see Supplementary Table  2 ). Using this subset of individuals, the first hypothesis regarding group differences in lateralization was tested using the first resting-state run (the Discovery dataset) and then the second resting-state run (the Replication dataset). To compare hemispheric lateralization between ASD and NT individuals, a series of multiple regressions was performed first within the Discovery dataset and then within the Replication dataset. Individual parcellations and lateralization values were calculated separately for the Discovery and Replication datasets. Models consisted of NSAR values as the dependent variable, group (ASD and NT) as the independent variable, and the following covariates: mean-centered age, mean-centered mean framewise displacement, and handedness. Multiple comparisons were addressed via Bonferroni correction. Any networks with group differences identified in the Discovery dataset were tested in the Replication dataset.

Following the hypothesis testing in the Discovery and Replication datasets, models were implemented in all of the participants, with individual parcellations and corresponding lateralization values derived from all available data (the Complete dataset). Note that the Complete dataset includes 21 participants with only one available scan, and that NSAR values for participants with two available scans were derived from a single individual parcellation created using both scans as input. Models consisted of NSAR values as the dependent variable, group (ASD and NT) as the independent variable, and the following covariates: mean-centered age, mean-centered mean framewise displacement, and handedness. Only networks with group differences identified in the Discovery or Replication datasets were tested in the Complete dataset, with a corresponding Bonferroni correction. Effect sizes (Cohen’s d ) for any potential group differences were calculated on contrasts extracted from the corresponding multiple regression model [ 60 ]. To provide additional rigor, sub-analyses using nearest neighbor matching between the ASD and NT groups on the basis of mean framewise displacement, percent volumes available, and full-scale IQ were implemented using the R package MatchIt [ 61 ].

Network lateralization and behavioral phenotypes

To address the second hypothesis and examine the relationship between language network lateralization and verbal IQ across ASD and NT individuals, a multiple regression was used within the Complete dataset. Covariates included mean-centered age, mean-centered mean framewise displacement, and handedness. A similar analysis including language lateralization as a predictor of autism symptom severity (measured via ADOS CSS scores) was also performed.

Lastly, the potential relationship between language delay and language lateralization in ASD was investigated within the Complete dataset. For these analyses, language lateralization measured via NSAR was the dependent variable while the predictor was group (NT, ASD Language Delay, and ASD No Language Delay), and covariates included mean-centered age, mean-centered mean framewise displacement, and handedness. All statistical analyses took place in R 4.2.0 [ 62 ].

In order to validate the neurotypical group as a reference group for the group analysis, multiple regressions were used to identify significantly lateralized networks, and eight networks were identified as being lateralized: Visual-B, Language, Dorsal Attention-A, Salience/Ventral Attention-A, Control-B, Control-C, Default-C, and Limbic-B (see Supplementary Table 3 and Supplementary Figure S1 ). Network names conform with those used in references [ 59 , 63 ] and are a slight variation on those from the 17-network parcellation in references [ 43 , 57 ]. The use of “-A” and “-B” denote that the networks are subnetworks of a larger network or potentially have a common function. For example, the Somatomotor-A and Somatomotor-B networks occupy cortical territory within the primary motor and primary somatomotor cortex. This result aligns with prior findings [ 59 ], validating the neurotypical group from the Complete dataset as a reference group.

To test the hypothesis regarding group differences in lateralization, regression models were first implemented in Discovery and Replication datasets (composing a within-dataset replication) followed by the Complete dataset.

The first hypothesis regarding group differences in lateralization was examined using a series of multiple regressions. Adjusted for multiple comparisons using a Bonferroni correction, group differences in the Discovery dataset were found in the following networks: Language ( t (92) = -3.18, p -adjusted = 0.02), Salience/Ventral Attention-A ( t (92) = 3.82, p -adjusted = 0.002), and Control-B ( t (92) = 3.06, p -adjusted = 0.02; see Fig.  1 Panel B). Significant group differences in lateralization were identified for the Replication dataset for the Language ( t (92) = -2.44, p -adjusted = 0.05) and Control-B ( t (92) = 2.55, p -adjusted = 0.04) networks, but not for the Salience/Ventral Attention-A network ( t (92) = 1.83, p -adjusted = 0.21; see Fig.  1 Panel C). For a depiction of lateralization for all eight lateralized networks across the Discovery and Replication datasets, see Supplementary Figure  S2 .

