Soft Pneumatic Robot Modulates Graph Theory Metrics of Brain Network for Hand Rehabilitation After Stroke

 From the picture there is no clue if this will work on spastic hands.

Soft Pneumatic Robot Modulates Graph Theory Metrics of Brain Network for Hand Rehabilitation After Stroke

DOI:

10.3791/68588

⸱

October 10th, 2025

Ze-Jian Chen 1, 

Jun-Wen Xia 1, 

Nan Xia 1, 

Ming-Hui Gu 1, 

Jia-Hui Bian 2, 

Zhi-Bing Dong 1, 

Sheng-Qiang Wang 1, 

Qiong Yang 1, 

Zhi-Wei Tang 1, 

Jiang Xu 1, 

Yong Chen 1

1  Department of Rehabilitation Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 

2  College of Sports Medicine, Wuhan Sports University

Summary

This study explores the effects of a configurable soft pneumatic robot on enhancing whole-brain network topology post-stroke. Graph theory analysis indicates significant improvements in clustering coefficient, path length, and global efficiency. Findings highlight the potential of programmable robotic protocols to modulate neuroplasticity and optimize functional recovery in stroke rehabilitation.

Abstract

Functional restoration of the cerebral cortex relies on activity-dependent neuroplasticity following stroke. However, optimizing robotic rehabilitation for hand recovery remains a significant challenge. This proof-of-concept study investigates the feasibility of a configurable soft pneumatic robot in modulating the brain network among ten participants with hand motor impairments after stroke. The programmable robotic intervention was administered in a randomized sequence for resting-state assessment, slow-mode, and fast-mode robotic therapy by adjusting working mode, action time, and interaction duration. Functional near-infrared spectroscopy (fNIRS) was employed to measure cortical activity and functional connectivity. Additionally, graph theory metrics, including clustering coefficient, average path length, small-world index, global efficiency, degree centrality, and eigenvector centrality, were derived from fNIRS-based functional connectivity matrices. The results demonstrated that robotic intervention significantly improved clustering coefficient (P = 0.034), average path length (P = 0.007), and global efficiency (P = 0.001). While small-world index, degree centrality, and eigenvector centrality showed an increasing trend, these differences did not reach statistical significance. Moreover, fast-mode therapy induced more substantial changes in clustering coefficient compared to slow-mode therapy, suggesting a potentially stronger effect on neural reorganization. These findings preliminarily support the use of soft robotics with adjustable paradigms to enhance brain network connectivity and facilitate neuroplasticity following stroke. The observed improvements in global efficiency and small-world properties indicate that robotic therapy optimizes cortical organization, promoting functional recovery. Further studies with larger sample sizes and personalized intervention protocols are needed to confirm these results and explore their long-term effects.

Introduction

Stroke remains a leading cause of mortality and long-term disability for adults worldwide1. Among its debilitating sequelae, hand motor impairments significantly compromise essential daily activities and functional independence, thereby diminishing social participation and life quality in stroke survivors2,3. While physical medicine and rehabilitation are critical for motor recovery, conventional therapeutic approaches often yield inconsistent outcomes due to the complexity and interindividual variability of post-stroke neural reorganization4.

In recent years, robotic-assisted therapy (RAT) has gained significant attention for its ability to deliver high-intensity, repetitive, and task-specific training to facilitate motor recovery5. To tailor such interventions more effectively, a better understanding of the neural biomarkers is crucial for direct modulation by robotic interventions. This can ultimately help optimize therapeutic strategies and promote hand dexterity. In a previous study, voxel-based lesion symptom mapping has indicated that post-stroke proximal impairments of the upper limb could be associated with deficits in descending tracts from corticomotor areas and striatum. Distinct from this, hand impairments result from isolated injuries to the brain cortex6. However, clinical outcomes for hand function remain variable, and the neural mechanisms underlying motor recovery are not yet fully elucidated7,8,9. Moreover, most commercial robotic training follows predefined protocols with limited adaptability. It fails to address patient-specific needs and potentially constrains their clinical effectiveness10.

Over the past decades, neuroimaging studies have highlighted the pivotal role of brain network reorganization in stroke recovery, characterized by alterations in functional activity and connectivity. Functional near-infrared spectroscopy (fNIRS), a non-invasive technique for monitoring cortical hemodynamics, has emerged as a valuable tool for assessing neural dynamics and guiding rehabilitation settings11. Its portability and robustness against motion artifacts make it particularly well-suited for real-time monitoring and information transfer during robotic therapy12. While previous studies have documented changes in cortical interconnectivity following motor training, the effects of RAT on whole-brain network topology remain insufficiently explored13.

