Enhancing Neuroplasticity for Post-Stroke Motor Recovery: Mechanisms, Models, and Neurotechnology

Nothing here even remotely suggests how to make neuroplasticity repeatable on demand. 

We don't SPECIFICALLY know why a neuron gives up its' current job and takes on a neighbors. Thus nothing on neuroplasticity is scientifically repeatable on demand. So, DEMAND your doctor give you EXACT PROTOCOLS to use. Don't allow your doctor to give you generalities or guidelines.

 Enhancing Neuroplasticity for Post-Stroke Motor Recovery: Mechanisms, Models,
and Neurotechnology

Wangwang Yan , Yuzhou Lin , Member, IEEE, Yi-Feng Chen , Member, IEEE, Yuling Wang,

Jingxin Wang, and Mingming Zhang 

Abstract— Stroke remains a significant global health challenge, imposing substantial socioeconomic burdens. Post-stroke neurorehabilitation aims to maximize functional recovery and mitigate persistent disability through effective neuromodulation, while many patients experience prolonged recovery periods with suboptimal outcomes. This review explores innovative neurotechnologies and therapeutic strategies enhancing neuroplasticity for post-stroke motor recovery, with a particular focus on the subacute and chronic phases. We examine key neuroplasticity mechanisms and rehabilitation models informing neurotechnology use, including the vicariation model, the interhemispheric competition model, and the bimodal balance-recovery model. Building on these theoretical foundations, current neurotechnologies are categorized into endogenous drivers of neuroplasticity (e.g., task-oriented training, brain-computer interfaces) and exogenous drivers (e.g., brain stimulation, muscular electrical stimulation, robot-assisted passive movement). However, most approaches lack tailored adjustments combining volitional behavior with brain neuromodulation. Given

the heterogeneous effects of current neurotechnologies, we propose that future directions should focus on personalized rehabilitation strategies and closed-loop neuromodulation. These advanced approaches may provide deeper insights into neuroplasticity and potentially expand recovery possibilities for stroke patients.

Received 9 December 2024; revised 20 February 2025 and 5 March 2025; accepted 12 March 2025. Date of current version 21 March 2025. This work was supported in part by the National Key Research and Development Program of China under Grant 2023YFF1205200, in part by Guangdong Basic and Applied Basic Research Founda-

tion under Grant 2024A1515012308 and Grant 2024B1515020108, in part by Shenzhen Science and Technology Program under Grant JCYJ20220530113811027 and Grant JCYJ20210324104203010, and

in part by Shenzhen Medical Research Fund under Grant D2402017.

(Corresponding author: Mingming Zhang.) Wangwang Yan, Yuzhou Lin, Yi-Feng Chen, and Mingming Zhang are with Shenzhen Key Laboratory of Smart Healthcare Engineering

and the Department of Biomedical Engineering, Southern Univer-

sity of Science and Technology, Shenzhen 518055, China (e-mail:zhangmm@sustech.edu.cn). Yuling Wang is with the Department of Rehabilitation Medicine, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou 510655, China (e-mail: wangyul@mail.sysu.edu.cn). Jingxin Wang is with the Department of Rehabilitation Medicine, Zhengzhou Central Hospital Affiliated, Zhengzhou University, Zhengzhou 450001, China (e-mail: kfkWangjxbs@163.com). Digital Object Identifier 10.1109/TNSRE.2025.3551753