The challenges and prospects of brain-based prediction of behaviour
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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).
Yeung, A. W. K., More, S., Wu, J. & Eickhoff, S. B. Reporting details of neuroimaging studies on individual traits prediction: a literature survey. NeuroImage 256, 119275 (2022).
Cirillo, D. & Valencia, A. Big data analytics for personalized medicine. Curr. Opin. Biotechnol. 58, 161–167 (2019).
Dadi, K. et al. Benchmarking functional connectome-based predictive models for resting-state fMRI. NeuroImage 192, 115–134 (2019).
Dhamala, E., Yeo, B. T. T. & Holmes, A. J. One size does not fit all: methodological considerations for brain-based predictive modelling in psychiatry. Biol. Psychiatry 93, 717–728 (2023).
Rosenberg, M. D. et al. A neuromarker of sustained attention from whole-brain functional connectivity. Nat. Neurosci. 19, 165–171 (2016).
Lee, M. H., Smyser, C. D. & Shimony, J. S. Resting-state fMRI: a review of methods and clinical applications. Am. J. Neuroradiol. 34, 1866–1872 (2013).
Finn, E. S. et al. Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity. Nat. Neurosci. 18, 1664–1671 (2015).
Ferguson, M. A., Anderson, J. S. & Spreng, R. N. Fluid and flexible minds: intelligence reflects synchrony in the brain’s intrinsic network architecture. Netw. Neurosci. 1, 192–207 (2017).
Li, J. et al. A neuromarker of individual general fluid intelligence from the white-matter functional connectome. Transl. Psychiatry 10, 147 (2020).
Kumar, S. et al. An information network flow approach for measuring functional connectivity and predicting behavior. Brain Behav. 9, e01346 (2019).
Rosenberg, M. D. et al. Functional connectivity predicts changes in attention observed across minutes, days, and months. Proc. Natl Acad. Sci. USA 117, 3797–3807 (2020).
Avery, E. W. et al. Distributed patterns of functional connectivity predict working memory performance in novel healthy and memory-impaired individuals. J. Cogn. Neurosci. 32, 241–255 (2020).
Pläschke, R. N. et al. Age differences in predicting working memory performance from network-based functional connectivity. Cortex 132, 441–459 (2020).
Zhang, H. et al. Do intrinsic brain functional networks predict working memory from childhood to adulthood? Hum. Brain Mapp. 41, 4574–4586 (2020).
Girault, J. B. et al. White matter connectomes at birth accurately predict cognitive abilities at age 2. NeuroImage 192, 145–155 (2019).
Jiang, R. et al. Multimodal data revealed different neurobiological correlates of intelligence between males and females. Brain Imaging Behav. 14, 1979–1993 (2020).
Rasero, J., Sentis, A. I., Yeh, F. C. & Verstynen, T. Integrating across neuroimaging modalities boosts prediction accuracy of cognitive ability. PLoS Comput. Biol. 17, e1008347 (2021).
Wei, L. et al. Grey matter volume in the executive attention system predict individual differences in effortful control in young adults. Brain Topogr. 32, 111–117 (2019).
Kaufmann, T. et al. Task modulations and clinical manifestations in the brain functional connectome in 1615 fMRI datasets. NeuroImage 147, 243–252 (2017).
Xiao, Y. et al. Predicting visual working memory with multimodal magnetic resonance imaging. Hum. Brain Mapp. 42, 1446–1462 (2021).
Scheinost, D. et al. Ten simple rules for predictive modeling of individual differences in neuroimaging. NeuroImage 193, 35–45 (2019).
Gabrieli, J. D. E., Ghosh, S. S. & Whitfield-Gabrieli, S. Prediction as a humanitarian and pragmatic contribution from human cognitive neuroscience. Neuron 85, 11–26 (2015).
Pervaiz, U., Vidaurre, D., Woolrich, M. W. & Smith, S. M. Optimising network modelling methods for fMRI. NeuroImage 221, 116604 (2020).
