Feature selection leads to divergent neurobiological interpretations of brain-based machine learning biomarkers

Machine Learning


  • Genon, S., Eickhoff, S. B. & Kharabian, S. Linking interindividual variability in brain structure to behaviour. Nat. Rev. Neurosci. 23, 307–318 (2022).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • 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 

  • 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).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

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

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Poldrack, R. A., Huckins, G. & Varoquaux, G. Establishment of best practices for evidence for prediction: a review. JAMA Psychiatry 77, 534–540 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Goltermann, J. et al. Cross-validation for the estimation of effect size generalizability in mass-univariate brain-wide association studies. Preprint at bioRxiv https://doi.org/10.1101/2023.03.29.534696 (2023).

  • Spisak, T., Bingel, U. & Wager, T. D. Multivariate BWAS can be replicable with moderate sample sizes. Nature 615, E4–E7 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Yarkoni, T. & Westfall, J. Choosing prediction over explanation in psychology: lessons from machine learning. Perspect. Psychol. Sci. 12, 1100–1122 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Williams, L. M. & Whitfield Gabrieli, S. Neuroimaging for precision medicine in psychiatry. Neuropsychopharmacology 50, 246–257 (2024).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Woo, C.-W., Chang, L. J., Lindquist, M. A. & Wager, T. D. Building better biomarkers: brain models in translational neuroimaging. Nat. Neurosci. 20, 365–377 (2017).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Greene, A. S. & Constable, R. T. Clinical promise of brain-phenotype modeling: a review. JAMA Psychiatry 80, 848 (2023).

    Article 
    PubMed 

    Google Scholar 

  • Cui, Z. & Gong, G. The effect of machine learning regression algorithms and sample size on individualized behavioral prediction with functional connectivity features. Neuroimage 178, 622–637 (2018).

    Article 
    PubMed 

    Google Scholar 

  • Dhamala, E., Yeo, B. T. T. & Holmes, A. J. One size does not fit all: methodological considerations for brain-based predictive modeling in psychiatry. Biol. Psychiatry 93, 717–728 (2023).

    Article 
    PubMed 

    Google Scholar 

  • Gao, S., Greene, A. S., Constable, R. T. & Scheinost, D. Combining multiple connectomes improves predictive modeling of phenotypic measures. Neuroimage 201, 116038 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Rosenblatt, M. et al. Connectome-based machine learning models are vulnerable to subtle data manipulations. Patterns 4, 100756 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Greene, A. S. et al. Brain-phenotype models fail for individuals who defy sample stereotypes. Nature 609, 109–118 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Li, J. et al. Cross-ethnicity/race generalization failure of behavioral prediction from resting-state functional connectivity. Sci. Adv. 8, eabj1812 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 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 

  • Kang, K. et al. Study design features increase replicability in brain-wide association studies. Nature 636, 719–727 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Klapwijk, E. T., van den Bos, W., Tamnes, C. K., Raschle, N. M. & Mills, K. L. Opportunities for increased reproducibility and replicability of developmental neuroimaging. Dev. Cogn. Neurosci. 47, 100902 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Liu, S., Abdellaoui, A., Verweij, K. J. H. & van Wingen, G. A. Replicable brain–phenotype associations require large-scale neuroimaging data. Nat. Hum. Behav. 7, 1344–1356 (2023).

    Article 
    PubMed 

    Google Scholar 

  • 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 

  • Rosenblatt, M. et al. Power and reproducibility in the external validation of brain-phenotype predictions. Nat. Hum. Behav. https://doi.org/10.1038/s41562-024-01931-7 (2024).