Next, multiple regressions were used to examine potential differences between the ASD and NT groups in lateralization in the Complete dataset for the three networks previously identified in the Discovery and Replication datasets. A significant group effect on lateralization was found for the three networks after Bonferroni correction: Language ( t (113) = -4.69, p -adjusted < 0.001, d = -0.89), Salience/Ventral Attention-A ( t (113) = 2.89, p -adjusted = 0.01, d = 0.55), and Control-B ( t (113) = 2.71, p -adjusted = 0.02, d = 0.51; see Fig.  1 Panel D). In order to understand which hemisphere was driving differences in lateralization, we examined network surface areas adjusted for mean-centered age, mean-centered mean framewise displacement, and handedness (see Fig.  2 ; lateralization for all eight lateralized networks in the Complete dataset is depicted in Supplementary Figure  S3 ). The symmetrical Language network in the autism group appears to be driven by increased surface area in the right hemisphere.

Group differences in network lateralization. Panel A depicts an individual parcellation from a neurotypical subject of three networks for which group differences in lateralization were identified. These networks include the Language (LANG), Salience/Ventral Attention-A (SAL-A), and Control-B (CTRL-B) networks. Panels B-D depict three networks on the y-axis and model-adjusted NSAR values on the x-axis, with negative values representing left hemisphere lateralization and positive values representing right hemisphere lateralization. NSAR values were adjusted by regressing out the effects of mean-centered age, mean-centered mean framewise displacement, and handedness using the following formula: NSAR adjusted = NSAR raw - [β 1 (mean-centered age raw - mean of mean-centered age raw ) + β 2 (mean-centered FD raw - mean of mean-centered FD raw ) + β 3 (group raw - mean group raw ) + β 4 (handedness raw - mean handedness raw )]. NSAR adjustment occurred separately for each network and each group. A significant group effect on lateralization was found for three networks following Bonferroni correction in the Discovery dataset: Language ( t (92) = -3.18, p -adjusted = 0.02), Salience/Ventral Attention-A ( t (92) = 3.82, p -adjusted = 0.002), and Control-B ( t (92) = 3.06, p -adjusted = 0.02). Significant group differences in lateralization for the Language ( t (92) = -2.44, p -adjusted = 0.05) and Control-B ( t (92) = 2.55, p -adjusted = 0.04) networks were replicated in the Replication dataset. In the Complete dataset, group differences in lateralization were identified for the Language ( t (113) = -4.69, p -adjusted < 0.001), Salience/Ventral Attention-A ( t (113) = 2.89, p -adjusted = 0.01), and Control-B ( t (113) = 2.71, p -adjusted = 0.02) networks

Given the significant difference in mean framewise displacement between the ASD and NT groups, a sub-analysis of participants matched on mean framewise displacement ( N  = 96, the Complete dataset) was used. Similar conclusions to the unmatched analysis were reached, with group differences in lateralization identified for the Language ( t (91) = -4.48, p -adjusted < 0.001), Salience/Ventral Attention-A ( t (91) = 2.65, p -adjusted = 0.03), and Control-B ( t (91) = 2.89, p -adjusted = 0.01) networks.

Previously, a significant group difference in the percent available volumes was identified, so a separate sub-analysis of participants matched on percent volumes available ( N  = 96, the Complete dataset) was undertaken. As with the unmatched analysis, group differences in lateralization were identified for the Language ( t (91) = -4.35, p -adjusted < 0.001) and Salience/Ventral Attention-A ( t (91) = 2.69, p -adjusted = 0.02) networks, but not for the Control-B network ( t (91) = 2.32, p -adjusted = 0.07).

Likewise, given the significant difference in full-scale IQ between the ASD and NT groups, a separate sub-analysis of participants matched on full-scale IQ scores was undertaken ( N  = 84, the Complete dataset). Group differences in lateralization were identified for the Language ( t (79) = -3.71, p -adjusted = 0.001), Salience/Ventral Attention-A ( t (79) = 2.67, p -adjusted = 0.03), and Control-B ( t (79) = 3.21, p -adjusted = 0.01) networks.