Poldrak, R. A., Huckins, G. & Varoquax, G. Establishment of best practices for evidence for prediction: a review. J. Am. Med. Assoc. Psychiatry 77, 534–540 (2020).
Sripada, C. et al. Prediction of neurocognition in youth from resting state fMRI. Mol. Psychiatry 25, 3413–3421 (2019).
He, T. et al. Deep neural networks and kernel regression achieve comparable accuracies for functional connectivity prediction of behavior and demographics. NeuroImage 206, 116276 (2020).
He, L. et al. Functional connectome prediction of anxiety related to the COVID-19 pandemic. Am. J. Psychiatry 178, 530–540 (2021).
Gao, S., Greene, A. S., Constable, R. T. & Scheinost, D. Combining multiple connectomes improves predictive modeling of phenotypic measures. NeuroImage 201, 116038 (2019).
Zalesky, A., Fornito, A., Cocchi, L., Gollo, L. L. & Breakspear, M. Time-resolved resting-state brain networks. Proc. Natl Acad. Sci. USA 111, 10341–10346 (2014).
Bahg, G., Evans, D. G., Galdo, M. & Turner, B. M. Gaussian process linking functions for mind, brain, and behavior. Proc. Natl Acad. Sci. USA 117, 29398–29406 (2020).
Mihalik, A. et al. Canonical correlation analysis and partial least squares for identifying brain–behaviour associations: a tutorial and a comparative study. Biol. Psychiatry 7, 1055–1067 (2022).
Gal, S., Tik, N., Bernstein-Eliav, M. & Tavor, I. Predicting individual traits from unperformed tasks. NeuroImage 249, 118920 (2022).
He, T. et al. Meta-matching as a simple framework to translate phenotypic predictive models from big to small data. Nat. Neurosci. 25, 795–804 (2022).
Takagi, Y., Hirayama, J. I. & Tanaka, S. C. State-unspecific patterns of whole-brain functional connectivity from resting and multiple task states predict stable individual traits. NeuroImage 201, 116036 (2019).
Burr, D. A. et al. Functional connectivity predicts the dispositional use of expressive suppression but not cognitive reappraisal. Brain Behav. 10, e01493 (2020).
Jiang, R. et al. Task-induced brain connectivity promotes the detection of individual differences in brain–behavior relationships. NeuroImage 207, 116370 (2020).
Ooi, L. Q. R. et al. Comparison of individualized behavioral predictions across anatomical, diffusion and functional connectivity MRI. NeuroImage 263, 119636 (2022).
Dhamala, E., Jamison, K. W., Jaywant, A., Dennis, S. & Kuceyeski, A. Distinct functional and structural connections predict crystallised and fluid cognition in healthy adults. Hum. Brain Mapp. 42, 3102–3118 (2021).
Mansour, L. S., Tian, Y., Yeo, B. T. T., Cropley, V. & Zalesky, A. High-resolution connectomic fingerprints: mapping neural identity and behavior. NeuroImage 229, 117695 (2021).
Pat, N. et al. Longitudinally stable, brain-based predictive models mediate the relationships between childhood cognition and socio-demographic, psychological and genetic factors. Hum. Brain Mapp. 43, 5520–5542 (2022).
Hurtz, G. M. & Donovan, J. J. Personality and job performance: the Big Five revisited. J. Appl. Psychol. 85, 869–879 (2000).
Kane, M. J., Conway, A. R. A., Miura, T. K. & Colflesh, G. J. H. Working memory, attention control, and the n-back task: a question of construct validity. J. Exp. Psychol. 33, 615–622 (2007).
Sanchez-Cubillo, I. et al. Construct validity of the Trail Making Test: role of task-switching, working memory, inhibition/interference control, and visuomotor abilities. J. Int. Neuropsychol. Soc. 15, 438–450 (2009).
Chen, J. et al. Shared and unique brain network features predict cognitive, personality, and mental health scores in the ABCD study. Nat. Commun. 13, 2217 (2022).
Wu, J. et al. A connectivity-based psychometric prediction framework for brain–behavior relationship studies. Cereb. Cortex 31, 3732–3751 (2021).
