The Challenge: Execution of voluntary movements relies critically on the functional integration of several motor cortical areas and the spinal circuitries. Surprisingly, after decades of research, how the motor cortices specify descending neural signals destined to the spinal neurons has remained obscure. Our quest for understanding motor cortical functions is made additionally challenging by the fact that successful production of any movement necessitates coordination of many muscles representing many degrees of freedom. Recently there has been evidence suggesting that the central nervous system may coordinate muscle activations through a linear combination of muscle synergies, each of which activates a group of muscles as a unit. Previous work has shown that muscle synergies may be encoded entirely by spinal circuitries. We therefore hypothesize that descending motor cortical signals may function to select and activate muscle synergies encoded downstream of the cortex. A critical technological challenge for testing the above hypothesis is that of developing a suitable computational method for identifying muscle synergies, objectively and efficiently, from experimentally derived electromyographic (EMG) data of multiple muscles. With this proposal we plan on addressing this challenge and performing experiments on stroke patients for testing this hypothesis.
Specific Aims : To address the technological challenge described above, we plan on applying a recently developed multivariate analytic technique - the nonnegative matrix factorization (NMF) algorithm (Lee and Seung, 1999) - to identify muscle synergies embedded within the collected EMGs. Specifically, we will perform two experiments involving surface EMG recordings from patients with unilateral ischemic strokes in the motor cortices, which severely affect generation of voluntary actions;we will then identify muscle synergies from the collected EMGs using NMF to test our hypothesis of cortical activations of muscle synergies. In Experiment 1, we will test the robustness of muscle synergies in subacute stroke patients by recording EMGs (16 muscles) from each of the normal and stroke-affected arms during a variety of motor tasks. Our hypothesis predicts that cortical lesions should leave the muscular compositions of the synergies unaffected, and thus, the synergies of the normal and affected arms should be very similar to each other. In Experiment 2, we will characterize activation changes of muscle synergies during neurorehabilitation. We will record EMGs from the stroke-affected arm of subacute stroke patients as they undergo a 6-week neurorehabilitation therapy based on the Armeo system, a robotic training program that has been shown to be effective in improving the paretic arm's motor functions. Recordings will be conducted at 4 different time points along the course of therapy, and changes in the patient's clinical outcome will be correlated with changes in the activations of muscle synergies. This experiment will allow us to know whether EMG changes during post-stroke improvement of motor functions can be explained as changes in the activation pattern of selected synergies. Potential Scientific and Clinical Impact: This proposal offers and tests the new hypothesis that the human motor cortex produces voluntary movements by selecting and activating muscle synergies encoded downstream of the cortex. Our proposed view, if true, implies that the many previously observed correlations between motor cortical activities and various movement parameters may be phenomena secondary to synergy activations. This change in perspective from one based on movement parameters to one based on muscle synergies amounts to a paradigm shift in our understanding of the motor cortex. Clinically, the view that muscle synergies are basic units for movement execution suggests that a rehabilitation program focusing on those synergies whose activations are altered by the stroke lesions may lead to better treatment outcome than non-specific physical therapies or methods focusing on individual muscles. Our use of a factorization algorithm offers a means to objectively identify the troubled muscle synergy as a specific target for interventions such as neuromuscular electrical stimulation techniques or other biofeedback-based methods. This new neurorehabilitation possibility may lead to a better treatment efficacy for severe and/or chronic stroke patients. Considering that in the US, ~700,000 individuals suffer from a new or recurrent stroke every year, the potential clinical impact of this project is highly relevant to the goals of NIMH and NIH.
This proposal plans on studying how the human motor cortices translate motor intentions to commands for muscle activations, as well as how the brain restructures motor programs after a stroke injury for improvement of limb functions. Thus, the knowledge gained from this research is highly relevant to future designs and strategies for neurorehabilitation for patients suffering from severe and/or chronic stroke. Every year, there are approximately 700,000 new or recurrent cases of stroke in the US alone, and stroke has been the leading cause of long term disability;considering these statistics, this project is extremely relevant to public health and the missions of the NIH.
|Coscia, Martina; Cheung, Vincent C K; Tropea, Peppino et al. (2014) The effect of arm weight support on upper limb muscle synergies during reaching movements. J Neuroeng Rehabil 11:22|
|Devarajan, Karthik; Cheung, Vincent C K (2014) On nonnegative matrix factorization algorithms for signal-dependent noise with application to electromyography data. Neural Comput 26:1128-68|
|Cheung, Vincent C K; Deboer, Caroline; Hanson, Elizabeth et al. (2013) Gene expression changes in the motor cortex mediating motor skill learning. PLoS One 8:e61496|
|Cheung, Vincent C K; Turolla, Andrea; Agostini, Michela et al. (2012) Muscle synergy patterns as physiological markers of motor cortical damage. Proc Natl Acad Sci U S A 109:14652-6|