    Article 
    PubMed 

    Google Scholar 

  • Adkinson, B. D. et al. Brain-phenotype predictions of language and executive function can survive across diverse real-world data: dataset shifts in developmental populations. Dev. Cogn. Neurosci. 70, 101464 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Marek, S. & Laumann, T. O. Replicability and generalizability in population psychiatric neuroimaging. Neuropsychopharmacology 50, 52–57 (2024).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Rosenberg, M. D. & Finn, E. S. How to establish robust brain–behavior relationships without thousands of individuals. Nat. Neurosci. 25, 835–837 (2022).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Wu, J. et al. Cross-cohort replicability and generalizability of connectivity-based psychometric prediction patterns. Neuroimage 262, 119569 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 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).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Kohoutová, L. et al. Toward a unified framework for interpreting machine-learning models in neuroimaging. Nat. Protoc. 15, 1399–1435 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Rudin, C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell. 1, 206–215 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Tejavibulya, L. et al. Predicting the future of neuroimaging predictive models in mental health. Mol. Psychiatry 27, 3129–3137 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Allen, E. A., Erhardt, E. B. & Calhoun, V. D. Data visualization in the neurosciences: overcoming the curse of dimensionality. Neuron 74, 603–608 (2012).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Tian, Y. & Zalesky, A. Machine learning prediction of cognition from functional connectivity: are feature weights reliable? Neuroimage 245, 118648 (2021).

    Article 
    PubMed 

    Google Scholar 

  • Chen, J. et al. Relationship between prediction accuracy and feature importance reliability: an empirical and theoretical study. Neuroimage 274, 120115 (2023).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 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 

  • 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).

    Article 
    PubMed 

    Google Scholar 

  • Noble, S., Mejia, A. F., Zalesky, A. & Scheinost, D. Improving power in functional magnetic resonance imaging by moving beyond cluster-level inference. Proc. Natl Acad. Sci. USA 119, e2203020119 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 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 

    Google Scholar 

  • Noble, S., Curtiss, J., Pessoa, L. & Scheinost, D. The tip of the iceberg: a call to embrace anti-localizationism in human neuroscience research. Imaging Neurosci. (Camb.) 2, imag-2-00138 (2024).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Alexander, L. M. et al. An open resource for transdiagnostic research in pediatric mental health and learning disorders. Sci. Data 4, 170181 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Casey, B. J. et al. The Adolescent Brain Cognitive Development (ABCD) study: imaging acquisition across 21 sites. Dev. Cogn. Neurosci. 32, 43–54 (2018).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Somerville, L. H. et al. The Lifespan Human Connectome Project in Development: a large-scale study of brain connectivity development in 5–21 year olds. Neuroimage 183, 456–468 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Satterthwaite, T. D. et al. The Philadelphia Neurodevelopmental Cohort: a publicly available resource for the study of normal and abnormal brain development in youth. Neuroimage 124, 1115–1119 (2016).

    Article 
    PubMed 

    Google Scholar 

  • Shen, X. et al. Using connectome-based predictive modeling to predict individual behavior from brain connectivity. Nat. Protoc. 12, 506–518 (2017).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Foster, M. L., Ye, J., Powers, A. R., Dvornek, N. C. & Scheinost, D. Connectome-based predictive modeling of early and chronic psychosis symptoms. Neuropsychopharmacology 50, 877–885 (2025).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Chandler, S. et al. Validation of the social communication questionnaire in a population cohort of children with autism spectrum disorders. J. Am. Acad. Child Adolesc. Psychiatry 46, 1324–1332 (2007).

    Article 
    PubMed 

    Google Scholar 

  • Hoerl, A. E. & Kennard, R. W. Ridge regression: applications to nonorthogonal problems. Technometrics 12, 69–82 (1970).

    Article 

    Google Scholar 

  • 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 

  • Burt, J. B., Helmer, M., Shinn, M., Anticevic, A. & Murray, J. D. Generative modeling of brain maps with spatial autocorrelation. Neuroimage 220, 117038 (2020).

    Article 
    PubMed 

    Google Scholar 

  • Shinn, M. et al. Functional brain networks reflect spatial and temporal autocorrelation. Nat. Neurosci. 26, 867–878 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Dubois, J., Galdi, P., Paul, L. K. & Adolphs, R. A distributed brain network predicts general intelligence from resting-state human neuroimaging data. Phil. Trans. R. Soc. B 373, 20170284 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Gonzalez-Castillo, J. et al. Whole-brain, time-locked activation with simple tasks revealed using massive averaging and model-free analysis. Proc. Natl Acad. Sci. USA 109, 5487–5492 (2012).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Westlin, C. et al. Improving the study of brain–behavior relationships by revisiting basic assumptions. Trends Cogn. Sci. 27, 246–257 (2023).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Machado, T. A., Kauvar, I. V. & Deisseroth, K. Multiregion neuronal activity: the forest and the trees. Nat. Rev. Neurosci. 23, 683–704 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Tremblay, S., Testard, C., Inchauspé, J. & Petrides, M. Epiphenomenal neural activity in the primate cortex. Preprint at bioRxiv https://doi.org/10.1101/2022.09.12.506984 (2023).

