Transfer Learning of Deep Neural Networks Pretrained using the ABCD Dataset for General Psychopathology Prediction in Korean Adolescents.

Background: This study examines whether a deep neural network (DNN), trained to predict the general psychopathology factor (p-factor) using functional magnetic resonance imaging (fMRI) data from adolescents in the Adolescent Brain Cognitive Development (ABCD) study, generalizes to Korean adolescents.

Method: We trained a scanner-generalization neural network (SGNN) to predict p-factor scores from resting-state functional connectivity (RSFC) data of 6,905 ABCD adolescents, controlling for MRI scanner-related confounds. We then transferred the pretrained SGNN to a DNN to predict p-factor scores for 125 adolescents, including healthy individuals and those with major depressive disorder, using data from Seoul National University Hospital (SNUH). We compared the transferred DNN's performance with that of kernel ridge regression (KRR) and a baseline DNN.

Results: The transferred DNN outperformed KRR (0.17 ± 0.16; 0.60 ± 0.07) and the baseline DNN (0.17 ± 0.16; 0.69 ± 0.11), achieving a higher Pearson's correlation coefficient (0.29 ± 0.18) and lower mean absolute error (0.59 ± 0.09; p < 0.005). We identified the default mode network (DMN) and visual network (VIS) as crucial functional networks (FNs) for predicting p-factors across both datasets. The dorsal attention network was specific to ABCD, while the cingulo-opercular and ventral attention networks were specific to SNUH.

Conclusion: The transferred SGNN successfully generalized to Korean adolescents. Altered RSFC in the DMN and VIS may serve as promising biomarkers for p-factor prediction across diverse populations, addressing heterogeneity in demographics, diagnoses, and MRI scanner characteristics.