Brain-Behavior Modeling from Multimodal MRI using Opportunistic Pretraining and Common-Cohort Adaptation

Md. Tanvir Rahman, Taslima Ferdaus Shuva, Nabil Anan Orka, Asaduzzaman Khan, Mohammad Ali Moni

Incomplete imaging coverage limits sample sizes for multimodal cognitive prediction. We evaluated a two-stage opportunistic deep ensemble using baseline Adolescent Brain Cognitive Development Study data to predict fluid, crystallized, and total cognition. Separate neural models processed structural/diffusion, resting-state, and task-based MRI data with covariates. Models were pretrained on eligible modality cohorts (up to 9,686 participants), adapted to common training cohorts, and fused via Ridge regression (trimodal cohort: 4,216 participants). Five-fold family-aware cross-validation compared this approach against constituent and covariate-only models. Two-stage training increased mean test coefficient of determination (R^2) in 24 of 27 comparisons (median gain: 0.0081). Fusion increased mean R^2 by 0.0051-0.0333 over the strongest constituent and reduced mean absolute error by 0.0067-0.0976. Task-plus-resting-state and trimodal ensembles reduced absolute error across all targets (nominal p <= 0.0108). Task-plus-resting-state fusion achieved the highest mean R^2 for fluid (0.2222) and total cognition (0.3665), while structural-plus-resting-state led for crystallized cognition…