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Record W4310704553 · doi:10.1126/sciadv.adc9970

Deep brain stimulation of the subgenual cingulum and uncinate fasciculus for the treatment of posttraumatic stress disorder

2022· article· en· W4310704553 on OpenAlexaff
Clement Hamani, Benjamin Davidson, Felipe Corchs, Agessandro Abrahão, Sean M. Nestor, Jennifer S. Rabin, Alexander Nyman, Liane Phung, Maged Goubran, Anthony Levitt, Omid Talakoub, Peter Giacobbe, Nir Lipsman

Bibliographic record

VenueScience Advances · 2022
Typearticle
Languageen
FieldMedicine
TopicNeurological disorders and treatments
Canadian institutionsHealth Sciences CentreToronto Rehabilitation InstituteUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsUncinate fasciculusCingulum (brain)FasciculusNeuroimagingRecallMedicineDeep brain stimulationPsychologyNeuroscienceInternal medicineMagnetic resonance imagingWhite matterRadiology

Abstract

fetched live from OpenAlex

Deep brain stimulation (DBS) has been investigated for neuropsychiatric disorders. In this phase 1 trial, we treated four posttraumatic stress disorder (PTSD) patients with DBS delivered to the subgenual cingulum and the uncinate fasciculus. In addition to validated clinical scales, patients underwent neuroimaging studies and psychophysiological assessments of fear conditioning, extinction, and recall. We show that the procedure is safe and potentially effective (55% reduction in Clinical Administered PTSD Scale scores). Posttreatment imaging data revealed metabolic activity changes in PTSD neurocircuits. During psychophysiological assessments, patients with PTSD had higher skin conductance responses when tested for recall compared to healthy controls. After DBS, this objectively measured variable was significantly reduced. Last, we found that a ratio between recall of extinguished and nonextinguished conditioned responses had a strong correlation with clinical outcome. As this variable was recorded at baseline, it may comprise a potential biomarker of treatment response.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.408
Threshold uncertainty score0.266

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.000

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 head0.018
GPT teacher head0.308
Teacher spread0.290 · 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.

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

Citations29
Published2022
Admission routes1
Has abstractyes

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