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.
Michael Meier and colleagues from Petra Schweinhardts’ lab at the
Balgrist University Hospital in Zurich, Switzerland, addressed this
ambiguity 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. Participants’ brain activity was predictive of
their scores on the various questionnaires. Importantly, different
questionnaires were associated with distinct patterns of neural
activity. These results suggest similar questionnaires may measure
different emotional states.