Task Complexity Amplifies the Stroke-Induced Temporal and Spatial Asymmetry in Muscle Synergy Plasticity

Task Complexity Amplifies the Stroke-Induced Temporal and Spatial Asymmetry in Muscle Synergy Plasticity | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Task Complexity Amplifies the Stroke-Induced Temporal and Spatial Asymmetry in Muscle Synergy Plasticity Yong Wang, Rui Sun, Lingling Zhong, Dantong Liao, Haoyang Song, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7058548/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Feb, 2026 Read the published version in Journal of NeuroEngineering and Rehabilitation → Version 1 posted 9 You are reading this latest preprint version Abstract Background : motor synergy patterns are recognized as physiological markers of motor cortical damage, providing insight into how motor cortex coordinates spinal motor modules to generate movement. However, how these patterns adapt to tasks of varying complexity following post-stroke cortical damage is not yet fully understood. Objective : we aimed to understand how motor synergy patterns are distorted across tasks of increasing complexity after stroke induced cortical damage, also to provide a reference for task selection when using muscle synergy patterns as biomarkers for stroke evaluation or intervention. Methods : This is a pilot, cross sectional study. We investigated the muscle synergies during 5 tasks with varying complexity in 20 healthy individuals (13 females and 7 males, aged 64.33 ±6.94 years) and in 12 chronic stroke participants (4 females and 8 males, aged 64.4 ±6.54 years) by recording the surface electromyographic activities of 16 upper limb muscles (eight muscles unilaterally). Non-negative matrix factorization was performed to extract the muscle synergies. We categorized the stroke-induced synergy plasticity based on healthy synergy centroids, compared the synergy plasticity between affected and unaffected limb, and investigated the correlation between synergy plasticity and patient’s motor function, Results: In healthy individuals, the number of muscle synergies exhibits a U-shaped pattern as task complexity increases, whereas in stroke patients, both the affected and unaffected limbs show a decreasing trend in muscle synergy number with increasing task complexity. The proportion of preservation synergies was significantly higher in the unaffected arm compared to the affected arm in moderate and high complexity tasks. In contrast, the number of mutation synergies as well as mutation synergy activation were lower in the unaffected arm than in the affected arm in moderate and high complexity task. Notably, this asymmetry in preservation is significantly correlated with motor function of stroke patients. Conclusion : This study is the first to investigate how task complexity influences muscle synergy plasticity and their asymmetry in stroke participants. stroke patients demonstrate spatial and temporal asymmetry in muscle synergies between the unaffected and affected sides. This asymmetry is magnified by task complexity and shows a strong correlation with motor performance. Therefore, we recommend that the use of muscle synergy patterns as biomarkers for stroke assessment or rehabilitation should also account for the factor of task complexity. Stroke Muscle synergy pattern Surface Electromyography Synergy Asymmetry Task complexity Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Impairment of upper limb motor function is one of the most common motor consequences of stroke, significantly affecting the ability to reach, grasp, and manipulate objects, thereby disrupting patients' daily lives and social participation 1 . Motor synergy patterns have been evidenced as physiological markers of motor cortical damage, reflecting the way motor cortical areas orchestrate motor modules in the spinal cord to generate movement 2 – 4 . The current mainstream view is that descending cortical signals represent neuronal drives that select, activate, and flexibly combine muscle synergies specified by networks in the spinal cord and/or brainstem 5 . This multilayered architecture allows the motor system to circumvent the need to directly control its large number of degrees of freedom. However, it remains unclear how cortical impairment affects the ability to process tasks of varying complexity and how these changes influence the control of motor modules in the spinal cord. To gain some insight into this question, we investigated the muscle activation patterns of stroke survivors performing tasks with different levels of complexity. Our goal was not only to understand how motor synergies are distorted across tasks of increasing complexity after cortical outflow is disrupted by stroke but also to provide a reference for selecting tasks when using muscle synergy patterns as biomarkers for stroke evaluation and intervention. Various methods have been applied to extract and calculate muscle synergy patterns, including Principal Component Analysis (PCA) 6 , Independent Component Analysis (ICA) 7 , Factor Analysis (FA) 8 , etc., each with its own advantages in performance 9 . However, the above methods allow negative values in their results, leading to spatial synergies with negative weightings for muscular variables, which do not align with the physiological non-negativity of muscle activation and are difficult to interpret. Evidence is presented for the increased neurophysiological relevance of the factors derived from Non-Negative Matrix Factorization (NMF) 10 . Therefore, NMF 11 has become the preferred method for studying muscle synergies in motor-related diseases, as it ensures non-negative results and provides a more physiologically meaningful representation of muscle activations. For example, Saito et al. used NMF to study altered coordination strategies of muscle synergies in individuals with chronic low back pain, revealing pain-adapted protective behaviors in muscle control strategies 12 . In healthy individuals, NMF-decomposed muscle synergies have been found to positively correlate with maximum joint torque fluctuations between motion cycles, indicating their efficiency 3 . For stroke patients, post-NMF indexes, such as number of merged muscle synergies or median scalar products, are found to be related to the motor function and stroke duction of patients 2 , 5 . Based on this, the present study also employed NMF to extract muscle synergy patterns. To address the limitation of manually selecting the number of synergies in NMF, the silhouette score was employed to evaluate the cluster quality of muscle synergies 13 . This approach facilitates the determination of the optimal number of synergies, thereby improving the objectivity of the analysis. Preservation, merging, and fractionation are the three distinct patterns of muscle coordination modes reflecting the multiple neural responses that occur poststroke 2 . In patients with mild impairment, as indicated by higher FMA scores, muscle synergies in the stroke-affected upper limb closely resemble those in the unaffected upper limb. This preservation of muscle synergies suggests that, despite observable variations in motor performance, the core neural control mechanisms in these individuals remain largely intact. However, as impairment severity increases, a noticeable divergence between the muscle synergies of the affected and unaffected upper limbs emerges. In severely affected limbs, two distinct coordination patterns—merging and fractionation—are commonly observed. Merging involves the combination of multiple muscle synergies into a single, less complex synergy, reflecting a loss of motor flexibility and adaptability. In contrast, fractionation refers to the splitting of a single muscle synergy into multiple fragmented components, indicating inefficient and disorganized motor control. Previous studies have primarily observed these three coordination patterns—preservation, merging, and fractionation—in the affected upper limb, relative to the unaffected limb. Thus, it has often been concluded that stroke primarily alters descending cortical signals without significantly influencing the control of motor modules in the spinal cord. However, we argue that the unaffected upper limb of stroke patients cannot serve as a reliable reference because its muscle synergy patterns may also undergo subtle changes after stroke. To address this limitation, we propose using healthy individuals as the reference standard. In this study, we performed clustering analysis on the muscle synergies of the same upper-limb muscles in healthy individuals. By analyzing tasks with varying levels of complexity, we extracted the typical centroids of muscle synergies, providing a robust baseline for comparison. In this study, we designed five upper-limb tasks with varying levels of complexity for both stroke patients and healthy participants. Muscle synergies were extracted from EMGs of all participants using non-negative matrix factorization (NMF) with 80% R2 EMG reconstruction and then compared the synergy number in the affected, unaffected and healthy arms. Using the centroids of muscle synergies from healthy individuals as a reference baseline, we identified distinct muscle coordination patterns in stroke patients. In addition to the widely recognized patterns of preservation, merging, and fractionation, we discovered a fourth distinct pattern, termed mutation. This pattern was observed not only in the affected upper limb of stroke patients but also, notably, in their unaffected upper limb. We analyzed the proportions of these four patterns between the affected and unaffected sides of stroke patients, investigating how muscle synergy distortion and imbalance evolve with increasing task complexity. The results manifested that task complexity amplifies the stroke-induced distortion and asymmetry of muscle synergy patterns in temporal and spatial, suggesting that we need to consider the task complexity when applying muscle synergy pattern as markers of the physiological status of stroke survivors. 2. Methods The study was a pilot, cross sectional study. The proposed study followed the guidelines of the Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) 14 . Ethical approval was obtained from the Human Subjects Ethics Sub-committee of the Hong Kong Polytechnic University (HSEARS 20240125003). 2.2 Participants Stroke participants were recruited in hospitals and the Hong Kong Stroke Association according to inclusion criteria: (1) age between 18–65 years; (2) First-time ischemic or hemorrhagic stroke; (3) The affected hand can hold a cup and drink water; (4) Modified Ashworth Scale (MAS) < 3; (5) Time from onset of stroke ≥ 6 months; (6) Sufficient cognitive ability to follow experimental procedure (Mini-Mental State Examination Score, MMSE ≥ 20). Exclusion criteria: (1) Evidence of severe verbal comprehension deficit, apraxia, and/or visuospatial neglect; (2) Severe respiratory and circulatory failure; (3) Presence of non-stabilized fractures; (4) Presence of traumatic brain injury; (5) Presence of drug-resistant epilepsy; (6) Presence of swallowing disorders and (7) Any pain during passive and active range of motion of upper limb. Age-matched healthy participants without any neurological or orthopedic disorder history were recruited in the form of volunteers through posters in community notices, online platforms or social networks. All patients signed the informed consent prior to the enrollment. 2.3 System setup and experimental procedure 2.3.1 System setup Before experiment, stroke patients were instructed to complete the cognitive assessment (MMSE), motor function clinical scale (FMA-UE and ARAT) and muscle spasticity assessments (MAS). A therapist who does not participate in the collection of EMG signals conducted these assessments. Thereafter, all participants were guided to perform five tasks (Fig. 1 A): maximum isometric contraction of elbow extension and flexion, placing an object on a low platform (10 cm), placing an object on a medium platform (20 cm), placing an object on a high platform (30 cm), and drinking from high platform (30cm) task. The details of the tasks and the definitions of their complexity can be found in Table 1 and Supplementary Fig. 1 . An experimenter provided real-time, step-by-step instructions and monitored the experimental process throughout. EMGs were recorded at a sampling rate of 1000 Hz using a wireless multichannel EMG system (The Biometrics Ltd., DataLITE, 16 channels; Fig. 1 B). Surface electrodes were placed along the longitudinal midline of the target muscles, following the specifications and anatomical guidelines outlined in the Surface Electromyography for the Non-Invasive Assessment of Muscles 15 . The activities of eight muscles in both limbs were recorded simultaneously (Fig. 1 B): upper trapezius (UT), lower trapezius (LT), anterior deltoid (ANDE), posterior deltoid (PODE), triceps brachii lateral head (TBLH), biceps brachii short head (BBSH), flexor digitorum superficialis (FDS), and extensor digitorum communis (EDC). After the sensors were positioned, muscle contractions were performed to verify signal quality. 