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Dissociable brain structural asymmetry patterns reveal unique phenome-wide profiles

Nature Human Behaviour volume 7, pages 251–268 (2023)Cite this article

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Abstract

Broca reported ~150 years ago that particular lesions of the left hemisphere impair speech. Since then, other brain regions have been reported to show lateralized structure and function. Yet, studies of brain asymmetry have limited their focus to pairwise comparisons between homologous regions. Here, we characterized separable whole-brain asymmetry patterns in grey and white matter structure from n = 37,441 UK Biobank participants. By pooling information on left–right shifts underlying whole-brain structure, we deconvolved signatures of brain asymmetry that are spatially distributed rather than locally constrained. Classically asymmetric regions turned out to belong to more than one asymmetry pattern. Instead of a single dominant signature, we discovered complementary asymmetry patterns that contributed similarly to whole-brain asymmetry at the population level. These asymmetry patterns were associated with unique collections of phenotypes, ranging from early lifestyle factors to demographic status to mental health indicators.

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Fig. 1: Left–right asymmetry representation ties together spatially dispersed brain features across two dimensions of interdependence.
Fig. 2: The leading whole-brain asymmetry pattern spotlights shifts in the frontal and temporal poles with concomitant contralateral shifts in the occipital pole.
Fig. 3: Second most explanatory whole-brain asymmetry pattern.
Fig. 4: Third whole-brain asymmetry pattern.
Fig. 5: Fourth whole-brain asymmetry pattern.
Fig. 6: Right-handedness-related regional asymmetries show rightward shifts in amygdala, tapetum and middle temporal gyrus and leftward shifts in Crus II of the cerebellum, postcentral gyrus, caudate, pars opercularis and pars triangularis of the inferior frontal gyrus.
Fig. 7: Sex-related regional asymmetries show leftward shifts in the occipital pole, ventral striatum and thalamus in females and rightward shifts in tapetum and planum temporale in females.
Fig. 8: High fluid IQ-related regional asymmetries show rightward shifts in the frontal pole and ventral striatum and leftward shifts in the hippocampus and pallidum.

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Data availability

The UKBB data are available to other investigators online (ukbiobank.ac.uk). https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Atlases. Source data are provided with this paper.

Code availability

A full collection of asymmetry patterns is freely available to and open for reuse by the reader at: https://github.com/ksaltoun/Dissociable_Asymmetry_Patterns. The analysis scripts that reproduce the results of the present study are available upon request.

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Acknowledgements

This study was supported by the Brain Canada Foundation, through the Canada Brain Research Fund, with the financial support of Health Canada, National Institutes of Health (grant nos. NIH R01 AG068563A and NIH R01 R01DA053301-01A1 to D.B.), the Canadian Institute of Health Research (grant nos. CIHR 438531 and CIHR 470425 to D.B.), the Healthy Brains Healthy Lives initiative (Canada First Research Excellence fund to D.B.), Google (Research Award, Teaching Award to D.B.) and by the CIFAR Artificial Intelligence Chairs programme (Canada Institute for Advanced Research to D.B.).

The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Authors and Affiliations

  1. McConnell Brain Imaging Centre, Montreal Neurological Institute (MNI), McGill University, Montreal, Quebec, Canada

    Karin Saltoun, Vaibhav Sharma & Danilo Bzdok

  2. Mila - Quebec Artificial Intelligence Institute, Montreal, Quebec, Canada

    Karin Saltoun, Vaibhav Sharma & Danilo Bzdok

  3. Department of Biomedical Engineering, Faculty of Medicine, McGill University, Montreal, Quebec, Canada

    Karin Saltoun

  4. School of Computer Science, McGill University, Quebec, Canada

    Karin Saltoun

  5. Division of the Humanities and Social Sciences, California Institute of Technology, Pasadena, CA, USA

    Ralph Adolphs & Lynn K. Paul

  6. Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA

    Ralph Adolphs

  7. International Research Consortium for the Corpus Callosum and Cerebral Connectivity (IRC5), Pasadena, CA, USA

    Lynn K. Paul

  8. Fuller Graduate School of Psychology, Travis Research Institute, Pasadena, CA, USA

    Lynn K. Paul

  9. The Brain and Mind Institute, Western University, London, Ontario, Canada

    Joern Diedrichsen

  10. Department of Computer Science, Western University, London, Ontario, Canada

    Joern Diedrichsen

  11. Department of Statistical and Actuarial Sciences, Western University, London, Ontario, Canada

    Joern Diedrichsen

  12. Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore

    B. T. Thomas Yeo

  13. Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, Singapore, Singapore

    B. T. Thomas Yeo

  14. N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore, Singapore

    B. T. Thomas Yeo

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  1. Karin Saltoun
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Contributions

K.S. and D.B. conceived, executed and wrote the paper. All authors analysed the data and edited the manuscript.

Corresponding author

Correspondence to Danilo Bzdok.

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Nature Human Behaviour thanks Xiang-Zhen Kong and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–7 and Table 1.

Reporting summary

Supplementary Data Fig. 1

Statistical source data of UMAP stability test.

Supplementary Data Fig. 2

Statistical source data of explained variance permutation tests; feature contribution of top 25 asymmetry patterns.

Supplementary Data Fig. 3

Statistical source data of bootstrap robustness test + permutation testing and explained variance permutation tests.

Supplementary Data Fig. 4

Comparison of number of phenotypic hits on a regional versus asymmetry pattern-based approach.

Supplementary Data Fig. 5

SVD-based non-parametric permutation test for consistent handedness-based asymmetry pattern deviations.

Supplementary Data Fig. 6

SVD-based non-parametric permutation test for consistent sex-based asymmetry pattern deviations.

Supplementary Data Fig. 7

SVD-Based non-parametric permutation test for consistent IQ-based asymmetry pattern deviations.

Supplementary Data Table 1

Effect of sex and age on asymmetry pattern expression ANOVA results.

Source data

Source Data Fig. 1

UMAP 1+2 coordinates; absolute average lateralization index.

Source Data Fig. 2

Asymmetry pattern 1 feature contributions; Manhattan plot; age-sex graph; representative brain feature measures.

Source Data Fig. 3

Asymmetry pattern 2 feature contributions; Manhattan plot; age-sex graph; representative brain feature measures.

Source Data Fig. 4

Asymmetry pattern 3 feature contributions; Manhattan plot; age-sex graph; representative brain feature measures.

Source Data Fig. 5

Asymmetry pattern 4 feature contributions; Manhattan plot; age-sex graph; representative brain feature measures.

Source Data Fig. 6

Regional non-parametric permutation test for consistent handedness-based asymmetry deviations.

Source Data Fig. 7

Regional non-parametric permutation test for consistent sex-based asymmetry deviations.

Source Data Fig. 8

Regional non-parametric permutation test for consistent IQ-based asymmetry deviations.

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Saltoun, K., Adolphs, R., Paul, L.K. et al. Dissociable brain structural asymmetry patterns reveal unique phenome-wide profiles. Nat Hum Behav 7, 251–268 (2023). https://doi.org/10.1038/s41562-022-01461-0

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