Corticolimbic hyper-response to emotion and glutamatergic function in people with high schizotypy: a multimodal fMRI-MRS study
Bibliographic record
Abstract
Animal models and human neuroimaging studies suggest that altered levels of glutamatergic metabolites within a corticolimbic circuit have a major role in the pathophysiology of schizophrenia. Rodent models propose that prefrontal glutamate dysfunction could lead to amygdala hyper-response to environmental stress and underlie hippocampal overdrive in schizophrenia. Here we determine whether changes in brain glutamate are present in individuals with high schizotypy (HS), which refers to the presence of schizophrenia-like characteristics in healthy individuals, and whether glutamate levels are related to altered corticolimbic response to emotion. Twenty-one healthy HS subjects and 22 healthy subjects with low schizotypy (LS) were selected based on their Oxford and Liverpool Inventory of Feelings and Experiences rating. Glutamate levels were measured in the anterior cingulate cortex (ACC) using proton magnetic resonance spectroscopy, followed by a functional magnetic resonance imaging (fMRI) scan to measure corticolimbic response during emotional processing. fMRI results and fMRI × glutamate interactions were considered significant after voxel-wise P<0.05 family-wise error correction. While viewing emotional pictures, HS individuals showed greater activation than did subjects with LS in the caudate, and marginally in the ACC, hippocampus, medial prefrontal cortex (MPFC) and putamen. Although no between-group differences were found in glutamate concentrations, within the HS group ACC glutamate was negatively correlated with striatal activation (left: z=4.30, P=0.004 and right: z=4.12 P=0.008 caudate; left putamen: z=3.89, P=0.018) and marginally with MPFC (z=3.55, P=0.052) and amygdala (left: z=2.88, P=0.062; right: z=2.79, P=0.079), correlations that were not present in LS subjects. These findings provide, to our knowledge, the first evidence that brain glutamate levels are associated with hyper-responsivity in brain regions thought to be critical in the pathophysiology of psychosis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".