2.3.2 Task complexity Participants were instructed to sit beside a table, with the table height adjusted to create a 30° angle between their shoulders and the tabletop. The sequence of the five experimental tasks were randomized for each participant and detailed information can refer to Table 1 . Each participant was asked to repeat each task 3 times with nature speed, starting with the dominant or non-affected arm 16 , 17 . We defined target complexity, temporal dynamics, and overall complexity based on specific task characteristics. Target complexity was determined by the precision and variability of the target: tasks with static and easily reachable targets, such as the elbow MVCE/F, reaching tasks at 10 cm, 20 cm, and 30 cm, were classified as having simple targets, while tasks involving multi-step or dynamic goals, such as the drinking task, were classified as having complex targets. Temporal dynamics referred to the time synchronization demands of the task: MVCE/F was static and thus had no dynamics; reaching tasks allowed for self-paced execution, making them low dynamics; and the drinking task required precise timing and coordination between actions, making it high dynamics. Overall complexity was determined by integrating factors such as range of motion, muscle coordination, target complexity, and temporal dynamics, which is similar to other studies involving task complexity as a factor 18 – 20 . Based on these criteria, task complexity was categorized progressively from low (e.g., MVCE/F, placing task at 10cm) to moderate (e.g., placing task at 20 and 30 cm) to high (e.g., drinking task). Table 1 Task Characteristics and Complexity Levels Based on Motion, Coordination, and Dynamics Task Content Range of Motion Muscle Coordination Temporal Dynamics Task Steps Overall Complexity MVCE/F Conduct IMVC of elbow extension and flexion for 5s (three repetitions with 2 minutes 21 ) Static Single muscle activation No dynamics Single step Low Placing low (10 cm) A 100 g wooden block was placed 30 cm from the table edge ( Fig. 1 A ) 22 . Upon a ‘ready and go’ command, participants used their affected, non-affected limb, or both to place the block on 10, 20, and 30 cm platforms for three repetitions with 10-second intervals, rested for 5 minutes 23 , and repeated the task on 20 and 30 cm platforms 24 . Small range Proximal muscles (elbow-dominant) Low dynamics Single step Low Placing moderate (20 cm) Moderate range Proximal muscles (elbow-dominant) Low dynamics Single step Low to Moderate Placing high (30 cm) Moderate range Proximal muscles (elbow-shoulder coordination) Low dynamics Single step Moderate Drinking A half-filled plastic cup was placed 30 cm from the table edge on a 30 cm platform (Fig. 1 A). Participants, seated with wrists on the table's edge, reached for the cup, used wrist pronation to take a sip, and returned it to its original position. Moderate range Proximal + distal muscle coordination High dynamics Multi-step High MVCE/F = maximum isometric contraction of elbow extension and flexion. 2.4 Non-negative Matrix Fraction to extract muscle synergies NMF was applied to EMGs from 8 upper limb muscles to extract muscle synergies, following a preprocessing pipeline to ensure biologically meaningful input. Raw EMG signals were first band-pass filtered (20–450Hz) to remove noise and artifacts, rectified by taking their absolute values, and then smoothed to extract their envelopes. The envelope was computed using MATLAB's envelope function in RMS mode, which calculates the root mean square (RMS) of the signal within sliding windows of a specified size. The RMS envelope is given by: $$\:RMS\left(t\right)=\sqrt{\frac{1}{N}\sum\:_{i=1}^{N}{x}_{i}^{2}}$$ where window size N = 500 in this study, and \(\:{x}_{i}\) are the signal samples within the window. This method captures the energy profile of the signal, providing a smooth and biologically relevant representation of muscle activation intensity (Fig. 1 C). The processed EMG matrix V (8×T) was decomposed into synergy matrix W (8×r) and activation matrix H (r×T) by minimizing reconstruction error ( \(\:{min}_{W,H}{‖\varvec{V}-\varvec{W}\varvec{H}‖}_{F}^{2}\) ​) under non-negativity constraints ( Fig. 1DEF ). This revealed a small number of synergies (r) that represent coordinated muscle activation patterns and their time-varying activations. 2.5 Categorizing muscle synergies To study the distortions in muscle synergy patterns in the affected and unaffected upper limbs of post-stroke patients, we used the muscle synergy centroids from healthy individuals as references. K-means algorithm was used to cluster muscle synergy centroids from the dominant side of healthy individuals, minimizing within-cluster variance via \(\:{min}_{C,u}\sum\:_{k=1}^{K}{\sum\:}_{{x}_{i}\in\:{C}_{k}}{‖{x}_{i}-{u}_{k}‖}_{2}^{2}\) . Where the K is the number of clusters, \(\:{C}_{k}\) represents the set of data points (synergies) in the k cluster. ​ \(\:{‖{x}_{i}-{u}_{k}‖}_{2}^{2}\) is the squared Euclidean distance between a data point and its cluster centroid. The silhouette coefficient \(\:s\left(i\right)=\frac{b\left(i\right)-a\left(i\right)}{\text{m}\text{a}\text{x}\{a\left(i\right),b(i\left)\right\}}\) was employed to evaluate clustering quality. Where \(\:a\left(i\right)\) is the average intra-cluster distance for point \(\:i\) , \(\:b\left(i\right)\) is the smallest average distance of \(\:i\) to all points in other clusters. The resulting cluster centroids, representing dominant-side synergy patterns in healthy individuals, were used as a baseline to analyse synergy deviations in stroke patients. These centroids are shown in Supplementary Fig. 2 . Through observation, we categorized the coordination patterns of muscle synergies into four types: Preservation, Merging, Fractionation, and Mutation (in Fig. 2 ). Each synergy from stroke participants was compared to healthy centroids and categorized based on their cosine similarity ( \(\:\text{cos}\theta\:=\frac{a\bullet\:b}{‖a‖‖b‖}\) ). Synergies that exceeded a predefined similarity threshold (0.80) 2 with any healthy centroid were categorized as “ Preservation ” ( see preservation example in Fig. 2 ). For non-preserved synergies, further classification involved assessing their similarity to linear combinations of healthy centroids (“ Merging ”, see merging example in Fig. 2 ). or determining whether the synergy could be represented as a linear combination of other synergies within the same subject to reconstruct any healthy synergy centroid (“ Fractionation” , see fractionation example in Fig. 2 ). Synergies that did not fit into the above categories were classified as “ Mutation ” ( see mutation example in Fig. 2 ), indicating a pattern distinct from those observed in healthy individuals. The linear combination weights were determined using non-negative least squares 25 . 2.6 Statistical analysis Muscle synergy numbers extracted using NMF were analyzed to compare differences across tasks (MVCE/F, placing 10cm, 20cm, 30cm, and drinking) and groups (affected limb, unaffected limb, and healthy dominant limb). Levene's test (p > 0.05) confirmed homogeneity of variances, enabling the use of two-way ANOVA to assess the effects of group, task, and their interaction. Post-hoc Least Significant Difference (LSD) tests were applied to identify specific group and task differences where significant effects were detected. Once all muscle synergies from stroke limbs were classified into Preservation, Merging, Fractionation, and Mutation plasticity, we analyzed the difference in the number and activation of muscle synergies across the five tasks between the affected and unaffected limbs. A two-way ANOVA was used to evaluate the effects of side (affected vs. unaffected) and tasks on synergy plasticity numbers and activation. To identify specific task-level differences, we conducted paired t-tests to assess which tasks exhibited significant differences. Adjustments were made for multiple comparisons to control for type I errors. We also investigated the relationship between synergy similarity (preservation) and distortion (merging, fractionation, mutation) with motor function, stroke duration of patients. Specifically, we calculated the Spearman’s rank correlation between the synergy numbers of each coordination category and the clinical scores including stroke duration, FMA-UL, ARAT, and MAS. Spearman’s rank correlation was used because a normal distribution was not observed in the data (tested using the Shapiro–Wilk test). The significance level for all tests was set at p = 0.05. The p values obtained from all tests were corrected using the Bonferroni correction for multiple comparisons 26 . 3. Results 3.1 Subject Demographics From May to July 2024, 12 chronic stroke patients and 20 age-matched healthy individuals were recruited. The demographic characteristics of the participants are in Supplementary Table 1 . Among the stroke patients, six had strokes affecting the right side, while the other six had strokes affecting the left side. All healthy participants were right-handed. There were no significant differences in age between the two groups. 3.2 Parameter testing in the NMF To determine the threshold of Variance Accounted For (VAF) in the NMF algorithm, we conducted parameter testing for each task. It can be observed in Fig. 3 , across all tasks, the number of muscle synergies in the affected limb of stroke patients was generally lower than in the unaffected limb and the dominant limb of healthy participants. This finding aligns with our expectations and previous research. However, the degree of distinction varied significantly depending on the VAF threshold. We highlighted the regions with noticeable distinctions with red dashed boxes. To ensure comparability of results across different tasks, it was necessary to determine a unified VAF threshold. At VAF = 0.98, the muscle synergy numbers across the five tasks effectively distinguished the affected limb, unaffected limb, and healthy dominant limb. Therefore, we selected VAF = 0.98 as the unified threshold in this study. 3.3 Spatial and temporal characteristics of muscle synergy by tasks and groups For stroke patients, the number of muscle synergies in both the affected and unaffected limbs showed a decreasing trend as task difficulty increased (Fig. 4 A). In contrast, for the dominant limb of healthy individuals, the number of muscle synergies followed a U-shaped curve, first decreasing and then increasing as task complexity rose (Fig. 4 A). Figure 4 B provides specific examples of muscle synergy patterns for stroke and healthy participants across the five tasks. Two-way ANOVA revealed a significant main effect of group (F(2, 205) = 7.823, p < 0.001, Partial η² = 0.071) and task (F(4, 205) = 11.118, p < 0.001, Partial η² = 0.178). The interaction effect between group and task was not significant (F(8, 205) = 1.717, p = 0.096, Partial η² = 0.063) (Fig. 4 C). Post-hoc comparisons showed that muscle synergy numbers for the affected limb were significantly lower than those for the healthy dominant limb (p < 0.001) but not significantly different from the unaffected limb (p = 0.179). The healthy dominant limb had significantly higher synergy numbers compared to the unaffected limb (p = 0.021). For task comparisons, the drinking task resulted in significantly lower muscle synergy numbers compared to MVCE/F (p < 0.001) and placing tasks at 20cm (p = 0.015). Additionally, MVCE/F exhibited higher synergy numbers compared to all other tasks, including placing at 10cm (p < 0.001), 20cm (p = 0.001), and 30cm (p < 0.001). No significant differences were observed among the placing tasks. Figure 5 A shows that the unaffected limb preserved more healthy-like synergies, while the affected limb had higher proportions of merging and mutation. This results are similar to the finding to the Cheung’s study 2 . Making a further step, we calculated the differences in synergy proportions between the two limbs to examine limb asymmetry. As shown in Fig. 5 B, task complexity increased the asymmetry in preservation and merging plasticity, while fractionation showed no clear trend. Mutation imbalance followed a U-shaped trend, increasing initially and then decreasing with task difficulty. For the number of synergies plasticity, significant differences were observed in preservation plasticity (F(1, 109) = 25.29, p < 0.001) and mutation plasticity (F(1, 109) = 25.29, p = 0.005) between the affected and unaffected sides. Paired t-tests revealed that the number of preservation synergies in the affected side was significantly different from that in the unaffected side for tasks of moderate and high complexity (Placing 30cm and Drinking tasks). Specifically, the preservation synergy numbers were lower in the affected side compared to the unaffected side for these tasks (Fig. 6 A; Placing 30cm: t(11) = -1.122, p = 0.010; Drinking tasks: t(11) = -1.285, p = 0.008). The mutation synergy numbers were higher in the affected side compared to the unaffected side for Drinking task (Fig. 6 D; t(11) = -1.3029, p = 0.009). These results indicated statistically significant differences in specific tasks for the synergy imbalance (Fig. 6 ). For muscle synergy activation, paired t test also indicated statistically significant differences in specific tasks ( Supplementary Fig. 3 ). Significant differences were only observed in the Placing 30cm task for mutation synergy activation ( Supplementary Fig. 3D ; t(11) = 1.1841, p = 0.041). 