Motor cortex proactively drives contralateral swing leg muscles during treadmill walking, counter to the traditional view of stereotyped human locomotion.
Ischemic damage to the brain triggers substantial reorganization of spared areas and pathways, which is associated with limited, spontaneous restoration of function. A better understanding of this plastic remodeling is crucial to develop more effective strategies for stroke rehabilitation. In this review article, we discuss advances in the comprehension of post-stroke network reorganization in patients and animal models. We first focus on rodent studies that have shed light on the mechanisms underlying neuronal remodeling in the perilesional area and contralesional hemisphere after motor cortex infarcts. Analysis of electrophysiological data has demonstrated brain-wide alterations in functional connectivity in both hemispheres, well beyond the infarcted area. We then illustrate the potential use of non-invasive brain stimulation (NIBS) techniques to boost recovery. We finally discuss rehabilitative protocols based on robotic devices as a tool to promote endogenous plasticity and functional restoration.
Background
To assist people with disabilities, exoskeletons must be provided with human-robot interfaces and smart algorithms capable to identify the user’s movement intentions. Surface electromyographic (sEMG) signals could be suitable for this purpose, but their applicability in shared control schemes for real-time operation of assistive devices in daily-life activities is limited due to high inter-subject variability, which requires custom calibrations and training. Here, we developed a machine-learning-based algorithm for detecting the user’s motion intention based on electromyographic signals, and discussed its applicability for controlling an upper-limb exoskeleton for people with severe arm disabilities
.
Methods
Ten healthy participants, sitting in front of a screen while wearing the exoskeleton, were asked to perform several reaching movements toward three LEDs, presented in a random order. EMG signals from seven upper-limb muscles were recorded. Data were analyzed offline and used to develop an algorithm that identifies the onset of the movement across two different events: moving from a resting position toward the LED (
Go-forward
), and going back to resting position (
Go
-
backward
). A set of subject-independent time-domain EMG features was selected according to information theory and their probability distributions corresponding to rest and movement phases were modeled by means of a two-component Gaussian Mixture Model (GMM). The detection of movement onset by two types of detectors was tested: the first type based on features extracted from single muscles, whereas the second from multiple muscles. Their performances in terms of sensitivity, specificity and latency were assessed for the two events with a leave one-subject out test method.
Results
The onset of movement was detected with a maximum sensitivity of 89.3% for
Go-forward
and 60.9% for
Go-backward
events. Best performances in terms of specificity were 96.2 and 94.3% respectively. For both events the algorithm was able to detect the onset before the actual movement, while computational load was compatible with real-time applications.
Conclusions
The detection performances and the low computational load make the proposed algorithm promising for the control of upper-limb exoskeletons in real-time applications. Fast initial calibration makes it also suitable for helping people with severe arm disabilities in performing assisted functional tasks.
Focal cortical stroke often leads to persistent motor deficits, prompting the need for more effective interventions. The efficacy of rehabilitation can be increased by ‘plasticity-stimulating’ treatments that enhance experience-dependent modifications in spared areas. Transcallosal pathways represent a promising therapeutic target, but their role in post-stroke recovery remains controversial. Here, we demonstrate that the contralesional cortex exerts an enhanced interhemispheric inhibition over the perilesional tissue after focal cortical stroke in mouse forelimb motor cortex. Accordingly, we designed a rehabilitation protocol combining intensive, repeatable exercises on a robotic platform with reversible inactivation of the contralesional cortex. This treatment promoted recovery in general motor tests and in manual dexterity with remarkable restoration of pre-lesion movement patterns, evaluated by kinematic analysis. Recovery was accompanied by a reduction of transcallosal inhibition and ‘plasticity brakes’ over the perilesional tissue. Our data support the use of combinatorial clinical therapies exploiting robotic devices and modulation of interhemispheric connectivity.
These results support the use of kinematic analysis in mice as a tool for both detection of poststroke functional impairments and tracking of motor improvements following rehabilitation. Similar studies could be performed in parallel with human studies to exploit the translational value of this skilled reaching analysis.
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