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Record W3104403497 · doi:10.1101/2020.11.09.375600

Reduction of collinear inhibition in observers with central vision loss using anodal transcranial direct current stimulation: A case series

2020· preprint· en· W3104403497 on OpenAlexafffund
Rajkumar Nallour Raveendran, Amy Chow, Katelyn Tsang, Arijit Chakraborty, Benjamin Thompson

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2020
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRetinal Development and Disorders
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTranscranial direct-current stimulationVisual cortexNeurosciencePeripheral visionStimulationPsychologyRetinalMedicineOphthalmologyComputer visionComputer science

Abstract

fetched live from OpenAlex

Abstract People with central vision loss (CVL) due to macular degeneration are forced to rely on their residual peripheral vision and often develop a preferred retinal locus (PRL), a region of intact peripheral retina that is used for fixation. At the PRL, visual processing is impaired due to crowding (cluttering of visual objects). The problem of crowding still persists when images are magnified to account for the lower resolution of peripheral vision. We assessed whether anodal transcranial direct stimulation (a-tDCS), a neuro-modulation technique that alters cortical inhibition, would reduce collinear inhibition (an early component of crowding) when applied to the visual cortex in patients with CVL. Our results showed that applying a-tDCS to the visual cortex for 20mins reduced crowding in three patients with CVL and that the effect was sustained for up to 30mins. Sham stimulation delivered in a separate session had no effect. These initial observations mandate further research into the use of a-tDCS to enhance cortical processing of residual retinal input in patients with CVL.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.016
GPT teacher head0.238
Teacher spread0.222 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
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

Citations0
Published2020
Admission routes2
Has abstractyes

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