3.5 Correlations between motor evaluation and muscle synergy pattern The Pearson correlation analysis shows significant negative correlations between FMA-UE, ARAT, and the asymmetry of preservation synergies in the drinking tasks (Fig. 7 A for correlation matrix and Fig. 7 B for the masked correlation matrix), with r = -0.71 (p = 0.009) and r = -0.73 (p = 0.007), respectively. Scatter plots (Fig. 7 C and D ) confirm these trends, with clear downward regression lines and narrow confidence intervals. Furthermore, MAS of elbow extension showed significant positive correlation with preservation synergies in the drinking tasks (r = 0.59, p = 0.042), as the scatter plot, regression line and confidence interval shown in Fig. 7 E. These findings indicate that higher asymmetry of preservation synergies is associated with lower FMA-UE and ARAT scores and higher MAS scores. 4. Discussion This study is the first to investigate how task complexity influences muscle synergy patterns and their distortion in stroke participants. Our findings demonstrated that complicated tasks requiring greater motor demands, such as Placing 30cm and Drinking, significantly amplify the distortion and asymmetry of muscle synergies between the affected and unaffected limbs. Specifically, preservation and mutation synergies exhibited notable difference in these high-complexity tasks, with statistically significant results for synergy asymmetry. Furthermore, correlation analyses revealed that higher asymmetry in preservation synergies during the Drinking task was strongly linked to reduced motor function and increased spasticity. These results underscore the importance of considering task complexity when using muscle synergy patterns as markers of motor impairment in stroke survivors and suggest that tasks with higher motor demands may provide a more sensitive assessment of stroke-induced motor deficits. 4.1 Classification of muscle synergy Preservation, merging, and fractionation represent three typical types of plasticity in muscle synergies observed in post-stroke patients 2 , 27 . These plasticity patterns were found to correlate with the level of motor impairment, as assessed by the Fugl-Meyer Scale, and time since the stroke onset 2 . These observations can be perfectly explained within the framework of the hierarchical structure of motor control, which consists of a high-level (cortical) layer responsible for planning and coordinating motor tasks, and a low-level (spinal cord) layer that executes specific muscle activations through predefined synergistic modules 5 , 28 . However, the majority of prior research has adopted the unaffected side as the reference to describe the change of muscle synergies in the paretic limb 2 , 5 , 29 . While this approach has the advantage of providing a direct within-subject comparison, highlighting the extent of motor impairment induced by stroke, it has a critical limitation: the motor control of the non-paretic side is often altered following a stroke. As a result, using the non-paretic side as a baseline may not accurately capture the full spectrum of stroke-induced changes in motor control. Our study addressed this limitation by using synergy centroids from healthy individuals as the reference to examine changes in muscle synergy patterns on both the paretic and non-paretic sides of stroke patients. Beyond the classical plasticity phenomena of preservation, merging, and fractionation 2 , 4 , 12 , 27 , we identified a new phenomenon, which we term 'mutation.' Notably, we are not the first to observe this phenomenon. Jinsook Roh et al. also reported systematic alterations in upper limb muscle synergies in stroke survivors with severe motor impairment, which differ from the typical preservation, merging, or fractionation 30 . Mutation synergy describes the emergence of significantly altered muscle synergy structural patterns post-stroke, which may reflect the central nervous system’s compensatory response arise from disrupted cortical control and altered descending inputs 31 . These disruptions could force the spinal circuits to engage in compensatory reorganization, leading to the formation of atypical synergy patterns. 4.2 Task complexity reveals divergent muscle synergy patterns in healthy individuals and stroke patients Previous studies on muscle synergy differences between stroke patients and healthy individuals have been inconsistent, with some reporting no difference 5 , 32 , 33 and others noting fewer synergies in stroke patients 34 , 35 . One potential reason for this inconsistency is the lack of consideration for task difficulty. To address this, we designed tasks of varying complexity and observed significant effects across groups. However, contrary to our expectations, the relationship between task complexity and muscle synergy patterns differed significantly between healthy individuals and stroke patients (Fig. 4 A). In healthy individuals, the number of muscle synergies exhibited a U-shaped trend as task complexity increased: moderate-complexity tasks required fewer synergies, while both simple and highly complex tasks involved more synergies to ensure stability or adaptability. This pattern highlights the neuromuscular system's ability to optimize energy efficiency and movement during moderate-difficulty tasks—often associated with familiar motor skills—by utilizing refined synergy patterns developed through practice or evolution. However, this nonlinear relationship disappears in stroke patients, as both the paretic and non-paretic sides show a continuous decline in the number of synergies with increasing task complexity. This suggests reduced flexibility in synergy recruitment, with the nervous system compensating by relying on fewer, less differentiated synergies, leading to motor inefficiency and limited adaptability. 4.3 Task complexity amplifies the synergy asymmetry in stroke patients The asymmetry of muscle synergies reflects the redistribution of workload and control strategies in post-stroke patients. Previous studies have demonstrated that muscle synergies in the lower limbs are highly sensitive to symmetry in stroke patients, with lateral symmetry (both spatial and temporal) improving as a result of rehabilitation interventions 36 – 38 . While these findings are primarily based on lower-limb studies, our study extends this understanding to the upper limbs, revealing consistent patterns of synergy asymmetry in stroke patients' upper extremities (Fig. 5 A). Moreover, we found that this asymmetry becomes more pronounced by increasing task complexity (Fig. 5 B). In individual level, our observations revealed distinct patterns for preservation, merging, and mutation synergies: the number (modified) of preservation synergies was significantly higher in the unaffected arm compared to the affected arm in moderate and high complexity tasks (Placing 30cm and Drinking tasks in Fig. 6 ). In contrast, the number (modified) of mutation synergies as well as mutation synergy activation were lower in the unaffected arm than in the affected arm in moderate and high complexity task (Drinking Task in Fig. 6 and Placing 30cm in Supplementary Fig. 3 ). Notably, this asymmetry in preservation is significantly correlated with motor function of stroke patients (Fig. 7 ). These findings highlight the critical importance of task selection in using muscle synergy analysis for stroke evaluation, as tasks of varying complexity elicit different synergy patterns. Higher complexity tasks are more sensitive to revealing asymmetrical and compensatory mechanisms, offering a more comprehensive assessment of motor function and recovery. 4.4 The parameter selection for NMF Finally, we would like to discuss our experience in selecting the muscle synergy number during the extraction process using the NMF algorithm. In studies employing NMF for extracting muscle synergies, the most prevalent approach is to use Variance Accounted For (VAF) to determine the number of synergies required for a given task 12 , 39 – 41 . Although this method is more robust than relying on experience to set the muscle synergy number, the selection of the VAF threshold itself remains somewhat subjective. For instance, some studies use a VAF threshold of 0.80 41 or 0.90 42 , while most of them between 0.80-1.00 43 , leading to significant inconsistencies. In this study, we tested and plotted the relationship between muscle synergy number and VAF for the unaffected and affected sides of stroke patients, as well as the dominant side of healthy individuals. By analyzing these curves across different tasks, we identified the VAF ranges that best distinguished the three groups. Combining the optimal ranges across tasks, we determined that a VAF threshold of 0.98 provides the best balance. This testing methodology can serve as a valuable reference for determining the VAF threshold and muscle synergy number in future related studies. 4.5 The disadvantages and future direction Although the sample size of this study is comparable to similar research 3 , 4 , 41 , 44 , one limitation is the relatively small number of stroke patients, which may affect the generalizability of the findings. Additionally, future studies could benefit from further refining the task complexity and testing a wider range of tasks to explore muscle synergy patterns under varying conditions. This would provide a more comprehensive understanding of how muscle synergy can be utilized as a reliable tool for stroke assessment and rehabilitation planning. 5. Conclusion Post-stroke muscle synergy plasticity, in both the unaffected and affected sides, can be categorized into preservation, merging, fractionation, and mutation. Unlike healthy individuals, whose muscle synergy numbers follow a U-shaped curve as task complexity increases, stroke patients exhibit a steady decline in muscle synergy numbers on both sides with increasing task complexity. Furthermore, stroke patients demonstrate spatial and temporal asymmetry in muscle synergies between the unaffected and affected sides. This asymmetry is magnified by task complexity and shows a strong correlation with motor performance, highlighting its potential relevance for stroke assessment and rehabilitation. Declarations Ethics approval and consent to participate The protocol was approved by the Institutional Review Board of The Hong Kong Polytechnic University (HSEARS20240125003). Participants gave informed consent before taking part in the study. Consent for publication Consent obtained from patient/family member. Competing interests I declare that the authors have no competing interests. Funding This study was supported by The Hong Kong Polytechnic University (P0045217). Author Contribution R. Sun, Y. Wang, P. Cao, R. Song, and R. K. Y. Tong conceptualized and designed the study. Y. Wang, L. Zhong, D. Liao, H. Song, and Q. Meng, C. H. Fong were responsible for data acquisition, analysis, and interpretation. R. Sun and Y. Wang drafted the manuscript. All authors critically reviewed, revised, and approved the final version of the manuscript for submission. Acknowledgement We sincerely thank all the stroke patients who participated in the study and their families for their support and cooperation. Data Availability The datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request. Please note that the data are provided exclusively for research purposes. References Langhorne P, Coupar F, Pollock A. Motor recovery after stroke: a systematic review. Lancet Neurol. 2009;8:741–54. Cheung VC, Turolla A, Agostini M, Silvoni S, Bennis C, Kasi P, Paganoni S, Bonato P, Bizzi E. Muscle synergy patterns as physiological markers of motor cortical damage. Proceedings of the national academy of sciences . 109, 14652–14656 (2012). Sheng Y, Zeng J, Liu J, Liu H. Metric-based muscle synergy consistency for upper limb motor functions. IEEE Trans Instrum Meas. 2021;71:1–11. 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Muscle synergy analysis for clinical characterization of upper limb motor recovery after stroke. Arch Phys Med Rehabil. (2025). Roh J, Rymer WZ, Perreault EJ, Yoo SB, Beer RF. Alterations in upper limb muscle synergy structure in chronic stroke survivors. J Neurophysiol. 2012;109:768–81. Ward NS, Newton JM, Swayne OB, Lee L, Thompson AJ, Greenwood RJ, Rothwell JC, Frackowiak RS. Motor system activation after subcortical stroke depends on corticospinal system integrity. Brain. 2006;129:809–19. Kim H, Lee J, Kim J. Muscle synergy analysis for stroke during two degrees of freedom reaching task on horizontal plane. Int J Precis Eng Manuf. 2020;21:319–28. Pellegrino L, Coscia M, Muller M, Solaro C, Casadio M. Evaluating upper limb impairments in multiple sclerosis by exposure to different mechanical environments. Sci Rep. 2018;8:2110. García-Cossio E, Broetz D, Birbaumer N, Ramos-Murguialday A. Cortex integrity relevance in muscle synergies in severe chronic stroke. Front Hum Neurosci. 2014;8:744. Runnalls KD, Ortega-Auriol P, McMorland AJ, Anson G, Byblow WD. Effects of arm weight support on neuromuscular activation during reaching in chronic stroke patients. Exp Brain Res. 2019;237:3391–408. Coscia M, Monaco V, Martelloni C, Rossi B, Chisari C, Micera S. Muscle synergies and spinal maps are sensitive to the asymmetry induced by a unilateral stroke. J Neuroeng Rehabil. 