Background: While manually-assisted body-weight supported treadmill training (BWSTT) has revealed improved locomotor function in persons with post-stroke hemiparesis, outcomes are inconsistent and it is very labor intensive. Thus an alternate treatment approach is desirable. Objectives of this pilot study were to: 1) compare the efficacy of body-weight supported treadmill training (BWSTT) combined with the Lokomat robotic gait orthosis versus manually-assisted BWSTT for locomotor training post-stroke, and 2) assess effects of fast versus slow treadmill training speed.

After cerebral ischemia, disruption and subsequent reorganization of functional connections occur both locally and remote to the lesion. However, the unpredictable timing and extent of sensorimotor recovery reflects a gap in understanding of these underlying neural mechanisms. We aimed to identify plasticity of alpha-band functional neural connections within the perilesional area and the predictive value of functional connectivity with respect to motor recovery of the upper extremity after stroke. Our results show improvements in upper extremity motor recovery in relation to distributed changes in MEG-based alpha band functional connectivity, both in the perilesional area and contralesional cortex. Motor recovery was found to be predicted by increased connectivity at baseline in the ipsilesional somatosensory area, supplementary motor area, and cerebellum, contrasted with reduced connectivity of contralesional motor regions, after controlling for age, stroke onset-time and lesion size. These findings support plasticity within a widely distributed neural network and define brain regions in which the extent of network participation predicts post-stroke recovery potential

Plasticity after stroke has traditionally been studied by observing changes only in the spatial distribution and laterality of focal brain activation during affected limb movement. However, neural reorganization is multifaceted and our understanding may be enhanced by examining dynamics of activity within large-scale networks involved in sensorimotor control of the limbs. Here, we review functional connectivity as a promising means of assessing the consequences of a stroke lesion on the transfer of activity within large-scale neural networks. We first provide a brief overview of techniques used to assess functional connectivity in subjects with stroke. Next, we review task-related and resting-state functional connectivity studies that demonstrate a lesion-induced disruption of neural networks, the relationship of the extent of this disruption with motor performance, and the potential for network reorganization in the presence of a stroke lesion. We conclude with suggestions for future research and theories that may enhance the interpretation of changing functional connectivity. Overall findings suggest that a network level assessment provides a useful framework to examine brain reorganization and to potentially better predict behavioral outcomes following stroke.

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The participants in this study appeared more likely to be "pushed" toward using CAM by negative experiences with the health care system than to be "pulled" toward CAM by perceptions about its safety or congruence with their beliefs about health and illness.