  • Dockès, J., Varoquaux, G. & Poline, J.-B. Preventing dataset shift from breaking machine-learning biomarkers. GigaScience 10, giab055 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Yarkoni, T. The generalizability crisis. Behav. Brain Sci. 45, e1 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Taylor, P. A. et al. Highlight results, don’t hide them: enhance interpretation, reduce biases and improve reproducibility. Neuroimage 274, 120138 (2023).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Makowski, C. et al. Leveraging the Adolescent Brain Cognitive Development study to improve behavioral prediction from neuroimaging in smaller replication samples. Cereb. Cortex 34, bhae223 (2024).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Tozzi, L., Fleming, S. L., Taylor, Z. D., Raterink, C. D. & Williams, L. M. Test–retest reliability of the human functional connectome over consecutive days: identifying highly reliable portions and assessing the impact of methodological choices. Netw. Neurosci. 4, 925–945 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Wang, Y. et al. Intra-session test–retest reliability of functional connectivity in infants. Neuroimage 239, 118284 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Amico, E. & Goñi, J. The quest for identifiability in human functional connectomes. Sci. Rep. 8, 8254 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Graff, K. et al. Benchmarking common preprocessing strategies in early childhood functional connectivity and intersubject correlation fMRI. Dev. Cogn. Neurosci. 54, 101087 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Horien, C., Shen, X., Scheinost, D. & Constable, R. T. The individual functional connectome is unique and stable over months to years. NeuroImage 189, 676–687 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Kaufmann, T. et al. Delayed stabilization and individualization in connectome development are related to psychiatric disorders. Nat. Neurosci. 20, 513–515 (2017).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • 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).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Vanderwal, T. et al. Individual differences in functional connectivity during naturalistic viewing conditions. Neuroimage 157, 521–530 (2017).

    Article 
    PubMed 

    Google Scholar 

  • Sizemore, A. E. et al. Cliques and cavities in the human connectome. J. Comput. Neurosci. 44, 115–145 (2018).

    Article 
    PubMed 

    Google Scholar 

  • Smith, S. M. et al. Brain aging comprises many modes of structural and functional change with distinct genetic and biophysical associations. Elife 9, e52677 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Niu, X., Taylor, A., Shinohara, R. T., Kounios, J. & Zhang, F. Multidimensional brain-age prediction reveals altered brain developmental trajectory in psychiatric disorders. Cereb. Cortex 32, 5036–5049 (2022).

    Article 
    PubMed 

    Google Scholar 

  • Anbarasi, J., Kumari, R., Ganesh, M. & Agrawal, R. Translational connectomics: overview of machine learning in macroscale connectomics for clinical insights. BMC Neurol. 24, 364 (2024).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Snoek, L., Miletić, S. & Scholte, H. S. How to control for confounds in decoding analyses of neuroimaging data. Neuroimage 184, 741–760 (2019).

    Article 
    PubMed 

    Google Scholar 

  • Brucar, L. R., Feczko, E., Fair, D. A. & Zilverstand, A. Current approaches in computational psychiatry for the data-driven identification of brain-based subtypes. Biol. Psychiatry 93, 704–716 (2023).

    Article 
    PubMed 

    Google Scholar 

  • Hong, S.-J. et al. Toward neurosubtypes in autism. Biol. Psychiatry 88, 111–128 (2020).

    Article 
    PubMed 

    Google Scholar 

  • Pettorruso, M. et al. Addiction biotypes: a paradigm shift for future treatment strategies? Mol. Psychiatry 29, 1450–1452 (2024).

    Article 
    PubMed 

    Google Scholar 

  • Williams, L. M. Defining biotypes for depression and anxiety based on large-scale circuit dysfunction: a theoretical review of the evidence and future directions for clinical translation. Depress. Anxiety 34, 9–24 (2017).

