Solmi M, Radua J, Olivola M, Croce E, Soardo L, Salazar de Pablo G, et al. Age at onset of mental disorders worldwide: large-scale meta-analysis of 192 epidemiological studies. Mol Psychiatry. 2022;27:281–95.
Article CAS PubMed Google Scholar
Gong B, Naveed S, Hafeez DM, Afzal KI, Majeed S, Abele J, et al. Neuroimaging in psychiatric disorders: a bibliometric analysis of the 100 most highly cited articles. J Neuroimaging. 2019;29:14–33.
Chaudhury D, Liu H, Han M-H. Neuronal correlates of depression. Cell Mol Life Sci. 2015;72:4825–48.
Article CAS PubMed PubMed Central Google Scholar
Barch DM. The neural correlates of transdiagnostic dimensions of psychopathology. Am J Psychiatry. 2017;174:613–5.
Mitelman SA. Transdiagnostic neuroimaging in psychiatry: a review. Psychiatry Res. 2019;277:23–38.
Paus T, Keshavan M, Giedd JN. Why do many psychiatric disorders emerge during adolescence? Nat Rev Neurosci. 2008;9:947–57.
Article CAS PubMed PubMed Central Google Scholar
Nielsen AN, Barch DM, Petersen SE, Schlaggar BL, Greene DJ. Machine learning with neuroimaging: evaluating its applications in psychiatry. Biol Psychiatry Cogn Neurosci Neuroimaging. 2020;5:791–8.
Rosenberg MD, Casey B, Holmes AJ. Prediction complements explanation in understanding the developing brain. Nat Commun. 2018;9:589.
Article PubMed PubMed Central Google Scholar
Yan W-J, Ruan Q-N, Jiang K. Challenges for artificial intelligence in recognizing mental disorders. Diagnostics. 2023;13:2.
Arbabshirani MR, Plis S, Sui J, Calhoun VD. Single subject prediction of brain disorders in neuroimaging: promises and pitfalls. Neuroimage. 2017;145:137–65.
Davatzikos C. Machine learning in neuroimaging: progress and challenges. Neuroimage. 2019;197:652.
Marek S, Tervo-Clemmens B, Calabro FJ, Montez DF, Kay BP, Hatoum AS, et al. Reproducible brain-wide association studies require thousands of individuals. Nature. 2022;603:654–60.
Article CAS PubMed PubMed Central Google Scholar
Venkataraman A, Kubicki M, Westin C-F, Golland P. Robust feature selection in resting-state fMRI connectivity based on population studies. 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, 2010. p. 63–70.
Hong S-J, Sisk LM, Caballero C, Mekhanik A, Roy AK, Milham MP, et al. Decomposing complex links between the childhood environment and brain structure in school-aged youth. Dev Cogn Neurosci. 2021;48:100919.
Article PubMed PubMed Central Google Scholar
Iannaccone R, Hauser TU, Ball J, Brandeis D, Walitza S, Brem S. Classifying adolescent attention-deficit/hyperactivity disorder (ADHD) based on functional and structural imaging. Eur Child Adolesc Psychiatry. 2015;24:1279–89.
Foland-Ross LC, Sacchet MD, Prasad G, Gilbert B, Thompson PM, Gotlib IH. Cortical thickness predicts the first onset of major depression in adolescence. Int J Dev Neurosci. 2015;46:125–31.
Article PubMed PubMed Central Google Scholar
Hart H, Marquand AF, Smith A, Cubillo A, Simmons A, Brammer M, et al. Predictive neurofunctional markers of attention-deficit/hyperactivity disorder based on pattern classification of temporal processing. J Am Acad Child Adolesc Psychiatry. 2014;53:569–78.
Cui Z, Pines AR, Larsen B, Sydnor VJ, Li H, Adebimpe A, et al. Linking individual differences in personalized functional network topography to psychopathology in youth. Biol Psychiatry. 2022;92:973–83.
Article PubMed PubMed Central Google Scholar
Hong J, Hwang J, Lee J-H. General psychopathology factor (p-factor) prediction using resting-state functional connectivity and a scanner-generalization neural network. J Psychiatr Res. 2023;158:114–25.
