Skip to main content
SpringerLink
Log in

Linking interindividual variability in brain structure to behaviour

  • Perspective
  • Published:

From Nature Reviews Neuroscience

View current issue Sign up to alerts

Abstract

What are the brain structural correlates of interindividual differences in behaviour? More than a decade ago, advances in structural MRI opened promising new avenues to address this question. The initial wave of research then progressively led to substantial conceptual and methodological shifts, and a replication crisis unveiled the limitations of traditional approaches, which involved searching for associations between local measurements of neuroanatomy and behavioural variables in small samples of healthy individuals. Given these methodological issues and growing scepticism regarding the idea of one-to-one mapping of psychological constructs to brain regions, new perspectives emerged. These not only embrace the multivariate nature of brain structure–behaviour relationships and promote generalizability but also embrace the representation of the relationships between brain structure and behavioural data by latent dimensions of interindividual variability. Here, we examine the past and present of the study of brain structure–behaviour associations in healthy populations and address current challenges and open questions for future investigations.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1: Examples of mapping between local brain morphology and behavioural traits or performance.
Fig. 2: Poor replicability of structural brain–behavioural associations.
Fig. 3: Machine learning and multivariate approaches to study structural brain–behaviour associations.

Similar content being viewed by others

References

  1. Kiesow, H. et al. 10,000 social brains: sex differentiation in human brain anatomy. Sci. Adv. 6, eaaz1170 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  2. Valk, S. L. et al. Personality and local brain structure: their shared genetic basis and reproducibility. NeuroImage 220, 117067 (2020).

    Article  PubMed  Google Scholar 

  3. Kanai, R. & Rees, G. The structural basis of inter-individual differences in human behaviour and cognition. Nat. Rev. Neurosci. 12, 231–242 (2011).

    Article  CAS  PubMed  Google Scholar 

  4. Colom, R., Jung, R. E. & Haier, R. J. General intelligence and memory span: evidence for a common neuroanatomic framework. Cogn. Neuropsychol. 24, 867–878 (2007).

    Article  PubMed  Google Scholar 

  5. Nostro, A. D., Müller, V. I., Reid, A. T. & Eickhoff, S. B. Correlations between personality and brain structure: a crucial role of gender. Cereb. Cortex 27, 3698–3712 (2017).

    PubMed  Google Scholar 

  6. Matsuo, K. et al. A voxel-based morphometry study of frontal gray matter correlates of impulsivity. Hum. Brain Mapp. 30, 1188–1195 (2009).

    Article  PubMed  Google Scholar 

  7. Kanai, R., Feilden, T., Firth, C. & Rees, G. Political orientations are correlated with brain structure in young adults. Curr. Biol. 21, 677–680 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Button, K. S. et al. Power failure: why small sample size undermines the reliability of neuroscience. Nat. Rev. Neurosci. 14, 365–376 (2013).

    Article  CAS  PubMed  Google Scholar 

  9. Collaboration, O. S. Estimating the reproducibility of psychological science. Science 349, aac4716 (2015).

    Article  CAS  Google Scholar 

  10. De Boeck, P. & Jeon, M. Perceived crisis and reforms: issues, explanations, and remedies. Psychol. Bull. 144, 757 (2018).

    Article  PubMed  Google Scholar 

  11. Poldrack, R. A. et al. Scanning the horizon: towards transparent and reproducible neuroimaging research. Nat. Rev. Neurosci. 18, 115 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Boekel, W. et al. A purely confirmatory replication study of structural brain–behavior correlations. Cortex 66, 115–133 (2015).

    Article  PubMed  Google Scholar 

  13. Boekel, W., Forstmann, B. U. & Wagenmakers, E.-J. Challenges in replicating brain–behavior correlations: rejoinder to Kanai (2015) and Muhlert and Ridgway (2015). Cortex 74, 348–352 (2016).

