Non-Invasive Photoacoustic Imaging of Cerebral Oxygenation and Hemoglobin Content in Awake Mice
Bibliographic record
Abstract
ABSTRACT Introduction Investigating cerebral oxygen saturation dynamics in awake animal models remains technically challenging due to motion artifacts and anesthesia-related biases. Here, we introduce a novel high-resolution ultrasound-photoacoustic (PA) imaging approach enabling real-time, non-invasive monitoring of deep cerebrovascular oxygenation dynamics in awake mice with intact skulls. Materials and Methods Swiss male and female mice (n = 5–6) were head-fixed using a customized holder adapted to the Neurotar Mobile HomeCage floating platform. High-resolution ultrasound combined with PA imaging (VevoLAZR-X, VisualSonics) was used to discriminate oxyhemoglobin, deoxyhemoglobin, and total hemoglobin in multiple brain regions. Cerebrovascular responses were assessed under three paradigms: (i) baseline awake state vs. 2% isoflurane anesthesia, and (ii) right whisker stimulation to probe sensory-driven hemodynamics. Results PA imaging successfully resolved deep-brain oxygenation in awake, intact-skull mice. Under isoflurane anesthesia, we observed a rapid and transient increase in cerebrovascular sO□ (p < 0.01). During whisker stimulation, we detected robust, region-specific increases in total hemoglobin, reflecting localized neurovascular coupling in awake mice. Conclusions This study establishes high-resolution PA imaging as a powerful, non-invasive tool to monitor cerebrovascular oxygenation dynamics in awake mice. By integrating baseline, anesthetic, and sensory paradigms, we demonstrate its potential to dissect neurovascular physiology without the confounding effects of anesthesia. These findings provide new opportunities for preclinical neuroscience research and translational applications investigating cerebral oxygen metabolism.
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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".