Xiao, Y., Brown, T. T., Snowden, L. R., Chow, J. C.-C. & Mann, J. J. COVID-19 policies, pandemic disruptions, and changes in child mental health and sleep in the United States. JAMA Netw. Open 6, e232716 (2023).
Google Scholar
Samji, H. et al. Review: Mental health impacts of the COVID-19 pandemic on children and youth – a systematic review. Child Adolesc. Ment. Health 27, 173–189 (2022).
Google Scholar
Kourgiantakis, T. et al. Navigating inequities in the delivery of youth mental health care during the COVID-19 pandemic: perspectives of youth, families, and service providers. Can. J. Public Health 113, 806–816 (2022).
Google Scholar
Schmidhuber, J. Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2015).
Google Scholar
Posner, J. The role of precision medicine in child psychiatry: what can we expect and when? J. Am. Acad. Child Adolesc. Psychiatry 57, 813 (2018).
Google Scholar
Romer, A. L., Ren, B. & Pizzagalli, D. A. Brain structure relations with psychopathology trajectories in the ABCD Study. J. Am. Acad. Child Adolesc. Psychiatry 62, 895–907 (2023).
Google Scholar
Sripada, C. et al. Widespread attenuating changes in brain connectivity associated with the general factor of psychopathology in 9- and 10-year olds. Transl. Psychiatry 11, 575 (2021).
Google Scholar
Voorhees, B. W. V. et al. Predicting future risk of depressive episode in adolescents: the Chicago Adolescent Depression Risk Assessment (CADRA). Ann. Fam. Med. 6, 503–511 (2008).
Google Scholar
King, M. et al. Development and validation of an international risk prediction algorithm for episodes of major depression in general practice attendees: the PredictD Study. Arch. Gen. Psychiatry 65, 1368–1376 (2008).
Google Scholar
Wilson, P. W. et al. Prediction of coronary heart disease using risk factor categories. Circulation 97, 1837–1847 (1998).
Google Scholar
Stroup, W. W. Generalized Linear Mixed Models: Modern Concepts, Methods and Applications (CRC, 2012).
Shah, J. et al. Multivariate prediction of emerging psychosis in adolescents at high risk for schizophrenia. Schizophr. Res. 141, 189–196 (2012).
Google Scholar
Bruni, O. et al. The Sleep Disturbance Scale for Children (SDSC). Construction and validation of an instrument to evaluate sleep disturbances in childhood and adolescence. J. Sleep Res. 5, 251–261 (1996).
Google Scholar
Pigeon, W. R., Bishop, T. M. & Krueger, K. M. Insomnia as a precipitating factor in new onset mental illness: a systematic review of recent findings. Curr. Psychiatry Rep. 19, 44 (2017).
Google Scholar
Pigeon, W. R., Pinquart, M. & Conner, K. Meta-analysis of sleep disturbance and suicidal thoughts and behaviors. J. Clin. Psychiatry 73, e1160–e1167 (2012).
Google Scholar
Telzer, E. H., Goldenberg, D., Fuligni, A. J., Lieberman, M. D. & Gálvan, A. Sleep variability in adolescence is associated with altered brain development. Dev. Cogn. Neurosci. 14, 16–22 (2015).
Google Scholar
Uccella, S. et al. Sleep deprivation and insomnia in adolescence: implications for mental health. Brain Sci. 13, 569 (2023).
Google Scholar
Freeman, D., Sheaves, B., Waite, F., Harvey, A. G. & Harrison, P. J. Sleep disturbance and psychiatric disorders. Lancet Psychiatry 7, 628–637 (2020).
Google Scholar
Harvey, A. G., Murray, G., Chandler, R. A. & Soehner, A. Sleep disturbance as transdiagnostic: consideration of neurobiological mechanisms. Clin. Psychol. Rev. 31, 225–235 (2011).
Google Scholar
Lunsford-Avery, J. R., Bidopia, T., Jackson, L. & Sloan, J. S. Behavioral treatment of insomnia and sleep disturbances in school-aged children and adolescents. Child Adolesc. Psychiatr. Clin. N. Am. 30, 101–116 (2021).
Google Scholar
Harvey, A. G. et al. A randomized controlled trial of the Transdiagnostic Intervention for Sleep and Circadian Dysfunction (TranS-C) to improve serious mental illness outcomes in a community setting. J. Consult. Clin. Psychol. 89, 537–550 (2021).
Google Scholar
Pettersson, E., Larsson, H., D’Onofrio, B. M. & Lichtenstein, P. Associations between general and specific psychopathology factors and 10-year clinically relevant outcomes in adult Swedish twins and siblings. JAMA Psychiatry 80, 728–737 (2023).
Google Scholar
Moore, T. M. et al. Development of a computerized adaptive screening tool for overall psychopathology (‘p’). J. Psychiatr. Res. 116, 26–33 (2019).
Google Scholar
Jones, J. D. et al. The general psychopathology ‘p’ factor in adolescence: multi-informant assessment and computerized adaptive testing. Res. Child Adolesc. Psychopathol. 52, 1753–1764 (2024).
Google Scholar
Marek, S. et al. Reproducible brain-wide association studies require thousands of individuals. Nature 603, 654–660 (2022).