    Article  PubMed  Google Scholar 

  14. Muhlert, N. & Ridgway, G. R. Failed replications, contributing factors and careful interpretations: commentary on Boekel et al. 2015. Cortex 74, 338–342 (2016).

    Article  PubMed  Google Scholar 

  15. Kanai, R. Open questions in conducting confirmatory replication studies: commentary on Boekel et al. 2015. Cortex 74, 343–347 (2016).

    Article  PubMed  Google Scholar 

  16. Genon, S. et al. Searching for behavior relating to grey matter volume in a-priori defined right dorsal premotor regions: lessons learned. NeuroImage 157, 144–156 (2017).

    Article  PubMed  Google Scholar 

  17. Masouleh, S. K., Eickhoff, S. B., Hoffstaedter, F., Genon, S. & Initiative, A. S. D. N. Empirical examination of the replicability of associations between brain structure and psychological variables. eLife 8, e43464 (2019).

    Article  Google Scholar 

  18. Avinun, R., Israel, S., Knodt, A. R. & Hariri, A. R. Little evidence for associations between the Big Five personality traits and variability in brain gray or white matter. NeuroImage 220, 117092 (2020).

    Article  PubMed  Google Scholar 

  19. Kharabian, S., Eickhoff, S. B. & Genon, S. Searching for replicable associations between cortical thickness and psychometric variables in healthy adults: empirical facts. Preprint at bioRxiv https://doi.org/10.1101/2020.01.10.901181 (2020).

    Article  Google Scholar 

  20. Botvinik-Nezer, R. et al. Variability in the analysis of a single neuroimaging dataset by many teams. Nature 582, 84–88 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Han, X. et al. Reliability of MRI-derived measurements of human cerebral cortical thickness: the effects of field strength, scanner upgrade and manufacturer. Neuroimage 32, 180–194 (2006).

    Article  PubMed  Google Scholar 

  22. Fortin, J.-P. et al. Harmonization of cortical thickness measurements across scanners and sites. NeuroImage 167, 104–120 (2018).

    Article  PubMed  Google Scholar 

  23. Gronenschild, E. H. B. M. et al. The effects of freesurfer version, workstation type, and Macintosh operating system version on anatomical volume and cortical thickness measurements. PLoS ONE 7, e38234 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Kharabian Masouleh, S. et al. Influence of processing pipeline on cortical thickness measurement. Cereb. Cortex 30, 5014–5027 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Martínez, K. et al. Reproducibility of brain–cognition relationships using three cortical surface-based protocols: an exhaustive analysis based on cortical thickness. Hum. Brain Mapp. 36, 3227–3245 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Climie, E. A. & Rostad, K. Test review: Wechsler Adult Intelligence Scale. J. Psychoeduc. Assess. 29, 6 (2011).

    Article  Google Scholar 

  27. Marek, S. et al. Reproducible brain-wide association studies require thousands of individuals. Nature 603, 654–660 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Miller, K. L. et al. Multimodal population brain imaging in the UK Biobank prospective epidemiological study. Nat. Neurosci. 19, 1523 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Steegen, S., Tuerlinckx, F., Gelman, A. & Vanpaemel, W. Increasing transparency through a multiverse analysis. Perspect. Psychol. Sci. 11, 702–712 (2016).

    Article  PubMed  Google Scholar 

  30. Albers, C. & Lakens, D. When power analyses based on pilot data are biased: inaccurate effect size estimators and follow-up bias. J. Exp. Soc. Psychol. 74, 187–195 (2018).

    Article  Google Scholar 

  31. Schönbrodt, F. D. & Perugini, M. At what sample size do correlations stabilize? J. Res. Personal. 47, 609–612 (2013).

