Implantable photonic neural probes for light-sheet fluorescence brain imaging
Bibliographic record
Abstract
ABSTRACT Significance Light-sheet fluorescence microscopy is a powerful technique for high-speed volumetric functional imaging. However, in typical light-sheet microscopes, the illumination and collection optics impose significant constraints upon the imaging of non-transparent brain tissues. Here, we demonstrate that these constraints can be surmounted using a new class of implantable photonic neural probes . Aim Mass manufacturable, silicon-based light-sheet photonic neural probes can generate planar patterned illumination at arbitrary depths in brain tissues without any additional micro-optic components. Approach We develop implantable photonic neural probes that generate light sheets in tissue. The probes were fabricated in a photonics foundry on 200 mm diameter silicon wafers. The light sheets were characterized in fluorescein and in free space. The probe-enabled imaging approach was tested in fixed and in vitro mouse brain tissues. Imaging tests were also performed using fluorescent beads suspended in agarose. Results The probes had 5 to 10 addressable sheets and average sheet thicknesses < 16 μm for propagation distances up to 300 μm in free space. Imaging areas were as large as ≈ 240 μm × 490 μm in brain tissue. Image contrast was enhanced relative to epifluorescence microscopy. Conclusions The neural probes can lead to new variants of light-sheet fluorescence microscopy for deep brain imaging and experiments in freely-moving animals.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".