Ooi LQR, Chen J, Zhang S, Kong R, Tam A, Li J, et al. Comparison of individualized behavioral predictions across anatomical, diffusion and functional connectivity MRI. NeuroImage. 2022;263:1–18.
Dhamala E, Ooi LQR, Chen J, Ricard JA, Berkeley E, Chopra S, et al. Brain-based predictions of psychiatric illness–linked behaviors across the sexes. Biol Psychiatry. 2023;94:479–91.
Parkes L, Moore TM, Calkins ME, Cook PA, Cieslak M, Roalf DR, et al. Transdiagnostic dimensions of psychopathology explain individuals’ unique deviations from normative neurodevelopment in brain structure. Transl Psychiatry. 2021;11:232.
Article PubMed PubMed Central Google Scholar
Chen J, Tam A, Kebets V, Orban C, Ooi LQR, Asplund CL, et al. Shared and unique brain network features predict cognitive, personality, and mental health scores in the ABCD study. Nat Commun. 2022;13:1–17.
Mansour L S, Tian Y, Yeo BTT, Cropley V, Zalesky A. High-resolution connectomic fingerprints: mapping neural identity and behavior. NeuroImage. 2021;229:117695.
Abd-alrazaq A, Alhuwail D, Schneider J, Toro CT, Ahmed A, Alzubaidi M, et al. The performance of artificial intelligence-driven technologies in diagnosing mental disorders: an umbrella review. Npj Digital Med. 2022;5:87.
Oh J, Oh B-L, Lee K-U, Chae J-H, Yun K. Identifying schizophrenia using structural MRI with a deep learning algorithm. Front Psychiatry. 2020;11:16.
Article PubMed PubMed Central Google Scholar
Koutsouleris N, Worthington M, Dwyer DB, Kambeitz-Ilankovic L, Sanfelici R, Fusar-Poli P, et al. Toward generalizable and transdiagnostic tools for psychosis prediction: an independent validation and improvement of the NAPLS-2 risk calculator in the multisite PRONIA cohort. Biol Psychiatry. 2021;90:632–42.
Article CAS PubMed PubMed Central Google Scholar
Koutsouleris N, Meisenzahl EM, Davatzikos C, Bottlender R, Frodl T, Scheuerecker J, et al. Use of neuroanatomical pattern classification to identify subjects in at-risk mental states of psychosis and predict disease transition. Arch Gen Psychiatry. 2009;66:700–12.
Article PubMed PubMed Central Google Scholar
Mikolas P, Marxen M, Riedel P, Bröckel K, Martini J, Huth F, et al. Prediction of estimated risk for bipolar disorder using machine learning and structural MRI features. Psychol Med. 2023;54:278–88.
Lee Y, Ragguett R-M, Mansur RB, Boutilier JJ, Rosenblat JD, Trevizol A, et al. Applications of machine learning algorithms to predict therapeutic outcomes in depression: a meta-analysis and systematic review. J Affect Disord. 2018;241:519–32.
Lalousis PA, Wood SJ, Schmaal L, Chisholm K, Griffiths SL, Reniers RLEP, et al. Heterogeneity and classification of recent onset psychosis and depression: a multimodal machine learning approach. Schizophr Bull. 2021;47:1130–40.
Article PubMed PubMed Central Google Scholar
Kochunov P, Zavaliangos-Petropulu A, Jahanshad N, Thompson PM, Ryan MC, Chiappelli J, et al. A white matter connection of schizophrenia and Alzheimer’s disease. Schizophr Bull. 2021;47:197–206.
Karcher NR, Michelini G, Kotov R, Barch DM. Associations between resting-state functional connectivity and a hierarchical dimensional structure of psychopathology in middle childhood. Biol Psychiatry Cogn Neurosci Neuroimaging. 2021;6:508–17.
Sripada C, Angstadt M, Taxali A, Kessler D, Greathouse T, Rutherford S, et al. Widespread attenuating changes in brain connectivity associated with the general factor of psychopathology in 9- and 10-year olds. Transl Psychiatry. 2021;11:575.
Article PubMed PubMed Central Google Scholar
Elliott ML, Romer A, Knodt AR, Hariri AR. A connectome-wide functional signature of transdiagnostic risk for mental illness. Biol Psychiatry. 2018;84:452–9.
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