    Article  Google Scholar 

  32. Genon, S., Reid, A., Langner, R., Amunts, K. & Eickhoff, S. B. How to characterize the function of a brain region. Trends Cogn. Sci. 22, 350–364 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Scoville, W. B. & Milner, B. Loss of recent memory after bilateral hippocampal lesions. J. Neurol. Neurosurg. Psychiatry 20, 11 (1957).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Poldrack, R. A. Mapping mental function to brain structure: how can cognitive neuroimaging succeed? Perspect. Psychol. Sci. 5, 753–761 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Pessoa, L. Understanding brain networks and brain organization. Phys. Life Rev. 11, 400–435 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Alexander-Bloch, A., Raznahan, A., Bullmore, E. & Giedd, J. The convergence of maturational change and structural covariance in human cortical networks. J. Neurosci. 33, 2889–2899 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Habeck, C. & Stern, Y. Multivariate data analysis for neuroimaging data: overview and application to Alzheimer’s disease. Cell Biochem. Biophys. 58, 53–67 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. McIntosh, A. R. & Mišić, B. Multivariate statistical analyses for neuroimaging data. Annu. Rev. Psychol. 64, 499–525 (2013).

    Article  PubMed  Google Scholar 

  39. van der Linden, D. et al. Overlap between the general factor of personality and emotional intelligence: a meta-analysis. Psychol. Bull. 143, 36 (2017).

    Article  PubMed  Google Scholar 

  40. Lyall, D. M. et al. Cognitive test scores in UK Biobank: data reduction in 480,416 participants and longitudinal stability in 20,346 participants. PLoS ONE 11, e0154222 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  41. Cox, S., Ritchie, S., Fawns-Ritchie, C., Tucker-Drob, E. & Deary, I. Structural brain imaging correlates of general intelligence in UK Biobank. Intelligence 76, 101376 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Watkins, M. W. Exploratory factor analysis: a guide to best practice. J. Black Psychol. 44, 219–246 (2018).

    Article  Google Scholar 

  43. Hilger, K. et al. Predicting intelligence from brain gray matter volume. Brain Struct. Funct. 225, 2111–2129 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  44. Wu, J. et al. A connectivity-based psychometric prediction framework for brain–behavior relationship studies. Cereb. Cortex 31, 3732–3751 (2014).

    Article  Google Scholar 

  45. Qin, S. et al. Amygdala subregional structure and intrinsic functional connectivity predicts individual differences in anxiety during early childhood. Biol. Psychiatry 75, 892–900 (2014).

    Article  PubMed  Google Scholar 

  46. Vo, L. T. et al. Predicting individuals’ learning success from patterns of pre-learning MRI activity. PLoS ONE 6, e16093 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Eldar, E., Hauser, T. U., Dayan, P. & Dolan, R. J. Striatal structure and function predict individual biases in learning to avoid pain. Proc. Natl Acad. Sci. USA 113, 4812–4817 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Chen, C., Yang, J., Lai, J., Li, H. & Yuan, J. Correlating gray matter volume with individual difference in the flanker interference effect. PLoS ONE 10, e0136877 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  49. Wei, L. et al. Grey matter volumes in the executive attention system predict individual differences in effortful control in young adults. Brain Topogr. 32, 111–117 (2019).

    Article  PubMed  Google Scholar 

  50. Wang, X. et al. Predicting trait-like individual differences in fear of pain in the healthy state using gray matter volume. Brain Imaging Behav. 13, 1468–1473 (2019).

    Article  PubMed  Google Scholar 

  51. Wang, L., Wee, C.-Y., Suk, H.-I., Tang, X. & Shen, D. MRI-based intelligence quotient (IQ) estimation with sparse learning. PLoS ONE 10, e0117295 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  52. Yang, J.-J. et al. Prediction for human intelligence using morphometric characteristics of cortical surface: partial least square analysis. Neuroscience 246, 351–361 (2013).

    Article  CAS  PubMed  Google Scholar 

  53. Scheinost, D. et al. Ten simple rules for predictive modeling of individual differences in neuroimaging. NeuroImage 193, 35–45 (2019).

