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Record W1983912180 · doi:10.1109/biocas.2013.6679655

A miniaturized system for imaging vascular response to deep brain stimulation

2013· article· en· W1983912180 on OpenAlexafffund
Xiao Zhang, M. Sohail Noor, Clinton B. McCracken, Zelma H. T. Kiss, Orly Yadid-Pecht, Kartikeya Murari

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Neural Engineering
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCMOSNoise (video)SIGNAL (programming language)Sensitivity (control systems)Computer scienceMaterials scienceOptoelectronicsElectronic engineeringEngineeringComputer vision

Abstract

fetched live from OpenAlex

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 cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> and 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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.260
Threshold uncertainty score0.430

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.259
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2013
Admission routes2
Has abstractyes

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