[en] 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.
Disciplines :
Neurosciences & behavior
Author, co-author :
Genon, Sarah ; Université de Liège - ULiège > Département des sciences cliniques > Neuroimagerie des troubles de la mémoire et revalidation cognitive ; Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, ; Institute of Neuroscience and Medicine (INM-7), Research Centre Jülich, Jülich
Eickhoff, Simon B; Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, ; Institute of Neuroscience and Medicine (INM-7), Research Centre Jülich, Jülich,
Kharabian, Shahrzad; Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf,
Language :
English
Title :
Linking interindividual variability in brain structure to behaviour.
Kiesow, H. et al. 10,000 social brains: sex differentiation in human brain anatomy. Sci. Adv. 6, eaaz1170 (2020).
Valk, S. L. et al. Personality and local brain structure: their shared genetic basis and reproducibility. NeuroImage 220, 117067 (2020).
Kanai, R. & Rees, G. The structural basis of inter-individual differences in human behaviour and cognition. Nat. Rev. Neurosci. 12, 231–242 (2011).
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).
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).
Matsuo, K. et al. A voxel-based morphometry study of frontal gray matter correlates of impulsivity. Hum. Brain Mapp. 30, 1188–1195 (2009).
Kanai, R., Feilden, T., Firth, C. & Rees, G. Political orientations are correlated with brain structure in young adults. Curr. Biol. 21, 677–680 (2011).
Button, K. S. et al. Power failure: why small sample size undermines the reliability of neuroscience. Nat. Rev. Neurosci. 14, 365–376 (2013).
Collaboration, O. S. Estimating the reproducibility of psychological science. Science 349, aac4716 (2015).
De Boeck, P. & Jeon, M. Perceived crisis and reforms: issues, explanations, and remedies. Psychol. Bull. 144, 757 (2018).
Poldrack, R. A. et al. Scanning the horizon: towards transparent and reproducible neuroimaging research. Nat. Rev. Neurosci. 18, 115 (2017).
Boekel, W. et al. A purely confirmatory replication study of structural brain–behavior correlations. Cortex 66, 115–133 (2015).
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).
Muhlert, N. & Ridgway, G. R. Failed replications, contributing factors and careful interpretations: commentary on Boekel et al. 2015. Cortex 74, 338–342 (2016).
Kanai, R. Open questions in conducting confirmatory replication studies: commentary on Boekel et al. 2015. Cortex 74, 343–347 (2016).
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).
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).
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).
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 10.1101/2020.01.10.901181 (2020). DOI: 10.1101/2020.01.10.901181
Botvinik-Nezer, R. et al. Variability in the analysis of a single neuroimaging dataset by many teams. Nature 582, 84–88 (2020).
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).
Fortin, J.-P. et al. Harmonization of cortical thickness measurements across scanners and sites. NeuroImage 167, 104–120 (2018).
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).
Kharabian Masouleh, S. et al. Influence of processing pipeline on cortical thickness measurement. Cereb. Cortex 30, 5014–5027 (2020).
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).
Climie, E. A. & Rostad, K. Test review: Wechsler Adult Intelligence Scale. J. Psychoeduc. Assess. 29, 6 (2011).
Marek, S. et al. Reproducible brain-wide association studies require thousands of individuals. Nature 603, 654–660 (2022).
Miller, K. L. et al. Multimodal population brain imaging in the UK Biobank prospective epidemiological study. Nat. Neurosci. 19, 1523 (2016).
Steegen, S., Tuerlinckx, F., Gelman, A. & Vanpaemel, W. Increasing transparency through a multiverse analysis. Perspect. Psychol. Sci. 11, 702–712 (2016).
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).
Schönbrodt, F. D. & Perugini, M. At what sample size do correlations stabilize? J. Res. Personal. 47, 609–612 (2013).
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).
Scoville, W. B. & Milner, B. Loss of recent memory after bilateral hippocampal lesions. J. Neurol. Neurosurg. Psychiatry 20, 11 (1957).
Poldrack, R. A. Mapping mental function to brain structure: how can cognitive neuroimaging succeed? Perspect. Psychol. Sci. 5, 753–761 (2010).