    Article  PubMed  Google Scholar 

  54. He, Q. et al. Decoding the neuroanatomical basis of reading ability: a multivoxel morphometric study. J. Neurosci. 33, 12835–12843 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Cui, Z., Su, M., Li, L., Shu, H. & Gong, G. Individualized prediction of reading comprehension ability using gray matter volume. Cereb. Cortex 28, 1656–1672 (2018).

    Article  PubMed  Google Scholar 

  56. Sui, J., Jiang, R., Bustillo, J. & Calhoun, V. Neuroimaging-based individualized prediction of cognition and behavior for mental disorders and health: methods and promises. Biol. Psychiatry 88, 818–828 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  57. Jiang, R. et al. Multimodal data revealed different neurobiological correlates of intelligence between males and females. Brain Imaging Behav. 14, 1979–1993 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  58. Ullman, H., Almeida, R. & Klingberg, T. Structural maturation and brain activity predict future working memory capacity during childhood development. J. Neurosci. 34, 1592–1598 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Wang, Y., Goh, J. O., Resnick, S. M. & Davatzikos, C. Imaging-based biomarkers of cognitive performance in older adults constructed via high-dimensional pattern regression applied to MRI and PET. PLoS ONE 8, e85460 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  60. Rasero, J., Sentis, A. I., Yeh, F.-C. & Verstynen, T. V. Integrating across neuroimaging modalities boosts prediction accuracy of cognitive ability. PLoS Comput. Biol. 17, e1008347 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Boeke, E. A., Holmes, A. J. & Phelps, E. A. Toward robust anxiety biomarkers: a machine learning approach in a large-scale sample. Biol. Psychiatry 5, 799–807 (2020).

    Google Scholar 

  62. Smith, S. M. & Nichols, T. E. Statistical challenges in “big data” human neuroimaging. Neuron 97, 263–268 (2018).

    Article  CAS  PubMed  Google Scholar 

  63. Krishnan, A., Williams, L. J., McIntosh, A. R. & Abdi, H. Partial least squares (PLS) methods for neuroimaging: a tutorial and review. Neuroimage 56, 455–475 (2011).

    Article  PubMed  Google Scholar 

  64. Wang, H.-T. et al. Finding the needle in a high-dimensional haystack: canonical correlation analysis for neuroscientists. NeuroImage 216, 116745 (2020).

    Article  PubMed  Google Scholar 

  65. Seidlitz, J. et al. Morphometric similarity networks detect microscale cortical organization and predict inter-individual cognitive variation. Neuron 97, 231–247.e7 (2018).

    Article  CAS  PubMed  Google Scholar 

  66. Smith, S. M. et al. A positive-negative mode of population covariation links brain connectivity, demographics and behavior. Nat. Neurosci. 18, 1565–1567 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Moser, D. A. et al. An integrated brain–behavior model for working memory. Mol. Psychiatry 23, 1974–1980 (2018).

    Article  CAS  PubMed  Google Scholar 

  68. Kebets, V. et al. Somatosensory-motor dysconnectivity spans multiple transdiagnostic dimensions of psychopathology. Biol. Psychiatry 86, 779–791 (2019).

    Article  PubMed  Google Scholar 

  69. Xia, C. H. et al. Linked dimensions of psychopathology and connectivity in functional brain networks. Nat. Commun. 9, 1–14 (2018).

    Article  CAS  Google Scholar 

  70. Drysdale, A. T. et al. Resting-state connectivity biomarkers define neurophysiological subtypes of depression. Nat. Med. 23, 28–38 (2017).

    Article  CAS  PubMed  Google Scholar 

  71. Grosenick, L. et al. Functional and optogenetic approaches to discovering stable subtype-specific circuit mechanisms in depression. Biol. Psychiatry Cogn. Neurosci. Neuroimaging 4, 554–566 (2019).

    PubMed  PubMed Central  Google Scholar 

  72. Mihalik, A. et al. Brain-behaviour modes of covariation in healthy and clinically depressed young people. Sci. Rep. 9, 11536 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  73. Van Essen, D. C. et al. The WU-Minn Human Connectome Project: an overview. Neuroimage 80, 62–79 (2013).

