![]() ![]() The precise mechanisms contributing to these motor symptoms in PD remain largely unknown. Mean (standard deviation) of the Pearson correlation coefficients between homologous temporal components and mean (standard deviation) of the normalized scalar products between weight coefficients are reported on both features of muscle synergies. Muscle synergy analysis may represent a new framework to examine the pathophysiological basis of specific motor symptoms in Parkinson’s disease (PD), including balance and gait disorders that are often unresponsive to treatment. Maximum muscle activities were compared using non- parametric analysis of variance. The Panel B shows primitive signals (on the left) and weight coefficients for homologous muscle synergies retained according to both criteria (the dark gray is for the eigenvalue > 1 and the light gray is for the eigenvalue > 0.5), and related to the fastest pace. Non-negative matrix factorization was used to compute muscle synergies. Through the statistical analysis, we found that Synergy 1 (which comprised about half of total variance accounted for all synergies) was highly correlated. ![]() The explained cumulative variance refers to the fastest walking speed. Mean numbers of synergies during level running at 2.5, 3.3, and 4.1 m/s were 4.1 0.6, 4.3 0.5, and 4.2 0.7, respectively, and those during uphill running at. On the top right, the explained cumulative variance (mean ± standard deviation errorbar) of five retained synergies is reported. Based on cumulative percentages of variance explained by each muscle synergy for each condition, three to five types of muscle synergy were identified across all subjects (Fig. For each limb (healthy in black, unaffected in dark gray, affected in light gray), bars represent the result achieved for all ordered walking speeds (i.e., 0.5, 0.7, 0.9, and 1.1 km/h). Synergies ensure that most of the variance is good. low pass filter cut-off had the effect of decreasing the variance accounted for by a set number of synergies and affected individual muscle contributions. The basis vectors of this low-dimensional subspace, termed muscle synergies, are hypothesized to reflect neurally-established functional muscle groupings. reduces the variance of the shape by using principal component analysis. On the top left, the percentage of subjects presenting different number of synergies, according to the criteria of the eigenvalue > 1 and the eigenvalue > 0.5, is reported. Good variance (V GOOD) does not affect the selected performance variable, while bad variance (V BAD) does. Evidence for muscle synergy during mastication is scarce, partly due to the. Muscle synergies: comparison between the retaining criteria. ![]()
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