Movement correction method for laser speckle contrast imaging of cerebral blood flow in cranial windows in rodents
Bibliographic record
Abstract
Laser speckle contrast imaging (LSCI) is used in clinical research to dynamically image blood flow. One drawback is its susceptibility to movement artifacts. We demonstrate a new, simple method to correct motion artifacts in LSCI signals measured in awake mice with cranial windows during sensory stimulation. The principle is to identify a region in the image in which speckle contrast (SC) is independent of blood flow and only varies with animal movement, then to regress out this signal from the data. We show that (1) the regressed signal correlates well with mouse head movement, (2) the corrected signal correlates better with independently measured blood volume and (3) it has a (59 ± 6)% higher signal-to-noise ratio. Compared to three alternative correction methods, ours has the best performance. Regressing out flow-independent global variations in SC is a simple and accessible way to improve the quality of LSCI measurements.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".