MRS-measured glutamate versus GABA reflects excitatory versus inhibitory neural activities in awake mice
Bibliographic record
Abstract
To assess if magnetic resonance spectroscopy (MRS)-measured Glutamate (Glu) and GABA reflect excitatory and inhibitory neural activities, respectively, we conducted MRS measurements along with two-photon mesoscopic imaging of calcium signals in excitatory and inhibitory neurons of living, unanesthetized mice. For monitoring stimulus-driven activations of a brain region, MRS signals and mesoscopic neural activities were measured during two consecutive sessions of 15-min prolonged sensory stimulations. In the first session, putative excitatory neuronal activities were increased, while inhibitory neuronal activities remained at the baseline level. In the second half, while excitatory neuronal activities remained elevated, inhibitory neuronal activities were significantly enhanced. We assessed regional neurochemical statuses by measuring MRS signals, which were overall in accordance with the neural activities, and neuronal activities and neurochemical statuses in a mouse model of Dravet syndrome under resting condition. Mesoscopic assessments showed that activities of inhibitory neurons in the cortex were diminished relative to wild-type mice in contrast to spared activities of excitatory neurons. Consistent with these observations, the Dravet model exhibited lower concentrations of GABA than wild-type controls. Collectively, the current investigations demonstrate that MRS-measured Glu and GABA can reflect spontaneous and stimulated activities of neurons producing and releasing these neurotransmitters in an awake condition.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".