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Record W4206235744 · doi:10.1002/alz.050923

Theta phase‐gamma amplitude coupling during working memory and its relationships with demographic, clinical, genetic, neurochemical, and neurostructural measures in older adults at risk for dementia

2021· article· en· W4206235744 on OpenAlexaff
Rachel Patterson, Heather Brooks, Mina Mirjalili, Neda Rashidi‐Ranjbar, Reza Zomorrodi, Daniel M. Blumberger, Sanjeev Kumar, Corinne E. Fischer, Alastair J. Flint, Ariel Graff‐Guerrero, Nathan Herrmann, Krista L. Lanctôt, Linda Mah, Benoit H. Mulsant, Bruce G. Pollock, Aristotle N. Voineskos, Tarek K. Rajji

Bibliographic record

VenueAlzheimer s & Dementia · 2021
Typearticle
Languageen
FieldChemistry
TopicAdvanced NMR Techniques and Applications
Canadian institutionsBaycrest HospitalUniversity of TorontoUniversity Health NetworkSt. Michael's HospitalSunnybrook Health Science CentreCentre for Addiction and Mental Health
Fundersnot available
KeywordsNeuroimagingWorking memoryWhite matterPsychologyDementiaFractional anisotropyMajor depressive disorderMedicineMagnetic resonance imagingAudiologyNeuroscienceCognitionInternal medicineRadiology

Abstract

fetched live from OpenAlex

Abstract Background Theta phase‐gamma amplitude coupling (TGC) is a neurophysiological mechanism that underlies working memory (WM)1. WM is also associated with demographic, clinical, genetic and neuroimaging measures. However, the relative contributions of TGC and these measures to WM, and the relationship between TGC and these measures, remains unclear. We examined the relative contributions of TGC and these measures to WM performance in a group of participants at‐risk for Alzheimer’s dementia. Method Older participants (age=71.2±6.0, N = 206) with Mild Cognitive Impairment (MCI), Major Depressive Disorder (MDD), or MCI+MDD completed clinical assessment, N‐back WM task with EEG to measure TGC, genetic testing, PET with [11C]‐Pittsburgh Compound B ([11C]‐PIB PET) and brain 3T MRI. Linear regressions were used to assess the relationships among 2‐back WM performance; demographic and clinical variables; TGC; ApoE4 carrier status; total beta‐amyloid SUVR ([11C]‐PIB PET); and regional cortical thickness, subcortical volumes, and white matter fractional anisotropy (MRI). Result 2‐back WM performance was associated with age and TGC after controlling for all other measures (Age: β=‐0.253; p=0.039, TGC: β=0.300; p=0.005). TGC was not associated with any other measures after correction for multiple comparisons. Conclusion TGC predicts WM performance in contrast to demographic, clinical, genetic, and PET and MRI imaging measures. Our findings underline the strong association between WM and TGC, a dynamic time‐based neurophysiological measure that is capturing a functional process which may not be captured by these static measures. Future studies could explore other dynamic measures with other imaging modalities and their relationships with TGC or other neurophysiological measures. 1. 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.

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)0.002
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
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.040
GPT teacher head0.302
Teacher spread0.262 · 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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Citations0
Published2021
Admission routes1
Has abstractyes

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