![]() ![]() 2– 4 Kinematic measures from gait analysis and posturography 5– 8 provide a more detailed and quantitative description of behaviors, but it is difficult to dissociate differences in kinematics due to neural versus musculoskeletal deficits. Behavioral tests like sit-to-stand, Timed Up and Go, and the Berg Balance Scale can only offer global and/or descriptive information about the behavior, such as the time for a person to complete or maintain a task. Moreover, an altered behavioral or kinematic outcome (eg, walking slowly) could be the result of multiple distinct neural abnormalities, and thus it is not a clear indication of what is altered at the neural level. 1 Even though such tests are descriptive of the overall motor behavior, they provide little information about the underlying differences between the healthy and impaired nervous system ( Figure 1). Such information could inform diagnostic tools and evidence-based interventions specifically targeted to a patient’s deficits.Īlthough neural dysfunction is central to many motor deficits, in diagnosis and rehabilitation of movement, clinical tests mainly focus on behavioral or kinematic outcomes. Muscle synergy analysis may thus offer the clinician a better view of the neural structure underlying motor behaviors and how they change in motor deficits and rehabilitation. For example, hemiparetic stroke patients exhibit differences in the number of muscle synergies, which may reflect disruptions in descending neural pathways and are correlated to deficits in motor function. These muscle synergies are hypothesized to represent motor modules recruited by the nervous system to flexibly perform biomechanical subtasks necessary for movement. Computational analyses can be used to extract muscle synergies from such datasets, revealing underlying patterns that may reflect different levels of neural function. Unfortunately electromyographic datasets can be large, highly variable, and difficult to interpret, precluding their clinical utility. Because muscle activity reflects motoneuron activity and generates the forces that produce behavioral outcomes, an analysis of muscle activity may provide a better understanding of the functional neural deficits in the impaired nervous system. However, altered behavioral or kinematic outcomes could be the result of multiple distinct neural abnormalities with very different muscle coordination patterns. Consequently, the majority of clinical tests focus on evaluating motor outputs at the behavioral and kinematic levels. Although neural dysfunction is central to many motor deficits, neural activity during movements is not directly measurable. We present a method called muscle synergy analysis, which can offer clinicians insight into both underlying neural strategies for movement and functional outcomes of muscle activity. ![]()
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