2015;12:39. Tan CK, Kadone H, Watanabe H, Marushima A, Yamazaki M, Sankai Y, Suzuki K. Lateral symmetry of synergies in lower limb muscles of acute post-stroke patients after robotic intervention. Front NeuroSci. 2018;12:276. Tan CK, Kadone H, Watanabe H, Marushima A, Hada Y, Yamazaki M, Sankai Y, Matsumura A, Suzuki K. Differences in muscle synergy symmetry between subacute post-stroke patients with bioelectrically-controlled exoskeleton gait training and conventional gait training. Front Bioeng Biotechnol. 2020;8:770. Wang W, Jiang N, Teng L, Sui M, Li C, Wang L, Li G. Synergy Analysis of Back Muscle Activities in Patients With Adolescent Idiopathic Scoliosis Based on High-Density Electromyogram. IEEE Trans Biomed Eng. 2022;69:2006–17. Goudriaan M, Papageorgiou E, Shuman BR, Steele KM, Dominici N, Van Campenhout A, Ortibus E, Molenaers G, Desloovere K. Muscle synergy structure and gait patterns in children with spastic cerebral palsy. Dev Med Child Neurol. 2022;64:462–8. Liu J, Wang J, Tan G, Sheng Y, Chang H, Xie Q, Liu H. Correlation Evaluation of Functional Corticomuscular Coupling With Abnormal Muscle Synergy After Stroke. IEEE Trans Biomed Eng. 2021;68:3261–72. Moiseev SA, Pukhov AM, Mikhailova EA, Gorodnichev RM. Methodological and Computational Aspects of Extracting Extensive Muscle Synergies in Moderate-Intensity Locomotions. J Evol Biochem Physiol. 2022;58:88–97. Turpin NA, Uriac S, Dalleau G. How to improve the muscle synergy analysis methodology? Eur J Appl Physiol. 2021;121:1009–25. Sheng Y, Tan G, Liu J, Chang H, Wang J, Xie Q, Liu H. Upper Limb Motor Function Quantification in Post-Stroke Rehabilitation Using Muscle Synergy Space Model. IEEE Trans Biomed Eng. 2022;69:3119–30. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterials.docx Cite Share Download PDF Status: Published Journal Publication published 02 Feb, 2026 Read the published version in Journal of NeuroEngineering and Rehabilitation → Version 1 posted Editorial decision: Revision requested 13 Aug, 2025 Reviews received at journal 13 Aug, 2025 Reviewers agreed at journal 06 Aug, 2025 Reviews received at journal 03 Aug, 2025 Reviewers agreed at journal 31 Jul, 2025 Reviewers invited by journal 24 Jul, 2025 Editor assigned by journal 15 Jul, 2025 Submission checks completed at journal 15 Jul, 2025 First submitted to journal 06 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7058548","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":491523357,"identity":"54ccb99a-cfa6-4668-a9af-d8525a2aaa6b","order_by":0,"name":"Yong Wang","email":"","orcid":"","institution":"The Hong Kong Polytechnic University","correspondingAuthor":false,"prefix":"","firstName":"Yong","middleName":"","lastName":"Wang","suffix":""},{"id":491523358,"identity":"1404c6e5-ee5a-4c2f-9403-404fba726d96","order_by":1,"name":"Rui 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University","correspondingAuthor":false,"prefix":"","firstName":"Song","middleName":"","lastName":"Rong","suffix":""},{"id":491523366,"identity":"245fc203-5331-4653-a406-fbfaf3884c65","order_by":9,"name":"Raymond Kai-Yu Tong","email":"","orcid":"","institution":"The Chinese University of Hong Kong","correspondingAuthor":false,"prefix":"","firstName":"Raymond","middleName":"Kai-Yu","lastName":"Tong","suffix":""}],"badges":[],"createdAt":"2025-07-06 14:53:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7058548/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7058548/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12984-026-01889-9","type":"published","date":"2026-02-02T15:57:27+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":87828239,"identity":"b9ff3f0d-5c7d-4eff-8e52-4387175a0c13","added_by":"auto","created_at":"2025-07-29 12:02:18","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2223691,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA schematic illustration of the experiment design.\u003c/strong\u003e \u003cstrong\u003eA.\u003c/strong\u003eTasks with increasing complexity. \u003cstrong\u003eB.\u003c/strong\u003e Eight muscles for collecting EMG signals in each upper limb. \u003cstrong\u003eC.\u003c/strong\u003e Raw EMG signals and envelopes extract from the raw EMG signals. \u003cstrong\u003eD.\u003c/strong\u003e The change of variance explained with an increased number of muscle synergies. \u003cstrong\u003eE.\u003c/strong\u003e A schematic illustrating how muscle synergies are linearly combined to generate EMGs. \u003cstrong\u003eF.\u003c/strong\u003e The comparison of EMG waveforms resulting from the activations of individual synergies are then summed together to reconstruct the EMGs (black lines) and recorded real EMGs (blue lines).\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7058548/v1/8f7906a169ae95fb3b00af2a.png"},{"id":87828240,"identity":"2587056b-8d8b-44a2-ade3-f20ebf02c758","added_by":"auto","created_at":"2025-07-29 12:02:18","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":703505,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExamples of muscle synergy coordination patterns (Preservation, Merging, Fractionation, and Mutation) in the limbs of stroke participants compared to muscle synergy centroids from healthy participants.\u003c/strong\u003e In Preservation plasticity, the affected limb synergy (A1, MVCE/F) closely matched a healthy synergy centroid (H centroid 6; cosine similarity = 0.90). In Merging plasticity, the affected limb synergy (A5, placing 20cm) was reconstructed by merging three healthy synergy centroids (H centroid 2, 3, 5). In Fractionation plasticity, the affected limb synergy (A1, placing 30cm) was fractionated by a healthy synergy centroid (H centroid 6). In Mutation plasticity, the unaffected limb synergy (U5, placing 10cm) had limited similarity to any healthy centroid (highest similarity = 0.63) but showed morphological resemblance to H centroid 3, suggesting it is a mutated version of this centroid.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7058548/v1/9689168026a01cab701e9985.png"},{"id":87829434,"identity":"478adf7c-e32f-4e7e-853a-a4609839f459","added_by":"auto","created_at":"2025-07-29 12:10:18","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":716732,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe curves of the muscle synergy number with the threshold of variance accounted for (VAF) in the task of:\u003c/strong\u003e \u003cstrong\u003eA.\u003c/strong\u003e elbow MVCE/F; \u003cstrong\u003eB.\u003c/strong\u003e placing 10cm; \u003cstrong\u003eC.\u003c/strong\u003eplacing 20cm; \u003cstrong\u003eD. \u003c/strong\u003eplacing 30cm; \u003cstrong\u003eE.\u003c/strong\u003e drinking.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7058548/v1/9847c3691d6e1dfb7daedb33.png"},{"id":87829435,"identity":"2022767a-c9e3-4bd6-a251-d07cd6f11d62","added_by":"auto","created_at":"2025-07-29 12:10:19","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":994817,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA. \u003c/strong\u003eComparison of muscle synergy numbers across tasks and groups; \u003cstrong\u003eB.\u003c/strong\u003e Examples of muscle synergy patterns for stroke and healthy participants across the five tasks; \u003cstrong\u003eC.\u003c/strong\u003eThe results of statistical analysis of group and task effects on muscle synergy numbers.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7058548/v1/9e356a1bb76a80ebcd388543.png"},{"id":87829939,"identity":"463234de-03b9-4640-b3c7-4ef034fd95d1","added_by":"auto","created_at":"2025-07-29 12:18:19","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":811567,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA.\u003c/strong\u003e Comparison of the proportion of muscle synergy preservation, merging, fractionation across tasks and groups based on the healthy synergy centroid. \u003cstrong\u003eB.\u003c/strong\u003e The trend of imbalance of four types of muscle synergy in stroke participants (unaffected - affected) with the increase of task complexity.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7058548/v1/5b5e0d9db02fe0cf50d0410e.png"},{"id":87829436,"identity":"cea84597-0a4d-4338-af42-449dca2d6b19","added_by":"auto","created_at":"2025-07-29 12:10:19","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":908248,"visible":true,"origin":"","legend":"\u003cp\u003eThe differences in synergy numbers of: \u003cstrong\u003eA. \u003c/strong\u003ePreservation, \u003cstrong\u003eB.\u003c/strong\u003e Merging, \u003cstrong\u003eC.\u003c/strong\u003e Fractionation, \u003cstrong\u003eD.\u003c/strong\u003e Mutation between the affected and unaffected limbs across five tasks (MVCE/F, placing 10cm, placing 20cm, placing 30cm, drinking).\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-7058548/v1/820f7bac2810a681ac586f0e.png"},{"id":87829444,"identity":"ad167a76-fd4d-47a3-a244-82949bfb1616","added_by":"auto","created_at":"2025-07-29 12:10:19","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":872357,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation between motor performance metrics and significantly asymmetric muscle synergy patterns. A.\u003c/strong\u003eCorrelation matrix showing relationships between FMA-UE, ARAT, MAS-elbow extension, and preservation synergies in drinking task. \u003cstrong\u003eB.\u003c/strong\u003e Masked correlation matrix highlighting significant correlations. \u003cstrong\u003eC–E.\u003c/strong\u003e Scatter plots with regression lines and confidence intervals illustrating correlations between preservation asymmetry in drinking task and \u003cstrong\u003eC.\u003c/strong\u003e FMA-UE, \u003cstrong\u003eD.\u003c/strong\u003eARAT, \u003cstrong\u003eE.\u003c/strong\u003e MAS elbow extension. Only muscle synergies with significant asymmetry, as identified in prior analyses, were included in the correlation analysis.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-7058548/v1/6c9ffca75c2d94ffe3b2c53d.png"},{"id":102234048,"identity":"053b8624-46e4-494d-b48e-a8127045aecc","added_by":"auto","created_at":"2026-02-09 16:05:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8852493,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7058548/v1/911f242d-870a-4cca-a853-10068432f3d8.pdf"},{"id":87829438,"identity":"83b25357-d2cc-4b01-b5f8-aea1eadfd378","added_by":"auto","created_at":"2025-07-29 12:10:19","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":561063,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-7058548/v1/ceb2030047bdbf428e62f5dc.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Task Complexity Amplifies the Stroke-Induced Temporal and Spatial Asymmetry in Muscle Synergy Plasticity","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eImpairment of upper limb motor function is one of the most common motor consequences of stroke, significantly affecting the ability to reach, grasp, and manipulate objects, thereby disrupting patients' daily lives and social participation\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Motor synergy patterns have been evidenced as physiological markers of motor cortical damage, reflecting the way motor cortical areas orchestrate motor modules in the spinal cord to generate movement\u003csup\u003e\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. The current mainstream view is that descending cortical signals represent neuronal drives that select, activate, and flexibly combine muscle synergies specified by networks in the spinal cord and/or brainstem\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. This multilayered architecture allows the motor system to circumvent the need to directly control its large number of degrees of freedom. However, it remains unclear how cortical impairment affects the ability to process tasks of varying complexity and how these changes influence the control of motor modules in the spinal cord. To gain some insight into this question, we investigated the muscle activation patterns of stroke survivors performing tasks with different levels of complexity. Our goal was not only to understand how motor synergies are distorted across tasks of increasing complexity after cortical outflow is disrupted by stroke but also to provide a reference for selecting tasks when using muscle synergy patterns as biomarkers for stroke evaluation and intervention.\u003c/p\u003e\u003cp\u003eVarious methods have been applied to extract and calculate muscle synergy patterns, including Principal Component Analysis (PCA)\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e, Independent Component Analysis (ICA)\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, Factor Analysis (FA)\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, etc., each with its own advantages in performance\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. However, the above methods allow negative values in their results, leading to spatial synergies with negative weightings for muscular variables, which do not align with the physiological non-negativity of muscle activation and are difficult to interpret. Evidence is presented for the increased neurophysiological relevance of the factors derived from Non-Negative Matrix Factorization (NMF)\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Therefore, NMF\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e has become the preferred method for studying muscle synergies in motor-related diseases, as it ensures non-negative results and provides a more physiologically meaningful representation of muscle activations. For example, Saito et al. used NMF to study altered coordination strategies of muscle synergies in individuals with chronic low back pain, revealing pain-adapted protective behaviors in muscle control strategies\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. In healthy individuals, NMF-decomposed muscle synergies have been found to positively correlate with maximum joint torque fluctuations between motion cycles, indicating their efficiency\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. For stroke patients, post-NMF indexes, such as number of merged muscle synergies or median scalar products, are found to be related to the motor function and stroke duction of patients\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Based on this, the present study also employed NMF to extract muscle synergy patterns. To address the limitation of manually selecting the number of synergies in NMF, the silhouette score was employed to evaluate the cluster quality of muscle synergies\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. This approach facilitates the determination of the optimal number of synergies, thereby improving the objectivity of the analysis.