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Record W3202519812 · doi:10.1002/alz.12464

Binaural processing deficit and cognitive impairment in Alzheimer's disease

2021· article· en· W3202519812 on OpenAlexaff
Changming Wang, Zhibin Wang, Beijia Xie, Xinrui Shi, Pengcheng Yang, Lei Liu, Tianshu Qu, Qi Qin, Yi Xing, Wei Zhu, Stefan Teipel, Jianping Jia, Guoguang Zhao, Liang Li, Yi Tang

Bibliographic record

VenueAlzheimer s & Dementia · 2021
Typearticle
Languageen
FieldPsychology
TopicMultisensory perception and integration
Canadian institutionsMinistry of Education and Child Care
FundersNatural Science Foundation of Beijing MunicipalityNational Natural Science Foundation of China
KeywordsBinaural recordingCognitionAudiologyPsychologyNeuroscienceMedicine

Abstract

fetched live from OpenAlex

Speech comprehension in noisy environments depends on central auditory functions, which are vulnerable in Alzheimer's disease (AD). Binaural processing exploits two ear sounds to optimally process degraded sound information; its characteristics are poorly understood in AD. We studied behavioral and electrophysiological alterations in binaural processing among 121 participants (AD = 27; amnestic mild cognitive impairment [aMCI] = 33; subjective cognitive decline [SCD] = 30; cognitively normal [CN] = 31). We observed impairment of binaural processing in AD and aMCI, and detected a U-shaped curve change in phase synchrony (declining from CN to SCD and to aMCI, but increasing from aMCI to AD). This improvement in phase synchrony accompanying more severe cognitive stages could reflect neural adaptation for binaural processing. Moreover, increased phase synchrony is associated with worse memory during the stages when neural adaptation apparently occurs. These findings support a hypothesis that neural adaptation for binaural processing deficit may exacerbate cognitive impairment, which could help identify biomarkers and therapeutic targets in AD.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.781
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.050
GPT teacher head0.342
Teacher spread0.292 · 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.

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

Citations16
Published2021
Admission routes1
Has abstractyes

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