Neural changes associated with appetite information processing in schizophrenic patients after 16 weeks of olanzapine treatment
Bibliographic record
Abstract
There is evidence that some atypical antipsychotics, including olanzapine, can produce unwanted metabolic side effects, weight gain and diabetes. However, neuronal correlates of change related to food information processing have not been investigated with these medications. We studied the effect of a pharmacological manipulation with an antipsychotic known to cause weight gain on metabolites, cognitive tasks and neural correlates related to food regulation. We used functional magnetic resonance imaging in conjunction with a task requiring visual processing of appetitive stimuli in schizophrenic patients and healthy controls before and after 16 weeks of antipsychotic medication with olanzapine. In patients, the psychological and neuronal changes associated following the treatment correlated with appetite control measures and metabolite levels in fasting blood samples. After 16 weeks of olanzapine treatment, the patients gained weight, increased their waist circumference, had fewer positive schizophrenia symptoms, a reduced ghrelin plasma concentration and an increased concentration of triglycerides, insulin and leptin. In premotor area, somatosensory cortices as well as bilaterally in the fusiform gyri, the olanzapine treatment increased the neural activity related to appetitive information in schizophrenic patients to similar levels relative to healthy individuals. However, a higher increase in sensitivity to appetitive stimuli after the treatment was observed in insular cortices, amygdala and cerebellum in schizophrenic patients as compared with healthy controls. Furthermore, these changes in neuronal activity correlated with changes in some metabolites and cognitive measurements related to appetite regulation.
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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) | 0.001 | 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".