Kelly P. Westlake

Pilot study of Lokomat versus manual-assisted treadmill training for locomotor recovery post-stroke.

BackgroundWhile 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.MethodsSixteen volunteers with chronic hemiparetic gait (0.62 ± 0.30 m/s) post-stroke were randomly allocated to Lokomat (n = 8) or manual-BWSTT (n = 8) 3×/wk for 4 weeks. Groups were also stratified by fast (mean 0.92 ± 0.15 m/s) or slow (0.58 ± 0.12 m/s) training speeds. The primary outcomes were self-selected overground walking speed and paretic step length ratio. Secondary outcomes included: fast overground walking speed, 6-minute walk test, and a battery of clinical measures.ResultsNo significant differences in primary outcomes were revealed between Lokomat and manual groups as a result of training. However, within the Lokomat group, self-selected walk speed, paretic step length ratio, and four of the six secondary measures improved (p = 0.04–0.05, effect sizes = 0.19–0.60). Within the manual group, only balance scores improved (p = 0.02, effect size = 0.57). Group differences between fast and slow training groups were not revealed (p ≥ 0.28).ConclusionResults suggest that Lokomat training may have advantages over manual-BWSTT following a modest intervention dose in chronic hemiparetic persons and further, that our training speeds produce similar gait improvements. Suggestions for a larger randomized controlled trial with optimal study parameters are provided.

Resting state alpha-band functional connectivity and recovery after stroke

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 the 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.

Functional connectivity in relation to motor performance and recovery after stroke.

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.

Capturing subject variability in fMRI data: A graph-theoretical analysis of GICA vs. IVA

BACKGROUND Recent studies using simulated functional magnetic resonance imaging (fMRI) data show that independent vector analysis (IVA) is a superior solution for capturing spatial subject variability when compared with the widely used group independent component analysis (GICA). Retaining such variability is of fundamental importance for identifying spatially localized group differences in intrinsic brain networks. NEW METHODS Few studies on capturing subject variability and order selection have evaluated real fMRI data. Comparison of multivariate components generated by multiple algorithms is not straightforward. The main difficulties are finding concise methods to extract meaningful features and comparing multiple components despite lack of a ground truth. In this paper, we present a graph-theoretical (GT) approach to effectively compare the ability of multiple multivariate algorithms to capture subject variability for real fMRI data for effective group comparisons. The GT approach is applied to components generated from fMRI data, collected from individuals with stroke, before and after a rehabilitation intervention. COMPARISON WITH EXISTING METHOD IVA is compared with widely used GICA for the purpose of group discrimination in terms of GT features. In addition, masks are applied for motor related components generated by both algorithms. CONCLUSIONS Results show that IVA better captures subject variability producing more activated voxels and generating components with less mutual information in the spatial domain than Group ICA. IVA-generated components result in smaller p-values and clearer trends in GT features.

Complex-Value Coherence Mapping Reveals Novel Abnormal Resting-State Functional Connectivity Networks in Task-Specific Focal Hand Dystonia

Resting-state imaging designs are powerful in modeling functional networks in movement disorders because they eliminate task performance related confounds. However, the most common metric for quantifying functional connectivity, i.e., bivariate magnitude coherence (Coh), can sometimes be contaminated by spurious correlations in blood-oxygen level dependent (BOLD) signal due to smoothing and seed blur, thereby limiting the identification of true interactions between neighboring neural populations. Here, we apply a novel functional connectivity metric., i.e., imaginary coherence (ICoh), to BOLD fMRI data in healthy individuals and patients with task-specific focal hand dystonia (tspFHD), in addition to the traditional magnitude Coh metric. We reconstructed resting-state sensorimotor, basal ganglia, and default-mode networks using both Coh and ICoh. We demonstrate that indeed the ICoh metric eliminates spatial blur around seed placement and reflects slightly different networks from Coh. We then identified significant reductions in resting-state connectivity within both the sensorimotor and basal ganglia networks in patients with tspFHD, primarily in the hemisphere contralateral to the affected hand. Collectively, these findings direct our attention to the fact that multiple networks are decoupled in tspFHD that can be unraveled by different functional connectivity metrics, and that this aberrant communication contributes to clinical deficits in the disorder.

Neural plasticity and implications for hand rehabilitation after neurological insult.