Percent surface area for 17 networks in ASD and NT individuals. Depicted in the top of Panel A is the model-adjusted percentage of the left hemisphere surface area occupied by a given lateralized network. Percent surface area was adjusted using the following formula: Surface area adjusted = Surface area raw - [β 1 (mean-centered age raw - mean of mean-centered age raw ) + β 2 (mean-centered FD raw - mean of mean-centered FD raw ) + β 3 (group raw - mean group raw ) + β 4 (handedness raw - mean handedness raw )]. Depicted in the bottom portion of Panel A is the model-adjusted percentage of the right hemisphere surface area occupied by a given network. Points represent individual outliers. Depicted in Panel B is the adjusted mean percentage of surface area occupied by a lateralized network, with 95% confidence intervals. The left and right hemisphere estimates are displayed side-by-side. Black boxes have been used to indicate the networks for which a significant group difference was found

Verbal ability, ASD symptom severity and language lateralization

To examine the potential relationship between verbal ability (measured via verbal IQ and Language network lateralization, a multiple regression with the covariates of mean-centered age, mean-centered mean framewise displacement, and handedness was used ( N  = 87; ASD = 45, NT = 42). Language lateralization was not a significant predictor of verbal IQ ( t (81) = -0.63, p  = .53).

Next, the relationship between language lateralization and autism symptom severity (measured via ADOS CSS scores, n  = 47 ASD) was examined using a multiple regression with the covariates of mean-centered age, mean-centered mean framewise displacement, and handedness. Language lateralization was not a significant predictor of ADOS CSS scores ( t (42) = 1.1, p  = .28).

Language delay and language lateralization

The potential relationship between language delay and language lateralization was investigated using a multiple regression with the covariates of mean-centered age, mean-centered mean framewise displacement, and handedness. A significant group difference was found between the ASD with Language Delay and NT groups ( t (109) = 4.62, p  < .001, Cohen’s d  = 1.05; see Fig.  3 ). A significant group difference was also found between the ASD No Language Delay and NT groups ( t (109) = -2.44, p  = .02; Cohen’s d  = 0.69). No significant group difference between the ASD Language Delay and ASD No Language Delay groups was found ( t (109) = 1.21, p  = .23).

Language lateralization and language delay. Participants were binned into NT ( n  = 70), ASD Language Delay ( n  = 29), and ASD No Language Delay ( n  = 16), with three participants missing language delay data. On the y-axis are model-adjusted NSAR values for the Language network, with negative values representing left hemisphere lateralization and positive values representing right hemisphere lateralization. NSAR values were adjusted by regressing out the effects of mean-centered age, mean-centered mean framewise displacement, and handedness using the following formula: NSAR adjusted = NSAR raw - [β 1 (mean-centered age raw - mean of mean-centered age raw ) + β 2 (mean-centered FD raw - mean of mean-centered FD raw ) + β 3 (group raw - mean group raw ) + β 4 (handedness raw - mean handedness raw )]. NSAR adjustment occurred separately for each group. A significant group effect on language lateralization was found between the NT and ASD Language Delay groups ( t (109) = 4.62, p  < .001) and between the NT and ASD No Language Delay groups ( t (109) = -2.44, p  = .02). Circles represent group mean adjusted NSAR values while bars represent the standard error of the mean

In this study, we examined network lateralization in autistic and neurotypical individuals using a network surface area-based approach. We first hypothesized that group differences in lateralization would be constrained to areas associated with language. As expected, we identified differences in lateralization for the Language network. However, differences in network lateralization did not end there, and included the Salience/Ventral Attention-A and Control-B networks. Common among these three group differences was a reduction in asymmetry in the autism group, trending towards symmetric distributions. Additionally, of these three networks, the group difference in lateralization for the Language network showed the greatest effect size. Together, these findings evidenced a nuanced pattern of differences in network lateralization in autism, which were not restricted to the Language network, nor were they pervasive across all examined lateralized networks.

