Transcranial photoacoustic imaging of NMDA-evoked focal circuit dynamics in rat hippocampus
Bibliographic record
Abstract
Abstract Transcranial functional photoacoustic (fPA) voltage-sensitive dye (VSD) imaging promises to overcome current temporal and spatial limitations of current neuroimaging modalities. The technique previously distinguished global seizure activity from control neural activity in groups of rats. To validate the focal specificity of transcranial fPA neuroimaging in vivo , we now present proofs-of-concept that the results differentiate between low- and high-dose N-methyl-D-aspartate (NMDA) evoked neural activity in rat hippocampus. Concurrent quantitative EEG (qEEG) and microdialysis recorded real-time circuit dynamics and glutamate concentration change, respectively. We hypothesized that location-specific fPA VSD contrast would identify the neural dynamics in hippocampus with the correlation to NMDA evoked focal glutamate release and time-specific EEG signals. To test the hypothesis, we infused 0.3 to 3.0 mM NMDA at 2 μl/min over 60 min via an implanted microdialysis probe. The dialysate samples collected every 20 min during the infusion were analyzed for focal changes in extracellular glutamate release. Transcranial fPA VSD imaging provided NMDA-evoked VSD responses with positive correlation to extracellular glutamate concentration change at the contralateral side of the microdialysis probe. The graded response represents the all-or-none gating system of the dentate gyrus (DG) in hippocampus. Quantitative EEG (qEEG) successfully confirmed induction of focal seizure activity during NMDA infusion. We conclude that transcranial fPA VSD imaging distinguished graded DG gatekeeping functions, based on the VSD redistribution mechanism sensitive to electrophysiologic membrane potential. The results suggest the potential future use of this emerging technology in clinics and science as an innovative and significant functional neuroimaging modality.
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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 | 0.000 |
| 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".