Cross-Cohort Prediction of Empathy from Task-Relevant Connectivity in Task-Free Data
Year: 2023
Type: preprint
Study design: Observational Study
Abstract: Deficits in empathic function have deleterious effects on individual, relational and community function, encouraging isolation, increasing the risk of unemployment and homelessness, and impacting long-term health outcomes. Assessing empathic function in vulnerable neurodivergent or nonverbal populations using self-reports and in-scanner tasks is frequently unfeasible. We need robust, easily applied, multivariate methods for assessing empathic function in any population. Encouraging evidence suggests that characteristic interactions between brain networks underlying empathy are observable at rest, as a river leaves characteristic grooves in bedrock. Leveraging this to assess empathic function without self-reports or in-scanner tasks could be invaluable for clinical practice. We tested whether multivariate machine learning-aided analysis (LASSO) of connectivity patterns within task-free (resting) fMRI data could predict subdimensions of empathy (empathic concern, personal distress, and perspective-taking) in 74 healthy participants. We found that all three could be robustly predicted from our theory-driven networks as well as classical intrinsic networks, suggesting that a priori task data-derived networks can be used to meaningfully constrain analyses of "resting" state data. We also examined the structure of salient predictive connections within each network. These data suggest empathic function emerges from complex, ongoing interactions within and between brain networks, visible at in the absence of explicit empathy tasks. They support prior findings implicating ongoing dynamic interaction between affective “resonance” and cognitive “control” networks in this construct. Interestingly, empathic concern emerges as the most widely subserved (and broadly predictable) of the subdimensions, suggesting it is a useful point of diagnosis and intervention for community function. Crucially, we may be able to robustly assess empathic function in subjects who are unable to complete traditional empathy tasks. We provide the model and dataset in full, in the hopes of fruitful applications by other groups, in vulnerable populations, and towards other cognitive functions.
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W4389764748