Brain-Computer Interface-Robot Training Enhances Upper Extremity Performance and Changes the Cortical Activation in Stroke Patients: A Functional Near-Infrared Spectroscopy Study

Lingyu Liu; Minxia Jin; Linguo Zhang; Qiuzhen Zhang; Dunrong Hu; Lingjing Jin; Zhiyu Nie

doi:10.3389/fnins.2022.809657

Brain-Computer Interface-Robot Training Enhances Upper Extremity Performance and Changes the Cortical Activation in Stroke Patients: A Functional Near-Infrared Spectroscopy Study

Front Neurosci. 2022 Apr 8:16:809657. doi: 10.3389/fnins.2022.809657. eCollection 2022.

Authors

Lingyu Liu¹, Minxia Jin¹, Linguo Zhang¹, Qiuzhen Zhang¹, Dunrong Hu¹, Lingjing Jin¹, Zhiyu Nie²

Affiliations

¹ Department of Neurorehabilitation, Shanghai Yangzhi Rehabilitation Hospital, Shanghai Sunshine Rehabilitation Center, School of Medicine, Tongji University, Shanghai, China.
² Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China.

Abstract

Introduction: We evaluated the efficacy of brain-computer interface (BCI) training to explore the hypothesized beneficial effects of physiotherapy alone in chronic stroke patients with moderate or severe paresis. We also focused on the neuroplastic changes in the primary motor cortex (M₁) after BCI training.

Methods: In this study, 18 hospitalized chronic stroke patients with moderate or severe motor deficits participated. Patients were operated on for 20 sessions and followed up after 1 month. Functional assessments were performed at five points, namely, pre1-, pre2-, mid-, post-training, and 1-month follow-up. Wolf Motor Function Test (WMFT) was used as the primary outcome measure, while Fugl-Meyer Assessment (FMA), its wrist and hand (FMA-WH) sub-score and its shoulder and elbow (FMA-SE) sub-score served as secondary outcome measures. Neuroplastic changes were measured by functional near-infrared spectroscopy (fNIRS) at baseline and after 20 sessions of BCI training. Pearson correlation analysis was used to evaluate functional connectivity (FC) across time points.

Results: Compared to the baseline, better functional outcome was observed after BCI training and 1-month follow-up, including a significantly higher probability of achieving a clinically relevant increase in the WMFT full score (ΔWMFT score = 12.39 points, F = 30.28, and P < 0.001), WMFT completion time (ΔWMFT time = 248.39 s, F = 16.83, and P < 0.001), and FMA full score (ΔFMA-UE = 12.72 points, F = 106.07, and P < 0.001), FMA-WH sub-score (ΔFMA-WH = 5.6 points, F = 35.53, and P < 0.001), and FMA-SE sub-score (ΔFMA-SE = 8.06 points, F = 22.38, and P < 0.001). Compared to the baseline, after BCI training the FC between the ipsilateral M₁ and the contralateral M₁ was increased (P < 0.05), which was the same as the FC between the ipsilateral M₁ and the ipsilateral frontal lobe, and the FC between the contralateral M₁ and the contralateral frontal lobe was also increased (P < 0.05).

Conclusion: The findings demonstrate that BCI-based rehabilitation could be an effective intervention for the motor performance of patients after stroke with moderate or severe upper limb paresis and represents a potential strategy in stroke neurorehabilitation. Our results suggest that FC between ipsilesional M₁ and frontal cortex might be enhanced after BCI training.

Clinical trial registration: www.chictr.org.cn, identifier: ChiCTR2100046301.

Keywords: brain–computer interface; functional connectivity; functional near-infrared spectroscopy; stroke rehabilitation; upper extremity.