A lateralized motor network in order to understand adaptation to visuomotor rotation.

Objective.The functional asymmetry between the two brain hemispheres in language and spatial processing is well documented. However, a description of difference in control between the two hemispheres in motor function is not well established. Our primary objective in this study was to examine the distribution of control in the motor hierarchy and its variation across hemispheres.Approach.We developed a computation model termed the bilateral control network and implemented the same in a neural network framework to be used to replicate certain experimental results. The network consists of a simple arm model capable of making movements in 2D space and a motor hierarchy with separate elements coding target location, estimated position of arm, direction, and distance to be moved by the arm, and the motor command sent to the arm. The main assumption made here is the division of direction and distance coding between the two hemispheres with distance coded in the non-dominant and direction coded in the dominant hemisphere.Main results.With this assumption, the network was able to show main results observed in visuomotor adaptation studies. Importantly it showed decrease in error exhibited by the untrained arm while the other arm underwent training compared to the corresponding naïve arm's performance-transfer of motor learning from trained to the untrained arm. It also showed how this varied depending on the performance variable used-with distance as the measure, the non-dominant arm showed transfer and with direction, dominant arm showed transfer.Significance.Our results indicate the possibility of shared control between the two hemispheres. If indeed found true, this result could have major significance in motor rehabilitation as treatment strategies will need to be designed in order to account for this and can no longer be confined to the arm contralateral to the affected hemisphere.

Interaction of network and rehabilitation therapy parameters in defining recovery after stroke in a Bilateral Neural Network.

BACKGROUND: Restoring movement after hemiparesis caused by stroke is an ongoing challenge in the field of rehabilitation. With several therapies in use, there is no definitive prescription that optimally maps parameters of rehabilitation with patient condition. Recovery gets further complicated once patients enter chronic phase. In this paper, we propose a rehabilitation framework based on computational modeling, capable of mapping patient characteristics to parameters of rehabilitation therapy. METHOD: To build such a system, we used a simple convolutional neural network capable of performing bilateral reaching movements in 3D space using stereovision. The network was designed to have bilateral symmetry to reflect the bilaterality of the cerebral hemispheres with the two halves joined by cross-connections. This network was then modified according to 3 chosen patient characteristics-lesion size, stage of recovery (acute or chronic) and structural integrity of cross-connections (analogous to Corpus Callosum). Similarly, 3 parameters were used to define rehabilitation paradigms-movement complexity (Exploratory vs Stereotypic), hand selection mode (move only affected arm, CIMT vs move both arms, BMT), and extent of plasticity (local vs global). For each stroke condition, performance under each setting of the rehabilitation parameters was measured and results were analyzed to find the corresponding optimal rehabilitation protocol. RESULTS: Upon analysis, we found that regardless of patient characteristics network showed better recovery when high complexity movements were used and no significant difference was found between the two hand selection modes. Contrary to these two parameters, optimal extent of plasticity was influenced by patient characteristics. For acute stroke, global plasticity is preferred only for larger lesions. However, for chronic, plasticity varies with structural integrity of cross-connections. Under high integrity, chronic prefers global plasticity regardless of lesion size, but with low integrity local plasticity is preferred. CONCLUSION: Clinically translating the results obtained, optimal recovery may be observed when paretic arm explores the available workspace irrespective of the hand selection mode adopted. However, the extent of plasticity to be used depends on characteristics of the patient mainly stage of stroke and structural integrity. By using systems as developed in this study and modifying rehabilitation paradigms accordingly it is expected post-stroke recovery can be maximized.

A Cortico- Basal Ganglia Model for choosing an optimal rehabilitation strategy in Hemiparetic Stroke.

To facilitate the selection of an optimal therapy for a stroke patient with upper extremity hemiparesis, we propose a cortico-basal ganglia model capable of performing reaching tasks under normal and stroke conditions. The model contains two hemispherical systems, each organized into an outer sensory-motor cortical loop and an inner basal ganglia (BG) loop, controlling their respective hands. The model is trained to simulate two therapeutic approaches: the constraint induced movement therapy (CIMT) in which the intact is arrested, and Bimanual Reaching in which the movements of the intact arm are found to aid the affected arm. Which of these apparently mutually conflicting approaches is right for a given patient? Based on our study on the effect of lesion size on arm performance, we hypothesize that the choice of the therapy depends on the lesion size. Whereas bimanual reaching is more suitable for smaller lesion size, CIMT is preferred in case of larger lesion sizes. By virtue of the model's ability to capture the experimental results effectively, we believe that it can serve as a benchmark for the development and testing of various rehabilitation strategies for stroke.