Recent Advancements of Transcranial Direct Current Stimulation and Machine Learning: Methods, Challenges, and Opportunities

TL;DR

This paper reviews how machine learning is being used to improve transcranial direct current stimulation (tDCS) by making it more precise and adaptive.

Contribution

The paper synthesizes recent tDCS and ML studies (2020–2025) to identify trends and opportunities for precision neuromodulation.

Findings

ML techniques like SVM and random forests are used to predict tDCS outcomes and identify biomarkers.

Current datasets are small and fragmented, limiting robustness but showing promise for precision neuromodulation.

Future directions include multimodal integration and adaptive closed-loop systems for personalized tDCS.

Abstract

Transcranial direct current stimulation (tDCS) has emerged as a versatile non-invasive neuromodulation approach that can alter cortical excitability and affect network plasticity. Recent advances in machine learning (ML) offer an opportunity to transform tDCS from largely heuristic practice into a quantitatively informed, adaptive intervention paradigm. Here, we synthesize developments from 2020 to 2025 at the intersection of tDCS and ML. Search results from structured PubMed and Google Scholar queries were screened for eligibility based on predefined inclusion criteria, retaining peer-reviewed studies that applied ML techniques to tDCS related studies. Eligible studies were evaluated for data integrity, and ML model validation methodology. Sixteen studies met inclusion criteria. Across these studies, ML was applied to heterogeneous datasets, including electroencephalography, neuroimaging, and clinico–demographic features, to predict stimulation outcomes, characterize neural responses, and identify biomarkers of tDCS sensitivity. Support vector machines and random forests remain prevalent, reflecting the modest scale and exploratory nature of current datasets; most studies rely on early-stage clinical or preclinical cohorts, resulting in promising yet fragmented evidence. Nevertheless, emerging results illustrate how ML can reveal latent physiological structure, guide dose–response optimization, and support the translation of tDCS toward precision neuromodulation. Drawing on this integrated analysis, we highlight key directions for the field: multimodal integration that unifies electrophysiological, structural, and behavioral signatures; incorporation of biophysically grounded forward models and pretrained deep-learning architectures; and development of adaptive, closed-loop control strategies capable of personalizing stimulation in real time. Together, these advances chart a pathway toward ML-guided tDCS systems that are mechanistically informed, clinically actionable, and scalable for widespread application.

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