Repetitive Transcranial Magnetic Stimulation Improves Neuropsychiatric Manifestations in Adult-onset Leukoencephalopathy with Axonal Spheroids and Pigmented Glia
Bibliographic record
Abstract
Personalizing the stimulation location of transcranial neurostimulation based on the subject's individual anatomy is becoming more important in clinical research.In the case of focal epilepsy, there often is a clear target to stimulate that can be found by analysis of multi-modal clinical data, including EEG source localization.Hypothesizing its clinical importance, we developed a procedure to exploit the spatial target information and optimize the tDCS montage such that the induced electric field has maximum overlap with the target.This could be beneficial to tailor the treatment on a per-patient basis, so every patient will be stimulated with the intended field at the intended location.While commercial workflows for personalized tDCS exist, a flexible, easyto-use and open-source software that integrates EEG source localization and tDCS optimization techniques in one application was not yet available.To support open research, we therefore combined open-source solutions into an intuitive MATLAB software tool for (clinical) research purposes.This tool interfaces intuitively with other open-source tools: the Brainstorm package (EEG processing and source localization) and SimNibs (neurostimulation optimization).The most relevant parameters such as tissue conductivity and optimization constraints can be changed via the user interface.Based on MRI data, a head segmentation can be made and converted into a FEM model for both software's.Clinical targets can be defined in subject space or calculated via source localization.A tDCS montage can be optimized for this target using either patch electrodes or high-definition electrodes.The field maps for the optimized montage and the field distribution histograms are presented in the user interface.Based on these analyses, the tool produces a report in which the flow from input to output can be analyzed.In this way, clinical researchers worldwide are offered a research tool for streamlining the research into dose-effect relations, as well as personalization possibilities.
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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 | 0.000 | 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) | 0.002 | 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".