A miniaturized system for imaging vascular response to deep brain stimulation
Bibliographic record
Abstract
We present a miniaturized system for spectroscopic imaging of the cerebrovascular response to deep brain stimulation (DBS). The system consists of an optical module with controllable light emitting diode (LED) illumination and focusing optics, and an electronic module with a high-sensitivity complementary metal oxide semiconductor (CMOS) image sensor, an off-chip controller and a microSD card for image storage. The system is a refinement of our previously described integrated imaging microscope (IIM). Key differences include a further reduced footprint with the head-stage occupying less than 1.5 cm3and weighing under 1.5 gm, pulse width modulation (PWM) control of illumination intensity and improved signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) performance. Electrical and optical characterization and simulation data, and experimental data from an anesthetized rat are presented. Combined with integrated instrumentation for electrical stimulation and electrophysiology, we expect the tether-free, animal mountable system to facilitate understanding the long-term vascular and electrical effects of deep brain stimulation in freely-moving animals.
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 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".