Time synchronization between parietal–frontocentral connectivity with MRCP and gait in post-stroke bipedal tasks

• Published in: Journal of neuroengineering and rehabilitation Vol. 21; no. 1; pp. 101 - 16
• Main Authors: Phang, Chun-Ren, Su, Kai-Hsiang, Cheng, Yuan-Yang, Chen, Chia-Hsin, Ko, Li-Wei
• Format: Journal Article
• Language: English
• Published: London BioMed Central 13.06.2024; BioMed Central Ltd; BMC
• Subjects: Adult; Aged; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedicine; Brain–computer interface; Electroencephalography; Evoked Potentials, Motor - physiology; Female; Frontal Lobe - physiology; Frontal Lobe - physiopathology; Functional connectivity; Gait - physiology; Humans; Male; Middle Aged; Motor Cortex - physiology; Motor Cortex - physiopathology; Motor execution; Motor preparation; Neurology; Neurophysiology; Neurosciences; Parietal Lobe - physiology; Parietal Lobe - physiopathology; Political aspects; Post-stroke; Rehabilitation; Rehabilitation Medicine; Stroke - complications; Stroke - physiopathology; Stroke Rehabilitation; Support Vector Machine; Functional connectivity; Motor execution; Post-stroke; Brain–computer interface; Motor preparation
• ISSN: 1743-0003; 1743-0003
• DOI: 10.1186/s12984-024-01330-z

Background In post-stroke rehabilitation, functional connectivity (FC), motor-related cortical potential (MRCP), and gait activities are common measures related to recovery outcomes. However, the interrelationship between FC, MRCP, gait activities, and bipedal distinguishability have yet to be investigated. Methods Ten participants were equipped with EEG devices and inertial measurement units (IMUs) while performing lower limb motor preparation (MP) and motor execution (ME) tasks. MRCP, FCs, and bipedal distinguishability were extracted from the EEG signals, while the change in knee degree during the ME phase was calculated from the gait data. FCs were analyzed with pairwise Pearson’s correlation, and the brain-wide FC was fed into support vector machine (SVM) for bipedal classification. Results Parietal–frontocentral connectivity (PFCC) dysconnection and MRCP desynchronization were related to the MP and ME phases, respectively. Hemiplegic limb movement exhibited higher PFCC strength than nonhemiplegic limb movement. Bipedal classification had a short-lived peak of 75.1% in the pre-movement phase. These results contribute to a better understanding of the neurophysiological functions during motor tasks, with respect to localized MRCP and nonlocalized FC activities. The difference in PFCCs between both limbs could be a marker to understand the motor function of the brain of post-stroke patients. Conclusions In this study, we discovered that PFCCs are temporally dependent on lower limb gait movement and MRCP. The PFCCs are also related to the lower limb motor performance of post-stroke patients. The detection of motor intentions allows the development of bipedal brain-controlled exoskeletons for lower limb active rehabilitation.