Functional magnetic resonance spectroscopy of glutamate in schizophrenia and major depressive disorder: anterior cingulate activity during a color-word Stroop task
Bibliographic record
Abstract
BACKGROUND: Glutamate abnormalities have been suggested to be associated with symptoms of schizophrenia. Using functional magnetic resonance spectroscopy ((1)H-fMRS), it is possible to monitor glutamate dynamically in the activated brain areas, which has yet to be reported in schizophrenia. It was hypothesized that subjects with schizophrenia would have weaker glutamatergic responses in the anterior cingulate to a color-word Stroop Task. AIMS: The aim of this study was to gain insight into the health of GLU neurotransmission and the GLU-GLN cycle in SZ using a (1)H-fMRS protocol. METHODS: Spectra were acquired from the anterior cingulate of 16 participants with schizophrenia, 16 healthy controls and 16 participants with major depressive disorder (MDD) while performing the Stroop task in a 7T magnetic resonance imaging scanner. (1)H-fMRS spectra were acquired for 20 min in which there were three 4-min blocks of cross fixation interleaved with two 4-min blocks of the Stroop paradigm. RESULTS: A repeated-measures analysis of variance revealed a main effect of time for glutamate concentrations of all groups (P<0.001). The healthy control group increased glutamate concentrations in the first run of the Stroop task (P=0.006) followed by a decrease in the recovery period (P=0.007). Neither the schizophrenia (P=0.107) nor MDD (P=0.081) groups had significant glutamate changes in the first run of the task, while the schizophrenia group had a significant increase in glutamine (P=0.005). The MDD group decreased glutamate concentrations in the second run of the task (P=0.003), as did all the groups combined (P=0.003). CONCLUSIONS: (1)H-fMRS data were successfully acquired from psychiatric subjects with schizophrenia and mood disorder using a cognitive paradigm for the first time. Future study designs should further elucidate the glutamatergic response to functional activation in schizophrenia.
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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".