biostudies-literatureUnknownVandewouw MMNIDA NIH HHSNIMH NIH HHSInternalizing problems (e.g., anxiety and depression) are associated with a wide range of adverse outcomes. While some predictors of internalizing problems are known (e.g., their frequent co-occurrence with neurodevelopmental (ND) conditions), the biological markers of internalizing problems are not well understood. Here, we used deep learning, a powerful tool for identifying complex and multi-dimensional brain-behaviour relationships, to predict cross-sectional and worsening longitudinal trajectories of internalizing problems. Data were extracted from four large-scale datasets: the Adolescent Brain Cognitive Development study, the Healthy Brain Network, the Human Connectome Project Development study, and the Province of Ontario Neurodevelopmental network. We developed deep learning modelsbioRxiv : the preprint server for biology2024.11.28.625869https://www.ebi.ac.uk/biostudies/studies/S-EPMC12248087biostudies-literatureUsing deep learning to predict internalizing problems from brain structure in youth.PMC12248087U01 DA051037U24 DA041123U01 DA041106U01 DA041117U01 DA041028U24 DA041147U01 DA041089U01 MH109589U01 DA041022U01 DA041120U01 DA041174U01 DA041148U01 DA041048U01 DA041025U01 DA041156U01 DA041134U01 DA051039U01 DA051016U01 DA051038U01 DA051018U01 DA050988U01 DA050987U01 DA050989U01 DA041093Crosbie JKushki AAnagnostou EKelley EVandewouw MMSchachar RJTaylor MJBarnett NLerch JPJones JArnold PDAyub MArias ASyed BIaboni AfalseUsing deep learning to predict internalizing problems from brain structure in youth.Internalizing problems (e.g., anxiety and depression) are associated with a wide range of adverse outcomes. While some predictors of internalizing problems are known (e.g., their frequent co-occurrence with neurodevelopmental (ND) conditions), the biological markers of internalizing problems are not well understood. Here, we used deep learning, a powerful tool for identifying complex and multi-dimensional brain-behaviour relationships, to predict cross-sectional and worsening longitudinal trajectories of internalizing problems. Data were extracted from four large-scale datasets: the Adolescent Brain Cognitive Development study, the Healthy Brain Network, the Human Connectome Project Development study, and the Province of Ontario Neurodevelopmental network. We developed deep learning models2025-01-01T00:00:00Z2025 May2026-04-25T03:23:23.112Z2025-08-30T03:05:59.77ZS-EPMC122480874065493610.1101/2024.11.28.625869