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Record W4407001138 · doi:10.1177/07067437251315515

Cognitive Outcomes After Transcranial Magnetic Stimulation for the Treatment of Late-Life Depression: Résultats cognitifs après la stimulation magnétique transcrânienne pour le traitement de la dépression chez les personnes âgées

2025· article· en· W4407001138 on OpenAlexafffundvenue
Katharina Göke, Shawn M. McClintock, Linda Mah, Tarek K. Rajji, Hyewon H. Lee, Sean M. Nestor, Jonathan Downar, Yoshihiro Noda, Zafiris J. Daskalakis, Benoit H. Mulsant, Daniel M. Blumberger

Bibliographic record

VenueThe Canadian Journal of Psychiatry · 2025
Typearticle
Languageen
FieldNeuroscience
TopicTranscranial Magnetic Stimulation Studies
Canadian institutionsHealth Sciences CentreUniversity of TorontoToronto Dementia Research AllianceBaycrest HospitalSunnybrook Health Science CentreCentre for Addiction and Mental Health
FundersDaiichi Sankyo EuropeOntario Ministry of Research and InnovationBiogenCentre for Addiction and Mental Health FoundationSchool of Medicine, Duke UniversityNational Institutes of HealthTeijin PharmaMochida Memorial Foundation for Medical and Pharmaceutical ResearchUniversity of TorontoBrightFocus FoundationSENSHIN Medical Research FoundationDainippon Sumitomo PharmaMagVentureJapan Society for the Promotion of ScienceOntario Ministry of Health and Long-Term CareCanadian Institutes of Health ResearchHealth Sciences Centre FoundationMiyuki GikenWeston Brain InstituteAlzheimer's SocietyWellcome TrustYoshitomiyakuhin CorporationBrainsWayFondation Brain CanadaIndiviorNational Institute of Mental HealthPatient-Centered Outcomes Research Institute
KeywordsCognitionCognitive trainingEffects of sleep deprivation on cognitive performancePsychologyTranscranial magnetic stimulationDorsolateral prefrontal cortexLate life depressionAudiologyMedicineClinical psychologyPrefrontal cortexPsychiatryStimulationNeuroscience

Abstract

fetched live from OpenAlex

BACKGROUND: Late-life depression (LLD) is often accompanied by cognitive impairment, which may persist despite antidepressant treatment. Repetitive transcranial magnetic stimulation (rTMS) is an efficacious treatment for depression, with potential benefits on cognitive functioning. However, research on cognitive effects is inconclusive, relatively sparse in LLD, and predominantly focused on group-level cognitive changes. This study aimed to explore individual-level cognitive changes following rTMS treatment in patients with LLD. METHOD: Data were analyzed from 153 patients with LLD from the FOUR-D study (ClinicalTrials.gov identifier: NCT02998580) who received bilateral standard rTMS or theta burst stimulation (TBS) targeting the dorsolateral prefrontal cortex (DLPFC). Cognitive function was assessed pre- and post-treatment using measures of executive function, information processing speed, and learning and memory. Reliable change indices, adjusted for practice effects and test-retest reliability, were employed to evaluate individual-level cognitive changes. Chi-square tests examined if proportions of cognitive improvers differed from expected proportions. RESULTS: Cognitive performance from baseline to end of treatment remained stable for most patients. Reliably improved performance was observed in 0.0% to 20.0% of participants across cognitive measures, while worsened performance was observed in 0.0% to 2.7%. A small but significant proportion (20.0%) of participants showed improvement in verbal learning. CONCLUSIONS: Bilateral standard rTMS or TBS of the DLPFC in LLD yielded no substantial cognitive enhancing effects, although a small proportion showed improved verbal learning after treatment. Importantly, both interventions were cognitively safe with relatively stable performance across time. Future research is needed to explore approaches to enhance the cognitive benefits of standard rTMS and TBS in patients with LLD.

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 score0.001
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
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)0.0010.000

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 head0.033
GPT teacher head0.298
Teacher spread0.265 · 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

Citations4
Published2025
Admission routes3
Has abstractyes

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