Modeling Transcranial Direct-Current Stimulation-Induced Electric Fields in Children and Adults
Bibliographic record
Abstract
Transcranial direct-current stimulation (tDCS) is a form of non-invasive brain stimulation that induces electric fields in neuronal tissue, modulating cortical excitability. Therapeutic applications of tDCS are rapidly expanding, and are being investigated in pediatrics for various clinical conditions. Anatomical variations are among a host of factors that influence the effects of tDCS, and pronounced anatomical differences between children and adults suggest that induced electric fields may be substantially different across development. The aim of this study was to determine the strength and distribution of tDCS-induced electric fields across development. Typically developing children, adolescents, and adults were recruited. Individualized finite-element method modeling of primary motor cortex (M1) targeting tDCS was performed. In the largest pediatric sample to date, we found significantly higher peak and mean M1 electric field strength, and more expansive electric field spread for children compared to adults. Electric fields were often comparable between adolescents and adults. Our results suggest that these differences may be associated with age-related differences in skull and extra-axial space thickness, as well as developmental changes occurring in gray and white matter. Individualized current modeling may be a valuable tool for personalizing effective doses of tDCS in future pediatric clinical trials.
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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 | 0.001 |
| 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 | 0.000 |
| 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 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".