Next, we explored the connection between behavioral phenotypes and language lateralization. No significant relationships between verbal ability or autism symptom severity and language lateralization were found. However, language delay was identified as a stratification marker of language lateralization, with the greatest group difference found between the ASD with Language Delay and NT groups. This result suggests that the difference in language lateralization between the ASD and NT groups was predominantly driven by autistic individuals who experienced delayed language onset during development and does not reflect current language ability. In combination with prior research identifying no developmental changes in lateralization between ages 11–36 years [ 59 ], this also suggests that differences in language lateralization occurring early in development are responsible for the differences in language lateralization in autism observed in the present study. Taken as a whole, these results provide further evidence for differences in functional lateralization in autistic males, which appear to be behaviorally and clinically relevant in the case of language lateralization and language delay.

In the context of our research, network lateralization refers to the organizational principle whereby specific brain networks are predominantly based in one hemisphere versus distributed equally between both hemispheres. Of particular significance to our investigation is the lateralization of brain regions associated with language, given that language dysfunction is addressed within the social and repetitive behavior symptom domains for the autism diagnosis. Our exploration extended beyond examining lateralization within eight networks, including the Language network, previously identified as lateralized in neurotypical individuals [ 59 ]. We also sought to uncover relationships between language lateralization and three behavioral phenotypes, which together with a prior study [ 59 ], point to a potential developmental timeframe in which differences in language lateralization in autism may emerge, although additional studies are needed to directly assess this.

Evidence for differences in functional lateralization in ASD

The present study shed light on three networks—Language, Salience/Ventral Attention-A, and Control-B—where lateralization differed between male ASD and NT individuals. Previously, language regions have been implicated in connectivity and asymmetry differences, leading to the postulation of the left hemisphere dysfunction theory of autism [ 13 ]. Interestingly, the direction of group differences identified here indicates that the Language network in ASD is less asymmetrical than in NT individuals (see Fig.  1 Panels B-D). This appears to be driven by an increase in Language network surface area in the right hemisphere compared with the NT group (see Fig.  2 ). Other functional work has similarly identified a rightward shift in asymmetry in autism [ 14 , 15 , 16 , 17 , 18 ].

Perhaps, as suggested by the expansion-fractionation-specialization hypothesis, differences in the fractionation or specialization of the interdigitated theory of mind and language networks may contribute to the development of autism symptoms [ 64 ]. This hypothesis proposes that as the cerebral cortex expands, certain core organizing areas act as anchors, while areas farther from these anchors self-organize into association cortex [ 65 ]. These untethered association regions may exhibit a proto-organization at birth, which then fractionates and specializes through processes including competition and inherent connectivity differences. Any differences in the processes of expansion or fractionation may impact network specialization and potentially network lateralization. Considering the interdigitated nature of functional networks such as language and default networks, disturbances in the expansion or fractionation of one core area are likely to impact multiple networks both directly and indirectly. Our findings, demonstrating differences in lateralization across multiple networks in autism, align with this hypothesis.

The present study also identified a decrease in lateralization in the Control-B network in autistic males. A mapping between resting-state functional connectivity and task activation has identified an executive control network as being associated with action–inhibition, emotion, and perception–somesthesis–pain [ 66 ]. This has since been disentangled into two functionally distinct control networks, which are linked to initiating and adapting control and the stable maintenance of goal-directed behavior [ 67 ]. In ASD specifically, prior evidence has supported differences in control network structure [ 68 ], as well as increased right-lateralization in frontoparietal network components [ 25 ]. However, because there is no standardized network taxonomy [ 69 ], we cannot definitively determine if the previous findings in control and frontoparietal networks directly relate to the observed lateralization differences in the Control-B network in the present study.

Unexpectedly, our research revealed a decrease in lateralization within the Salience/Ventral Attention-A network in ASD compared with NT individuals. Although this outcome was surprising, it could be partly attributed to the individualized approach taken in the present study, which may be more sensitive to differences in lateralization than group-averaged approaches. Regardless, the salience network is thought to identify relevant stimuli from internal and external inputs in order to direct behavior and is distinct from executive control networks [ 70 , 71 ]. Complementary in function to the salience network, the ventral attention network is involved in spatial selective attention [ 72 , 73 ]. Our finding is intriguing considering that attention-deficit/hyperactivity disorder, a condition characterized by deficits in attention, is a frequent co-occurring diagnosis with autism, with some recent estimates ranging between 38.5 and 87% co-occurrence [ 74 , 75 , 76 , 77 , 78 , 79 ]. Neuroimaging studies have complemented this observation. Of note, Farrant & Uddin [ 80 ] reported hyperconnectivity in the ventral and dorsal attention networks in children with ASD, while hypo-connectivity was observed in the dorsal attention network in adults. However, the present study specifically identified decreased lateralization in the ventral attention network in autistic males. Regardless, a salience network dysfunction theory of ASD has been proposed, suggesting that deviations in the salience network and anterior insula in particular may contribute to social communication and theory of mind deficits in ASD [ 81 , 82 ].