\u003c/p\u003e\u003cp\u003ePreservation, merging, and fractionation are the three distinct patterns of muscle coordination modes reflecting the multiple neural responses that occur poststroke\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. In patients with mild impairment, as indicated by higher FMA scores, muscle synergies in the stroke-affected upper limb closely resemble those in the unaffected upper limb. This preservation of muscle synergies suggests that, despite observable variations in motor performance, the core neural control mechanisms in these individuals remain largely intact. However, as impairment severity increases, a noticeable divergence between the muscle synergies of the affected and unaffected upper limbs emerges. In severely affected limbs, two distinct coordination patterns\u0026mdash;merging and fractionation\u0026mdash;are commonly observed. Merging involves the combination of multiple muscle synergies into a single, less complex synergy, reflecting a loss of motor flexibility and adaptability. In contrast, fractionation refers to the splitting of a single muscle synergy into multiple fragmented components, indicating inefficient and disorganized motor control. Previous studies have primarily observed these three coordination patterns\u0026mdash;preservation, merging, and fractionation\u0026mdash;in the affected upper limb, relative to the unaffected limb. Thus, it has often been concluded that stroke primarily alters descending cortical signals without significantly influencing the control of motor modules in the spinal cord. However, we argue that the unaffected upper limb of stroke patients cannot serve as a reliable reference because its muscle synergy patterns may also undergo subtle changes after stroke. To address this limitation, we propose using healthy individuals as the reference standard. In this study, we performed clustering analysis on the muscle synergies of the same upper-limb muscles in healthy individuals. By analyzing tasks with varying levels of complexity, we extracted the typical centroids of muscle synergies, providing a robust baseline for comparison.\u003c/p\u003e\u003cp\u003eIn this study, we designed five upper-limb tasks with varying levels of complexity for both stroke patients and healthy participants. Muscle synergies were extracted from EMGs of all participants using non-negative matrix factorization (NMF) with 80% R2 EMG reconstruction and then compared the synergy number in the affected, unaffected and healthy arms. Using the centroids of muscle synergies from healthy individuals as a reference baseline, we identified distinct muscle coordination patterns in stroke patients. In addition to the widely recognized patterns of preservation, merging, and fractionation, we discovered a fourth distinct pattern, termed mutation. This pattern was observed not only in the affected upper limb of stroke patients but also, notably, in their unaffected upper limb. We analyzed the proportions of these four patterns between the affected and unaffected sides of stroke patients, investigating how muscle synergy distortion and imbalance evolve with increasing task complexity. The results manifested that task complexity amplifies the stroke-induced distortion and asymmetry of muscle synergy patterns in temporal and spatial, suggesting that we need to consider the task complexity when applying muscle synergy pattern as markers of the physiological status of stroke survivors.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003eThe study was a pilot, cross sectional study. The proposed study followed the guidelines of the Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Ethical approval was obtained from the Human Subjects Ethics Sub-committee of the Hong Kong Polytechnic University (HSEARS 20240125003).\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Participants\u003c/h2\u003e\u003cp\u003eStroke participants were recruited in hospitals and the Hong Kong Stroke Association according to inclusion criteria: (1) age between 18\u0026ndash;65 years; (2) First-time ischemic or hemorrhagic stroke; (3) The affected hand can hold a cup and drink water; (4) Modified Ashworth Scale (MAS)\u0026thinsp;\u0026lt;\u0026thinsp;3; (5) Time from onset of stroke\u0026thinsp;\u0026ge;\u0026thinsp;6 months; (6) Sufficient cognitive ability to follow experimental procedure (Mini-Mental State Examination Score, MMSE\u0026thinsp;\u0026ge;\u0026thinsp;20). Exclusion criteria: (1) Evidence of severe verbal comprehension deficit, apraxia, and/or visuospatial neglect; (2) Severe respiratory and circulatory failure; (3) Presence of non-stabilized fractures; (4) Presence of traumatic brain injury; (5) Presence of drug-resistant epilepsy; (6) Presence of swallowing disorders and (7) Any pain during passive and active range of motion of upper limb. Age-matched healthy participants without any neurological or orthopedic disorder history were recruited in the form of volunteers through posters in community notices, online platforms or social networks. All patients signed the informed consent prior to the enrollment.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.3 System setup and experimental procedure\u003c/h2\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\u003ch2\u003e2.3.1 System setup\u003c/h2\u003e\u003cp\u003eBefore experiment, stroke patients were instructed to complete the cognitive assessment (MMSE), motor function clinical scale (FMA-UE and ARAT) and muscle spasticity assessments (MAS). A therapist who does not participate in the collection of EMG signals conducted these assessments. Thereafter, all participants were guided to perform five tasks (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA): maximum isometric contraction of elbow extension and flexion, placing an object on a low platform (10 cm), placing an object on a medium platform (20 cm), placing an object on a high platform (30 cm), and drinking from high platform (30cm) task. The details of the tasks and the definitions of their complexity can be found in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cb\u003eSupplementary Fig.\u0026nbsp;1\u003c/b\u003e. An experimenter provided real-time, step-by-step instructions and monitored the experimental process throughout. EMGs were recorded at a sampling rate of 1000 Hz using a wireless multichannel EMG system (The Biometrics Ltd., DataLITE, 16 channels; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Surface electrodes were placed along the longitudinal midline of the target muscles, following the specifications and anatomical guidelines outlined in the \u003cem\u003eSurface Electromyography for the Non-Invasive Assessment of Muscles\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. The activities of eight muscles in both limbs were recorded simultaneously (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB): upper trapezius (UT), lower trapezius (LT), anterior deltoid (ANDE), posterior deltoid (PODE), triceps brachii lateral head (TBLH), biceps brachii short head (BBSH), flexor digitorum superficialis (FDS), and extensor digitorum communis (EDC). After the sensors were positioned, muscle contractions were performed to verify signal quality.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e2.3.2 Task complexity\u003c/h2\u003e\u003cp\u003eParticipants were instructed to sit beside a table, with the table height adjusted to create a 30\u0026deg; angle between their shoulders and the tabletop. The sequence of the five experimental tasks were randomized for each participant and detailed information can refer to Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Each participant was asked to repeat each task 3 times with nature speed, starting with the dominant or non-affected arm\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. We defined target complexity, temporal dynamics, and overall complexity based on specific task characteristics. Target complexity was determined by the precision and variability of the target: tasks with static and easily reachable targets, such as the elbow MVCE/F, reaching tasks at 10 cm, 20 cm, and 30 cm, were classified as having simple targets, while tasks involving multi-step or dynamic goals, such as the drinking task, were classified as having complex targets. Temporal dynamics referred to the time synchronization demands of the task: MVCE/F was static and thus had no dynamics; reaching tasks allowed for self-paced execution, making them low dynamics; and the drinking task required precise timing and coordination between actions, making it high dynamics. Overall complexity was determined by integrating factors such as range of motion, muscle coordination, target complexity, and temporal dynamics, which is similar to other studies involving task complexity as a factor \u003csup\u003e\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Based on these criteria, task complexity was categorized progressively from low (e.g., MVCE/F, placing task at 10cm) to moderate (e.g., placing task at 20 and 30 cm) to high (e.g., drinking task).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eTask Characteristics and Complexity Levels Based on Motion, Coordination, and Dynamics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTask\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eContent\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRange of Motion\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMuscle Coordination\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTemporal Dynamics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTask Steps\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eOverall Complexity\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMVCE/F\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eConduct IMVC of elbow extension and flexion for 5s (three repetitions with 2 minutes\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStatic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSingle muscle activation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNo dynamics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSingle step\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003eLow\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePlacing low\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e(10 cm)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eA 100 g wooden block was placed 30 cm from the table edge \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Upon a \u0026lsquo;ready and go\u0026rsquo; command, participants used their affected, non-affected limb, or both to place the block on 10, 20, and 30 cm platforms for three repetitions with 10-second intervals, rested for 5 minutes\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, and repeated the task on 20 and 30 cm platforms\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSmall range\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eProximal muscles (elbow-dominant)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLow dynamics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSingle step\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003eLow\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePlacing moderate\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e(20 cm)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModerate range\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eProximal muscles (elbow-dominant)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLow dynamics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSingle step\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003eLow to Moderate\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePlacing high\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e(30 cm)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModerate range\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eProximal muscles (elbow-shoulder coordination)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLow dynamics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSingle step\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003eModerate\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDrinking\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eA half-filled plastic cup was placed 30 cm from the table edge on a 30 cm platform (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Participants, seated with wrists on the table's edge, reached for the cup, used wrist pronation to take a sip, and returned it to its original position.