    Article 
    PubMed 

    Google Scholar 

  • Zhao, Q. et al. The transition from homogeneous to heterogeneous machine learning in neuropsychiatric research. Biol. Psychiatry Glob. Open Sci. 5, 100397 (2024).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Buch, A. M. et al. Molecular and network-level mechanisms explaining individual differences in autism spectrum disorder. Nat. Neurosci. 26, 650–663 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Chen, P. et al. Four distinct subtypes of Alzheimer’s disease based on resting-state connectivity biomarkers. Biol. Psychiatry 93, 759–769 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Dinga, R. et al. Evaluating the evidence for biotypes of depression: methodological replication and extension of Drysdale et al. (2017). Neuroimage Clin. 22, 101796 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 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 

  • Esterman, M. et al. Evaluating the evidence for a neuroimaging subtype of posttraumatic stress disorder. Sci. Transl. Med. 12, eaaz9343 (2020).

    Article 
    PubMed 

    Google Scholar 

  • Etkin, A. et al. Using fMRI connectivity to define a treatment-resistant form of post-traumatic stress disorder. Sci. Transl. Med. 11, eaal3236 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Jiang, Y. et al. Neuroimaging biomarkers define neurophysiological subtypes with distinct trajectories in schizophrenia. Nat. Ment. Health 1, 186–199 (2023).

    Article 

    Google Scholar 

  • Murray, L., Frederick, B. B. & Janes, A. C. Data-driven connectivity profiles relate to smoking cessation outcomes. Neuropsychopharmacology 49, 1007–1013 (2024).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Pagani, M. et al. Biological subtyping of autism via cross-species fMRI. Preprint at bioRxiv https://doi.org/10.1101/2025.03.04.641400 (2025).

  • Wen, Z. et al. Neuroimaging-based variability in subtyping biomarkers for psychiatric heterogeneity. Mol. Psychiatry https://doi.org/10.1038/s41380-024-02807-y (2024).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Lee, T. Y., Jo, H. J., Koike, S. & Raballo, A. Editorial: biotyping in psychiatry. Front. Psychiatry 13, 844206 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Bolognini, N. & Ro, T. Transcranial magnetic stimulation: disrupting neural activity to alter and assess brain function. J. Neurosci. 30, 9647–9650 (2010).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Luber, B. et al. Using diffusion tensor imaging to effectively target TMS to deep brain structures. Neuroimage 249, 118863 (2022).

    Article 
    PubMed 

    Google Scholar 

  • McPartland, J. C. Developing clinically practicable biomarkers for autism spectrum disorder. J. Autism Dev. Disord. 47, 2935–2937 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Yip, S. W., Kiluk, B. & Scheinost, D. Toward 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).

    PubMed 

    Google Scholar 

  • Chekroud, A. M. et al. Illusory generalizability of clinical prediction models. Science 383, 164–167 (2024).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Kopal, J., Uddin, L. Q. & Bzdok, D. The end game: respecting major sources of population diversity. Nat. Methods 20, 1122–1128 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Quinonero-Candela, J., Sugiyama, M., Schwaighofer, A. & Lawrence, N. D. Dataset Shift in Machine Learning (MIT Press, 2022).

  • Horien, C., Greene, A. S., Constable, R. T. & Scheinost, D. Regions and connections: complementary approaches to characterize brain organization and function. Neuroscientist 26, 117–133 (2020).

    Article 
    PubMed 

    Google Scholar 

  • Bzdok, D., Wolf, G. & Kopal, J. Harnessing population diversity: in search of tools of the trade. GigaScience 13, giae068 (2024).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Ge, J. et al. Increasing diversity in connectomics with the Chinese Human Connectome Project. Nat. Neurosci. 26, 163–172 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Satterthwaite, T. D. et al. Neuroimaging of the Philadelphia Neurodevelopmental Cohort. Neuroimage 86, 544–553 (2014).

    Article 
    PubMed 

    Google Scholar 

  • Gur, R. C. et al. A cognitive neuroscience based computerized battery for efficient measurement of individual differences: standardization and initial construct validation. J. Neurosci. Methods 187, 254–262 (2010).

    Article 
    PubMed 

    Google Scholar 

  • Wilkinson, G. S. & Robertson, G. J. Wide Range Achievement Test 4 (APA PsycTests, 2006); https://doi.org/10.1037/t27160-000.

  • Dickens, R. H., Meisinger, E. B. & Tarar, J. M. Test review: Comprehensive Test of Phonological Processing—2nd ed. (CTOPP-2) by Wagner, R. K., Torgesen, J. K., Rashotte, C. A., & Pearson, N. A. Can. J. Sch. Psychol. 30, 155–162 (2015).

