Introduction

Movement is one of the basic skills of human beings. We can perform various daily tasks almost effortless. However, the organization of human movement is not straightforward, as it involves complex cooperative interactions between the central nervous system (CNS) and the musculoskeletal system. As each joint of the musculoskeletal system can afford up to 6 degrees of freedom movement, it makes the motor control extremely delicate. Muscle synergy hypothesis, which describes muscle activation of a set of muscles contributing to a particular movement, has been proposed to simplify the motor control (d'Avella et al., 2003). Neural control of movements can be accomplished by a hierarchal framework where muscle synergies are at the bottom and the task-related conceptual parameters are manipulated by higher neural centers (Loeb et al., 1999; Scott, 2004; Todorov et al., 2005). The hypothesis of muscle synergy has been explored by many previous studies. For example, Ting and Macpherson (2005) and Overduin et al. (2008) demonstrated that synergies represented a generalized control strategy in postural control of cats and rhesus macaques. In upper limb movement, the muscle activity can be fully characterized by a relatively limited number of muscle synergies among various motor tasks (d'Avella and Lacquaniti, 2013). Lacquaniti et al. (2012) showed that the muscle activity of human locomotion can be formed by a combination of basic muscle synergies timed at different phases of the gait cycle. These studies showed that muscle synergies could simplify the motor behavior generation and reduce the dimensionality of redundant musculature control problem. Furthermore, muscle synergies are robust and shared across behaviors (d'Avella and Bizzi, 2005; Chia Bejarano et al., 2017; Nazifi et al., 2017; Saito et al., 2018). Dominici et al. (2011) observed that the two basic patterns extracted from newborn babies' locomotor were retained through development and another two new patterns were first revealed in toddlers. The common primitives may relate to a common ancestral neural network.

The purpose of muscle synergies analysis in people who suffered from motor deficits due to inappropriate muscle coordination is to reveal the underlying physiological mechanisms and offer suggestions on efficient recovery process (Safavynia et al., 2011; Casadio et al., 2013). Some studies have been conducted to find out how muscle synergies were affected after stroke. For instance, Clark et al. (2010) illustrated that motor modules in stroke patients locomotion were altered and the number of modules was correlated with biomechanical and clinical walking performance variables. Gizzi et al. (2011) extended the results by analyzing muscle synergies of stroke patients walking at a comfortable speed. They noted that the temporal profile of activation coefficient was preserved while the muscle synergies of the paretic limb were different from those in the contralateral as well as in healthy controls. Similarly, in upper limb motions, Roh et al. (2013, 2015) found alterations in synergy composition from chronic stroke participants. In the study of muscle synergies control during hand-reaching, mildly impaired stroke patients modulated synergies in different ways from the control group (Israely et al., 2018). Another study about the longitudinal changes in upper limb muscle synergies of stroke survivors showed the changes in the number of muscle synergies and the recruitment of muscles during the therapy (Hesam-Shariati et al., 2017). And proper intervention such as physical therapy on the standing-up motion of stroke survivors have been proved to improve the disordered and inadequate muscle synergy structure (Kogami et al., 2018). Furthermore, Cheung et al. (2012) observed that distinct muscle organization patterns such as merging, preservation, and fractionation of muscle synergies occurred after cortical damage. Hashiguchi et al. (2016) also found the merging and fractionation of muscle synergies in subacute stroke patients during gait, and the merging extent was relied on motor function. The abnormal patterns may be explained as compensation strategies of brainstem and spinal control. These results indicated that muscle synergies can provide physiological markers to assess the status of post-stroke survivors. Besides the muscle synergies, data from inertial sensors can also be used as helper methods in rehabilitation process since they can provide precise information on how the limb moves through space (Voinea et al., 2017). However, no former research has examined the potential alterations in structure and recruitment of muscle synergies from stroke patients at different Brunnstrom stages. Brunnstrom Approach is one of the measurements used to assess the motor control restoration throughout the body after stroke, which emphasizes the synergic patterns of movement developed during the recovery (Brunnstrom, 1966, 1970).

In this paper, we examined the alterations of muscle synergy structure and the recruitment patterns in subacute stroke survivors at different Brunnstrom stages during the voluntary reaching movement. Surface electromyography (EMG) and inertial sensor data from 35 stroke survivors ranging from Brunnstrom Stage III to VI and 25 age-matched control subjects were collected. Muscle synergies and recruitment patterns identified by non-negative matrix factorization (NMF) from stroke and healthy groups were compared. This study can provide suggestions on how to make use of the abnormal synergy patterns to accelerate the rehabilitation process by focusing on the exercise of specific muscles.

