Beyond fear centers: a distributed fMRI-based neuromarker for the subjective experience of fear

Abstract

The specific neural systems underlying the subjective feeling of fear remain vigorously debated in affective neuroscience. Here, we combined functional MRI with machine learning to identify and evaluate a sensitive and generalizable neural signature predictive of the momentary self-reported subjective fear experience across discovery (n=67), validation (n=20) and generalization (n=31) cohorts. We systematically demonstrate that accurate fear prediction crucially requires distributed brain systems, with important contributions from cortical (e.g., prefrontal, midcingulate and insular cortices) and subcortical (e.g., thalamus, periaqueductal gray, basal forebrain and amygdala) regions. We further demonstrate that the neural representation of subjective fear is distinguishable from the representation of conditioned threat and general negative affect. Overall, our findings suggest that subjective fear, which exhibits distinct neural representation with some other aversive states, is encoded in distributed systems rather than isolated fear centers. This signature provides a neuromarker for monitoring fear-related neuropathology and evaluating novel treatments targeting pathological fear.