Machine learning and individual variability in electrical field characteristics predict tDCS treatment response for anxiety in older adults in the ACT trial.

ABSTRACT:

Introduction

Anxiety is highly prevalent in older adults and often co-occurs with neurodegenerative disorders, worsening cognitive decline and quality of life. Transcranial direct current stimulation (tDCS) has shown therapeutic potential, but outcomes remain inconsistent due to individual neurophysiological variability.

Method

We trained machine learning (ML) models to predict state anxiety reduction following active tDCS paired with BrainHQ cognitive training (tDCS + CT) in older adults with moderate/severe baseline state anxiety symptoms from the Augmenting Cognitive Training in Older Adults (the ACT trial, NCT02851511). Our models predicted outcomes based on MRI-derived current density maps (J-maps) generated via finite element modeling. We evaluated the model using (1)

SUBMITTER: Cheng J 

PROVIDER: S-EPMC13473359 | biostudies-literature | 2026

REPOSITORIES: biostudies-literature