Regenerative Glutamate Release by Presynaptic NMDA Receptors Contributes to Spreading Depression
Bibliographic record
Abstract
Spreading depression (SD) is a slowly propagating neuronal depolarization that underlies certain neurologic conditions. The wave-like pattern of its propagation suggests that SD arises from an unusual form of neuronal communication. We used enzyme-based glutamate electrodes to show that during SD induced by transiently raising extracellular K(+) concentrations ([K(+)]o) in rat brain slices, there was a rapid increase in the extracellular glutamate concentration that required vesicular exocytosis but unlike fast synaptic transmission, still occurred when voltage-gated sodium and calcium channels (VGSC and VGCC) were blocked. Instead, presynaptic N-methyl-D-aspartate (NMDA) receptors (NMDARs) were activated during SD and could generate substantial glutamate release to support regenerative glutamate release and propagating waves when VGSCs and VGCCs were blocked. In calcium-free solutions, high [K(+)]o still triggered SD-like waves and glutamate efflux. Under such a condition, glutamate release was blocked by mitochondrial Na(+)/Ca(2+) exchanger inhibitors that likely blocked calcium release from mitochondria secondary to NMDA-induced Na(+) influx. Therefore presynaptic NMDA receptor activation is sufficient for triggering vesicular glutamate release during SD via both calcium entry and release from mitochondria by mitochondrial Na(+)/Ca(2+) exchanger. Our observations suggest that presynaptic NMDARs contribute to a cycle of glutamate-induced glutamate release that mediate high [K(+)]o-triggered SD.
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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) | 0.000 | 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 | 0.001 |
| 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".