    Article  PubMed  Google Scholar 

  74. Han, F., Gu, Y., Brown, G. L., Zhang, X. & Liu, X. Neuroimaging contrast across the cortical hierarchy is the feature maximally linked to behavior and demographics. Neuroimage 215, 116853 (2020).

    Article  PubMed  Google Scholar 

  75. Llera, A., Wolfers, T., Mulders, P. & Beckmann, C. F. Inter-individual differences in human brain structure and morphology link to variation in demographics and behavior. eLife 8, e44443 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Modabbernia, A., Janiri, D., Doucet, G. E., Reichenberg, A. & Frangou, S. Multivariate patterns of brain–behavior–environment associations in the Adolescent Brain and Cognitive Development study. Biol. Psychiatry 89, 510–520 (2021).

    Article  PubMed  Google Scholar 

  77. Alnæs, D., Kaufmann, T., Marquand, A. F., Smith, S. M. & Westlye, L. T. Patterns of sociocognitive stratification and perinatal risk in the child brain. Proc. Natl Acad. Sci. USA 117, 12419–12427 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  78. Nooner, K. B. et al. The NKI-Rockland sample: a model for accelerating the pace of discovery science in psychiatry. Front. Neurosci. 6, 152 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  79. Avants, B. B., Cook, P. A., Ungar, L., Gee, J. C. & Grossman, M. Dementia induces correlated reductions in white matter integrity and cortical thickness: a multivariate neuroimaging study with sparse canonical correlation analysis. Neuroimage 50, 1004–1016 (2010).

    Article  PubMed  Google Scholar 

  80. Genon, S. et al. Relating pessimistic memory predictions to Alzheimer’s disease brain structure. Cortex 85, 151–164 (2016).

    Article  PubMed  Google Scholar 

  81. Sui, J. et al. Multimodal neuromarkers in schizophrenia via cognition-guided MRI fusion. Nat. Commun. 9, 1–14 (2018).

    Article  CAS  Google Scholar 

  82. Moser, D. A. et al. Multivariate associations among behavioral, clinical, and multimodal imaging phenotypes in patients with psychosis. JAMA Psychiatry 75, 386–395 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  83. Ing, A. et al. Identification of neurobehavioural symptom groups based on shared brain mechanisms. Nat. Hum. Behav. 3, 1306–1318 (2019).

    Article  PubMed  Google Scholar 

  84. Wasserman, J. D. & Bracken, B. A. Fundamental psychometric considerations in assessment. Handb. Psychol. https://doi.org/10.1002/9781118133880.hop210003 (2012).

    Article  Google Scholar 

  85. Chen, J. et al. Exploration of scanning effects in multi-site structural MRI studies. J. Neurosci. Methods 230, 37–50 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  86. Holmes, A. J. et al. Brain Genomics Superstruct Project initial data release with structural, functional, and behavioral measures. Sci. Data 2, 1–16 (2015).

    Article  Google Scholar 

  87. Habeck, C., Gazes, Y., Razlighi, Q. & Stern, Y. Cortical thickness and its associations with age, total cognition and education across the adult lifespan. PLoS ONE 15, e0230298 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Peelle, J. E., Cusack, R. & Henson, R. N. A. Adjusting for global effects in voxel-based morphometry: gray matter decline in normal aging. NeuroImage 60, 1503–1516 (2012).

    Article  PubMed  Google Scholar 

  89. Dayan, E., Hamann, J. M., Averbeck, B. B. & Cohen, L. G. Brain structural substrates of reward dependence during behavioral performance. J. Neurosci. 34, 16433–16441 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Valk, S. L., Bernhardt, B. C., Böckler, A., Kanske, P. & Singer, T. Substrates of metacognition on perception and metacognition on higher-order cognition relate to different subsystems of the mentalizing network. Hum. Brain Mapp. 37, 3388–3399 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  91. May, A. Experience-dependent structural plasticity in the adult human brain. Trends Cogn. Sci. 15, 475–482 (2011).

