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The title of this Special Issue is: “Underlying Mechanisms and Neurorehabilitation of Gait after Stroke” [...]

With discoveries of brain and spinal cord mechanisms that control gait, and disrupt gait coordination after disease or injury, and that respond to motor training for those with neurological disease or injury, there is greater ability to construct more efficacious gait coordination training paradigms. Therefore, it is critical in these contemporary times, to use the most precise, sensitive, homogeneous (i.e., domain-specific), and comprehensive measures available to assess gait coordination, dyscoordination, and changes in response to treatment. Gait coordination is defined as the simultaneous performance of the spatial and temporal components of gait. While kinematic gait measures are considered the gold standard, the equipment and analysis cost and time preclude their use in most clinics. At the same time, observational gait coordination scales can be considered. Two independent groups identified the Gait Assessment and Intervention Tool (G.A.I.T.) as the most suitable scale for both research and clinical practice, compared to other observational gait scales, since it has been proven to be valid, reliable, sensitive to change, homogeneous, and comprehensive. The G.A.I.T. has shown strong reliability, validity, and sensitive precision for those with stroke or multiple sclerosis (MS). The G.A.I.T. has been translated into four languages (English, Spanish, Taiwanese, and Portuguese (translation is complete, but not yet published)), and is in use in at least 10 countries. As a contribution to the field, and in view of the evidence for continued usefulness and international use for the G.A.I.T. measure, we have provided this update, as well as an open access copy of the measure for use in clinical practice and research, as well as directions for administering the G.A.I.T.

The central nervous system (CNS) control of human gait is complex, including descending cortical control, affective ascending neural pathways, interhemispheric communication, whole brain networks of functional connectivity, and neural interactions between the brain and spinal cord. Many important studies were conducted in the past, which administered gait training using externally targeted methods such as treadmill, weight support, over-ground gait coordination training, functional electrical stimulation, bracing, and walking aids. Though the phenomenon of CNS activity-dependent plasticity has served as a basis for more recently developed gait training methods, neurorehabilitation gait training has yet to be precisely focused and quantified according to the CNS source of gait control. Therefore, we offer the following hypotheses to the field: Hypothesis 1. Gait neurorehabilitation after stroke will move forward in important ways if research studies include brain structural and functional characteristics as measures of response to treatment. Hypothesis 2. Individuals with persistent gait dyscoordination after stroke will achieve greater recovery in response to interventions that incorporate the current and emerging knowledge of CNS function by directly engaging CNS plasticity and pairing it with peripherally directed, plasticity-based motor learning interventions. These hypotheses are justified by the increase in the study of neural control of motor function, with emerging research beginning to elucidate neural factors that drive recovery. Some are developing new measures of brain function. A number of groups have developed and are sharing sophisticated, curated databases containing brain images and brain signal data, as well as other types of measures and signal processing methods for data analysis. It will be to the great advantage of stroke survivors if the results of the current state-of-the-art and emerging neural function research can be applied to the development of new gait training interventions.

Introduction: The purpose of this study was to compare the following three treatments of upper limb motor impairment in chronic moderate/severely impaired stroke survivors: 1) Customized whole limb motor learning (Whole ML), including shoulder/elbow robotics (ROB) and functional electrical stimulation (FES); 2) prescribed shoulder/elbow ROB + motor learning (S/E ROB ML); and 3) prescribed wrist/hand FES + ML (W/H FES ML). Methods: Subjects with chronic stroke were stratified according to relative level of impairment (Fugl-Meyer coordination Test) in wrist/hand versus shoulder/elbow. Their stratification category dictated the treatment group to which they were allocated, as follows: 1) WHOLE ML (n=18), equal distal and proximal impairment; 2) S/E ROB ML (n=10 ), greater shoulder/elbow impairment; and 3) FES ML (n=8), greater wrist/hand impairment. Treatment was 5 days/wk, 5 hrs/day, 60 sessions. Primary measure was the Arm Motor Assessment Tool, 13 complex tasks, Functional Scale (AMAT-F), acquired at baseline and at follow-up, 3 months after last treatment. Secondary measures were subscales, AMAT-F Shoulder/Elbow (AMAT-F S/E) and AMAT Wrist/Hand (AMAT-F W/H). Statistical analyses were Kruskal Wallace model to account for group size differences; group comparisons and within-group pre-/follow-up comparisons were analyzed (and multiple comparisons corrections). Results: All three groups had significant within-group gain for AMAT-F (p=.0007; p=.039; p=.047, respectively; similar results were shown for AMAT S/E subscale. Only the Whole ML group had significant gain in AMAT W/H (p=.007). Gain for all three groups exceeded the threshold for clinically significant gain in AMAT-F (.21 points), as follows: Whole ML (.66 points, 3 x’s the threshold); S/E ROB ML (.53 points, 2.5 x’s); W/H FES ML (.46 points, 2 x’s). Conclusion: For the customized whole limb motor learning group there was greater improvement in upper limb function versus the two groups that received prescribed, focused training for either shoulder/elbow or wrist/hand, according to the AMAT-F clinically significant threshold and according to the AMAT-F W/H subscale.