Local neurodynamics and tDCS effects: a guide for symptom mitigation interventions
Bibliographic record
Abstract
Volume conductor modeling of the human head is an essential tool for neurophysiological characterization and the diagnosis of various neurological disorders.Among the non-invasive brain stimulation techniques, transcranial direct current stimulation (tDCS) is widely used in both clinical practice and neuroscience research.However, to achieve personalized tDCS, it is crucial to develop a pipeline that generates individualized head models to ensure target-specific stimulation.This process necessitates the electrical conductivity modeling of head tissues from magnetic resonance imaging (MRI) data, a task prone to segmentation errors, particularly for low-contrast tissues.Furthermore, traditional volume conductor models typically assign uniform electrical conductivity to each tissue, an assumption that does not accurately reflect the complex conductivity distribution in human tissues.In this study, we present a comparative analysis of head models with and without tissue segmentation in the application to tDCS.We introduce a novel approach for the rapid and automated estimation of electrical conductivity in the human head.The impact of these head modeling techniques on the induced electric field (EF) is systematically evaluated using 20 distinct head models.Our findings underscore the importance of accurate head modeling in optimizing stimulation protocols and advancing personalized neurostimulation therapies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.013 |
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".