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Record W2917811671 · doi:10.3390/brainsci9020045

Neuromodulation Strategies in Post-Traumatic Stress Disorder: From Preclinical Models to Clinical Applications

2019· review· en· W2917811671 on OpenAlexaff
Flavia Venetucci Gouveia, Darryl C. Gidyk, Peter Giacobbe, Enoch Ng, Ying Meng, Benjamin Davidson, Agessandro Abrahão, Nir Lipsman, Clement Hamani

Bibliographic record

VenueBrain Sciences · 2019
Typereview
Languageen
FieldNeuroscience
TopicTranscranial Magnetic Stimulation Studies
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook HospitalSunnybrook Health Science Centre
FundersNational Institute of Mental HealthNational Institutes of HealthU.S. Department of Veterans Affairs
KeywordsNeuromodulationDeep brain stimulationTranscranial magnetic stimulationAnxietyTranscranial direct-current stimulationStimulationPsychologyTraumatic stressMedicineDeep transcranial magnetic stimulationPsychiatryClinical psychologyPhysical medicine and rehabilitationNeuroscienceDiseaseParkinson's diseaseInternal medicine

Abstract

fetched live from OpenAlex

Post-traumatic stress disorder (PTSD) is an often debilitating disease with a lifetime prevalence rate between 5⁻8%. In war veterans, these numbers are even higher, reaching approximately 10% to 25%. Although most patients benefit from the use of medications and psychotherapy, approximately 20% to 30% do not have an adequate response to conventional treatments. Neuromodulation strategies have been investigated for various psychiatric disorders with promising results, and may represent an important treatment option for individuals with difficult-to-treat forms of PTSD. We review the relevant neurocircuitry and preclinical stimulation studies in models of fear and anxiety, as well as clinical data on the use of transcranial direct current stimulation (tDCS), repetitive transcranial magnetic stimulation (rTMS), and deep brain stimulation (DBS) for the treatment of PTSD.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.402
GPT teacher head0.488
Teacher spread0.085 · 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 designNot applicable
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

Citations36
Published2019
Admission routes1
Has abstractyes

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