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Record W4406201528 · doi:10.1002/alz.091649

Impaired Complex Activities of Daily Living Correlated with Altered EEG Oscillations in the Dementia Spectrum Disorder

2024· article· en· W4406201528 on OpenAlexaboutno aff
Po‐Yu Chen, Cheng‐En Wu, Yu-Hua Huang, Jung‐Lung Hsu, I‐Ching Chuang, Sietske A.M. Sikkes

Bibliographic record

VenueAlzheimer s & Dementia · 2024
Typearticle
Languageen
FieldNeuroscience
TopicEEG and Brain-Computer Interfaces
Canadian institutionsnot available
Fundersnot available
KeywordsDementiaMontreal Cognitive AssessmentElectroencephalographyActivities of daily livingCognitionPsychologyAudiologyBiomarkerCognitive declineBrain activity and meditationResting state fMRIMemory clinicPsychiatryCognitive impairmentMedicineNeuroscienceInternal medicineDiseaseBiology

Abstract

fetched live from OpenAlex

Abstract Background Literature on biomarker studies suggests that pathological changes begin approximately 10 to 20 years before the first cognitive symptom appears in dementia populations. It is an emerging era for developing methods to detect the early signs of progressive cognitive decline. Recently, the instrumental activity of living (IADL) capacity has been regarded as a functional biomarker to predict the progression from MCI to dementia. EEG, combined with cognitive performance, such as IADL performance, might be able to provide a more sophisticated biomarker candidate. Thus, the present study aimed to adopt EEG combined with the Amsterdam Instrument of Daily Living Questionnaire‐Short Version Traditional Chinese (A‐IADL‐SQ‐TC) to early detect cognitive decline in the preclinical stages. Method We recruited 76 participants, including those with normal cognition (n=25), SCD (n=21), MCI (n=17), and dementia (n=13) in both community and memory clinics. All of the participants were measured with the Mini‐Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), and eye‐closed EEG recording. Furthermore, all proxies of the participants filled out the A‐IADL‐SQ‐TC. Result Individuals with MCI and dementia exhibited larger relative power in the low‐frequency bands, specifically the delta (0.5‐4 Hz) and theta bands (4‐8 Hz), and smaller relative power in the high‐frequency band, specifically the alpha (8‐13 Hz) and beta bands (13‐20Hz). These findings are consistent with previous research. Additionally, we discovered that the A‐IDAL‐Q‐SV‐TC T‐score negatively correlated with theta relative power across the scalp (r = ‐.55 to ‐.62, all p**<.001), while positively correlated with beta relative power across the scalp (r =.35 to ‐.46, all p<**.001). Regarding the global theta relative power, the one‐way ANOVA showed significant group differences, F (3,72) = 19.768, p<.001. However, the post‐hoc effect revealed only the dementia group showed a significantly higher relative power than the other groups. Conclusion We have discovered that the resting‐state EEG oscillations exhibited different patterns across different stages of dementia. Furthermore, A‐IADL‐SQ‐TC was associated with the spectral intensity of the resting‐state EEG oscillations, and together they provide more sensitive indicators for early detection.

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 score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.036
GPT teacher head0.273
Teacher spread0.237 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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