Researchers applied a machine learning technique that could potentially translate patterns of activity in fear-processing brain regions into scores on questionnaires used to assess a patient's fear of pain. This neuroscientific approach, reported in eNeuro, may help reconcile self-reported emotions and their neural underpinnings.
Pain-related fear is typically assessed with various questionnaires, often used interchangeably, that ask patients how they feel about their clinical pain. However, it is unclear to what extent these self-reports measure fear and anxiety, which are known to involve different brain regions, and perhaps other psychological constructs.
Researchers applied a pattern regression approach in 20 human patients by imaging the brains of patients with low back pain as they watched video clips evoking harmful (bending) and harmless (walking) activities for the back to reveal predictive relationships between fear-related neural pattern information and different pain-related fear questionnaires. More specifically, the applied Multiple Kernel Learning approach allowed generating models to predict the questionnaire scores based on a hierarchical ranking of fear-related neural patterns induced by viewing videos of activities potentially harmful for the back.
Authors sought to find evidence for or against overlapping pain- related fear constructs by comparing the questionnaire prediction models according to their predictive abilities and associated neural contributors. By demonstrating evidence of non-overlapping neural predictors within fear processing regions, the results underpin the diversity of pain-related fear constructs.
This neuroscientific approach might ultimately help to further understand and dissect psychological pain-related fear constructs. Importantly, different questionnaires were associated with distinct patterns of neural activity. These results suggest similar questionnaires may measure different emotional states.
Brain activity predicts fear of pain
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