Characterizing changes in brain network topology offers a valuable window into the neuroplastic processes that underlie functional recovery after brain ischemic or hemorrhagic injuries. The human brain operates as a complex network, where neurological function or repair relies on both regional integrity and the dynamic interactions among multiple areas14. Under these circumstances, graph theory provides a robust framework for quantifying large-scale brain network architecture, offering insights into cortical organization at nodal levels15. By computing key network metrics such as clustering coefficient, average path length, and global efficiency, researchers may evaluate how RAT influences neural information processing and transfer capacity. As shown in previous literature, stroke-induced disruptions to network integrity often manifest as alterations in small-world properties and overall topological organization16,17. Rehabilitation, particularly task-specific and repetitive training, has been associated with cortical reorganization. However, the precise impact of varied robotic interventions on these network dynamics remains inadequately understood18.

This study aims to investigate the feasibility of a programmable soft pneumatic robot in modulating brain network dynamics in people who have had a stroke. Compared with hand flexion/extension exercise in existing robotic paradigms, the current pneumatic robot has advantages in customizing the working mode, action time, and interaction duration for the desired movements, such as grasp, release, and pinch19,20. These diverse modes of human-robot interaction may provide more targeted stimulation to the sensorimotor cortical areas. Such a paradigm can potentially promote neuroplasticity in regions associated with hand function recovery13. In addition, fNIRS-based graph theory analysis across diverse therapeutic conditions was utilized to assess how individualized robotic strategies may optimize hand motor recovery. Moreover, the neural mechanisms underlying soft robotic rehabilitation could be preliminarily elucidated. Understanding these network-level adaptations can inform the development of targeted, neurophysiology-driven rehabilitation protocols, ultimately enhancing hand recovery in stroke survivors.

Protocol

This study was approved by the Ethics Committee of the Tongji Hospital, Wuhan, China. All research protocols adhered to the principles outlined in the Declaration of Helsinki. Written informed consent was obtained from all participants before their inclusion in the study. The equipment and software used are listed in the Table of Materials.

1. Participants

1. Consider the following inclusion criteria:
   1. Age between 18 and 75 years.
   2. First-ever ischemic or hemorrhagic stroke confirmed by CT or MRI.
   3. Medically stable condition.
   4. Sign informed consent in person or from their authorized representative(s).
2. Consider the following exclusion criteria:
   1. Severe aphasia, cognitive impairments, consciousness disorders, or inability to cooperate with the intervention or assessment protocol.
   2. Inability to remain seated for at least 20 min.
   3. Severe muscle tone abnormalities (Modified Ashworth Scale score ≥2). (So, leaving survivors behind, which is a no-no in rehab. Cherry picking to make your intervention look better should not be allowed!)

2. Soft pneumatic robotic system

1. Equipment connection and startup
   1. Plug the power cable of the robotic system into an electrical outlet.
   2. Turn on the computer by pressing the button located on the computer tower.
   3. Connect the two air pump power cables to the power outlet and the soft pneumatic glove.
   4. Press the green power button on the back of the robotic system to activate the device.
2. Wearing the soft pneumatic robot
   1. Assist the participant in wearing the soft pneumatic robot on the affected hand, ensuring a secure fit around the palm and fingers.
   2. Fasten the robot using the Velcro strap, ensuring it is securely positioned from the dorsal side of the thumb to the thenar eminence on the palmar side for optimal stability.
3. Robotic control and mode selection
   1. Press the green Connect button below the imaging device.
   2. On the computer interface, select the Robotic Inhalation Mode.
   3. Configure the communication port (Com 7) to ensure proper device connectivity.
   4. Select the appropriate inflation-deflation mode and confirm parameter settings without causing discomfort to patients. Slow Mode: 2000 ms action time (0.5 Hz) with 75% interaction duration. Fast Mode: 200 ms action time (5Hz) with 75% interaction duration21 (Figure 1).

Figure 1: Experimental equipment and configuration. (A) Experimental setup. (B) Soft robotic configuration. (C) Soft pneumatic robot. (D) fNIRS configuration and calibration. Please click here to view a larger version of this figure.

3. Functional Near-Infrared Spectroscopy (fNIRS) system

1. Equipment connection and initialization
   1. Use the continuous wave fNIRS system to record data from all participants. The system emits near-infrared light at wavelengths of 690 nm and 830 nm, penetrating 2-3 cm beneath the cerebral cortex, with a