Increased orbitofrontal cortex activation associated with “pro-obsessive” antipsychotic treatment in patients with schizophrenia
Bibliographic record
Abstract
BACKGROUND: Patients with schizophrenia have an approximately 10-fold higher risk for obsessive-compulsive symptoms (OCS) than the general population. A large subgroup seems to experience OCS as a consequence of second-generation antipsychotic agents (SGA), such as clozapine. So far little is known about underlying neural mechanisms. METHODS: To investigate the role of SGA treatment on neural processing related to OCS in patients with schizophrenia, we stratified patients according to their monotherapy into 2 groups (group I: clozapine or olanzapine; group II: amisulpride or aripiprazole). We used an fMRI approach, applying a go/no-go task assessing inhibitory control and an n-back task measuring working memory. RESULTS: We enrolled 21 patients in group I and 19 patients in group II. Groups did not differ regarding age, sex, education or severity of psychotic symptoms. Frequency and severity of OCS were significantly higher in group I and were associated with pronounced deficits in specific cognitive abilities. Whereas brain activation patterns did not differ during working memory, group I showed significantly increased activation in the orbitofrontal cortex (OFC) during response inhibition. Alterations in OFC activation were associated with the severity of obsessions and mediated the association between SGA treatment and co-occurring OCS on a trend level. LIMITATIONS: The main limitation of this study is its cross-sectional design. CONCLUSION: To our knowledge, this is the first imaging study conducted to elucidate SGA effects on neural systems related to OCS. We propose that alterations in brain functioning reflect a pathogenic mechanism in the development of SGA-induced OCS in patients with schizophrenia. Longitudinal studies and randomized interventions are needed to prove the suggested causal interrelations.
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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 | 0.000 |
| 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".