Quantitative spatial comparison of diffuse optical imaging with blood oxygen level-dependent and arterial spin labeling-based functional magnetic resonance imaging
Bibliographic record
Abstract
Akin to functional magnetic resonance imaging (fMRI), diffuse optical imaging (DOI) is a noninvasive method for measuring localized changes in hemoglobin levels within the brain. When combined with fMRI methods, multimodality approaches could offer an integrated perspective on the biophysics, anatomy, and physiology underlying each of the imaging modalities. Vital to the correct interpretation of such studies, control experiments to test the consistency of both modalities must be performed. Here, we compare DOI with blood oxygen level-dependent (BOLD) and arterial spin labeling fMRI-based methods in order to explore the spatial agreement of the response amplitudes recorded by these two methods. Rather than creating optical images by regularized, tomographic reconstructions, we project the fMRI image into optical measurement space using the optical forward problem. We report statistically better spatial correlation between the fMRI-BOLD response and the optically measured deoxyhemoglobin (R=0.71, p=1x10(-7)) than between the BOLD and oxyhemoglobin or total hemoglobin measures (R=0.38, p=0.04|0.37, p=0.05, respectively). Similarly, we find that the correlation between the ASL measured blood flow and optically measured total and oxyhemoglobin is stronger (R=0.73, p=5x10(-6) and R=0.71, p=9x10(-6), respectively) than the flow to deoxyhemoglobin spatial correlation (R=0.26, p=0.10).
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".