CMOS Image Sensor and System for Imaging Hemodynamic Changes in Response to Deep Brain Stimulation
Bibliographic record
Abstract
Deep brain stimulation (DBS) is a therapeutic intervention used for a variety of neurological and psychiatric disorders, but its mechanism of action is not well understood. It is known that DBS modulates neural activity which changes metabolic demands and thus the cerebral circulation state. However, it is unclear whether there are correlations between electrophysiological, hemodynamic and behavioral changes and whether they have any implications for clinical benefits. In order to investigate these questions, we present a miniaturized system for spectroscopic imaging of brain hemodynamics. The system consists of a 144 ×144, [Formula: see text] pixel pitch, high-sensitivity, analog-output CMOS imager fabricated in a standard 0.35 μm CMOS process, along with a miniaturized imaging system comprising illumination, focusing, analog-to-digital conversion and μSD card based data storage. This enables stand alone operation without a computer, nor electrical or fiberoptic tethers. To achieve high sensitivity, the pixel uses a capacitive transimpedance amplifier (CTIA). The nMOS transistors are in the pixel while pMOS transistors are column-parallel, resulting in a fill factor (FF) of 26%. Running at 60 fps and exposed to 470 nm light, the CMOS imager has a minimum detectable intensity of 2.3 nW/cm(2) , a maximum signal-to-noise ratio (SNR) of 49 dB at 2.45 μW/cm(2) leading to a dynamic range (DR) of 61 dB while consuming 167 μA from a 3.3 V supply. In anesthetized rats, the system was able to detect temporal, spatial and spectral hemodynamic changes in response to DBS.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".