A distributed fMRI-based signature for the subjective experience of fear.
Feng ZhouWeihua ZhaoZiyu QiYayuan GengShuxia YaoKeith M KendrickTor D WagerBenjamin BeckerPublished in: Nature communications (2021)
The specific neural systems underlying the subjective feeling of fear are debated in affective neuroscience. Here, we combine 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'.
Keyphrases
- prefrontal cortex
- functional connectivity
- machine learning
- resting state
- sleep quality
- magnetic resonance imaging
- small molecule
- white matter
- high throughput
- high resolution
- multiple sclerosis
- contrast enhanced
- deep brain stimulation
- depressive symptoms
- artificial intelligence
- magnetic resonance
- big data
- physical activity
- blood brain barrier
- high frequency
- transcranial magnetic stimulation