Accelerated sequential bilateral theta burst repetitive transcranial magnetic stimulation in late-life depression
Bibliographic record
Abstract
aging.Therefore, incorporating age-related anatomical decline in older adults, such as brain atrophy and white matter hyperintensity, into electric field models is important to produce a more accurate tDCS estimation.We found significant negative correlation (p<0.001) for brain atrophy and isolated current densities in selected subregions conducted in a large sample of older adults (N¼587).Integrating white matter lesions into the model resulted an overall decrease of computed current densities in the intact brain tissue by 7%.In addition, accurately representing electrode location in tDCS models is also important for estimating current distribution in the aging brain.For instance, artificially created electrodes versus real electrodes segmented from imaging data resulted in up to a 35% difference in current density in the brain.While both electrode models showed significant correlation to brain volumes, their correlation coefficients were not identical.This serves as a practical application of electric field models and demonstrates the impact of electrode selection on results.Overall, inter-individual variability and model set-up remain crucial in current dose delivery from electrical stimulation at the brain level, especially in older adults.Future studies may consider practical applications to model tDCS in the aging population to compensate for agerelated factors and increase the accuracy of predictions with these models.
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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".