Materials and Methods

Participants

Thirty-five patients with stroke and 25 age-matched control subjects were recruited for this study from Peking University First Hospital. Inclusion criteria: (1) diagnosed with stroke for the first time; (2) the duration of stroke was no longer than 6 months; (3) shoulder lift with voluntary at least 30° without help; (4) had no history of other nervous system diseases. The patients enrolled were all suffered cerebral ischemic stroke, and the impairment region was at dominated area of middle cerebral artery. Healthy participants without neurological nor muscular injuries in upper limb were enrolled as control group. The general information for both stroke and control participants are demonstrated in Table 1. Brunnstrom Stages of recruited stroke patients were assessed by professional rehabilitation therapist from Peking University First Hospital. This research has been approved by the Ethics Committee of Peking University First Hospital and all the subjects gave informed consent before experimentations.

Data Acquisition

To start with, the subjects sat upright in front of the desk with palms facing the thighs. Then the participants were instructed to perform voluntary upward reaching by flexing shoulder at 90° and thumbs up with each arm and hold on for 2 s. The task was repeated three times for each subject, and with an interval of 3 min. Through the tasks, EMG activity was recorded from 7 upper-limb muscles by ME6000 multi-channel bipolar EMG recording system (Mega Electronics Ltd., Kuopio, Finland) at 1,000 Hz. The recorded muscles were pectoralis major (PECM), upper trapezius (TRA), anterior deltoid (DELA), medial deltoid (DELM), biceps brachii (BIC), triceps brachii (TRI), and brachioradialis (BRAC). Electrodes were placed longitudinally along the muscle fiber directions on corresponding muscles based on the guidelines of the Surface Electromyography for the Non-Invasive Assessment of Muscles (SENIAM) (Hermens et al., 1999). At the same time, the motion information during voluntary reaching task was collected (50Hz) using four MPU-9150 (InvenSense Inc., USA) sensors, each including a tri-axial accelerometer, tri-axial magnetometer and tri-axial gyroscope. The four inertial sensors were attached to the center of the waist as the root, lateral center of upper arm, lateral center of forearm, and lateral center of wrist, respectively.

Elbow Joint Angle Estimate

In order to observe the change of upper limb behaviors of stroke survivors compared to control subjects, we calculated elbow joint angle from motion data recorded by inertial sensors. We assume that upper arm, forearm, and hand are all rigid bodies, rotating around their corresponding joints. The quaternions were obtained by fusing data from accelerometer, gyroscope and magnetometer according to previous studies (Zhang and Wu, 2011).

where the superscript means global, means body, means sensor. The subscript means the initial of time, and means at time t. So qoGB is the quaternion of body in global coordinates in the initial. qoBS remains the same at different time, which means qtBS=qoBS. Then the position vector of joint is calculated by:

where means child joint and means the father joint. In the hierarchical biomechanical model, shoulder is the father joint of elbow and elbow is the father joint of wrist. is the vector between father joint and child joint. The elbow joint angle was computed as the angle between forearm vectors of start point to endpoint based on hierarchical biomechanical model (Huang et al., 2012).

where superscript of and represent the end and start point. Because in our hierarchical biomechanical model, the elbow joint was moved due to the movement of shoulder joint. Thus, we used the qshoulder-1 to eliminate the influence of shoulder joint.

Equation 4 can be simplified by the above equations and we obtained the elbow angle:

where qelbowe and qshouldere are the endpoint of elbow and shoulder quaternion respectively.

Muscle Synergy Extraction And Analysis

Identification of Muscle Synergies

In order to minimize the disturbances that would affect the EMG signals, preprocessing was conducted before the extraction of muscle synergies. EMG signals were high-pass filtered by window-based finite impulse response filter (50th order, cutoff of 50 Hz), rectified, low-pass filtered by window-based finite impulse response filter (50th order, cutoff of 20 Hz), and integrated over 20-ms intervals sequentially (Cheung et al., 2012). To avoid that the extraction of muscle synergies was biased into describing only the muscles with high-amplitude, we normalized EMG signals of each muscle from each individual within the task by the maximum value, and resampled into 200 points per trial (Burden, 2010).

