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Record W2015871527 · doi:10.1016/j.jalz.2011.05.2122

P4‐101: Functional neural mechanisms underlying executive functioning in mild cognitive impairment and Alzheimer's disease: An EEG coherence study

2011· article· en· W2015871527 on OpenAlexaff
Erin Johns, Stephannie Davies, Natalie A. Phillips

Bibliographic record

VenueAlzheimer s & Dementia · 2011
Typearticle
Languageen
FieldNeuroscience
TopicFunctional Brain Connectivity Studies
Canadian institutionsConcordia University
Fundersnot available
KeywordsElectroencephalographyDisconnectionPsychologyAudiologyNeuropsychologyNeuroscienceCoherence (philosophical gambling strategy)Executive dysfunctionExecutive functionsCognitionWorking memoryCognitive impairmentMedicinePhysics

Abstract

fetched live from OpenAlex

Executive dysfunction is present in mild cognitive impairment (MCI) and early Alzheimer's disease (AD); however, its neural substrates remain unclear. AD may involve disconnection between brain areas, thus, we measured functional connection between brain areas using EEG coherence in normal elderly controls (NECs), MCI, and AD during executive task performance to determine the relationship between executive functioning (EF) and functional connections in these groups. 30 NECs, 15 MCI, and 10 early AD patients completed neuropsychological and EEG testing. EEG was recorded while performing two EF tasks: go/no-go (inhibitory control) and n-back (working memory). EEG coherence was calculated for frontal (F3-F4) and parietal (P3-P4) interhemispheric electrode pairs as well as fronto-parietal interhemispheric (F3-P4, F4-P3) and intrahemispheric (F3-P3, F4-P4) electrode pairs in the theta and gamma frequency bands. Preliminary analyses revealed that AD patients performed worse than NECs on all EF measures and MCI patients exhibited a deficit on inhibition measures, but not other EF tasks. There were no group differences for the go/no-go task. For n-back, AD patients had slower reaction times, and AD and MCI patients had lower accuracy. EEG coherence in conditions requiring EF was higher than in control conditions for all groups and all electrode pairs in the theta and gamma bands. Coherence in the theta band during go/no-go was lower for AD versus NECs for F3-F4, P3-P4, and F3-P4 electrode pairs. Correlations for coherence variables that showed group differences in AD patients revealed a that higher coherence was related to better performance on select neuropsychological tests for F3-F4, F3-P3, F3-P4, and F4-P3 electrode pairs. This study demonstrated that EEG coherence in gamma and theta bands changes parametrically with the manipulation of executive demands in NECs, MCI, and AD. Decreased coherence was observed in AD for certain electrode pairs in the theta band, and coherence for those pairs correlated with performance on selected neuropsychological measures. Thus, decreased interhemispheric coherence in frontal and fronto-parietal regions may be an underlying neural mechanism for executive dysfunction in early AD. The current findings may provide a useful, inexpensive method of demonstrating functional disconnections between brain areas due to the neuropathology of 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 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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.000

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 head0.131
GPT teacher head0.303
Teacher spread0.172 · 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

Citations2
Published2011
Admission routes1
Has abstractyes

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