Neuronal correlates of appetite regulation in patients with schizophrenia: Is there a basis for future appetite dysfunction?
Bibliographic record
Abstract
BACKGROUND: Given the undesired metabolic side effects of atypical antipsychotic medication it is important to understand the neuronal basis related to processing of appetite regulation in patients affected by schizophrenia. METHODS: Here we used functional magnetic resonance imaging (fMRI) to assess brain activity in response to food cues and neutral stimuli in twenty patients with schizophrenia and eleven healthy individuals. In addition to clinical and dietary habits assessments, we collected, in patients, measurements of fasting glucose, ghrelin, leptin, insulin, prolactin and lipids blood concentration and we correlated the cerebral activity with clinical and metabolic measures. RESULTS: Both groups engaged a common neuronal network while processing food cues, which included the left insula, primary sensorimotor areas, and inferior temporal and parietal cortices. Cerebral responses to appetitive stimuli in thalamus, parahippocampus and middle frontal gyri were specific only to schizophrenic patients, with parahippocampal activity related to hunger state and increasing linearly over time. Antipsychotic medication dosage correlated positively with a cognitive measure reflecting food cravings, whereas the severity of the disease correlated negatively with a cognitive measure indicating dietary restraint in eating habits. These cognitive variables correlated, in turn, with parahippocampal and thalamic neuronal activities, respectively. CONCLUSIONS: We identified a specific neural substrate underlying cognitive processing of appetitive stimuli in schizophrenia, which may contribute to appetite dysfunction via perturbations in processing of homeostatic signals in relation to external stimuli. Our results also suggest that both antipsychotic medication and the disease severity per se could amplify these effects, via different mechanisms and neuronal networks.
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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".