Glutamate and Dysconnection in the Salience Network: Neurochemical, Effective Connectivity, and Computational Evidence in Schizophrenia
Bibliographic record
Abstract
BACKGROUND: Functional dysconnection in schizophrenia is underwritten by a pathophysiology of the glutamate neurotransmission that affects the excitation-inhibition balance in key nodes of the salience network. Physiologically, this manifests as aberrant effective connectivity in intrinsic connections involving inhibitory interneurons. In computational terms, this produces a pathology of evidence accumulation and ensuing inference in the brain. Finally, the pathophysiology and aberrant inference would partially account for the psychopathology of schizophrenia as measured in terms of symptoms and signs. We refer to this formulation as the 3-level hypothesis. METHODS: We tested the hypothesis in core nodes of the salience network (the dorsal anterior cingulate cortex [dACC] and the anterior insula) of 20 patients with first-episode psychosis and 20 healthy control subjects. We established 3-way correlations between the magnetic resonance spectroscopy measures of glutamate, effective connectivity of resting-state functional magnetic resonance imaging, and correlations between measures of this connectivity and estimates of precision (inherent in evidence accumulation in the Stroop task) and psychopathology. RESULTS: Glutamate concentration in the dACC was associated with higher and lower inhibitory connectivity in the dACC and in the anterior insula, respectively. Crucially, glutamate concentration correlated negatively with the inhibitory influence on the excitatory neuronal population in the dACC of subjects with first-episode psychosis. Furthermore, aberrant computational parameters of the Stroop task performance were associated with aberrant inhibitory connections. Finally, the strength of connections from the dACC to the anterior insula correlated negatively with severity of social withdrawal. CONCLUSIONS: These findings support a link between glutamate-mediated cortical disinhibition, effective-connectivity deficits, and computational performance in psychosis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".