Theoretical and Experimental Optimization of Laser Speckle Contrast Imaging for High Specificity to Brain Microcirculation
Bibliographic record
Abstract
The functional spatial resolution in most of hemodynamics-based functional neuroimaging techniques is limited by the fineness of hemodynamic control with the active vascular beds likely at submillimeter resolution. This study was designed to visualize changes of cerebral blood flow (CBF) at submillimeter spatial scale on the prolonged isoflurane-anesthetized rats model by using laser speckle contrast imaging (LSCI) technique. Recently, this old method has attracted an increasing interest in studies of brain activities under normal and pathophysiologic conditions. However, some paramount assumptions behind this imaging technique have been kept ignored in this field since 1981 firstly proposed by Fercher and Briers. Most recently, these assumptions are claimed as serious mistakes that made LSCI fail to reproducibly and correctly measure blood flow speed. In our study, these issues are also re-examined theoretically and re-evaluated experimentally based on the results from the classical carbon dioxide challenge model. The detailed distribution of CBF responses to the stimulation induced by different levels of carbon dioxide pressure was obtained with tens of micron spatial resolution. The relative CBF images over the exposed cortical area acquired by LSCI were also compared with laser-Doppler measurements. Our results show that these assumptions would not produce any significant errors on investigating changes of blood flow and also achieve high specificity to assess cerebral microcirculation, as would facilitate its broad application in functional imaging field.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".