Transcranial magnetic stimulation electric field modelling in three pediatric brain regions
Bibliographic record
Abstract
The effect of repetitive transcranial magnetic stimulation (rTMS) has also been examined in patients with major depressive disorder (MDD), bipolar disorder (BP) and schizophrenia (SZ).For patients with MDD, rTMS protocols are gradually being confirmed for use, but remains inconsistent among patients with BP and SZ.The aim of this study is to investigate computational models of electric field strength for transcranial magnetic stimulation (TMS) of the left dorsolateral prefrontal cortex (DLPFC) based on individual MRI data of patients with SZ, MDD, BP, and healthy controls (HC).In addition, we explore the association of electric field intensities with age, gender and intracranial volume.The subjects were 23 patients with SZ, 24 patients with MDD, 23 patients with BP, and 23 HC.Based on individual MRI sequences, electric fields were computationally modeled by two independent investigators using SimNIBS ver.2.1.1.There was no significant difference in electric field strength intensities between the groups (HC vs SZ, HC vs MDD, HC vs BP, SZ vs MDD, SZ vs BP, MDD vs BP).Female subjects showed higher electric field intensities in widespread areas than males with age as covariate.With age and estimated total intracranial volume (eTIV) as covariates, however, most part of higher electric field strength in female subjects were no longer of statistical significance except for a part of frontal lobe or brainstem.Female subjects showed significantly lower eTIV than male subjects.Age was positively significantly associated with electric field strength in the left parahippocampal area as observed.These results suggest differences in electric field strength of left DLPFC TMS for gender and age.The intracranial volume may contribute to the difference between males and females and the left parahippocampal area may be subject to age-related changes.It may open future avenues for individually modeling TMS based on structural MRI data.
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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".