Predicting Empathy from Task-Positive Connectivity Patterns Within Task-Free Data: A Cross-Cohort Approach
Christov-Moore, L.1, Reggente, N.1
1Institute For Advanced Consciousness Studies, Santa Monica, CA, USA
PrePrint: https://osf.io/preprints/psyarxiv/t4v7q
Quick Summary
We predicted multiple dimensions of empathic function from connectivity patterns in resting-state fMRI data using a cross-cohort approach.We trained models on two groups of participants (n=51,42) and tested each group’s model on the other. We found that we could robustly predict all dimensions of empathy from task-free data, and that a priori task-associated networks performed better than classical networks. Empathic concern was the most complexly represented aspect of empathic function. Cross-cohort approaches are a useful step toward robust prognostic neuroscience.