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Record W4205948577 · doi:10.3390/brainsci12010048

Repetitive Transcranial Magnetic Stimulation for Comorbid Major Depressive Disorder and Alcohol Use Disorder

2021· review· en· W4205948577 on OpenAlexafffund
Victor M. Tang, Bernard Le Foll, Daniel M. Blumberger, Daphne Voineskos

Bibliographic record

VenueBrain Sciences · 2021
Typereview
Languageen
FieldNeuroscience
TopicTranscranial Magnetic Stimulation Studies
Canadian institutionsCanada Research ChairsUniversity of TorontoCentre for Addiction and Mental Health
FundersNational Institute on Drug AbuseCentre for Addiction and Mental Health
KeywordsTranscranial magnetic stimulationMajor depressive disorderAlcohol use disorderDorsolateral prefrontal cortexComorbidityPsychologyDeep transcranial magnetic stimulationPsychiatryNeuromodulationClinical psychologyPrefrontal cortexMedicineNeuroscienceMoodStimulationCognitionAlcohol

Abstract

fetched live from OpenAlex

Major depressive disorder (MDD) and alcohol use disorder (AUD) are leading causes of disability, and patients are frequently affected by both conditions. This comorbidity is known to confer worse outcomes and greater illness severity. Repetitive transcranial magnetic stimulation (rTMS) is a non-invasive neuromodulation method that has demonstrated antidepressant effects. However, the study of rTMS for patients with MDD and commonly associated comorbidities, such as AUD, has been largely overlooked, despite significant overlap in clinical presentation and neurobiological mechanisms. This narrative review aims to highlight the interrelated aspects of the literature on rTMS for MDD and rTMS for AUD. First, we summarize the available evidence on the effectiveness of rTMS for each condition, both most studied through stimulation of the dorsolateral prefrontal cortex (DLPFC). Second, we describe common symptom constructs that can be modulated by rTMS, such as executive dysfunction, that are transdiagnostic across these disorders. Lastly, we describe promising approaches in the personalization and optimization of rTMS that may be applicable to both AUD and MDD. By bridging the gap between research efforts in MDD and AUD, rTMS is well positioned to be developed as a treatment for the many patients who have both conditions concurrently.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.991
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.131
GPT teacher head0.385
Teacher spread0.254 · 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 designOther design
Domainnot available
GenreReview

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

Citations13
Published2021
Admission routes2
Has abstractyes

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