Induction of human motor cortex plasticity by theta burst transcranial ultrasound stimulation
Bibliographic record
Abstract
in the brain from MR-measurements of the current-induced magnetic field B z .Aim: We test the performance of a standard reconstruction algorithm ("projected current density algorithm", PCD, Jeong et al. 2014) for human brain data.We compare it with current flow simulations using personalized head models.Methods: 1.We generated ground-truth data for the TES current flow and Bz-field using a detailed head model and SimNIBS (www.simnibs.org).We applied the PCD algorithm to the B z -field and quantified the reconstruction performance by comparison with the ground-truth current flow.We additionally compared the PCD results with simulations using a simple head model ("3c" with scalp, bone and a homogeneous intracranial compartment).2. We reconstructed the current flow from in-vivo MRCDI data (G€ oksu et al, 2018) with the PCD algorithm.We also used head models of different complexities ("3c" and "4c": scalp, skull, CSF & brain) and optimized their conductivities to minimize the root-mean-square difference between the measured and simulated B z .Results: 1.For simulated B z data, the PCD algorithm only coarsely reconstructed the true current flow.Even the simple head model performed better.2. For measured B z data, current flows obtained with personalized head models and fitted conductivities explained the measurements better than the current flow reconstructed with the PCD algorithm.This was already the case for the simple head model (3c).The more detailed model (4c) resulted in further statistically significant improvements.However, for all models, the unexplained variance stayed above the noise floor, indicating remaining differences to unknown true current flow. Conclusions:The PCD algorithm has low accuracy for MRCDI data of the brain.However, MRCDI is useful for evaluations and improvements of current flow simulations with anatomically detailed personalized head models.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".