Experience dependent plasticity refers to ability of the brain to adapt to new experiences by changing its structure and function. The purpose of this paper is to provide a brief review the neurophysiological and structural correlates of neural plasticity that occur during and following motor learning. We also consider that the extent of plastic reorganization is dependent upon several key principals and that the resulting behavioral consequences can be adaptive or maladaptive. In light of this research, we conclude that an increased understanding of the complexities of brain plasticity will translate into enhanced treatment opportunities for the clinician to optimize hand function.

Towards a Robotic Hand Rehabilitation Exoskeleton for Stroke Therapy

A majority of stroke patients suffer from the loss of effective motor function, which compromises their ability to control grasping motion. Hand rehabilitation is therefore important to improve their motor function and quality of life in activities of daily living (ADLs). In this initial work, we present the design and development of a partial hand exoskeleton actuated by shape memory alloy (SMA) spring actuators. The SMA spring actuators are cooled by forced convection and the individual joints of the finger are actuated via tendons. In this design, pre-tension in the passive springs enables the restoration of the original configuration when the SMA springs are not actuated. To address the slow cooling rate of SMA springs that limits the control performance, we developed a cooling unit for each SMA spring actuator. We utilized computer vision to identify an object and provide 3-D coordinates of the optimal grasping points on the object. We then utilized vision-based control to move the fingertips towards the grasping points. The experimental results showed that each individual joint was able to return to its original configuration significantly faster as well as to follow a sinusoidal trajectory with the proposed cooling strategy.Copyright

Capturing subject variability in data driven fMRI analysis: A graph theoretical comparison

Recent simulation studies, using functional magnetic resonance imaging (fMRI) like data, have shown that independent vector analysis (IVA) is a superior solution for capturing subject variability when compared to the popular group independent component analysis. This is of fundamental importance for identifying group differences which is a common goal of medical research. Nevertheless, there have not been similar studies on the effectiveness of IVA using real fMRI data. The main difficulties when working with real data are the lack of a ground truth and the high variability among subjects when performing the analysis. In this paper, we present a graph-theoretic approach to effectively compare an algorithms ability to capture subject variability for real fMRI data and also address the important issue of order selection for capturing subject variability.

Link Between Parkinson Disease and Rapid Eye Movement Sleep Behavior Disorder With Dream Enactment: Possible Implications for Early Rehabilitation

The purpose of this article is 2-fold: first, to inform readers of the link between the loss of motor inhibition during rapid eye movement (REM) sleep dreaming, diagnosed as REM sleep behavior disorder, and the future onset of neurodegenerative disorders, such as Parkinson disease and dementia with Lewy bodies; it has been reported that motor disinhibition during REM sleep often precedes the onset of these disorders by years or even decades; second, to consider that the identification of REM sleep behavior disorder and the early involvement of rehabilitation and/or development of home exercise plans may aid in prolonging and even increasing function, independence, and quality of life, should such neurodegenerative disorders develop later in life.

Handedness results from Complementary Hemispheric Dominance, not Global Hemispheric Dominance: Evidence from Mechanically Coupled Bilateral Movements

Two contrasting views of handedness can be described as 1) complementary dominance, in which each hemisphere is specialized for different aspects of motor control, and 2) global dominance, in which the hemisphere contralateral to the dominant arm is specialized for all aspects of motor control. The present study sought to determine which motor lateralization hypothesis best predicts motor performance during common bilateral task of stabilizing an object (e.g., bread) with one hand while applying forces to the object (e.g., slicing) using the other hand. We designed an experimental equivalent of this task, performed in a virtual environment with the unseen arms supported by frictionless air-sleds. The hands were connected by a spring, and the task was to maintain the position of one hand while moving the other hand to a target. Thus the reaching hand was required to take account of the spring load to make smooth and accurate trajectories, while the stabilizer hand was required to impede the spring load to keep a constant position. Right-handed subjects performed two task sessions (right-hand reach and left-hand stabilize; left-hand reach and right-hand stabilize) with the order of the sessions counterbalanced between groups. Our results indicate a hand by task-component interaction such that the right hand showed straighter reaching performance whereas the left hand showed more stable holding performance. These findings provide support for the complementary dominance hypothesis and suggest that the specializations of each cerebral hemisphere for impedance and dynamic control mechanisms are expressed during bilateral interactive tasks. NEW & NOTEWORTHY We provide evidence for interlimb differences in bilateral coordination of reaching and stabilizing functions, demonstrating an advantage for the dominant and nondominant arms for distinct features of control. These results provide the first evidence for complementary specializations of each limb-hemisphere system for different aspects of control within the context of a complementary bilateral task.