Neural correlates of an attentional bias to health-threatening stimuli in individuals with pathological health anxiety
Bibliographic record
Abstract
BACKGROUND: An attentional bias to health-threat stimuli is assumed to represent the primary pathogenetic factor for the development and maintenance of pathological health anxiety (PHA; formerly termed "hypochondriasis"). However, little is known about the neural basis of this attentional bias in individuals with PHA. METHODS: A group of patients with PHA, a group of depressed patients and a healthy control group completed an emotional Stroop task with health-threat (body symptom and illness) words and neutral control words while undergoing functional MRI. RESULTS: We included 33 patients with PHA, 28 depressed patients and 31 controls in our analyses. As reflected in reaction times, patients with PHA showed a significantly stronger attentional bias to health-threat words than both control groups. In addition, patients with PHA showed increased amygdala and rostral anterior cingulate cortex activation for body symptom, but not for illness words. Moreover, only in patients with PHA amygdala activation in response to symptom words was positively associated with higher arousal and more negative valence ratings of the body symptom word material. LIMITATIONS: A control group of patients with an anxiety disorder but without PHA would have helped to define the specificity of the results for PHA. CONCLUSION: The attentional bias observed in patients with PHA is associated with hyperactivation in response to body symptom words in brain regions that are crucial for an arousal-related fear response (e.g., the amygdala) and for resolving emotional interference (e.g., the rostral anterior cingulate cortex). The findings have important implications for the nosological classification of PHA and suggest the application of innovative exposure-based interventions for the treatment of PHA.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".