Pessoa, L. Understanding brain networks and brain organization. Phys. Life Rev. 11, 400–435 (2014).
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).
Habeck, C. & Stern, Y. Multivariate data analysis for neuroimaging data: overview and application to Alzheimer’s disease. Cell Biochem. Biophys. 58, 53–67 (2010).
McIntosh, A. R. & Mišić, B. Multivariate statistical analyses for neuroimaging data. Annu. Rev. Psychol. 64, 499–525 (2013).
van der Linden, D. et al. Overlap between the general factor of personality and emotional intelligence: a meta-analysis. Psychol. Bull. 143, 36 (2017).
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).
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).
Watkins, M. W. Exploratory factor analysis: a guide to best practice. J. Black Psychol. 44, 219–246 (2018).
Hilger, K. et al. Predicting intelligence from brain gray matter volume. Brain Struct. Funct. 225, 2111–2129 (2020).
Wu, J. et al. A connectivity-based psychometric prediction framework for brain–behavior relationship studies. Cereb. Cortex 31, 3732–3751 (2014).
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).
Vo, L. T. et al. Predicting individuals’ learning success from patterns of pre-learning MRI activity. PLoS ONE 6, e16093 (2011).
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).
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).
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).
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).
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).
Yang, J.-J. et al. Prediction for human intelligence using morphometric characteristics of cortical surface: partial least square analysis. Neuroscience 246, 351–361 (2013).
Scheinost, D. et al. Ten simple rules for predictive modeling of individual differences in neuroimaging. NeuroImage 193, 35–45 (2019).
He, Q. et al. Decoding the neuroanatomical basis of reading ability: a multivoxel morphometric study. J. Neurosci. 33, 12835–12843 (2013).
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).
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).
Jiang, R. et al. Multimodal data revealed different neurobiological correlates of intelligence between males and females. Brain Imaging Behav. 14, 1979–1993 (2020).
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).
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).
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).
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).
Smith, S. M. & Nichols, T. E. Statistical challenges in “big data” human neuroimaging. Neuron 97, 263–268 (2018).
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).
Wang, H.-T. et al. Finding the needle in a high-dimensional haystack: canonical correlation analysis for neuroscientists. NeuroImage 216, 116745 (2020).
Seidlitz, J. et al. Morphometric similarity networks detect microscale cortical organization and predict inter-individual cognitive variation. Neuron 97, 231–247.e7 (2018).
Smith, S. M. et al. A positive-negative mode of population covariation links brain connectivity, demographics and behavior. Nat. Neurosci. 18, 1565–1567 (2015).
Moser, D. A. et al. An integrated brain–behavior model for working memory. Mol. Psychiatry 23, 1974–1980 (2018).
Kebets, V. et al. Somatosensory-motor dysconnectivity spans multiple transdiagnostic dimensions of psychopathology. Biol. Psychiatry 86, 779–791 (2019).
Xia, C. H. et al. Linked dimensions of psychopathology and connectivity in functional brain networks. Nat. Commun. 9, 1–14 (2018).
Drysdale, A. T. et al. Resting-state connectivity biomarkers define neurophysiological subtypes of depression. Nat. Med. 23, 28–38 (2017).
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).
Mihalik, A. et al. Brain-behaviour modes of covariation in healthy and clinically depressed young people. Sci. Rep. 9, 11536 (2019).
Van Essen, D. C. et al. The WU-Minn Human Connectome Project: an overview. Neuroimage 80, 62–79 (2013).
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).
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).
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).
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).
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).
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).
Genon, S. et al. Relating pessimistic memory predictions to Alzheimer’s disease brain structure. Cortex 85, 151–164 (2016).
Sui, J. et al. Multimodal neuromarkers in schizophrenia via cognition-guided MRI fusion. Nat. Commun. 9, 1–14 (2018).
Moser, D. A. et al. Multivariate associations among behavioral, clinical, and multimodal imaging phenotypes in patients with psychosis. JAMA Psychiatry 75, 386–395 (2018).