Language delay as a stratification marker for ASD

The present study identified a significant difference in language lateralization between NT and ASD with Language Delay individuals, similar to a previous study which found that language delay explained the most variance in extreme rightward deviations of laterality in autism [ 28 ]. This finding is notable considering the disparities in datasets and modeling techniques between this study and that of Floris et al. [ 28 ]. In the prior study, gray matter voxels were the subject of laterality, as opposed to functional connectivity-derived language network surface area. Additionally, significant group differences were identified using individual deviations from a normative pattern of brain laterality across development rather than from group mean comparisons. Another challenge, highlighted by Marek et al. [ 83 ] and Liu et al. [ 84 ], is the difficulty of establishing relationships between scanner-derived data (such as functional connectivity) and out-of-scanner behavioral measures. This is of particular concern with the use of the ADI for determining language delay, since this measure is retrospective and susceptible to memory errors such as telescoping [ 85 ]. Thus, there is a clear need for prospective investigations of the relationship between language delay and language lateralization.

Regardless of these challenges, the causal direction and origins of the relationship between language delay in ASD and language lateralization remain unknown. Bishop [ 86 ] proposed several explanations for these differences. It was suggested that genetic risk may lead to language impairment, subsequently resulting in weak laterality (the neuroplasticity model). Alternatively, genetic risk might independently cause weak laterality and language impairment (the pleiotropy model), or weak laterality caused by genetic risk could subsequently lead to language impairment (the endophenotype model). An alternative model suggested by Berretz and Packheiser [ 87 ] posits that within any given neurodevelopmental or psychiatric condition, there is a singular, distinct endophenotype uniquely associated with altered asymmetries. Evidence from Nielsen et al. [ 26 ] suggests that deficits in language development may result in the abnormal language lateralization observed in ASD. This is supported by several pieces of evidence observed in the present study as well as in unpublished data [ 59 ]. Notably, no consistent age-related effects on lateralization were identified previously [ 59 ], and the present study evidenced no direct relationship between language lateralization and verbal ability. However, language delay was found to act as a stratification marker for language lateralization, with the greatest effect occurring between the ASD with Language Delay and NT groups. Together, this suggests that differences in language lateralization likely occurring early in development could underlie the differences in language lateralization observed in autism, although additional studies are needed to assess this claim.

Limitations

It should be noted that the dataset chosen for this study has certain characteristics which restrict the generalizability of our findings. First, the participant sample consisted entirely of males, which restricts the applicability of our results to females with ASD and may overlook potential sex differences. Additionally, the overwhelming representation of high verbal and cognitive performance individuals within the dataset further impacts the generalizability of our findings.

Further investigations should focus on replicating these findings in larger and more diverse samples, as well as exploring the longitudinal trajectories of network lateralization in individuals with ASD. Given the present evidence suggesting that differences in language lateralization may be occurring early in development, it may be informative to explore differences in network lateralization in infancy and early childhood. Additionally, the incorporation of multimodal neuroimaging techniques could provide a more comprehensive understanding of the relationship between language network lateralization and language delay in ASD.

In this study, we examined network lateralization in ASD and NT male individuals using an individual-level approach based on participant network parcellations. First, we hypothesized that group differences in lateralization would be constrained to language-relevant regions. We identified group differences in lateralization for the Language, Salience/Ventral Attention-A, and Control-B networks, evidencing a selective pattern of functional lateralization differences in autism rather than a pervasive one. Additionally, we hypothesized that language delay would stratify language lateralization, such that the greatest group differences would be found between the NT and ASD with Language Delay groups. Support for this hypothesis was found, suggesting that language lateralization is behaviorally and clinically relevant to autism.

