Optogenetic Analysis of Neuronal Excitability during Global Ischemia Reveals Selective Deficits in Sensory Processing following Reperfusion in Mouse Cortex
Bibliographic record
Abstract
We have developed an approach to directly probe neuronal excitability during the period beginning with induction of global ischemia and extending after reperfusion using transgenic mice expressing channelrhodopsin-2 (ChR2) to activate deep layer cortical neurons independent of synaptic or sensory stimulation. Spontaneous, ChR2, or forepaw stimulation-evoked electroencephalogram (EEG) or local field potential (LFP) records were collected from the somatosensory cortex. Within 20 s of ischemia, a >90% depression of spontaneous 0.3-3 Hz EEG and LFP power was detected. Ischemic depolarization followed EEG depression with a ∼2 min delay. Surprisingly, neuronal excitability, as assessed by the ChR2-mediated EEG response, was intact during the period of strong spontaneous EEG suppression and actually increased before ischemic depolarization. In contrast, a decrease in the somatosensory-evoked potential (forepaw-evoked potential, reflecting cortical synaptic transmission) was coincident with the EEG suppression. After 5 min of ischemia, the animal was reperfused, and the ChR2-mediated response mostly recovered within 30 min (>80% of preischemia value). However, the recovery of the somatosensory-evoked potential was significantly delayed compared with the ChR2-mediated response (<40% of preischemia value at 60 min). By assessing intrinsic optical signals in combination with EEG, we found that neuronal excitability approached minimal values when the spreading ischemic depolarization wave propagated to the ChR2-stimulated cortex. Our results indicate that the ChR2-mediated EEG/LFP response recovers much faster than sensory-evoked EEG/LFP activity in vivo following ischemia and reperfusion, defining a period where excitable but synaptically silent neurons are present.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".