    Article  PubMed  Google Scholar 

  92. Geng, X. et al. Structural and maturational covariance in early childhood brain development. Cereb. Cortex 27, 1795–1807 (2017).

    PubMed  Google Scholar 

  93. Zielinski, B. A., Gennatas, E. D., Zhou, J. & Seeley, W. W. Network-level structural covariance in the developing brain. Proc. Natl Acad. Sci. USA 107, 18191–18196 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Lanphear, B. P. The impact of toxins on the developing brain. Annu. Rev. Public Health 36, 211–230 (2015).

    Article  PubMed  Google Scholar 

  95. Farah, M. J. The neuroscience of socioeconomic status: correlates, causes, and consequences. Neuron 96, 56–71 (2017).

    Article  CAS  PubMed  Google Scholar 

  96. Teicher, M. H., Samson, J. A., Anderson, C. M. & Ohashi, K. The effects of childhood maltreatment on brain structure, function and connectivity. Nat. Rev. Neurosci. 17, 652 (2016).

    Article  CAS  PubMed  Google Scholar 

  97. Palmer, C. E. et al. Fluid and crystallised intelligence are associated with distinct regionalisation patterns of cortical morphology. Preprint at bioRxiv https://doi.org/10.1101/2020.02.13.948596 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  98. Noble, K. G. et al. Family income, parental education and brain structure in children and adolescents. Nat. Neurosci. 18, 773 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Decker, A. L., Duncan, K., Finn, A. S. & Mabbott, D. J. Children’s family income is associated with cognitive function and volume of anterior not posterior hippocampus. Nat. Commun. 11, 1–11 (2020).

    Article  CAS  Google Scholar 

  100. Kanai, R., Bahrami, B., Roylance, R. & Rees, G. Online social network size is reflected in human brain structure. Proc. R. Soc. B: Biol. Sci. 279, 1327–1334 (2012).

    Article  CAS  Google Scholar 

  101. Rice, K. & Redcay, E. Spontaneous mentalizing captures variability in the cortical thickness of social brain regions. Soc. Cogn. Affect. Neurosci. 10, 327–334 (2015).

    Article  PubMed  Google Scholar 

  102. Foster, N. E. & Zatorre, R. J. Cortical structure predicts success in performing musical transformation judgments. Neuroimage 53, 26–36 (2010).

    Article  PubMed  Google Scholar 

  103. Good, C. D. et al. A voxel-based morphometric study of ageing in 465 normal adult human brains. Neuroimage 14, 21–36 (2001).

    Article  CAS  PubMed  Google Scholar 

  104. Mechelli, A., Price, C. J., Friston, K. J. & Ashburner, J. Voxel-based morphometry of the human brain: methods and applications. Curr. Med. Imaging 1, 105–113 (2005).

    Article  Google Scholar 

  105. Fischl, B. & Dale, A. M. Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc. Natl Acad. Sci. USA 97, 11050–11055 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  106. Greve, D. N. & Fischl, B. False positive rates in surface-based anatomical analysis. Neuroimage 171, 6–14 (2018).

    Article  PubMed  Google Scholar 

  107. Glasser, M. F. & Van Essen, D. C. Mapping human cortical areas in vivo based on myelin content as revealed by T1- and T2-weighted MRI. J. Neurosci. 31, 11597–11616 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  108. Toschi, N. & Passamonti, L. Intra-cortical myelin mediates personality differences. J. Personal. 87, 889–902 (2019).

    Article  Google Scholar 

  109. Le Bihan, D. et al. Diffusion tensor imaging: concepts and applications. J. Magn. Reson. Imaging 13, 534–546 (2001).

    Article  PubMed  Google Scholar 

  110. Forkel, S. J., Friedrich, P., Thiebaut de Schotten, M. & Howells, H. White matter variability, cognition, and disorders: a systematic review. Brain Struct. Funct. 227, 529–544 (2020).