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModerate range\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eProximal\u0026thinsp;+\u0026thinsp;distal muscle coordination\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHigh dynamics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMulti-step\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003eHigh\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eMVCE/F\u0026thinsp;=\u0026thinsp;maximum isometric contraction of elbow extension and flexion.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Non-negative Matrix Fraction to extract muscle synergies\u003c/h2\u003e\u003cp\u003eNMF was applied to EMGs from 8 upper limb muscles to extract muscle synergies, following a preprocessing pipeline to ensure biologically meaningful input. Raw EMG signals were first band-pass filtered (20\u0026ndash;450Hz) to remove noise and artifacts, rectified by taking their absolute values, and then smoothed to extract their envelopes. The envelope was computed using MATLAB's \u003cem\u003eenvelope\u003c/em\u003e function in RMS mode, which calculates the root mean square (RMS) of the signal within sliding windows of a specified size. The RMS envelope is given by:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:RMS\\left(t\\right)=\\sqrt{\\frac{1}{N}\\sum\\:_{i=1}^{N}{x}_{i}^{2}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere window size N\u0026thinsp;=\u0026thinsp;500 in this study, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{x}_{i}\\)\u003c/span\u003e\u003c/span\u003e are the signal samples within the window. This method captures the energy profile of the signal, providing a smooth and biologically relevant representation of muscle activation intensity (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). The processed EMG matrix \u003cb\u003eV\u003c/b\u003e (8\u0026times;T) was decomposed into synergy matrix \u003cb\u003eW\u003c/b\u003e (8\u0026times;r) and activation matrix \u003cb\u003eH\u003c/b\u003e (r\u0026times;T) by minimizing reconstruction error (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{min}_{W,H}{‖\\varvec{V}-\\varvec{W}\\varvec{H}‖}_{F}^{2}\\)\u003c/span\u003e\u003c/span\u003e​) under non-negativity constraints (\u003cb\u003eFig.\u0026nbsp;1DEF\u003c/b\u003e). This revealed a small number of synergies (r) that represent coordinated muscle activation patterns and their time-varying activations.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Categorizing muscle synergies\u003c/h2\u003e\u003cp\u003eTo study the distortions in muscle synergy patterns in the affected and unaffected upper limbs of post-stroke patients, we used the muscle synergy centroids from healthy individuals as references. K-means algorithm was used to cluster muscle synergy centroids from the dominant side of healthy individuals, minimizing within-cluster variance via \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{min}_{C,u}\\sum\\:_{k=1}^{K}{\\sum\\:}_{{x}_{i}\\in\\:{C}_{k}}{‖{x}_{i}-{u}_{k}‖}_{2}^{2}\\)\u003c/span\u003e\u003c/span\u003e. Where the K is the number of clusters, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{C}_{k}\\)\u003c/span\u003e\u003c/span\u003e represents the set of data points (synergies) in the k cluster. ​\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{‖{x}_{i}-{u}_{k}‖}_{2}^{2}\\)\u003c/span\u003e\u003c/span\u003e is the squared Euclidean distance between a data point and its cluster centroid. The silhouette coefficient \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:s\\left(i\\right)=\\frac{b\\left(i\\right)-a\\left(i\\right)}{\\text{m}\\text{a}\\text{x}\\{a\\left(i\\right),b(i\\left)\\right\\}}\\)\u003c/span\u003e\u003c/span\u003e was employed to evaluate clustering quality. Where \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:a\\left(i\\right)\\)\u003c/span\u003e\u003c/span\u003e is the average intra-cluster distance for point \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:b\\left(i\\right)\\)\u003c/span\u003e\u003c/span\u003e is the smallest average distance of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e to all points in other clusters. The resulting cluster centroids, representing dominant-side synergy patterns in healthy individuals, were used as a baseline to analyse synergy deviations in stroke patients. These centroids are shown in \u003cb\u003eSupplementary Fig.\u0026nbsp;2\u003c/b\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThrough observation, we categorized the coordination patterns of muscle synergies into four types: Preservation, Merging, Fractionation, and Mutation (in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Each synergy from stroke participants was compared to healthy centroids and categorized based on their cosine similarity (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{cos}\\theta\\:=\\frac{a\\bullet\\:b}{‖a‖‖b‖}\\)\u003c/span\u003e\u003c/span\u003e). Synergies that exceeded a predefined similarity threshold (0.80)\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e with any healthy centroid were categorized as \u0026ldquo;\u003cem\u003ePreservation\u003c/em\u003e\u0026rdquo; (\u003cb\u003esee preservation example in\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). For non-preserved synergies, further classification involved assessing their similarity to linear combinations of healthy centroids (\u0026ldquo;\u003cem\u003eMerging\u003c/em\u003e\u0026rdquo;, \u003cb\u003esee merging example in\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). or determining whether the synergy could be represented as a linear combination of other synergies within the same subject to reconstruct any healthy synergy centroid (\u0026ldquo;\u003cem\u003eFractionation\u0026rdquo;\u003c/em\u003e, \u003cb\u003esee fractionation example in\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Synergies that did not fit into the above categories were classified as \u0026ldquo;\u003cem\u003eMutation\u003c/em\u003e\u0026rdquo; (\u003cb\u003esee mutation example in\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), indicating a pattern distinct from those observed in healthy individuals. The linear combination weights were determined using non-negative least squares\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Statistical analysis\u003c/h2\u003e\u003cp\u003eMuscle synergy numbers extracted using NMF were analyzed to compare differences across tasks (MVCE/F, placing 10cm, 20cm, 30cm, and drinking) and groups (affected limb, unaffected limb, and healthy dominant limb). Levene's test (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) confirmed homogeneity of variances, enabling the use of two-way ANOVA to assess the effects of group, task, and their interaction. Post-hoc Least Significant Difference (LSD) tests were applied to identify specific group and task differences where significant effects were detected.\u003c/p\u003e\u003cp\u003eOnce all muscle synergies from stroke limbs were classified into Preservation, Merging, Fractionation, and Mutation plasticity, we analyzed the difference in the number and activation of muscle synergies across the five tasks between the affected and unaffected limbs. A two-way ANOVA was used to evaluate the effects of side (affected vs. unaffected) and tasks on synergy plasticity numbers and activation. To identify specific task-level differences, we conducted paired t-tests to assess which tasks exhibited significant differences. Adjustments were made for multiple comparisons to control for type I errors.\u003c/p\u003e\u003cp\u003eWe also investigated the relationship between synergy similarity (preservation) and distortion (merging, fractionation, mutation) with motor function, stroke duration of patients. Specifically, we calculated the Spearman\u0026rsquo;s rank correlation between the synergy numbers of each coordination category and the clinical scores including stroke duration, FMA-UL, ARAT, and MAS. Spearman\u0026rsquo;s rank correlation was used because a normal distribution was not observed in the data (tested using the Shapiro\u0026ndash;Wilk test). The significance level for all tests was set at p\u0026thinsp;=\u0026thinsp;0.05. The p values obtained from all tests were corrected using the Bonferroni correction for multiple comparisons\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Subject Demographics\u003c/h2\u003e\u003cp\u003eFrom May to July 2024, 12 chronic stroke patients and 20 age-matched healthy individuals were recruited. The demographic characteristics of the participants are in \u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e. Among the stroke patients, six had strokes affecting the right side, while the other six had strokes affecting the left side. All healthy participants were right-handed. There were no significant differences in age between the two groups.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Parameter testing in the NMF\u003c/h2\u003e\u003cp\u003eTo determine the threshold of Variance Accounted For (VAF) in the NMF algorithm, we conducted parameter testing for each task. It can be observed in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, across all tasks, the number of muscle synergies in the affected limb of stroke patients was generally lower than in the unaffected limb and the dominant limb of healthy participants. This finding aligns with our expectations and previous research. However, the degree of distinction varied significantly depending on the VAF threshold. We highlighted the regions with noticeable distinctions with red dashed boxes. To ensure comparability of results across different tasks, it was necessary to determine a unified VAF threshold. At VAF\u0026thinsp;=\u0026thinsp;0.98, the muscle synergy numbers across the five tasks effectively distinguished the affected limb, unaffected limb, and healthy dominant limb. Therefore, we selected VAF\u0026thinsp;=\u0026thinsp;0.98 as the unified threshold in this study.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Spatial and temporal characteristics of muscle synergy by tasks and groups\u003c/h2\u003e\u003cp\u003eFor stroke patients, the number of muscle synergies in both the affected and unaffected limbs showed a decreasing trend as task difficulty increased (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). In contrast, for the dominant limb of healthy individuals, the number of muscle synergies followed a U-shaped curve, first decreasing and then increasing as task complexity rose (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB provides specific examples of muscle synergy patterns for stroke and healthy participants across the five tasks.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTwo-way ANOVA revealed a significant main effect of group (F(2, 205)\u0026thinsp;=\u0026thinsp;7.823, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Partial η\u0026sup2; = 0.071) and task (F(4, 205)\u0026thinsp;=\u0026thinsp;11.118, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Partial η\u0026sup2; = 0.178). The interaction effect between group and task was not significant (F(8, 205)\u0026thinsp;=\u0026thinsp;1.717, p\u0026thinsp;=\u0026thinsp;0.096, Partial η\u0026sup2; = 0.063) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Post-hoc comparisons showed that muscle synergy numbers for the affected limb were significantly lower than those for the healthy dominant limb (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) but not significantly different from the unaffected limb (p\u0026thinsp;=\u0026thinsp;0.179). The healthy dominant limb had significantly higher synergy numbers compared to the unaffected limb (p\u0026thinsp;=\u0026thinsp;0.021). For task comparisons, the drinking task resulted in significantly lower muscle synergy numbers compared to MVCE/F (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and placing tasks at 20cm (p\u0026thinsp;=\u0026thinsp;0.015). Additionally, MVCE/F exhibited higher synergy numbers compared to all other tasks, including placing at 10cm (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), 20cm (p\u0026thinsp;=\u0026thinsp;0.001), and 30cm (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). No significant differences were observed among the placing tasks.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA shows that the unaffected limb preserved more healthy-like synergies, while the affected limb had higher proportions of merging and mutation. This results are similar to the finding to the Cheung\u0026rsquo;s study\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Making a further step, we calculated the differences in synergy proportions between the two limbs to examine limb asymmetry. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB, task complexity increased the asymmetry in preservation and merging plasticity, while fractionation showed no clear trend. Mutation imbalance followed a U-shaped trend, increasing initially and then decreasing with task difficulty.