Neural interface-based motor neuroprosthesis in post-stroke upper limb neurorehabilitation: An individual patient data meta-analysis

Yu Tung Lo; Mervyn Jun Rui Lim; Chun Yen Kok; Shilin Wang; Sebastiaan Zhiyong Blok; Ting Yao Ang; Vincent Yew Poh Ng; Jai Prashanth Rao; Karen Sui Geok Chua

doi:10.1016/j.apmr.2024.04.001

Neural interface-based motor neuroprosthesis in post-stroke upper limb neurorehabilitation: An individual patient data meta-analysis

Arch Phys Med Rehabil. 2024 Apr 3:S0003-9993(24)00910-9. doi: 10.1016/j.apmr.2024.04.001. Online ahead of print.

Authors

Yu Tung Lo¹, Mervyn Jun Rui Lim², Chun Yen Kok³, Shilin Wang³, Sebastiaan Zhiyong Blok⁴, Ting Yao Ang³, Vincent Yew Poh Ng³, Jai Prashanth Rao⁵, Karen Sui Geok Chua⁶

Affiliations

¹ Department of Neurosurgery, National Neuroscience Institute, Singapore; Duke-NUS Medical School, Singapore. Electronic address: jacklo@nus.edu.sg.
² Department of Neurosurgery, National University Hospital, Singapore.
³ Department of Neurosurgery, National Neuroscience Institute, Singapore.
⁴ National University of Singapore, Yong Loo Lin School of Medicine, Singapore.
⁵ Department of Neurosurgery, National Neuroscience Institute, Singapore; Duke-NUS Medical School, Singapore.
⁶ National University of Singapore, Yong Loo Lin School of Medicine, Singapore; Institute of Rehabilitation Excellence, Tan Tock Seng Hospital Rehabilitation Centre, Singapore; Lee Kong Chian School of Medicine, Nanyang Technological University.

PMID: 38579958
DOI: 10.1016/j.apmr.2024.04.001

Abstract

Objective: To determine the efficacy of neural interface-, including brain-computer interface (BCI), based neurorehabilitation through conventional and individual patient data (IPD) meta-analysis, and to assess clinical parameters associated with positive response to neural interface-based neurorehabilitation.

Data sources: PubMed, EMBASE, and Cochrane Library databases up to February 2022 were reviewed.

Study selection: Studies using neural interface-controlled physical effectors (FES and/or powered exoskeletons) and reported Fugl-Meyer Assessment-upper extremity (FMA-UE) scores were identified. This meta-analysis was prospectively registered on PROSPERO (#CRD42022312428). PRISMA guidelines were followed.

Data extraction: Change in FMA-UE scores were pooled to estimate the mean effect size. Subgroup analyses were performed on clinical parameters and neural interface parameters with both study-level variables and IPD.

Data synthesis: Forty-six studies containing 617 patients were included. Twenty-nine studies involving 214 patients reported IPD. FMA-UE score increased by a mean of 5.23 (95% CI: 3.85 to 6.61). Systems that used motor attempt resulted in greater FMA-UE gain than motor imagery, as did training lasting >4 versus ≤4 weeks. On IPD analysis, the mean time-to-improvement above MCID was 12 weeks (95% CI: 7 to not reached). At 6 months, 58% improved above MCID (95% CI: 41 to 70%). Patients with severe impairment (p=0.042) and age >50 years (p=0.0022) correlated with the failure to improve above the MCID on univariate log-rank tests. However, these factors were only borderline significant on multivariate Cox analysis (HR 0.15, p = 0.08 and HR 0.47, p = 0.06, respectively).

Conclusion: Neural interface-based motor rehabilitation resulted in significant though modest reductions in post-stroke impairment and should be considered for wider applications in stroke neurorehabilitation.

Keywords: BCI; BMI; brain-computer interface; brain-machine interface; neuroprosthesis; neurorehabilitation; stroke.

Publication types

Review