Optimization Simulations of Transcranial Direct Current Stimulation Montages in Children With Perinatal Stroke
Bibliographic record
Abstract
BACKGROUND: Perinatal stroke (PS) is a vascular brain injury that causes most hemiparetic cerebral palsy. Transcranial direct current stimulation (tDCS) applies a weak electric field (EF) to the scalp, and targeting motor cortex (M1) paired with therapy may improve motor function. However, owing to developmental differences and idiosyncratic anatomy after early injury, optimal electrode placements are not known. We optimized electrode placements on the basis of individual anatomy and explored the resulting EF propagation patterns. OBJECTIVE/HYPOTHESIS: We hypothesized that children with PS would have greater electrode displacement distances from standard montages and that optimizations could improve the strength and direction of EF at M1 targets. MATERIALS AND METHODS: Magnetic resonance images of participants with PS and of controls were preprocessed, segmented, and converted to three-dimensional meshes. SimNIBS (Thielscher, Copenhagen, Denmark) modeled EF for various tDCS electrode placements. Optimal placements were modeled to maximize EF strength or direction at the targeted M1. Electrode displacement distances and directions in addition to EF metrics were compared in groups and optimization strategies. RESULTS: Optimal electrode displacement distance was greater in the arterial ischemic stroke group when EF strength in the lesioned M1 was optimized (W = 4.31, p < 0.01), located further posterior than controls. The opposite trend was observed when current direction was optimized (W = 3.68, p = 0.025). Displacement direction had higher variability in children with PS across all optimizations. Montage optimization improved EF metrics. Specifically, the anodal nondirectionally optimized protocol caused greater EF strength in simulations of participants with PS. Directionally optimized montages altered average current angle through the target M1, making it closer to perpendicular to the posterior bank of the precentral gyrus in all groups. CONCLUSIONS: Individualized electrode placements may optimize tDCS current propagation in children with PS, with tradeoffs between current direction and EF strength. tDCS current optimization may improve noninvasive neuromodulation therapies in children with disabilities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".