Multimodal Magnetic Resonance Imaging Biomarkers of Pediatric Traumatic Brain Injury Identified by Ensemble Learning Are Associated with Psychopathology

Background: Pediatric traumatic brain injury (TBI) is associated with increased psychopathology, but the neural alterations underlying this vulnerability remain poorly understood. We used interpretable machine learning to identify multimodal MRI features associated with pediatric TBI and their relationship with psychopathology. Methods: Baseline multimodal MRI data from 775 children aged 9-10 years in the Adolescent Brain Cognitive Development Study were analyzed, including 365 children with TBI and 410 controls. Structural MRI, diffusion MRI, and resting-state functional MRI features were evaluated using nested cross-validated ensemble classification. Permutation-based feature importance identified discriminative features, which were subsequently examined using partial least squares (PLS) regression in the TBI and control groups. Results: Classification performance was above chance across models, with accuracy ranging from 0.612-0.665 and AUC from 0.644-0.714, and was comparable in an independent validation sample. Twenty neuroimaging features spanning all three modalities were reliably discriminative, including measures of white matter microstructure, gray matter volume, regional functional variability, and functional connectivity. In the TBI group, a significant PLS component was associated with overall psychopathology scores (r = 0.22, permuted p = 0.008), whereas no significant component was identified in controls. Ten discriminative features contributed substantially to this association. Conclusions: Pediatric TBI is characterized by a distributed multimodal neuroimaging signature specifically associated with psychopathology following injury. Interpretable machine learning combined with multivariate brain-behavior modelling may help identify neural substrates of psychiatric vulnerability after pediatric TBI.