Relevance of transcranial modeling in the planning of neurostimulation with low intensity focused ultrasound for deep targets
Bibliographic record
Abstract
Abstract Introduction: Transcranial focused ultrasound is an emerging non-invasive neuromodulation technique that can reach subcortical targets in the human brain. However, the skull can degrade focusing of the FUS beam. Modeling can predict the effects of the skull bone and inform corrective actions. This study reports the degree of mistargeting observed when FUS targets deep brain structures without subject-specific corrections. Methods: A front-end application called BabelBrain (based on the https://github.com/ProteusMRIgHIFU/BabelViscoFDTD library) was developed to take advantage of emerging GPU technology (Apple Silicon). A 128-element concaved array (SonicConcepts, diameter=150 mm, F#=0.9) and a 4-ring concaved array (NeuroFUS, diameter=64mm, F#=0.98) were simulated. Planning was performed in neuronavigation software (Brainsight, Rogue Research). Calculations were performed with a MacBook Pro M1 Max system. The phased array targeted the left subthalamic nucleus (LSTN, n=3) at frequencies of 250 kHz and 700 kHz. The 4-ring array operating at 500 kHz targeted the cerebellum regions of the right dentate nucleus (RDN, n=1), and the left (LCLR, n=1) and right (RCLR, n=1) locomotion regions. Off-target distance and changes in focal volume at -6dB were calculated. Results: The phased array test at 250 kHz showed a mean (s.d.) off-target distance of 2.1 (1.2) mm and an increase of 12 (25) % in the focal volume. For 700 kHz, severe degradation of focusing was observed, with an off-target distance of 6.1 (3.7) mm and focal volume increase of 248 (86)%. The 4-ring array test at 500 kHz showed an off-target distance for RDN, LCLR, and RCLR of 2.4, 9, and 22.8 mm, respectively, with corresponding focal volume increases of 150, 118, and 70%. Conclusion: Without correction, significant targeting errors and focus degradation can occur. They can be mitigated by repositioning the transducer and adjusting steering for the 4-ring array, and with back-propagation refocusing for the 128-element phased array. Research Category and Technology and Methods Translational Research: 6. Pulsed Ultrasound (pUS) Keywords: Modeling, Planning, Deep targets
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".