Long-term Training With a Brain-Machine Interface-Based Gait Protocol Induces Partial Neurological Recovery in Paraplegic Patients

Liu, Jingyi BA; Abd-El-Barr, Muhammad MD, PhD; Chi, John H. MD

doi: 10.1227/01.neu.0000508601.15824.39
Science Times

Brigham & Women's Hospital, Boston, Massachusetts

Article Outline

Spinal cord injury (SCI) rehabilitation remains a major clinical challenge, especially in cases involving chronic, complete injury. Existing interventions for assisting patients with SCI in walking, including body weight support systems, robotic assistance, and functional electrostimulation of the legs, have not shown evidence of generating significant clinical improvement in somatosensory function below the level of the injury. In the past 2 decades, brain-machine interfaces (BMIs) have become popular tools for restoring limb function in paralyzed patients, although no study has suggested that long-term training with BMI-based paradigms and physical training could trigger neurological recovery, particularly in patients with complete SCI.1 However, the prospect of neurological recovery is supported by postmortem anatomical studies that have shown that 60% to 80% of patients with “complete” SCI show viable axons crossing the level of the SCI. In this study, Donati et al2 show partial neurological and clinical improvement in patients with SCI subjected to long-term training with a multistage BMI-based gait protocol called the Walk Again Neurorehabilitation protocol (WA-NR).

Donati et al implemented WA-NR in 8 patients with chronic (> 1 year) paraplegic SCI. Seven patients had complete SCI (American Spinal Injury Association Impairment Scale A), and 1 patient had partial SCI (American Spinal Injury Association Impairment Scale B). The 6-component protocol of WA-NR (Figure, A) started with seated virtual reality and progressed to gait training with a brain-controlled exoskeleton.

Although the original goal of the study was to explore how much such a long-term BMI-based protocol could help patients with SCI regain their ability to walk autonomously using the brain-controlled exoskeleton, the scientists realized after the first 12 months of training that all patients experienced a significant clinical improvement in their ability to perceive somatic sensations and to exert voluntary motor control below the original SCI. Sensory recovery was more vigorous and consistent for nociceptive perception (>5 dermatomes on average) than for tactile, vibration, or proprioception (1-2 dermatomes) and temperature (no significant improvement). Improvements were clinically significant after 7 months, peaking at the 10th month of training. Figure, B summarizes improvements in walking ability as measured by the Walking Index Spinal Cord Injury.

The authors propose that even in SCI up to 27% of the total area of spinal cord white matter may be preserved. Direct brain control of virtual or robotic legs and a continuous stream of tactile stimulation feedback from the legs and robotic actuators may induce plasticity through activation of central pattern generators and cortical afferents in patients with SCI. The full extent to which this mechanism of recovery can occur is of course still unclear. Other factors such as the timing of intervention and plasticity modulators that are still ill defined are, of course, unanswered questions at the current time.

Nonetheless, this is the first clinical study to report the occurrence of consistent, reproducible, and significant partial neurological recovery in multiple patients with chronic SCI. These findings suggest that long-term exposure to BMI-based protocols enriched with tactile feedback and combined with robotic gait training may induce cortical and subcortical plasticity even in patients diagnosed with chronic SCI. The results obtained in the study suggest that BMI applications should be upgraded from a type of assistive technology to improve mobility to a potentially new neurorehabilitation therapy. It is indeed an exciting time to be involved in the care of these complicated patients.

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1. Lebedev MA, Nicolelis MA. Brain-machine interfaces: past, present and future. Trends Neurosci. 2006;29(9):536–546.
2. Donati ARC, Shokur S, Morya E, et al. Long-term training with a brain-machine interface-based gait protocol induces partial neurological recovery in paraplegic patients. Sci Rep. 2016;6(30383):1–16.
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