1000 Section - Effects of sequential dual-target TMS on the dynamic characteristics of brain networks as revealed by fNIRS

Tuesday, May 19, 2026

6:00 PM - 7:30 PM PT

Presenting Author/Autor expositor(s)

ZL

Zengyong Li, PhD

Professor
National Research Center For Rehabilitation Technical Aids
Beijing, Beijing, China (People's Republic)

Co-Author(s)

CH

Congcong Huo, PhD

Dr
National Research Center For Rehabilitation Technical Aids
Beijing, Beijing, China (People's Republic)
JZ

Jingsha Zhang, MS

Senior Engineer
National Research Center For Rehabilitation Technical Aids
beijing, Beijing, China (People's Republic)
YM

Yanqiang Ma, MS

Engineer
National Research Center For Rehabilitation Technical Aids
beijing, Beijing, China (People's Republic)

Objectives : This study investigates the immediate effects of single- and dual-target TMS on brain functional networks in stroke patients using functional near-infrared spectroscopy (fNIRS) and dynamic functional connectivity (dFNC) analysis.

Design: Fifteen subacute stroke patients and fifteen healthy controls underwent three interventions: single-target SMA-rTMS, single-target LDLPFC-rTMS, and sequential dual-target stimulation (Dual-rTMS). fNIRS data were analyzed via dFNC to identify connectivity states (weak, moderate, strong) and evaluate time fraction, state transitions, global efficiency, and total signal power.

Results: Results revealed three dFNC states: State 1 (weak connectivity, 44.24%), State 2 (moderate, 39.44%), and State 3 (strong, 16.32%). Single-target TMS (especially SMA-rTMS) increased time fraction in State 1, indicating low-connectivity induction, while Dual-rTMS further prolonged State 1 and reduced inefficient transitions (e.g., State 1→2: p < 0.005). Stroke patients showed impaired transitions from State 2→3 (p = 0.024) versus controls. Time fraction in State 3 correlated positively with motor function scores (FMA-LE: r = 0.52, p = 0.045; BBS: r = 0.52, p = 0.047), while State 1 correlated negatively (FMA-LE: r = -0.52, p = 0.045).

Conclusion:

Sequential dual-target TMS optimizes network dynamics by sustaining low-energy states and suppressing maladaptive transitions, outperforming single-target approaches. dFNC temporal patterns may serve as biomarkers for post-stroke motor function, supporting sequential dual-target TMS as a promising rehabilitation tool.