Diffuse optical-MRI fusion and applications
Bibliographic record
Abstract
Diffuse optical imaging (DOI) is a relatively new functional imaging modality offering the possibility to record changes in hemoglobin concentrations. It is based on the propagation of near-infrared light through biological tissues. By measuring the optical absorption of the blood in the cortex, DOI enables the estimation of changes of deoxy-hemoglobin (HbR) and oxy-hemoglobin (HbO<sub>2</sub>) concentrations. It thus provides indirect information on neuronal activity. Drawbacks of optical imaging are its lack of quantification abilities as well as poor spatial resolution. Although not much can be done concerning the second issue, diffusion being the limiting factor, one can aim at more quantitative data by the use of extra information. As an example, the determination of baseline concentrations done by fitting a temporal or frequency curve to recover background concentrations is not expected to be accurate due to the heterogeneity of the underlying tissues. The vascular architecture, unknown when doing DOI alone, also plays a significant role in the signal detected. Partial volume effects due to an optode pair overlapping a large vein will lead to confounding data and create difficulties in analyzing the neuronal activation. Here we show that fusion with MRI, but done outside the scanner, may help solving some of these issues.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".