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Record W2084220936 · doi:10.1016/j.nicl.2015.03.011

Morphological differences in the lateral geniculate nucleus associated with dyslexia

2015· article· en· W2084220936 on OpenAlexaff
Mónica Giraldo‐Chica, John P. Hegarty, Keith A. Schneider

Bibliographic record

VenueNeuroImage Clinical · 2015
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsYork University
FundersDana Foundation
KeywordsDyslexiaParvocellular cellLateral geniculate nucleusPsychologyNeuroscienceMagnocellular cellDevelopmental dyslexiaGeniculateReading (process)Cognitive psychologyRetinaNucleus

Abstract

fetched live from OpenAlex

Developmental dyslexia is a common learning disability characterized by normal intelligence but difficulty in skills associated with reading, writing and spelling. One of the most prominent, albeit controversial, theories of dyslexia is the magnocellular theory, which suggests that malfunction of the magnocellular system in the brain is responsible for the behavioral deficits. We sought to test the basis of this theory by directly measuring the lateral geniculate nucleus (LGN), the only location in the brain where the magnocellular and parvocellular streams are spatially disjoint. Using high-resolution proton-density weighted MRI scans, we precisely measured the anatomical boundaries of the LGN in 13 subjects with dyslexia (five female) and 13 controls (three female), all 22-26 years old. The left LGN was significantly smaller in volume in subjects with dyslexia and also differed in shape; no differences were observed in the right LGN. The functional significance of this asymmetry is unknown, but these results are consistent with the magnocellular theory and support theories of dyslexia that involve differences in the early visual system.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.182
GPT teacher head0.394
Teacher spread0.211 · 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 designObservational
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

Citations72
Published2015
Admission routes1
Has abstractyes

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