Effects of visual attention modulation on dynamic functional connectivity during own-face viewing in body dysmorphic disorder
Bibliographic record
Abstract
Body dysmorphic disorder (BDD) is characterized by preoccupations with misperceptions of one's physical appearance. Previous neuroimaging studies in BDD have yet to examine dynamic functional connectivity (FC) patterns between brain areas, necessary to capture changes in activity in response to stimuli and task conditions. We used Leading Eigenvector Dynamics Analysis to examine whole-brain dynamic FC from fMRI data during an own-face viewing task in 29 unmedicated adults with BDD with facial concerns and 30 healthy controls. The task involved two parts: (1) unconstrained, naturalistic viewing and (2) holding visual attention in the center of the image, to reduce scanning and fixation on perceived facial flaws. An FC state consisting of bilateral medial orbitofrontal cortex regions occurred significantly less often during the visual attention condition and afterward during the unconstrained face viewing in BDD participants, compared to the first unconstrained face viewing, a pattern that differed from controls. Moreover, the probability of this state during the second unconstrained face viewing was associated with severity of obsessions and compulsions and degree of poor insight in BDD, suggesting its clinical significance. These findings have implications for understanding the pathophysiology of own-face viewing in BDD and how it is affected by modification of viewing patterns, which may have implications for novel perceptual retraining treatment designs.
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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 | 0.000 | 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".