Transcranial Pulse Stimulation with Ultrasound in Alzheimer’s disease – A new navigated focal brain therapy
Bibliographic record
Abstract
Abstract Ultrasound-based brain stimulation techniques offer an exciting potential to modulate the human brain in a highly focal and precisely targeted manner. However, for clinical applications the current techniques have to be further developed. We introduce a new ultrasound stimulation technique, based on single ultrashort ultrasound pulses (transcranial pulse stimulation, TPS) and describe a first navigable clinical TPS system. Feasibility, safety and preliminary (uncontrolled) efficacy data in Alzheimer’s disease (AD) are provided. Simulation data, in vitro measurements with rat and human skulls/brains and clinical data in 35 AD patients were acquired in a multicentric setting (including CERAD scores and functional MRI). Preclinical results show large safety margins and patient results show high treatment tolerability. Neuropsychological scores improved significantly when tested immediately as well as 1 and 3 months after stimulation and fMRI data displayed significant connectivity increases within the memory network. The results encourage broad neuroscientific application and translation of the new method to clinical therapy and randomized sham-controlled studies.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".