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Record W2091027097 · doi:10.1002/dev.20509

From motor cortex to visual cortex: The application of noninvasive brain stimulation to amblyopia

2010· review· en· W2091027097 on OpenAlexafffund
Benjamin Thompson, Behzad Mansouri, Lisa Koski, Robert F. Hess

Bibliographic record

VenueDevelopmental Psychobiology · 2010
Typereview
Languageen
FieldNeuroscience
TopicTranscranial Magnetic Stimulation Studies
Canadian institutionsMontreal Neurological Institute and HospitalUniversity of ManitobaMcGill University
FundersCanadian Institutes of Health Research
KeywordsNeurosciencePsychologyMotor cortexStimulationBrain stimulationVisual cortexStroke (engine)Cortex (anatomy)Physical medicine and rehabilitationMedicine

Abstract

fetched live from OpenAlex

Noninvasive brain stimulation is a technique for inducing changes in the excitability of discrete neural populations in the human brain. A current model of the underlying pathological processes contributing to the loss of motor function after stroke has motivated a number of research groups to investigate the potential therapeutic application of brain stimulation to stroke rehabilitation. The loss of motor function is modeled as resulting from a combination of reduced excitability in the lesioned motor cortex and an increased inhibitory drive from the nonlesioned hemisphere over the lesioned hemisphere. This combination of impaired neural function and pathological suppression resonates with current views on the cause of the visual impairment in amblyopia. Here, we discuss how the rationale for using noninvasive brain stimulation in stroke rehabilitation can be applied to amblyopia, review a proof-of-principle study demonstrating that brain stimulation can temporarily improve amblyopic eye function, and propose future research avenues.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.052
GPT teacher head0.378
Teacher spread0.326 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations28
Published2010
Admission routes2
Has abstractyes

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