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Record W3113079987 · doi:10.1002/alz.038614

Assessing the longitudinal relationship between theta‐gamma coupling and working memory performance

2020· article· en· W3113079987 on OpenAlexaff
Heather Brooks, Wei Wang, Sanjeev Kumar, Michelle S. Goodman, Reza Zomorrodi, Daniel M. Blumberger, Zafiris J. Daskalakis, Benoit H. Mulsant, Aristotle N. Voineskos, Tarek K. Rajji

Bibliographic record

VenueAlzheimer s & Dementia · 2020
Typearticle
Languageen
FieldNeuroscience
TopicFunctional Brain Connectivity Studies
Canadian institutionsToronto Dementia Research AllianceUniversity of TorontoCentre for Addiction and Mental Health
Fundersnot available
KeywordsWorking memoryPrefrontal cortexCognitionEffects of sleep deprivation on cognitive performancePsychologyCognitive declineExecutive functionsElectroencephalographyDementian-backAudiologyPhysical medicine and rehabilitationMedicineNeuroscienceInternal medicine

Abstract

fetched live from OpenAlex

Abstract Background Individuals with a history of depression exhibit cognitive deficits and are at high‐risk for developing dementia. In particular, they show deficits in prefrontal cortical function, e.g. executive functioning and working memory. Theta‐gamma coupling (TGC) is a neurophysiologic marker of prefrontal cortical function. In cross‐sectional studies, TGC is associated with performance on several prefrontal cortex dependent cognitive tasks. Thus, it is a promising target for interventions to enhance cognition and possibly prevent cognitive decline. However, the relationship between change in TGC and change in cognitive performance is not yet characterized. The aim of this study is to determine whether change in TGC over repeated assessments is associated with change in working memory performance at these assessments. Methods Twenty‐seven older individuals with a history of depression (mean age = 66.18, SD = 5.43) completed the N‐back, a working memory task, at three time‐points (T0, T1 (2 weeks post T0) and T2 (90 days post T0). TGC was recorded using electroencephalography during the N‐back task. Change in TGC and N‐back performance among the three time‐points was calculated for each participant. Regression analyses were run, with change in N‐back dprime as the dependent variable, change in TGC as the independent variable, and time‐point as the fixed factor. To control for any effect of change in theta and gamma oscillations, we included also changes in theta and gamma powers as covariates. Results We found a positive association between change in TGC and change in N‐back dprime (B = 96.5, p = 0.006), but no association between change in theta (B = 7.66e‐6, p = 0.28) or gamma (B = 1.96E‐6, p = 0.93) power. There was also no effect of time‐point (B = ‐0.32, p = 0.07), and no interaction between time‐point and TGC (B = ‐67.17, p = 0.23). Conclusion Our results suggest enhancing TGC could enhance working memory performance irrespective of whether this enhancement happens over days or weeks. They also further support the use of TGC as a target for intervention to enhance prefrontal cortical function in individuals at high‐risk for cognitive decline, and potentially prevent decline and dementia.

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)0.002
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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
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 integrity0.0000.001
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 head0.215
GPT teacher head0.325
Teacher spread0.110 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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