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A distributed fMRI-based signature for the subjective experience of fear.

ABSTRACT: 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'.

SUBMITTER: Zhou F

PROVIDER: S-EPMC8599690 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature

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A distributed fMRI-based signature for the subjective experience of fear.

Zhou Feng F Zhao Weihua W Qi Ziyu Z Geng Yayuan Y Yao Shuxia S Kendrick Keith M KM Wager Tor D TD Becker Benjamin B

Nature communications 20211117 1

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 i ...[more]

PMID: 34789745

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Project description:ObjectivePrevious work suggests that the perception of pain is subjective and dependent on individual differences in physiological, emotional, and cognitive states. Functional magnetic resonance imaging (FMRI) studies have used both stimulus-related (nociceptive properties) and percept-related (subjective experience of pain) models to identify the brain networks associated with pain. Our objective was to identify the network involved in processing subjective pain during cold stimuli.MethodsThe current FMRI study directly contrasted a stimulus-related model with a percept-related model during blocks of cold pain stimuli in healthy adults. Specifically, neuronal activation was modelled as a function of changes in stimulus intensity vs as a function of increasing/decreasing levels of subjective pain corresponding to changes in pain ratings. In addition, functional connectivity analyses were conducted to examine intrinsic correlations between three proposed subnetworks (sensory/discriminative, affective/motivational, and cognitive/evaluative) involved in pain processing.ResultsThe percept-related model captured more extensive activation than the stimulus-related model and demonstrated an association between higher subjective pain and activation in expected cortical (dorsolateral prefrontal cortex, ventrolateral prefrontal cortex, insula, dorsal anterior cingulate cortex [dACC] extending into pre-supplementary motor area) and subcortical (thalamus, striatum) areas. Moreover, connectivity results supported the posited roles of dACC and insula as key relay sites during neural processing of subjective pain. In particular, anterior insula appeared to link sensory/discriminative regions with regions in the other subnetworks, and dACC appeared to serve as a hub for affective/motivational, cognitive/evaluative, and motor subnetworks.ConclusionsUsing a percept-related model, brain regions involved in the processing of subjective pain during the application of cold stimuli were identified. Connectivity analyses identified linkages between key subnetworks involved in processing subjective pain.

Project description:BackgroundPersistent pain is measured by means of self-report, the sole reliance on which hampers diagnosis and treatment. Functional magnetic resonance imaging (fMRI) holds promise for identifying objective measures of pain, but brain measures that are sensitive and specific to physical pain have not yet been identified.MethodsIn four studies involving a total of 114 participants, we developed an fMRI-based measure that predicts pain intensity at the level of the individual person. In study 1, we used machine-learning analyses to identify a pattern of fMRI activity across brain regions--a neurologic signature--that was associated with heat-induced pain. The pattern included the thalamus, the posterior and anterior insulae, the secondary somatosensory cortex, the anterior cingulate cortex, the periaqueductal gray matter, and other regions. In study 2, we tested the sensitivity and specificity of the signature to pain versus warmth in a new sample. In study 3, we assessed specificity relative to social pain, which activates many of the same brain regions as physical pain. In study 4, we assessed the responsiveness of the measure to the analgesic agent remifentanil.ResultsIn study 1, the neurologic signature showed sensitivity and specificity of 94% or more (95% confidence interval [CI], 89 to 98) in discriminating painful heat from nonpainful warmth, pain anticipation, and pain recall. In study 2, the signature discriminated between painful heat and nonpainful warmth with 93% sensitivity and specificity (95% CI, 84 to 100). In study 3, it discriminated between physical pain and social pain with 85% sensitivity (95% CI, 76 to 94) and 73% specificity (95% CI, 61 to 84) and with 95% sensitivity and specificity in a forced-choice test of which of two conditions was more painful. In study 4, the strength of the signature response was substantially reduced when remifentanil was administered.ConclusionsIt is possible to use fMRI to assess pain elicited by noxious heat in healthy persons. Future studies are needed to assess whether the signature predicts clinical pain. (Funded by the National Institute on Drug Abuse and others.).

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A distributed fMRI-based signature for the subjective experience of fear.

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A distributed fMRI-based signature for the subjective experience of fear.

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