Strengths and weaknesses of transcranial ultrasound stimulation and its promise in psychiatry: an overview of the technology and a systematic review of the clinical applications
Bibliographic record
Abstract
This article reviews early studies that have demonstrated the ability of low intensity ultrasound waves to modulate brain activity. It also reviews the technological developments that have enabled transcranial ultrasound stimulation (TUS) to achieve millimetric spatial accuracy. This allows precise, noninvasive and reversible brain stimulation, a unique capability when compared to existing techniques such as transcranial magnetic stimulation, transcranial direct-current stimulation, and deep brain stimulation with implanted electrodes. TUS is now technologically ready for clinical translation. As psychiatric disorders have a high prevalence in the general population, and suffer from unmet noninvasive deep brain stimulation clinical needs, this article focuses on the potential application of TUS in psychiatry and reviews recently published clinical proofs of concept that have addressed depression, anxiety, schizophrenia and substance use disorders. Finally, the strengths and weaknesses of TUS technology are discussed, with reference to its clinical translation.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".