Noble, S., Scheinost, D. & Constable, R. T. A decade of test–retest reliability of functional connectivity: a systematic review and meta-analysis. NeuroImage 203, 116157 (2019).
Elliott, M. L. et al. What is the test–retest reliability of common task-functional MRI measures? New empirical evidence and a meta-analysis. Psychol. Sci. 31, 792–806 (2020).
Patriat, R. et al. The effect of resting condition on resting-state fMRI reliability and consistency: a comparison between resting with eyes open, closed, and fixated. NeuroImage 78, 463–473 (2013).
Birn, R. M. et al. The effect of scan length on the reliability of resting-state fMRI connectivity estimates. NeuroImage 83, 550–558 (2013).
Bennett, C. M. & Miller, M. B. fMRI reliability: influences of task and experimental design. Cogn. Affect. Behav. Neurosci. 13, 690–702 (2013).
Cremers, H. R., Wager, T. D. & Yarkoni, T. The relation between statistical power and inference in fMRI. PLoS ONE 12, e0184923 (2017).
Kharabian Masouleh, S., Eickhoff, S. B., Hoffstaedter, F. & Genon, S., Alzheimer’s Disease Neuroimaging Initiative. Empirical examination of the replicability of associations between brain structure and psychological variables. eLife 8, e43464 (2019).
Genon, S., Eickhoff, S. B. & Kahrabian, S. Linking interindividual variability in brain structure to behaviour. Nat. Rev. Neurosci. 23, 307–318 (2022).
Marek, S. et al. Reproducible brain-wide association studies require thousands of individuals. Nature 603, 654–660 (2022).
Beaty, R. E. et al. Robust prediction of individual creative ability from brain functional connectivity. Proc. Natl Acad. Sci. USA 115, 1087–1092 (2018).
Liu, P. et al. The functional connectome predicts feeling of stress on regular days and during the COVID-19 pandemic. Neurobiol. Stress 14, 100285 (2021).
Ren, Z. et al. Connectome-based predictive modeling of creativity anxiety. NeuroImage 225, 117469 (2021).
Fong, A. H. C. et al. Dynamic functional connectivity during task performance and rest predicts individual differences in attention across studies. NeuroImage 188, 14–25 (2019).
Wu, J. et al. Cross-cohort replicability and generalizability of connectivity-based psychometric prediction patterns. NeuroImage 262, 119569 (2022).
Tervo-Clemmens, B. et al. Reply to: Multivariate BWAS can be replicable with moderate sample sizes. Nature 615, E8–E12 (2023).
Rosenberg, M. D. & Finn, E. S. How to establish robust brain–behavior relationships without thousands of individuals. Nat. Neurosci. 25, 835–837 (2022).
Spisak, T., Bingel, U. & Wager, T. Replicable multivariate BWAS with moderate sample sizes. Preprint at bioRxiv https://doi.org/10.1101/2022.06.22.497072 (2022).
Van Essen, D. C. et al. The WU-Minh Human Connectome Project: an overview. NeuroImage 80, 62–79 (2013).
Glasser, M. F. et al. The minimal preprocessing pipelines for the Human Connectome Project. NeuroImage 80, 105–124 (2013).
Harms, M. P. et al. Extending the Human Connectome Project across ages: imaging protocols for the lifespan development and aging projects. NeuroImage 183, 972–984 (2018).
Chouldechova, A., Benavides-Prado, D., Fialko, O. & Vaithianathan, R. A case study of algorithm-assisted decision making in child maltreatment hotline screening decisions. Proc. Mach. Learn. Res. 81, 134–148 (2018).
Martin, A. R. et al. Clinical use of current polygenic risk scores may exacerbate healthy disparities. Nat. Genet. 51, 584–591 (2019).
Obermeyer, Z., Powers, B., Vogeli, C. & Mullainathan, S. Dissecting racial bias in an algorithm used to manage the health of populations. Science 366, 447–453 (2019).
