• Personalized transcranial elec...

*Result*: Personalized transcranial electrical stimulation: a review of computational modeling and optimization.

Title:
Personalized transcranial electrical stimulation: a review of computational modeling and optimization.
Authors:
Wang M; Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, People's Republic of China.; Department of Computer Science, University of Warwick, Coventry CV4 7AL, United Kingdom., Zheng K; Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, People's Republic of China., Xin Y; Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, People's Republic of China., Chen X; Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, People's Republic of China., Liu Y; Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, People's Republic of China., Luo H; Department of Anesthesiology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China.; Key Laboratory of Anesthesiology (Shanghai Jiao Tong University), Ministry of Education, Shanghai, People's Republic of China., Tang J; Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, People's Republic of China., Yuan T; Shanghai Key Laboratory of Psychotic Disorders, Brain Health Institute, National Center for Mental Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine and School of Psychology, Shanghai, People's Republic of China., Wen H; Department of Computer Science, University of Warwick, Coventry CV4 7AL, United Kingdom., Wei P; School of Biological Science and Medical Engineering, State Key Laboratory of Digital Medicine, Southeast University, Nanjing, People's Republic of China., Liu Q; Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, People's Republic of China.
Source:
Journal of neural engineering [J Neural Eng] 2026 Mar 13; Vol. 23 (2). Date of Electronic Publication: 2026 Mar 13.
Publication Type:
Journal Article; Review
Language:
English
Journal Info:
Publisher: Institute of Physics Pub Country of Publication: England NLM ID: 101217933 Publication Model: Electronic Cited Medium: Internet ISSN: 1741-2552 (Electronic) Linking ISSN: 17412552 NLM ISO Abbreviation: J Neural Eng Subsets: MEDLINE
Imprint Name(s):
Original Publication: Bristol, U.K. : Institute of Physics Pub., 2004-
MeSH Terms:
Transcranial Direct Current Stimulation*/methods , Precision Medicine*/methods , Brain*/physiology , Computer Simulation* , Models, Neurological*, Humans
Contributed Indexing:
Keywords: computational modeling; head model; neuromodulation; transcranial electrical stimulation
Entry Date(s):
Date Created: 20260304 Date Completed: 20260313 Latest Revision: 20260313
Update Code:
20260313
DOI:
10.1088/1741-2552/ae4d8d
PMID:
41780164
Database:
MEDLINE

*Further Information*

*Objective. Personalized transcranial electrical stimulation (tES) has gained increasing attention due to the substantial inter-individual variability in brain anatomy and physiology. While previous reviews have discussed the physiological mechanisms and clinical applications of tES, there remains a critical gap in up-to-date syntheses focused on the computational modeling frameworks that enable individualized stimulation optimization.Approach. This review presents a comprehensive overview of recent advances in computational techniques supporting personalized tES. We systematically examine developments in forward modeling for simulating individualized electric fields, as well as inverse modeling approaches for optimizing stimulation parameters. We critically evaluate progress in head modeling pipelines, optimization algorithms, and the integration of multimodal brain data.Main results. Recent advances have substantially accelerated the construction of subject-specific head conductor models and expanded the landscape of optimization methods, including multi-objective optimization and brain network-informed optimization. These advances allow for dynamic and individualized stimulation planning, moving beyond empirical trial-and-error approaches.Significance. By integrating the latest developments in computational modeling for personalized tES, this review highlights current challenges, emerging opportunities, and future directions for achieving precision neuromodulation.
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