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Record W2967907067 · doi:10.1093/sleep/zsz182

Dynamic modulation of theta–gamma coupling during rapid eye movement sleep

2019· article· en· W2967907067 on OpenAlexaff
Mojtaba Bandarabadi, Richard Boyce, Carolina Gutierrez Herrera, Claudio L. Bassetti, Sylvain Williams, Kaspar Schindler, Antoine Adamantidis

Bibliographic record

VenueSLEEP · 2019
Typearticle
Languageen
FieldNeuroscience
TopicSleep and Wakefulness Research
Canadian institutionsMcGill University
FundersH2020 European Research CouncilHuman Frontier Science Program
KeywordsEye movementSleep (system call)Rapid eye movement sleepModulation (music)Coupling (piping)PhysicsPsychologyNeuroscienceNon-rapid eye movement sleepAudiologyCommunicationMedicineComputer scienceMaterials scienceAcoustics

Abstract

fetched live from OpenAlex

Theta phase modulates gamma amplitude in hippocampal networks during spatial navigation and rapid eye movement (REM) sleep. This cross-frequency coupling has been linked to working memory and spatial memory consolidation; however, its spatial and temporal dynamics remains unclear. Here, we first investigate the dynamics of theta-gamma interactions using multiple frequency and temporal scales in simultaneous recordings from hippocampal CA3, CA1, subiculum, and parietal cortex in freely moving mice. We found that theta phase dynamically modulates distinct gamma bands during REM sleep. Interestingly, we further show that theta-gamma coupling switches between recorded brain structures during REM sleep and progressively increases over a single REM sleep episode. Finally, we show that optogenetic silencing of septohippocampal GABAergic projections significantly impedes both theta-gamma coupling and theta phase coherence. Collectively, our study shows that phase-space (i.e. cross-frequency coupling) coding of information during REM sleep is orchestrated across time and space consistent with region-specific processing of information during REM sleep including learning and memory.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.438
Threshold uncertainty score0.608

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.017
GPT teacher head0.274
Teacher spread0.257 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations68
Published2019
Admission routes1
Has abstractyes

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