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Record W3112313144 · doi:10.1002/alz.047573

Theta‐gamma coupling and ApoE genotype in patients at risk for Alzheimer’s dementia

2020· article· en· W3112313144 on OpenAlexaff
Heather Brooks, Reza Zomorrodi, Sanjeev Kumar, Daniel M. Blumberger, Ariel Graff‐Guerrero, Corinne E. Fischer, Alastair J. Flint, Nathan Herrmann, Krista L. Lanctôt, Linda Mah, Benoit H. Mulsant, Bruce G. Pollock, Tarek K. Rajji

Bibliographic record

VenueAlzheimer s & Dementia · 2020
Typearticle
Languageen
FieldNeuroscience
TopicEEG and Brain-Computer Interfaces
Canadian institutionsBaycrest HospitalUniversity of TorontoUniversity Health NetworkSt. Michael's HospitalSunnybrook Health Science CentreCentre for Addiction and Mental Health
Fundersnot available
KeywordsApolipoprotein EMajor depressive disorderInternal medicineMemory clinicPsychologyElectroencephalographyDementiaMedicineNeuroscienceAudiologyPsychiatryCognition

Abstract

fetched live from OpenAlex

Abstract Background Theta‐gamma coupling (TGC) is an electroencephalography (EEG) marker of working memory and depends on robust synaptic plasticity within the recurrent prefrontal cortical networks (Rajji et al. 2017, Buszaski et al. 2012). ApoE4 allele has deleterious effects on synaptic plasticity (Teter et al. 2004). Thus, the primary objective of this analysis is to determine whether there is a relationship between ApoE4 genotype and TGC during the performance of a working memory task (N‐back). Method We used baseline data from a large Alzheimer’s dementia prevention trial. Participants were diagnosed with Mild Cognitive Impairment (MCI), past history of Major Depressive Disorder (MDD), or both (MCI+MDD) and had completed N‐back‐EEG and ApoE genotyping. TGC was measured across right and left frontal electrodes. Participants were grouped based on E4 presence (ApoE2/E3 or ApoE3/E3 [noncarriers] vs. ApoE3/E4 or ApoE4/E4 [carriers]). Result We included 248 participants (MCI= 125; MDD= 44, MCI+MDD= 79; 153 females/95 males). There were 182 ApoE4 noncarriers (age= 71±6.5; Theta power= 16,290±14,764; Gamma power= 5793±5663; TGC= 0.00251±0.00227; PiB frontal amyloid=1.4371±0.32) and 66 noncarriers (age=71±5.0; Theta power= 17,235±14,312; Gamma power= 6473±5287; TGC= 0.002184±0.001622; PiB frontal amyloid= 2.09±0.78). Multiple linear regression results show that ApoE4 carriers had reduced TGC (β= 0.205, p= 0.042, Cohen’s d= 0.28) after controlling for age, sex, diagnosis, theta power, gamma power, and PiB frontal amyloid. Gamma power was also a significant predictor of TGC. Conclusion Our results suggest that presence of ApoE4 allele contributes to the modulation of TGC during a working memory task. As ApoE impacts synaptic plasticity, our findings suggest that synaptic plasticity or other ApoE‐related mechanisms underlie TGC. Rajji, T.K., et al., Ordering Information in Working Memory and Modulation of Gamma by Theta Oscillations in Humans. Cerebral Cortex, 2017. 27(2): p. 1482‐1490. Buzsaki, G. and X.J. Wang, Mechanisms of Gamma Oscillations, in Annual Review of Neuroscience, Vol 35, S.E. Hyman, Editor. 2012. p. 203‐225. Teter, B., ApoE‐dependent plasticity in Alzheimer's disease. Journal of Molecular Neuroscience, 2004. 23(3): p. 167‐179.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.051
GPT teacher head0.273
Teacher spread0.222 · 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".

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Citations2
Published2020
Admission routes1
Has abstractyes

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