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFor the number of synergies plasticity, significant differences were observed in preservation plasticity (F(1, 109)\u0026thinsp;=\u0026thinsp;25.29, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and mutation plasticity (F(1, 109)\u0026thinsp;=\u0026thinsp;25.29, p\u0026thinsp;=\u0026thinsp;0.005) between the affected and unaffected sides. Paired t-tests revealed that the number of preservation synergies in the affected side was significantly different from that in the unaffected side for tasks of moderate and high complexity (Placing 30cm and Drinking tasks). Specifically, the preservation synergy numbers were lower in the affected side compared to the unaffected side for these tasks (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA; Placing 30cm: t(11) = -1.122, p\u0026thinsp;=\u0026thinsp;0.010; Drinking tasks: t(11) = -1.285, p\u0026thinsp;=\u0026thinsp;0.008). The mutation synergy numbers were higher in the affected side compared to the unaffected side for Drinking task (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD; t(11) = -1.3029, p\u0026thinsp;=\u0026thinsp;0.009). These results indicated statistically significant differences in specific tasks for the synergy imbalance (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFor muscle synergy activation, paired t test also indicated statistically significant differences in specific tasks (\u003cb\u003eSupplementary Fig.\u0026nbsp;3\u003c/b\u003e). Significant differences were only observed in the Placing 30cm task for mutation synergy activation (\u003cb\u003eSupplementary Fig.\u0026nbsp;3D\u003c/b\u003e; t(11)\u0026thinsp;=\u0026thinsp;1.1841, p\u0026thinsp;=\u0026thinsp;0.041).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Correlations between motor evaluation and muscle synergy pattern\u003c/h2\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe Pearson correlation analysis shows significant negative correlations between FMA-UE, ARAT, and the asymmetry of preservation synergies in the drinking tasks (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA for correlation matrix and Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB for the masked correlation matrix), with r = -0.71 (p\u0026thinsp;=\u0026thinsp;0.009) and r = -0.73 (p\u0026thinsp;=\u0026thinsp;0.007), respectively. Scatter plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC \u003cb\u003eand D\u003c/b\u003e) confirm these trends, with clear downward regression lines and narrow confidence intervals. Furthermore, MAS of elbow extension showed significant positive correlation with preservation synergies in the drinking tasks (r\u0026thinsp;=\u0026thinsp;0.59, p\u0026thinsp;=\u0026thinsp;0.042), as the scatter plot, regression line and confidence interval shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE. These findings indicate that higher asymmetry of preservation synergies is associated with lower FMA-UE and ARAT scores and higher MAS scores.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study is the first to investigate how task complexity influences muscle synergy patterns and their distortion in stroke participants. Our findings demonstrated that complicated tasks requiring greater motor demands, such as Placing 30cm and Drinking, significantly amplify the distortion and asymmetry of muscle synergies between the affected and unaffected limbs. Specifically, preservation and mutation synergies exhibited notable difference in these high-complexity tasks, with statistically significant results for synergy asymmetry. Furthermore, correlation analyses revealed that higher asymmetry in preservation synergies during the Drinking task was strongly linked to reduced motor function and increased spasticity. These results underscore the importance of considering task complexity when using muscle synergy patterns as markers of motor impairment in stroke survivors and suggest that tasks with higher motor demands may provide a more sensitive assessment of stroke-induced motor deficits.\u003c/p\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Classification of muscle synergy\u003c/h2\u003e\u003cp\u003ePreservation, merging, and fractionation represent three typical types of plasticity in muscle synergies observed in post-stroke patients\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. These plasticity patterns were found to correlate with the level of motor impairment, as assessed by the Fugl-Meyer Scale, and time since the stroke onset\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. These observations can be perfectly explained within the framework of the hierarchical structure of motor control, which consists of a high-level (cortical) layer responsible for planning and coordinating motor tasks, and a low-level (spinal cord) layer that executes specific muscle activations through predefined synergistic modules\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. However, the majority of prior research has adopted the unaffected side as the reference to describe the change of muscle synergies in the paretic limb\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. While this approach has the advantage of providing a direct within-subject comparison, highlighting the extent of motor impairment induced by stroke, it has a critical limitation: the motor control of the non-paretic side is often altered following a stroke. As a result, using the non-paretic side as a baseline may not accurately capture the full spectrum of stroke-induced changes in motor control.\u003c/p\u003e\u003cp\u003eOur study addressed this limitation by using synergy centroids from healthy individuals as the reference to examine changes in muscle synergy patterns on both the paretic and non-paretic sides of stroke patients. Beyond the classical plasticity phenomena of preservation, merging, and fractionation\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, we identified a new phenomenon, which we term 'mutation.' Notably, we are not the first to observe this phenomenon. Jinsook Roh et al. also reported systematic alterations in upper limb muscle synergies in stroke survivors with severe motor impairment, which differ from the typical preservation, merging, or fractionation\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Mutation synergy describes the emergence of significantly altered muscle synergy structural patterns post-stroke, which may reflect the central nervous system\u0026rsquo;s compensatory response arise from disrupted cortical control and altered descending inputs\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. These disruptions could force the spinal circuits to engage in compensatory reorganization, leading to the formation of atypical synergy patterns.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Task complexity reveals divergent muscle synergy patterns in healthy individuals and stroke patients\u003c/h2\u003e\u003cp\u003ePrevious studies on muscle synergy differences between stroke patients and healthy individuals have been inconsistent, with some reporting no difference\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e and others noting fewer synergies in stroke patients\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. One potential reason for this inconsistency is the lack of consideration for task difficulty. To address this, we designed tasks of varying complexity and observed significant effects across groups. However, contrary to our expectations, the relationship between task complexity and muscle synergy patterns differed significantly between healthy individuals and stroke patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). In healthy individuals, the number of muscle synergies exhibited a U-shaped trend as task complexity increased: moderate-complexity tasks required fewer synergies, while both simple and highly complex tasks involved more synergies to ensure stability or adaptability. This pattern highlights the neuromuscular system's ability to optimize energy efficiency and movement during moderate-difficulty tasks\u0026mdash;often associated with familiar motor skills\u0026mdash;by utilizing refined synergy patterns developed through practice or evolution. However, this nonlinear relationship disappears in stroke patients, as both the paretic and non-paretic sides show a continuous decline in the number of synergies with increasing task complexity. This suggests reduced flexibility in synergy recruitment, with the nervous system compensating by relying on fewer, less differentiated synergies, leading to motor inefficiency and limited adaptability.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Task complexity amplifies the synergy asymmetry in stroke patients\u003c/h2\u003e\u003cp\u003eThe asymmetry of muscle synergies reflects the redistribution of workload and control strategies in post-stroke patients. Previous studies have demonstrated that muscle synergies in the lower limbs are highly sensitive to symmetry in stroke patients, with lateral symmetry (both spatial and temporal) improving as a result of rehabilitation interventions\u003csup\u003e\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. While these findings are primarily based on lower-limb studies, our study extends this understanding to the upper limbs, revealing consistent patterns of synergy asymmetry in stroke patients' upper extremities (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Moreover, we found that this asymmetry becomes more pronounced by increasing task complexity (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). In individual level, our observations revealed distinct patterns for preservation, merging, and mutation synergies: the number (modified) of preservation synergies was significantly higher in the unaffected arm compared to the affected arm in moderate and high complexity tasks (Placing 30cm and Drinking tasks in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). In contrast, the number (modified) of mutation synergies as well as mutation synergy activation were lower in the unaffected arm than in the affected arm in moderate and high complexity task (Drinking Task in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and Placing 30cm in \u003cb\u003eSupplementary Fig.\u0026nbsp;3\u003c/b\u003e). Notably, this asymmetry in preservation is significantly correlated with motor function of stroke patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). These findings highlight the critical importance of task selection in using muscle synergy analysis for stroke evaluation, as tasks of varying complexity elicit different synergy patterns. Higher complexity tasks are more sensitive to revealing asymmetrical and compensatory mechanisms, offering a more comprehensive assessment of motor function and recovery.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e4.4 The parameter selection for NMF\u003c/h2\u003e\u003cp\u003eFinally, we would like to discuss our experience in selecting the muscle synergy number during the extraction process using the NMF algorithm. In studies employing NMF for extracting muscle synergies, the most prevalent approach is to use Variance Accounted For (VAF) to determine the number of synergies required for a given task\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Although this method is more robust than relying on experience to set the muscle synergy number, the selection of the VAF threshold itself remains somewhat subjective. For instance, some studies use a VAF threshold of 0.80\u003csup\u003e41\u003c/sup\u003e or 0.90\u003csup\u003e42\u003c/sup\u003e, while most of them between 0.80-1.00\u003csup\u003e43\u003c/sup\u003e, leading to significant inconsistencies. In this study, we tested and plotted the relationship between muscle synergy number and VAF for the unaffected and affected sides of stroke patients, as well as the dominant side of healthy individuals. By analyzing these curves across different tasks, we identified the VAF ranges that best distinguished the three groups. Combining the optimal ranges across tasks, we determined that a VAF threshold of 0.98 provides the best balance. This testing methodology can serve as a valuable reference for determining the VAF threshold and muscle synergy number in future related studies.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e4.5 The disadvantages and future direction\u003c/h2\u003e\u003cp\u003eAlthough the sample size of this study is comparable to similar research\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e, one limitation is the relatively small number of stroke patients, which may affect the generalizability of the findings. Additionally, future studies could benefit from further refining the task complexity and testing a wider range of tasks to explore muscle synergy patterns under varying conditions. This would provide a more comprehensive understanding of how muscle synergy can be utilized as a reliable tool for stroke assessment and rehabilitation planning.