Background/Objectives: Deficits in empathic function have deleterious effects on individual, relational, and community well-being, impacting long-term health outcomes. However, assessing empathic function in neurodivergent or nonverbal populations using self-reports and in-scanner tasks is frequently unfeasible. Encouraging evidence suggests that characteristic interactions in brain networks underlying empathy are observable at rest, though single-cohort approaches have been shown to limit replicability and generalizability. Methods: We tested whether machine learning-aided (LASSO) models trained and tested on connectivity matrices derived from task-free fMRI data in two cohorts of healthy participants (N = 96) could predict subdimensions of empathy when trained on one cohort and tested on the other. Results: We found that all subdimensions could be robustly predicted from our theory-driven networks, while classical resting-state networks only significantly predicted empathic concern and personal distress. Theory-driven networks matched or outperformed whole-brain models despite containing orders of magnitude fewer features, suggesting that a priori task-derived networks constrain resting-state analyses by concentrating predictive signal rather than relying on regularization to filter large feature spaces. Conclusions: Empathic function emerges from characteristic interactions within and between affective “resonance” and cognitive “control” networks. Empathic concern emerges as the most widely subserved and predictable sub-dimension, suggesting utility for diagnosis and intervention. Cross-cohort approaches hold potential for robustly assessing empathic function in heterogeneous cohorts, even those unable to complete traditional empathy tasks. We provide the model and dataset, to facilitate research and clinical applications by other groups and in vulnerable populations.
Reggente et al. (Thu,) studied this question.