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Record W3111534826 · doi:10.3390/brainsci10120993

The Mediation Role of Dynamic Multisensory Processing Using Molecular Genetic Data in Dyslexia

2020· article· en· W3111534826 on OpenAlexaff
Sara Mascheretti, Valentina Riva, Bei Feng, Vittoria Trezzi, Chiara Andreola, Roberto Giorda, Marco Villa, Ginette Dionne, Simone Gori, Cecilia Marino, Andrea Facoetti

Bibliographic record

VenueBrain Sciences · 2020
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental HealthUniversité Laval
FundersMinistero della Salute
KeywordsDyslexiaEndophenotypePsychologyReading (process)Visual processingMediationReading disabilityCognitive psychologyCognitionDevelopmental psychologyNeuroscience

Abstract

fetched live from OpenAlex

Although substantial heritability has been reported and candidate genes have been identified, we are far from understanding the etiopathogenetic pathways underlying developmental dyslexia (DD). Reading-related endophenotypes (EPs) have been established. Until now it was unknown whether they mediated the pathway from gene to reading (dis)ability. Thus, in a sample of 223 siblings from nuclear families with DD and 79 unrelated typical readers, we tested four EPs (i.e., rapid auditory processing, rapid automatized naming, multisensory nonspatial attention and visual motion processing) and 20 markers spanning five DD-candidate genes (i.e., DYX1C1, DCDC2, KIAA0319, ROBO1 and GRIN2B) using a multiple-predictor/multiple-mediator framework. Our results show that rapid auditory and visual motion processing are mediators in the pathway from ROBO1-rs9853895 to reading. Specifically, the T/T genotype group predicts impairments in rapid auditory and visual motion processing which, in turn, predict poorer reading skills. Our results suggest that ROBO1 is related to reading via multisensory temporal processing. These findings support the use of EPs as an effective approach to disentangling the complex pathways between candidate genes and behavior.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.764
Threshold uncertainty score0.165

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.056
GPT teacher head0.373
Teacher spread0.317 · 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 designOther design
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

Citations17
Published2020
Admission routes1
Has abstractyes

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