A Miniaturized Platform for Laser Speckle Contrast Imaging
Bibliographic record
Abstract
Imaging the brain in animal models enables scientists to unravel new biological insights. Despite critical advancements in recent years, most laboratory imaging techniques comprise of bulky bench top apparatus that require the imaged animals to be anesthetized and immobilized. Thus, animals are imaged in their non-native state severely restricting the scope of behavioral experiments. To address this gap, we report a miniaturized microscope that can be mounted on a rat's head for imaging in awake and unrestrained conditions. The microscope uses laser speckle contrast imaging (LSCI), a high resolution yet wide field imaging modality for imaging blood vessels and perfusion. Design details of both the image formation and acquisition modules are presented. A Monte Carlo simulation was used to estimate the depth of tissue penetration achievable by the imaging system while the produced speckle Airy disc patterns were simulated using Fresnel's diffraction theory. The microscope system weighs only 7 g and occupies less than 5 cm³ and was successfully used to generate proof of concept LSCI images of rat brain vasculature. We validated the utility of the head-mountable system in an awake rat brain model by confirming no impairment to the rat's native behavior.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".