Google Scholar
Antoniou, T. & Mamdani, M. Evaluation of machine learning solutions in medicine. Can. Med. Assoc. J. 193, E1425–E1429 (2021).
Google Scholar
Tibshirani, R. Regression shrinkage and selection via the Lasso. J. R. Stat. Soc. Series B 58, 267–288 (1996).
Google Scholar
Quinonero-Candela, J., Sugiyama, M., Schwaighofer, A. & Lawrence, N. D. Dataset Shift in Machine Learning (MIT, 2022).
Garavan, H. et al. Recruiting the ABCD sample: design considerations and procedures. Dev. Cogn. Neurosci. 32, 16–22 (2018).
Google Scholar
Karcher, N. R. & Barch, D. M. The ABCD Study: understanding the development of risk for mental and physical health outcomes. Neuropsychopharmacology 46, 131–142 (2021).
Google Scholar
Felitti, V. J. et al. Relationship of childhood abuse and household dysfunction to many of the leading causes of death in adults: the Adverse Childhood Experiences (ACE) Study. Am. J. Prev. Med. 14, 245–258 (1998).
Google Scholar
Kind, A. J. et al. Neighborhood socioeconomic disadvantage and 30-day rehospitalization: a retrospective cohort study. Ann. Intern. Med. 161, 765–774 (2014).
Google Scholar
Parlatini, V. et al. White matter alterations in attention-deficit/hyperactivity disorder (ADHD): a systematic review of 129 diffusion imaging studies with meta-analysis. Mol. Psychiatry 28, 4098–4123 (2023).
Google Scholar
Xia, J., Chen, N. & Qiu, A. Unraveling multimodal brain signatures: deciphering transdiagnostic dimensions of psychopathology in adolescents. Adv. Intell. Syst. 5, 2300577 (2024).
Google Scholar
Gordon, E. M. et al. Generation and evaluation of a cortical area parcellation from resting-state correlations. Cereb. Cortex 26, 288–303 (2016).
Google Scholar
Verbruggen, F. & Logan, G. D. Response inhibition in the stop-signal paradigm. Trends Cogn. Sci. 12, 418–424 (2008).
Google Scholar
Knutson, B., Westdorp, A., Kaiser, E. & Hommer, D. FMRI visualization of brain activity during a monetary incentive delay task. Neuroimage 12, 20–27 (2000).
Google Scholar
Cohen, A., Conley, M., Dellarco, D. & Casey B. The impact of emotional cues on short-term and long-term memory during adolescence. [Abstract]. In Proc. Soc. Neurosci. (2016).
Clark, D. A. et al. The general factor of psychopathology in the Adolescent Brain Cognitive Development (ABCD) Study: a comparison of alternative modeling approaches. Clin. Psychol. Sci. 9, 169–182 (2021).
Google Scholar
Murtagh, F. Multilayer perceptrons for classification and regression. Neurocomputing 2, 183–197 (1991).
Google Scholar
Yu, Y., Si, X., Hu, C. & Zhang, J. A review of recurrent neural networks: LSTM cells and network architectures. Neural Comput. 31, 1235–1270 (2019).
Google Scholar
Graves, A. Supervised Sequence Labelling with Recurrent Neural Networks (Springer, 2012).
Vaswani, A. et al. Attention is all you need. Adv. Neural Inf. Process. Syst. 30, 5998–6008 (2017).
Bottou, L. Large-scale machine learning with stochastic gradient descent. In Proc. COMPSTAT2010 (eds Lechevallier, Y. & Saporta, G.) 177–186 (Physica-Verlag, 2010).
Nesterov, Y. E. A method for solving a convex programming problem with convergence rate O(1/k2). Dokl. Akad. Nauk. 543–547 (1983).
Bergstra, J., Yamins, D. & Cox D. Making a science of model search: hyperparameter optimization in hundreds of dimensions for vision architectures. In Proc. 30th Int. Conf. Mach. Learn (eds Dasgupta, S. & McAllester, E.) 115–123 (PMLR, 2013).
Krogh, A. & Hertz, J. A simple weight decay can improve generalization. Adv. Neural Inf. Process. Syst. 4, 950–957 (1991).
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I. & Salakhutdinov, R. Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1929–1958 (2014).
Efron, B. in Breakthroughs in Statistics, Vol. 2 (eds Kotz, S. & Johnson, N. L.) 569–593 (Springer, 1992).
Lundberg, S. M. & Lee, S.-I. A unified approach to interpreting model predictions. Adv. Neural. Inf. Process. Syst. 30, 4765–4774 (2017).
Sundararajan, M., Taly, A. & Yan, Q. Axiomatic attribution for deep networks. In Proc. Int. Conf. Mach. Learn (eds Precup, D. & Teh, Y. W.) 3319–3328 (PMLR, 2017).
Elliott, M. R. & Valliant, R. Inference for nonprobability samples. Stat. Sci. 32, 249–264 (2017).
Google Scholar
Hill, E. D. Elliot-D-Hill / abcd. GitHub (2025).
link
More Stories
AI predicts adolescent mental health risk before symptoms emerge
Children’s screen time must be limited to protect their mental health
AI Model Predicts Risks and Potential Causes of Adolescent Mental Illness