Neuromodulation as a therapeutic approach for post-traumatic stress disorder: the evidence to date
Bibliographic record
Abstract
INTRODUCTION: Post-traumatic stress disorder (PTSD) can have debilitating effects on quality of life, and conventional treatments show mixed results. Neuromodulation is emerging as a promising approach for treating PTSD. This review examines current neuromodulatory treatments for PTSD, and highlights methodologies, clinical outcomes, and gaps in the literature to help guide future research. AREAS COVERED: A PubMed search identified 252 studies on PTSD and neuromodulation, of which 61 were selected for full review. These included 37 studies on repetitive transcranial magnetic stimulation (rTMS), 10 on transcranial direct current stimulation (tDCS),4 on deep brain stimulation (DBS) and 2 on focused ultrasound (FUS). EXPERT OPINION: The present review supports the potential of neuromodulation to reduce PTSD symptoms. rTMS and tDCS targeting the dlPFC appear effective through modulating neural circuits involved in fear processing and conditioning, however, literature varies regarding efficacy of stimulation frequencies and hemispheric targets. DBS targeting the amygdala or subcallosal cingulate white matter tracts improves treatment of refractory PTSD with sustained benefits, while FUS may improve symptoms through targeted modulation of brain structures such as the amygdala, though this technique is in the early stages of exploration. Future research should refine established neuromodulatory approaches and address gaps in emerging modalities to enhance treatment efficacy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".