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003ePost-stroke muscle synergy plasticity, in both the unaffected and affected sides, can be categorized into preservation, merging, fractionation, and mutation. Unlike healthy individuals, whose muscle synergy numbers follow a U-shaped curve as task complexity increases, stroke patients exhibit a steady decline in muscle synergy numbers on both sides with increasing task complexity. Furthermore, stroke patients demonstrate spatial and temporal asymmetry in muscle synergies between the unaffected and affected sides. This asymmetry is magnified by task complexity and shows a strong correlation with motor performance, highlighting its potential relevance for stroke assessment and rehabilitation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\u003cp\u003e The protocol was approved by the Institutional Review Board of The Hong Kong Polytechnic University (HSEARS20240125003). Participants gave informed consent before taking part in the study.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cp\u003eConsent obtained from patient/family member.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003cp\u003eI declare that the authors have no competing interests.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis study was supported by The Hong Kong Polytechnic University (P0045217).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eR. Sun, Y. Wang, P. Cao, R. Song, and R. K. Y. Tong conceptualized and designed the study. Y. Wang, L. Zhong, D. Liao, H. Song, and Q. Meng, C. H. Fong were responsible for data acquisition, analysis, and interpretation. R. Sun and Y. Wang drafted the manuscript. All authors critically reviewed, revised, and approved the final version of the manuscript for submission.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe sincerely thank all the stroke patients who participated in the study and their families for their support and cooperation.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request. Please note that the data are provided exclusively for research purposes.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLanghorne P, Coupar F, Pollock A. Motor recovery after stroke: a systematic review. Lancet Neurol. 2009;8:741\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCheung VC, Turolla A, Agostini M, Silvoni S, Bennis C, Kasi P, Paganoni S, Bonato P, Bizzi E. 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Early Stroke Rehabilitation of the Upper Limb Assisted with an Electromyography-Driven Neuromuscular Electrical Stimulation-Robotic Arm. Front Neurol. 8, (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSoylu AR, Arpinar-Avsar P. Detection of surface electromyography recording time interval without muscle fatigue effect for biceps brachii muscle during maximum voluntary contraction. J Electromyogr Kinesiol Official J Int Soc Electrophysiological Kinesiol. 2010;20:773\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHo S, Tong R, Chen M, Zhou H, Chan T. Hand Rehabilitation Robot using Electromyography. Biomechatronics in Medicine and Healthcare; 2011.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShourijeh MS, Flaxman TE, Benoit DL. An approach for improving repeatability and reliability of non-negative matrix factorization for muscle synergy analysis. J Electromyogr Kinesiol. 2016;26:36\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHolm S. A simple sequentially rejective multiple test procedure. Scand J Stat. 65\u0026ndash;70 (1979).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSheng Y, Wang J, Tan G, Chang H, Xie Q, Liu H. Muscle Synergy Plasticity in Motor Function Recovery After Stroke. IEEE Trans Neural Syst Rehabil Eng. 2024;32:1657\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBizzi E, Cheung VC, d'Avella A, Saltiel P, Tresch M. Combining modules for movement. Brain Res Rev. 2008;57:125\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePregnolato G, Severini G, Maistrello L, Rimini D, Lencioni T, Carpinella I, Ferrarin M, Jonsdottir J, Cheung VCK, Turolla A. Muscle synergy analysis for clinical characterization of upper limb motor recovery after stroke. Arch Phys Med Rehabil. (2025).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRoh J, Rymer WZ, Perreault EJ, Yoo SB, Beer RF. Alterations in upper limb muscle synergy structure in chronic stroke survivors. J Neurophysiol. 2012;109:768\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWard NS, Newton JM, Swayne OB, Lee L, Thompson AJ, Greenwood RJ, Rothwell JC, Frackowiak RS. Motor system activation after subcortical stroke depends on corticospinal system integrity. Brain. 2006;129:809\u0026ndash;19.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKim H, Lee J, Kim J. Muscle synergy analysis for stroke during two degrees of freedom reaching task on horizontal plane. Int J Precis Eng Manuf. 2020;21:319\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePellegrino L, Coscia M, Muller M, Solaro C, Casadio M. Evaluating upper limb impairments in multiple sclerosis by exposure to different mechanical environments. Sci Rep. 2018;8:2110.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGarc\u0026iacute;a-Cossio E, Broetz D, Birbaumer N, Ramos-Murguialday A. Cortex integrity relevance in muscle synergies in severe chronic stroke. Front Hum Neurosci. 2014;8:744.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRunnalls KD, Ortega-Auriol P, McMorland AJ, Anson G, Byblow WD. Effects of arm weight support on neuromuscular activation during reaching in chronic stroke patients. Exp Brain Res. 2019;237:3391\u0026ndash;408.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCoscia M, Monaco V, Martelloni C, Rossi B, Chisari C, Micera S. Muscle synergies and spinal maps are sensitive to the asymmetry induced by a unilateral stroke. J Neuroeng Rehabil. 2015;12:39.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTan CK, Kadone H, Watanabe H, Marushima A, Yamazaki M, Sankai Y, Suzuki K. Lateral symmetry of synergies in lower limb muscles of acute post-stroke patients after robotic intervention. Front NeuroSci. 2018;12:276.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTan CK, Kadone H, Watanabe H, Marushima A, Hada Y, Yamazaki M, Sankai Y, Matsumura A, Suzuki K. Differences in muscle synergy symmetry between subacute post-stroke patients with bioelectrically-controlled exoskeleton gait training and conventional gait training. Front Bioeng Biotechnol. 2020;8:770.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang W, Jiang N, Teng L, Sui M, Li C, Wang L, Li G. Synergy Analysis of Back Muscle Activities in Patients With Adolescent Idiopathic Scoliosis Based on High-Density Electromyogram. IEEE Trans Biomed Eng. 2022;69:2006\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGoudriaan M, Papageorgiou E, Shuman BR, Steele KM, Dominici N, Van Campenhout A, Ortibus E, Molenaers G, Desloovere K. Muscle synergy structure and gait patterns in children with spastic cerebral palsy. Dev Med Child Neurol. 2022;64:462\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu J, Wang J, Tan G, Sheng Y, Chang H, Xie Q, Liu H. Correlation Evaluation of Functional Corticomuscular Coupling With Abnormal Muscle Synergy After Stroke. IEEE Trans Biomed Eng. 2021;68:3261\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMoiseev SA, Pukhov AM, Mikhailova EA, Gorodnichev RM. Methodological and Computational Aspects of Extracting Extensive Muscle Synergies in Moderate-Intensity Locomotions. J Evol Biochem Physiol. 2022;58:88\u0026ndash;97.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTurpin NA, Uriac S, Dalleau G. How to improve the muscle synergy analysis methodology? Eur J Appl Physiol. 2021;121:1009\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSheng Y, Tan G, Liu J, Chang H, Wang J, Xie Q, Liu H. Upper Limb Motor Function Quantification in Post-Stroke Rehabilitation Using Muscle Synergy Space Model. IEEE Trans Biomed Eng. 2022;69:3119\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-neuroengineering-and-rehabilitation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jner","sideBox":"Learn more about [Journal of NeuroEngineering and Rehabilitation](http://jneuroengrehab.biomedcentral.com/)","snPcode":"12984","submissionUrl":"https://submission.nature.com/new-submission/12984/3","title":"Journal of NeuroEngineering and Rehabilitation","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Stroke, Muscle synergy pattern, Surface Electromyography, Synergy Asymmetry, Task complexity","lastPublishedDoi":"10.21203/rs.3.rs-7058548/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7058548/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: motor synergy patterns are recognized as physiological markers of motor cortical damage, providing insight into how motor cortex coordinates spinal motor modules to generate movement. However, how these patterns adapt to tasks of varying complexity following post-stroke cortical damage is not yet fully understood.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e: we aimed to understand how motor synergy patterns are distorted across tasks of increasing complexity after stroke induced cortical damage, also to provide a reference for task selection when using muscle synergy patterns as biomarkers for stroke evaluation or intervention.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: This is a pilot, cross sectional study. We investigated the muscle synergies during 5 tasks with varying complexity in 20 healthy individuals (13 females and 7 males, aged 64.33 ±6.94 years) and in 12 chronic stroke participants (4 females and 8 males, aged 64.4 ±6.54 years) by recording the surface electromyographic activities of 16 upper limb muscles (eight muscles unilaterally). Non-negative matrix factorization was performed to extract the muscle synergies. We categorized the stroke-induced synergy plasticity based on healthy synergy centroids, compared the synergy plasticity between affected and unaffected limb, and investigated the correlation between synergy plasticity and patient’s motor function,\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e In healthy individuals, the number of muscle synergies exhibits a U-shaped pattern as task complexity increases, whereas in stroke patients, both the affected and unaffected limbs show a decreasing trend in muscle synergy number with increasing task complexity. The proportion of preservation synergies was significantly higher in the unaffected arm compared to the affected arm in moderate and high complexity tasks. In contrast, the number of mutation synergies as well as mutation synergy activation were lower in the unaffected arm than in the affected arm in moderate and high complexity task. Notably, this asymmetry in preservation is significantly correlated with motor function of stroke patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: This study is the first to investigate how task complexity influences muscle synergy plasticity and their asymmetry in stroke participants. stroke patients demonstrate spatial and temporal asymmetry in muscle synergies between the unaffected and affected sides. This asymmetry is magnified by task complexity and shows a strong correlation with motor performance. Therefore, we recommend that the use of muscle synergy patterns as biomarkers for stroke assessment or rehabilitation should also account for the factor of task complexity.\u003c/p\u003e","manuscriptTitle":"Task Complexity Amplifies the Stroke-Induced Temporal and Spatial Asymmetry in Muscle Synergy Plasticity","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-29 12:02:14","doi":"10.21203/rs.3.rs-7058548/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-13T18:13:35+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-13T18:03:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"104583677404080640434375894135680660830","date":"2025-08-06T21:18:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-03T20:15:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"177016324652433270361263093662016127378","date":"2025-07-31T21:16:44+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-24T18:08:19+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-15T22:21:12+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-15T22:20:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of NeuroEngineering and Rehabilitation","date":"2025-07-06T14:45:32+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-neuroengineering-and-rehabilitation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jner","sideBox":"Learn more about [Journal of NeuroEngineering and Rehabilitation](http://jneuroengrehab.biomedcentral.com/)","snPcode":"12984","submissionUrl":"https://submission.nature.com/new-submission/12984/3","title":"Journal of NeuroEngineering and Rehabilitation","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d0e2d1a8-d9e5-47dd-bb4c-497e2b8e20f3","owner":[],"postedDate":"July 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-09T16:01:46+00:00","versionOfRecord":{"articleIdentity":"rs-7058548","link":"https://doi.org/10.1186/s12984-026-01889-9","journal":{"identity":"journal-of-neuroengineering-and-rehabilitation","isVorOnly":false,"title":"Journal of NeuroEngineering and Rehabilitation"},"publishedOn":"2026-02-02 15:57:27","publishedOnDateReadable":"February 2nd, 2026"},"versionCreatedAt":"2025-07-29 12:02:14","video":"","vorDoi":"10.1186/s12984-026-01889-9","vorDoiUrl":"https://doi.org/10.1186/s12984-026-01889-9","workflowStages":[]},"version":"v1","identity":"rs-7058548","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7058548","identity":"rs-7058548","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}