Stroke is the leading cause of disability in the United States. Despite the high incidence and mortality of stroke, sensitive and specific brain-based biomarkers predicting persisting disabilities are lacking. Both neuroimaging techniques like electroencephalography (EEG) and non-invasive brain stimulation (NIBS) techniques such as transcranial magnetic stimulation (TMS) have proven useful in predicting prognosis, recovery trajectories and response to rehabilitation in individuals with stroke. We propose, however, that additional synergetic effects can be achieved by simultaneously combining both approaches. Combined TMS-EEG is able to activate discrete cortical regions and directly assess local cortical reactivity and effective connectivity within the network independent of the integrity of descending fiber pathways and also outside the motor system. Studying cortical reactivity and connectivity in patients with stroke TMS-EEG may identify salient neural mechanisms underlying motor disabilities and lead to novel biomarkers of stroke pathophysiology which can then be used to assess, monitor, and refine rehabilitation approaches for individuals with significant disability to improve outcomes and quality of life after stroke.

When walking on a split-belt treadmill where one belt moves faster than the other, the nervous system consistently attempts to maintain symmetry between legs, quantified as deviation from double support time or step length symmetry. It is known that the cerebellum plays a critical role in locomotor adaptation. Less is known about the role of corticospinal drive in maintaining this type of proprioceptive-driven locomotor adaptation. The objective of this study was to examine the functional role of oscillatory drive in relation to changes in spatiotemporal gait parameters during split-belt walking adaptation. Eighteen healthy participants adapted and deadapted on a split-belt treadmill; 13 out of 18 participants repeated the paradigm two more times to examine the effects of reexposure. Coherence analysis was used to quantify the coupling between electromyography (EMG) from the proximal (TAprox) and distal tibialis anterior (TAdist) muscle during the swing phase of walking. EMG-EMG coherence was examined within the alpha (8–15 Hz), beta (15–30 Hz), and gamma (30–45 Hz) frequencies. Our results showed that 1) beta- and gamma-band coherence (markers of corticospinal drive) increased during early split-belt walking compared with baseline walking in the slow leg, 2) beta-band coherence decreased from early to late split-belt adaptation in the fast leg, 3) alpha-, beta-, and gamma-band coherence decreased from first to third split-belt exposure in the fast leg, and 4) there was a relationship between higher beta coherence in the slow leg TA and smaller double support asymmetry. Our results suggest that corticospinal drive may play a functional role in the temporal control of split-belt walking adaptation. NEW & NOTEWORTHY This is the first study to examine the functional role of intramuscular coherence in relation to changes in spatiotemporal gait parameters during split-belt walking adaptation. We found that the corticospinal drive measured by intramuscular coherence in tibialis anterior changes with adaptation and that the corticospinal drive is related to temporal but not spatial parameters. This study may give insight as to the specific role of the motor cortex during gait.

Walking patterns are adaptable in response to different environmental demands, which requires neural input from spinal and supraspinal structures. With an increase in age, there are changes in walking adaptation and in the neural control of locomotion, but the age-related changes in the neural control of locomotor adaptation is unclear. The purpose of this narrative review is to establish a framework where the age-related changes of neural control of human locomotor adaptation can be understood in terms of reactive feedback and predictive feedforward control driven by sensory feedback during locomotion. We parse out the effects of aging on (a) reactive adaptation to split-belt walking, (b) predictive adaptation to split-belt walking, (c) reactive visuomotor adaptation, and (d) predictive visuomotor adaptation, and hypothesize that specific neural circuits are influenced differentially with age, which influence locomotor adaptation. The differences observed in the age-related changes in walking adaptation across different locomotor adaptation paradigms will be discussed in light of the age-related changes in the neural mechanisms underlying locomotion.

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