Non-Invasive Modulation and Robotic Mapping of Motor Cortex in the Developing Brain
Bibliographic record
Abstract
Mapping the motor cortex with transcranial magnetic stimulation (TMS) has potential to interrogate motor cortex physiology and plasticity but carries unique challenges in children. Similarly, transcranial direct current stimulation (tDCS) can improve motor learning in adults but has only recently been applied to children. The use of tDCS and emerging techniques like high-definition tDCS (HD-tDCS) require special methodological considerations in the developing brain. Robotic TMS motor mapping may confer unique advantages for mapping, particularly in the developing brain. Here, we aim to provide a practical, standardized approach for two integrated methods capable of simultaneously exploring motor cortex modulation and motor maps in children. First, we describe a protocol for robotic TMS motor mapping. Individualized, MRI-navigated 12x12 grids centered on the motor cortex guide a robot to administer single-pulse TMS. Mean motor evoked potential (MEP) amplitudes per grid point are used to generate 3D motor maps of individual hand muscles with outcomes including map area, volume, and center of gravity. Tools to measure safety and tolerability of both methods are also included. Second, we describe the application of both tDCS and HD-tDCS to modulate the motor cortex and motor learning. An experimental training paradigm and sample results are described. These methods will advance the application of non-invasive brain stimulation in children.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".