Using a multimodal near-infrared spectroscopy and MRI to quantify gray matter metabolic rate for oxygen: A hypothermia validation study
Bibliographic record
Abstract
Non-invasive quantitative imaging of cerebral oxygen metabolism (CMRO2) in small animal models is crucial to understand the role of oxidative metabolism in healthy and diseased brains. In this study, we developed a multimodal method combining near-infrared spectroscopy (NIRS) and MRI to non-invasively study oxygen delivery and consumption in the cortex of mouse and rat models. The term CASNIRS is proposed to the technique that measures CMRO2 with ASL and NIRS. To determine the reliability of this method, CMRO2 values were compared with reported values measured with other techniques. Also, the sensitivity of the CASNIRS technique to detect changes in CMRO2 in the cortex of the animals was assessed by applying a reduction in core temperature, which is known to reduce CMRO2. Cerebral blood flow (CBF) and CMRO2 were measured in five mice and five rats at a core temperature of 37 °C followed by another measurement at 33 °C. CMRO2 was 7.8 ± 1.8 and 3.7 ± 0.9 (ml/100 g/min, mean ± SD) in mice and rats respectively. These values are in good agreement with reported values measured by 15O PET, 17O NMR, and BOLD fMRI. In hypothermia, we detected a significant decrease of 37% and 32% in CMRO2 in the cortex of mice and rats, respectively. Q10 was calculated to be 3.2 in mice and 2.7 in rats. In this study we showed that it is possible to assess absolute values of metabolic correlates such as CMRO2, CBF and oxygen extraction fraction (OEF) noninvasively in living brain of mice and rats by combining NIRS with MRI. This will open new possibilities for studying brain metabolism in patients as well as the many mouse/rat models of brain disorders.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".