Li, J. et al. Cross-ethnicity/race generalization failure of behavioral prediction from resting-state functional connectivity. Sci. Adv. 8, eabj1812 (2022).
Greene, A. S. et al. Brain-phenotype models fail for individuals who defy sample stereotypes. Nature 609, 109–118 (2022).
Greene, A. S., Gao, S., Scheinost, D. & Constable, R. T. Task-induced brain state manipulation improves prediction of individual traits. Nat. Commun. 9, 2807 (2018).
Nostro, A. D. et al. Predicting personality from network-based resting-state functional connectivity. Brain Struct. Funct. 223, 2699–2719 (2018).
Tian, Y. & Zalesky, A. Machine learning prediction of cognition from functional connectivity: are feature weights reliable? NeuroImage 245, 118648 (2021).
Haufe, S. et al. On the interpretation of weight vectors of linear models in multivariate neuroimaging. NeuroImage 87, 96–110 (2014).
Chen, J. et al. Relationship between prediction accuracy and feature importance reliability: an empirical and theoretical study. NeuroImage 274, 120115 (2023).
Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).
Yip, S. W., Kiluk, B. & Scheinost, D. Towards addiction prediction: an overview of cross-validated predictive modeling findings and considerations for future neuroimaging research. Biol. Psychiatry Cogn. Neurosci. Neuroimaging 5, 748–758 (2020).
Jiang, R., Woo, C. W., Qi, S., Wu, J. & Sui, J. Interpreting brain biomarkers: challenges and solutions in interpreting machine learning-based predictive neuroimaging. IEEE Signal Process. Mag. 39, 107–118 (2022).
Chormai, P. et al. Machine learning of large-scale multimodal brain imaging data reveals neural correlates of hand preference. NeuroImage 262, 119534 (2022).
Zou, H. & Hastie, T. Regularization and variable selection via the elastic net. J. R. Stat. Soc. B 67, 301–320 (2005).
Lundberg, S. M. & Lee, S.-I. A unified approach to interpreting model prediction. In Advances in Neural Information Processing Systems 30 (NIPS 2017) (eds Guyon, I. et al.) (Curran Associates, 2017).
Pat, N., Wang, Y., Bartonicek, A., Candia, J. & Stringaris, A. Explainable machine learning approach to predict and explain the relationship between task-based fMRI and individual differences in cognition. Cereb. Cortex 33, 2682–2703 (2023).
Kingma, D. P. & Welling, M. Auto-encoding variational Bayes. Preprint at arXiv https://doi.org/10.48550/arXiv.1312.6114 (2013).
Goodfellow, I. J. et al. Generative adversarial networks. Commun. ACM 63, 139–144 (2020).
van den Oord, A., Kalchbrenner, N. & Kavukcuoglu, K. Pixel recurrent neural networks. In Proceedings of the 33rd International Conference on Machine Learning (eds Balcan, M. F. & Weinberger, K. Q.) 48 1747–1756 (Proceedings of Machine Learning Research, 2016).
Fried, D. et al. Speaker-follower models for vision-and-language navigation. In Advances in Neural Information Processing Systems 31 (NeurIPS 2018) (eds Bengio, S. et al.) (Curran Associates, 2018).
Rosenblatt, M. et al. Connectome-based machine learning models are vulnerable to subtle data manipulations. Patterns (in the press).
Finlayson, S. G. et al. Adversarial attacks on medical machine learning. Science 363, 1287–1289 (2019).
Finlayson, S. G., Chung, H. W., Kohane, I. S. & Beam, A. L. Adversarial attacks against medical deep learning systems. Preprint at arXiv https://doi.org/10.48550/arXiv.1804.05296 (2019).
Dubois, J., Galdi, P., Han, Y., Paul, L. K. & Adolphs, R. Resting-state functional brain connectivity best predicts the personality dimension of openness to experience. Pers. Neurosci. 1, E6 (2018).
Jiang, R. et al. Connectome-based individualized prediction of temperament trait scores. NeuroImage 183, 366–374 (2018).
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