A systematic review of preclinical and clinical transcranial ultrasound neuromodulation and opportunities for functional connectomics
Bibliographic record
Abstract
BACKGROUND: Low-intensity transcranial ultrasound has surged forward as a non-invasive and disruptive tool for neuromodulation with applications in basic neuroscience research and the treatment of neurological and psychiatric conditions. OBJECTIVE: To provide a comprehensive overview and update of preclinical and clinical transcranial low intensity ultrasound for neuromodulation and emphasize the emerging role of functional brain mapping to guide, better understand, and predict responses. METHODS: A systematic review was conducted by searching the Web of Science and Scopus databases for studies on transcranial ultrasound neuromodulation, both in humans and animals. RESULTS: 187 relevant studies were identified and reviewed, including 116 preclinical and 71 clinical reports with subjects belonging to diverse cohorts. Milestones of ultrasound neuromodulation are described within an overview of the broader landscape. General neural readouts and outcome measures are discussed, potential confounds are noted, and the emerging use of functional magnetic resonance imaging is highlighted. CONCLUSION: Ultrasound neuromodulation has emerged as a powerful tool to study and treat a range of conditions and its combination with various neural readouts has significantly advanced this platform. In particular, the use of functional magnetic resonance imaging has yielded exciting inferences into ultrasound neuromodulation and has the potential to advance our understanding of brain function, neuromodulatory mechanisms, and ultimately clinical outcomes. It is anticipated that these preclinical and clinical trials are the first of many; that transcranial low intensity focused ultrasound, particularly in combination with functional magnetic resonance imaging, has the potential to enhance treatment for a spectrum of neurological conditions.
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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 | 0.008 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 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".