    Article 

    Google Scholar 

  • Tarar, J. M., Meisinger, E. B. & Dickens, R. H. Test review: Test of Word Reading Efficiency—Second Edition (TOWRE-2) by Torgesen, J. K., Wagner, R. K., & Rashotte, C. A. Can. J. Sch. Psychol. 30, 320–326 (2015).

    Article 

    Google Scholar 

  • Weintraub, S. et al. Cognition assessment using the NIH Toolbox. Neurology 80, S54–S64 (2013).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Goodman, A., Lamping, D. L. & Ploubidis, G. B. When to use broader internalising and externalising subscales instead of the hypothesised five subscales on the Strengths and Difficulties Questionnaire (SDQ): data from British parents, teachers and children. J. Abnorm. Child Psychol. 38, 1179–1191 (2010).

    Article 
    PubMed 

    Google Scholar 

  • Goodman, R. The Strengths and Difficulties Questionnaire: a research note. J. Child Psychol. Psychiatry 38, 581–586 (1997).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Birmaher, B. et al. Psychometric properties of the Screen for Child Anxiety Related Emotional Disorders (SCARED): a replication study. J. Am. Acad. Child Adolesc. Psychiatry 38, 1230–1236 (1999).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Conners, C. K. Conners’ Rating Scales—Revised (North Multi-Health Systems, 2001).

  • Young, K. S. Internet addiction: the emergence of a new clinical disorder. Cyberpsychol. Behav. 1, 237–244 (1998).

    Article 

    Google Scholar 

  • 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).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Garavan, H. et al. Recruiting the ABCD sample: design considerations and procedures. Dev. Cogn. Neurosci. 32, 16–22 (2018).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Papademetris, X. et al. BioImage Suite: an integrated medical image analysis suite: an update. Insight J 2006, 209 (2006).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Shen, X., Tokoglu, F., Papademetris, X. & Constable, R. T. Groupwise whole-brain parcellation from resting-state fMRI data for network node identification. Neuroimage 82, 403–415 (2013).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Elliott, M. L. et al. General functional connectivity: shared features of resting-state and task fMRI drive reliable and heritable individual differences in functional brain networks. Neuroimage 189, 516–532 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Yeh, F.-C., Liu, L., Hitchens, T. K. & Wu, Y. L. Mapping immune cell infiltration using restricted diffusion MRI. Magn. Reson. Med. 77, 603–612 (2017).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Yeh, F.-C., Wedeen, V. J. & Tseng, W.-Y. I. Generalized q-sampling imaging. IEEE Trans. Med. Imaging 29, 1626–1635 (2010).

    Article 
    PubMed 

    Google Scholar 

  • Gu, S. et al. The energy landscape of neurophysiological activity implicit in brain network structure. Sci. Rep. 8, 2507 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Sun, H. et al. Network controllability of structural connectomes in the neonatal brain. Nat. Commun. 14, 5820 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Yeh, F.-C., Verstynen, T. D., Wang, Y., Fernández-Miranda, J. C. & Tseng, W.-Y. I. Deterministic diffusion fiber tracking improved by quantitative anisotropy. PLoS ONE 8, e80713 (2013).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Adkinson, B. D. & Chekroud, A. M. Broadening the use of machine learning in psychiatry. Biol. Psychiatry 93, 4–5 (2023).

    Article 
    PubMed 

    Google Scholar 

  • Chen, J., Patil, K. R., Yeo, B. T. T. & Eickhoff, S. B. Leveraging machine learning for gaining neurobiological and nosological insights in psychiatric research. Biol. Psychiatry 93, 18–28 (2023).

    Article 
    PubMed 

    Google Scholar 

  • Hsu, W.-T., Rosenberg, M. D., Scheinost, D., Constable, R. T. & Chun, M. M. Resting-state functional connectivity predicts neuroticism and extraversion in novel individuals. Soc. Cogn. Affect. Neurosci. 13, 224–232 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Garrison, K. A. et al. Transdiagnostic connectome-based prediction of craving. Am. J. Psychiatry 180, 445–453 (2023).

    Article 
    PubMed 

    Google Scholar 



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