    Article  Google Scholar 

  111. Weiskopf, N. et al. Quantitative multi-parameter mapping of R1, PD*, MT, and R2* at 3 T: a multi-center validation. Front. Neurosci. 7, 95 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  112. Menon, V. et al. Microstructural organization of human insula is linked to its macrofunctional circuitry and predicts cognitive control. eLife 9, e53470 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  113. Carey, D. et al. Quantitative MRI provides markers of intra-, inter-regional, and age-related differences in young adult cortical microstructure. Neuroimage 182, 429–440 (2018).

    Article  PubMed  Google Scholar 

  114. Cremers, H. R., Wager, T. D. & Yarkoni, T. The relation between statistical power and inference in fMRI. PLoS ONE 12, e0184923 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  115. Mwangi, B., Tian, T. S. & Soares, J. C. A review of feature reduction techniques in neuroimaging. Neuroinformatics 12, 229–244 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  116. Dinga, R. et al. Evaluating the evidence for biotypes of depression: methodological replication and extension of. NeuroImage Clin. 22, 101796 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  117. Mihalik, A. et al. Multiple holdouts with stability: improving the generalizability of machine learning analyses of brain–behavior relationships. Biol. Psychiatry 87, 368–376 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  118. Hardoon, D. R. & Shawe-Taylor, J. Sparse canonical correlation analysis. Mach. Learn. 83, 331–353 (2011).

    Article  Google Scholar 

  119. Fukumizu, K., Bach, F. R. & Gretton, A. Statistical consistency of kernel canonical correlation analysis. J. Mach. Learn. Res. 8, 361–383 (2007).

    Google Scholar 

  120. Helmer, M., Ji, J. L., Anticevic, A. & Murray, J. On discovery of brain–phenotype relationships: detection, estimation, and prediction. Biol. Psychiatry 87, S207 (2020).

    Article  Google Scholar 

  121. Haufe, S. et al. On the interpretation of weight vectors of linear models in multivariate neuroimaging. Neuroimage 87, 96–110 (2014).

    Article  PubMed  Google Scholar 

  122. Helmer, M. et al. On stability of canonical correlation analysis and partial least squares with application to brain–behavior associations. Preprint at bioRxiv https://doi.org/10.1101/2020.08.25.265546 (2021).

    Article  Google Scholar 

Download references

Acknowledgements

The authors are supported by the Deutsche Forschungsgemeinschaft (DFG) (GE 2835/2–1), the National Institute of Mental Health (R01-MH074457), the Helmholtz Portfolio Theme ‘Supercomputing and Modelling for the Human Brain’, the European Union’s Horizon 2020 research and innovation programme under Grant Agreements 785907 (HBP SGA2) and 945539 (HBP SGA3), and The Virtual Brain Cloud (EU H2020, no. 826421).

Author information

Authors and Affiliations

  1. Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany

    Sarah Genon, Simon B. Eickhoff & Shahrzad Kharabian

  2. Institute of Neuroscience and Medicine (INM-7), Research Centre Jülich, Jülich, Germany

    Sarah Genon & Simon B. Eickhoff

Authors
  1. Sarah Genon
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Simon B. Eickhoff
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shahrzad Kharabian
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

S.G. and S.K. researched data for the article. S.G. wrote the first draft. All authors subsequently provided a substantial contribution to discussion of its content and reviewed/edited the manuscript across revision steps.

Corresponding author

Correspondence to Sarah Genon.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Reviews Neuroscience thanks C. Habeck, A. Hariri, who co-reviewed with E. Whitman, and A. Kuceyeski for their contribution to the peer review of this work.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Genon, S., Eickhoff, S.B. & Kharabian, S. Linking interindividual variability in brain structure to behaviour. Nat Rev Neurosci 23, 307–318 (2022). https://doi.org/10.1038/s41583-022-00584-7

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41583-022-00584-7

  • Springer Nature Limited

This article is cited by

Search

Navigation