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Record W4407933674 · doi:10.1016/j.brs.2024.12.934

Optimizing Antidepressant Benefits: Effect of Theta Burst Stimulation Treatment in Physically Active People with Treatment-Resistant Depression

2025· article· en· W4407933674 on OpenAlexaff
Arthur R. Chaves, Jennifer Cuda, Stacey Shim, Jessica Drodge, Youssef Nasr, Ram Brender, Ruxandra Antochi, Lara A. Pilutti, Sara Tremblay

Bibliographic record

VenueBrain stimulation · 2025
Typearticle
Languageen
FieldNeuroscience
TopicTranscranial Magnetic Stimulation Studies
Canadian institutionsRoyal Ottawa Mental Health CentreUniversité du Québec en OutaouaisMental Health Research CanadaUniversity of Ottawa
FundersH. Lundbeck A/SLundbeckfondenInnovationsfonden
KeywordsAntidepressantDepression (economics)StimulationTreatment-resistant depressionPsychologyMedicinePsychiatryAnesthesiaNeuroscience

Abstract

fetched live from OpenAlex

Methods: Thirty-three healthy, young individuals were divided into two groups undergoing the active (N17) and the sham (N16) protocol.We continuously recorded brain activity during a 10-minute iTBS intervention (2 seconds on, 8 seconds off) targeting the left DLPFC under stereotactic guidance based on each subject's structural MRI.EEG data were preprocessed after extracting 7-second epochs (0.5 to 7.5 seconds) during off -stimulation period between burst trains.We subtracted the power changes of each trial compared to the 7-second epoch before the first stimulation in both groups and averaged over consecutive sets of 10 trials.Data were separately compared for theta and alpha frequency bands across three channels below the coil and between the two intervention groups.Results: Our findings showed a statistically significant effect of intervention in regional alpha power changes between the consecutive burst trains.Conclusion: Compared to sham control, focal iTBS of the left DLPFC has acute effects on the regional expression of alpha oscillatory activity at the sensor level.We will perform source analysis to better delineate the spatial specificity of this findings.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.695
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.289
Teacher spread0.272 · 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.

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

Citations0
Published2025
Admission routes1
Has abstractyes

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