Compensatory hyperactivations as markers of latent working memory dysfunctions in patients with obsessive–compulsive disorder: an fMRI study
Bibliographic record
Abstract
OBJECTIVE: Behavioural studies have implicated working memory (WM) deficits in obsessive-compulsive disorder (OCD). However, findings are inconsistent, which could be explained by compensation strategies used by a subgroup of OCD patients. To test this hypothesis, we examined patients without a behavioural deficit in WM during performance of different WM tasks using functional magnetic resonance imaging (fMRI). METHODS: We scaned 11 patients and 11 matched control subjects while they performed 3 verbal and spatial item-recognition tasks. RESULTS: Patients and healthy subjects engaged the same set of brain regions. However, in direct comparison, the patients exhibited significantly greater task-related activation in several frontal and parietal brain areas known to underlie WM. CONCLUSION: Patients without manifest WM deficits exhibit increased activation in frontal and parietal brain areas relative to healthy subjects during WM task performance. These hyperactivations may permit them to compensate for reduced efficiency of their WM systems and may thus serve as markers of latent WM dysfunctions.
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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".