Predicting Long-Term ADAS-Cog Change From Early Response After Repetitive Transcranial Magnetic Stimulation in Alzheimer Disease
Abstract
Early cognitive response after repetitive transcranial magnetic stimulation (rTMS) may predict later cognitive change in Alzheimer disease, but the incremental value of domain-specific response is unclear. We analyzed data from 112 randomized participants; 101 entered a Week-5 landmark cohort and 91 had complete predictors and outcomes at 8, 16, and 24 weeks post-treatment. Nested predictor-set comparisons used ridge regression to evaluate baseline information, trial variables, early total-score response, and domain-specific response. Model-class comparisons included ridge, multi-task elastic net, restricted random forest, and longitudinal mixed effects. Validation used repeated treatment-site-stratified 5-fold cross-validation with five repetitions, nested hyperparameter tuning, and paired participant-level bootstrap comparisons with 10,000 resamples. Adding Week-3 and Week-5 ADAS-Cog total-score response improved prediction. The early total-response ridge achieved overall MAE 3.067, RMSE 3.885, and R2=0.384. Replacing two total-response variables with 22 component-level changes worsened performance (MAE 3.363, RMSE 4.269, R2=0.257). The paired MAE difference was -0.297 points (95% CI -0.485 to -0.115). Mixed effects was competitive (MAE 3.162), with an uncertain overall difference from total-response ridge. Sensitivity analyses using absolute future scores and excluding Week-3 response supported the main findings. In this sample, early aggregate ADAS-Cog response was more informative than a higher-dimensional domain-specific representation. The model is an internally validated early-response updating tool, not a pretreatment selection model or evidence of causal rTMS efficacy.
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BibTeX
@misc{chowdhury2026predicting,
title = {{Predicting Long-Term ADAS-Cog Change From Early Response After Repetitive Transcranial Magnetic Stimulation in Alzheimer Disease}},
author = {Mohammad Alamgir Chowdhury and Hina Shaheen},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.09629},
eprint = {2610.09629},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.09629},
}
RIS
TY - UNPB
TI - Predicting Long-Term ADAS-Cog Change From Early Response After Repetitive Transcranial Magnetic Stimulation in Alzheimer Disease
AU - Mohammad Alamgir Chowdhury
AU - Hina Shaheen
PY - 2026
DA - 2026/10/07
PB - arXiv
DO - 10.48550/arxiv.2610.09629
UR - https://arxiv.org/abs/2610.09629
AB - Early cognitive response after repetitive transcranial magnetic stimulation (rTMS) may predict later cognitive change in Alzheimer disease, but the incremental value of domain-specific response is unclear. We analyzed data from 112 randomized participants; 101 entered a Week-5 landmark cohort and 91 had complete predictors and outcomes at 8, 16, and 24 weeks post-treatment. Nested predictor-set comparisons used ridge regression to evaluate baseline information, trial variables, early total-score response, and domain-specific response. Model-class comparisons included ridge, multi-task elastic net, restricted random forest, and longitudinal mixed effects. Validation used repeated treatment-site-stratified 5-fold cross-validation with five repetitions, nested hyperparameter tuning, and paired participant-level bootstrap comparisons with 10,000 resamples. Adding Week-3 and Week-5 ADAS-Cog total-score response improved prediction. The early total-response ridge achieved overall MAE 3.067, RMSE 3.885, and R^2=0.384. Replacing two total-response variables with 22 component-level changes worsened performance (MAE 3.363, RMSE 4.269, R^2=0.257). The paired MAE difference was -0.297 points (95% CI -0.485 to -0.115). Mixed effects was competitive (MAE 3.162), with an uncertain overall difference from total-response ridge. Sensitivity analyses using absolute future scores and excluding Week-3 response supported the main findings. In this sample, early aggregate ADAS-Cog response was more informative than a higher-dimensional domain-specific representation. The model is an internally validated early-response updating tool, not a pretreatment selection model or evidence of causal rTMS efficacy.
ER -