Data availability

The data reported on in the present study can be accessed on the NIMH Data Archive under #2400 ( https://nda.nih.gov/edit_collection.html?id=2400 ). Preprocessing and individual parcellation pipeline code are available through the CBIG repository on GitHub at https://github.com/ThomasYeoLab/CBIG . Code used to implement the processing pipelines and perform statistical analyses are also available on GitHub at https://github.com/Nielsen-Brain-and-Behavior-Lab/AutismHemisphericSpecialization2023 .

Abbreviations

Autism diagnostic interview

Autism diagnostic interview-revised

Autism diagnostic observation schedule

• Autism spectrum disorder

Computational brain imaging group

Calibrated severity scores

Functional magnetic resonance imaging

FMRIB software library

Intelligence quotient

Magnetization prepare 2 rapid acquisition gradient echoes

Neuroimaging informatics technology initiative

Neurotypical

Repetition time

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Acknowledgements

We thank former members of the Utah Collaborative Programs of Excellence in Autism for their assistance during the early stages of this project. We sincerely thank the children, adolescents, and adults with autism and the individuals with typical development who participated in this study, and their families. We are also grateful to Ru Kong for her assistance implementing the individual parcellation pipeline. Furthermore, we acknowledge the support of the Office of Research Computing at Brigham Young University.

This dataset was collected with support by the National Institute of Mental Health of the National Institutes of Health under Award Numbers R01MH080826, K08 MH092697, and K08 MH100609. The research in this publication was also supported in part by the National Center for Advancing Translational Sciences of the NIH under Award Number UL1TR002538. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. DLF is supported by funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 101025785.

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Madeline Peterson, Erin D. Bigler & Jared A. Nielsen

Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, UT, 84108, USA

Molly B. D. Prigge, Brandon A. Zielinski & Jace B. King

Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA

Molly B. D. Prigge, Andrew L. Alexander & Janet E. Lainhart

Methods of Plasticity Research, Department of Psychology, University of Zurich, Zurich, Switzerland

Dorothea L. Floris

Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen Medical Center, Nijmegen, The Netherlands

Neuroscience Center, Brigham Young University, Provo, UT, 84604, USA

Erin D. Bigler & Jared A. Nielsen

Department of Neurology, University of Utah, Salt Lake City, UT, 84108, USA

Erin D. Bigler & Brandon A. Zielinski

Department of Neurology, University of California-Davis, Davis, CA, USA

Erin D. Bigler

Department of Pediatrics, University of Utah, Salt Lake City, UT, 84108, USA

Brandon A. Zielinski

Division of Pediatric Neurology, Departments of Pediatrics, Neurology, and Neuroscience, College of Medicine, University of Florida, Florida, FL, 32610, USA

Department of Psychiatry, Harvard Medical School, Boston, MA, 02115, USA

Nicholas Lange

Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, 53719, USA

Andrew L. Alexander & Janet E. Lainhart

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Contributions

MP: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Writing – original draft, Writing – review & editing, Visualization. MBDP: Methodology, Software, Formal analysis, Investigation, Resources, Data curation, Writing – review & editing, Project administration. DLF: Methodology, Writing – review & editing. EDB: Writing – review & editing, Project administration, Funding acquisition. BZ: Writing – review & editing, Project administration, Funding acquisition. JBK: Writing – review & editing, Project administration. NL: Methodology, Writing – review & editing, Project administration, Funding acquisition. ALA: Writing – review & editing, Project administration, Funding acquisition. JEL: Resources, Writing – review & editing, Project administration, Funding acquisition. JAN: Conceptualization, Methodology, Investigation, Resources, Data curation, Writing – review & editing, Supervision, Project administration. All authors read and approved the final manuscript.

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Peterson, M., Prigge, M.B.D., Floris, D.L. et al. Reduced lateralization of multiple functional brain networks in autistic males. J Neurodevelop Disord 16 , 23 (2024). https://doi.org/10.1186/s11689-024-09529-w

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Accepted : 26 March 2024

Published : 08 May 2024

DOI : https://doi.org/10.1186/s11689-024-09529-w

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