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Record W4394976648 · doi:10.1093/sleep/zsae067.0971

0971 Repetitive Transcranial Magnetic Stimulation for Depression Changes Sleep Architecture: Preliminary Results

2024· article· en· W4394976648 on OpenAlexaffabout
Karina Fonseca, David Smith, Caitlin Higginson, Stacey Shim, Jennifer Cuda, Jessica Drodge, Malika Lanthier, Meggan Porteous, Lisa McMurray, Sara Tremblay, Rébecca Robillard

Bibliographic record

VenueSLEEP · 2024
Typearticle
Languageen
FieldNeuroscience
TopicTranscranial Magnetic Stimulation Studies
Canadian institutionsRoyal Ottawa Mental Health CentreUniversity of Ottawa
FundersNational Institutes of Health
KeywordsTranscranial magnetic stimulationSleep architectureDepression (economics)Sleep (system call)StimulationNeuroscienceMedicineAudiologyPhysical medicine and rehabilitationPsychologyPolysomnographyElectroencephalographyComputer science

Abstract

fetched live from OpenAlex

Abstract Introduction There is a bi-directional relationship between sleep and depression, with sleep difficulties contributing to the risk of treatment resistant depression. The existing literature regarding the influence of repetitive transcranial magnetic stimulation (rTMS) treatment on sleep is limited. The present study seeks to investigate changes in brain activity during sleep in individuals with treatment resistant depression undergoing rTMS treatment. Methods As part of an ongoing randomized, double-blind clinical trial, participants underwent four or six weeks of daily rTMS sessions targeting the left or bilateral dorsolateral prefrontal cortex. Sleep was monitored at home with an ambulatory EEG device (Muse-S, Interaxon) or in the laboratory with standard polysomnography (N7000, Embla) for two consecutive nights before starting rTMS (the first night serving as an adaptation night) and one night after up to four weeks of rTMS. Results Our preliminary sample consisted of eight participants (25% females, age range: 31-64 years, M = 47.4, SD = 12.3). At baseline, all participants scored at least 15 on the Hamilton Rating Scale for Depression which corresponds to mild depression severity. On average, total sleep time did not change significantly from pre-intervention (6.2 + 1.3 hours) to after the last rTMS session (6.1 + 1.6 hours, p>.050). After the last rTMS session (M=4.6, SD = 3.1), the percentage of NREM1 sleep shortened significantly relative to what was observed before the intervention (M=5.9, SD = 2.8; p = .042) . Changes in other sleep stages did not reach statistical significance. Conclusion These preliminary findings suggest that rTMS treatment may reduce light sleep without affecting overall sleep duration in individuals with treatment resistant depression. If replicated in larger samples, such findings may highlight rTMS as a potential means of alleviating poor sleep commonly linked to depression. Support (if any) This work was supported by the Royal’s Institute of Mental Health Research’s Emerging Research Innovators in Mental Health award, the Research Scholar Junior 1 grant (grant number 297133) from the Fonds de Recherche en Santé – Québec, and an anonymous donation.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
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)0.0030.001

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 head0.029
GPT teacher head0.285
Teacher spread0.256 · 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 designNon-randomized trial
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 routes2
Has abstractyes

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