From Blood Oxygenation Level Dependent (BOLD) signals to brain temperature maps
Bibliographic record
Abstract
Abstract A theoretical framework is presented for converting Blood Oxygenation Level Dependent (BOLD) images to temperature maps, based on the idea that disproportional local changes in cerebral blood flow (CBF) as compared with cerebral metabolic rate of oxygen consumption (CMRO2) during functional brain activity, lead to both brain temperature changes and the BOLD effect. Using an oxygen limitation model and a BOLD signal model we obtain a transcendental equation relating CBF and CMRO2 changes with the corresponding BOLD signal, which is solved in terms of the Lambert W function. Inserting this result in the dynamic bio-heat equation describing the rate of temperature changes in the brain, we obtain a non autonomous ordinary differential equation that depends on the BOLD response, which is solved numerically for each brain voxel. In order to test the method, temperature maps obtained from a real BOLD dataset are calculated showing temperature variations in the range: (-0.15, 0.1) which is consistent with experimental results. The method could find potential clinical uses as it is an improvement over conventional methods which require invasive probes and can record only few locations simultaneously. Interestingly, the statistical analysis revealed that significant temperature variations are more localized than BOLD activations. This seems to exclude the use of temperature maps for mapping neuronal activity as areas where it is well known that electrical activity occurs (such as V5 bilaterally) are not activated in the obtained maps. But it also opens questions about the nature of the information processing and the underlying vascular network in visual areas that give rise to this result.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".