Increased glutamate levels observed upon functional activation in the anterior cingulate cortex using the Stroop Task and functional spectroscopy
Bibliographic record
Abstract
It has been shown in recent studies that it is possible to detect changes in the main excitatory neurotransmitter, glutamate, upon functional activation with visual and motor paradigms using a 7 T MRI and functional magnetic resonance spectroscopy. A cognitive task would be desirable for this technique because it could then be used to examine psychiatric disorders that have cognitive deficiencies. The aim of the work presented here was to use functional magnetic resonance spectroscopy with a 7 T MRI to show that increases in glutamate can be observed within the anterior cingulate cortex using the Stroop Task as the activation paradigm in healthy controls. Significant glutamate increases (0.24±0.09 µmol/g, P<0.025), comparable with what has been reported in the studies of the occipital cortex and motor cortex, were observed when the participants (n=7) performed the task, followed by a trend toward returning to baseline in the post-task recovery period (-0.23±0.13 µmol/g). This method would be ideal for the study of neuropsychiatric disorders that have been shown to have abnormal resting glutamate levels and cognitive deficiencies in the anterior cingulate cortex, such as schizophrenia. This exploratory study is the first to demonstrate functional magnetic resonance spectroscopy in the anterior cingulate with a cognitive task using a 7 T MRI.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".