Salience network and olanzapine in schizophrenia: implications for treatment in anorexia nervosa.
Bibliographic record
Abstract
UNLABELLED: The salience network (SN), a set of brain regions composed of the anterior fronto-insular cortex (aFI) and the anterior cingulate cortex (ACC), is usually involved in interoception, self-regulating, and action selection. Accumulating evidence indicates that dysfunctions in this network are associated with various pathophysiological deficits in both schizophrenia and eating disorders, stemming mainly from dysfunctional information processing of internal or external stimuli. In addition, the metabolic side effects of some antipsychotics (APs), as well as their pharmacological mechanisms of action, also suggest a link between the functional and neurophysiological changes in the brain in both schizophrenia and in eating disorders. Nevertheless, there is still a knowledge gap in explicitly and directly linking the metabolic side effects associated with AP treatment with the dysfunction in SN associated with processing of food-related information in schizophrenia. Here we provide neuroimaging evidence for such a link, by presenting data on a group of schizophrenia patients who followed 16 weeks of olanzapine treatment and undertook a passive viewing task while their brain activity was recorded. In response to food-related dynamic stimuli (video clips), we observed a decreased activity in SN (aFI and ACC) after the treatment, which also correlated with ghrelin plasma concentration and a measure of dietary restraint. Taken together with past findings regarding the role of SN in both schizophrenia and eating disorders, our results suggest that enhancing the reactivity in the SN has the potential to be a treatment strategy in people with anorexia nervosa. CLINICAL TRIAL REGISTRATION NUMBER: NCT 00290121.
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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) | 0.002 | 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".