Ing, A. et al. Identification of neurobehavioural symptom groups based on shared brain mechanisms. Nat. Hum. Behav. 3, 1306–1318 (2019).
Wasserman, J. D. & Bracken, B. A. Fundamental psychometric considerations in assessment. Handb. Psychol. 10.1002/9781118133880.hop210003 (2012). DOI: 10.1002/9781118133880.hop210003
Chen, J. et al. Exploration of scanning effects in multi-site structural MRI studies. J. Neurosci. Methods 230, 37–50 (2014).
Holmes, A. J. et al. Brain Genomics Superstruct Project initial data release with structural, functional, and behavioral measures. Sci. Data 2, 1–16 (2015).
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).
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).
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).
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).
May, A. Experience-dependent structural plasticity in the adult human brain. Trends Cogn. Sci. 15, 475–482 (2011).
Geng, X. et al. Structural and maturational covariance in early childhood brain development. Cereb. Cortex 27, 1795–1807 (2017).
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).
Lanphear, B. P. The impact of toxins on the developing brain. Annu. Rev. Public Health 36, 211–230 (2015).
Farah, M. J. The neuroscience of socioeconomic status: correlates, causes, and consequences. Neuron 96, 56–71 (2017).
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).
Palmer, C. E. et al. Fluid and crystallised intelligence are associated with distinct regionalisation patterns of cortical morphology. Preprint at bioRxiv 10.1101/2020.02.13.948596 (2020). DOI: 10.1101/2020.02.13.948596
Noble, K. G. et al. Family income, parental education and brain structure in children and adolescents. Nat. Neurosci. 18, 773 (2015).
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).
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).
Rice, K. & Redcay, E. Spontaneous mentalizing captures variability in the cortical thickness of social brain regions. Soc. Cogn. Affect. Neurosci. 10, 327–334 (2015).
Foster, N. E. & Zatorre, R. J. Cortical structure predicts success in performing musical transformation judgments. Neuroimage 53, 26–36 (2010).
Good, C. D. et al. A voxel-based morphometric study of ageing in 465 normal adult human brains. Neuroimage 14, 21–36 (2001).
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).
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).
Greve, D. N. & Fischl, B. False positive rates in surface-based anatomical analysis. Neuroimage 171, 6–14 (2018).
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).
Toschi, N. & Passamonti, L. Intra-cortical myelin mediates personality differences. J. Personal. 87, 889–902 (2019).
Le Bihan, D. et al. Diffusion tensor imaging: concepts and applications. J. Magn. Reson. Imaging 13, 534–546 (2001).
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).
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).
Menon, V. et al. Microstructural organization of human insula is linked to its macrofunctional circuitry and predicts cognitive control. eLife 9, e53470 (2020).
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).
Cremers, H. R., Wager, T. D. & Yarkoni, T. The relation between statistical power and inference in fMRI. PLoS ONE 12, e0184923 (2017).
Mwangi, B., Tian, T. S. & Soares, J. C. A review of feature reduction techniques in neuroimaging. Neuroinformatics 12, 229–244 (2014).
Dinga, R. et al. Evaluating the evidence for biotypes of depression: methodological replication and extension of. NeuroImage Clin. 22, 101796 (2019).
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).
Hardoon, D. R. & Shawe-Taylor, J. Sparse canonical correlation analysis. Mach. Learn. 83, 331–353 (2011).
Fukumizu, K., Bach, F. R. & Gretton, A. Statistical consistency of kernel canonical correlation analysis. J. Mach. Learn. Res. 8, 361–383 (2007).
Helmer, M., Ji, J. L., Anticevic, A. & Murray, J. On discovery of brain–phenotype relationships: detection, estimation, and prediction. Biol. Psychiatry 87, S207 (2020).
Haufe, S. et al. On the interpretation of weight vectors of linear models in multivariate neuroimaging. Neuroimage 87, 96–110 (2014).
Helmer, M. et al. On stability of canonical correlation analysis and partial least squares with application to brain–behavior associations. Preprint at bioRxiv 10.1101/2020.08.25.265546 (2021). DOI: 10.1101/2020.08.25.265546
