Pipeline for Planning and Execution of Transcranial Ultrasound Neuromodulation Experiments in Humans
Bibliographic record
Abstract
Transcranial ultrasound stimulation (TUS) is an emerging non-invasive neuromodulation technique capable of manipulating both cortical and subcortical structures with high precision. Conducting experiments involving humans necessitates careful planning of acoustic and thermal simulations. This planning is essential to adjust for bone interference with the ultrasound beam's shape and trajectory and to ensure TUS parameters meet safety requirements. T1- and T2-weighted, along with zero-time echo (ZTE) magnetic resonance imaging (MRI) scans with 1 mm isotropic resolution, are acquired (alternatively computed tomography x-ray (CT) scans) for skull reconstruction and simulations. Target and trajectory mapping are performed using a neuronavigational platform. SimNIBS is used for the initial segmentation of the skull, skin, and brain tissues. Simulation of TUS is carried over with the BabelBrain tool, which uses the ZTE scan to produce synthetic CT images of the skull to be converted into acoustic properties. We use a phased array ultrasound transducer with electrical steering capabilities. Z-steering is adjusted to ensure that the target depth is reached. Other transducer configurations are also supported in the planning tool. Thermal simulations are run to ensure temperature and mechanical index requirements are within the acoustic guidelines for TUS in human subjects as recommended by the FDA. During TUS delivery sessions, a mechanical arm assists in the movement of the transducer to the required location using a frameless stereotactic localization system.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.041 | 0.010 |
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".