We modeled EMG patterns () as linear combinations of few time-invariant muscle synergies (), each recruited by a time-varying coefficient () (Cheung et al., 2005; Tresch et al., 2006). The recruitment coefficients may reflect the temporal modulation of neural command to muscle synergy and specify how much each synergy contributes to EMG signal of each muscle (Torres-Oviedo et al., 2006). Muscle activation pattern can be expressed as:

where specifies the muscle synergy number. To extract muscle synergies and associated activation coefficients, we performed the algorithm of NMF to the EMG dataset (Lee and Seung, 1999, 2001). In this decomposition process, the elements in synergy and coefficient matrixes were first initialized with random values from a 0 to 1 uniform distribution. Then the values in the two matrixes were iteratively updated using updating rules in Equation (8) until convergence. The synergy extraction process was repeated 50 times for each subject and the synergies with the highest EMG-reconstruction R² was selected for further analyses to maximize the chance of applying R² according with the global optimum of NMF decomposition.

Estimating Muscle Synergy Number

We used the criterion of variance accounted for (VAF) to determine muscle synergy number, shown as Equation (9) (Cheung et al., 2005; Roh et al., 2011, 2012). VAF was computed from dataset of each subject for 50 times with random initial values of W and C matrix when the number of synergies varied from one to seven. We defined muscle synergy number as the minimum number required to achieve a mean VAF lager than 0.95, which was sufficient to capture the spatial features of the EMG patterns.

Quantifying Similarity of Synergies

In order to evaluate the similarity between synergies derived from different dataset, we calculated scalar product between muscle synergies (Tresch et al., 1999). We matched synergies that provided the highest total scalar product to compare individual synergies from two datasets directly, and each synergy was paired only once to the synergy in another dataset. As for the similarity between the activation coefficients, we used another metric of cross-correlation (Hug, 2011; Hug et al., 2011), shown as Equation (10).

where is the time index and X¯, Y¯ are the mean value of and , separately. The cross-correlation can give information on the possible shift in time and take the temporal profile into account (Dorel et al., 2009). The maximum of the cross-correlation between two activation coefficients where k = 0 was used to assess the differences across signals.

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To define a normative synergy template, we first randomly selected one set of three synergies and matched the synergies from remaining synergies then group-averaged to generate mean synergies for each group. The choice of initial dataset has little effect on the group-averaged synergies according to previous studies (Roh et al., 2015). We then used the group mean synergy of control group as the template. For each subject, we calculated the similarities between the three individual synergies and the corresponding template synergy. We excluded the subject's synergy set if the mean similarity was smaller than 0.85, and then re-calculated the means as an updated template, until all the included synergies were similar to the template (Roh et al., 2015). Synergy template for each Brunnstrom stage in stroke survivors was defined by a similar procedure. The normative activation coefficient templates were obtained by group-averaging of the coefficients corresponding to the normative synergy template. Subsequently, we calculated the scalar product as similarity between corresponding synergies in normative synergies of stroke and control to examine the alterations of synergy structure in post-stroke, and the Spearman correlation was calculated to quantify the correlation between similarities and the Brunnstrom Stage. The cross-correlations between activation coefficients were also calculated to test whether alterations also existed in activation coefficients.

Merging of Synergies

The adapted algorithm proposed by Cheung. (Cheung et al., 2012) was used to identify how the control synergies merged together in the stroke synergy templates. In the model of synergy merging, stroke synergy from each Brunnstrom Stage could be constructed by linear combinations of normative synergy template:

where wia is the th affected-arm synergy from a Brunnstrom stage, wkc is the synergy from the control group's normative synergy template, equals the synergy number of control group, specifies the number of affected-arm synergy from a Brunnstrom stage, and mki represents the nonnegative coefficient indicating how much the th synergy from normative template contributes to the th synergy's structure in a Brunnstrom stage. A normative synergy was considered as a significant contribution in the merging process if the coefficient mki was higher than 0.3 (Barroso et al., 2014). Similarity between reconstructed wia and the initial affected-arm synergy was quantified using scalar product between corresponding columns described as above.

Data analysis were performed using MATLAB 2017a (The Mathworks, Natick, USA). The significant level of statistical tests was fixed at 0.05.

Results

Figure 1 summarizes the pre-processed EMG activity of seven muscles recorded during the reaching task from a representative subject in each stroke and control group. It can be observed that the difference of EMG signals between control and stroke subjects was mainly in the pattern of trapezius activation. The activation of TRA in stoke survivors at Brunnstrom Stage III–V appeared more highly correlated with DELA and DELM than control group. Muscle synergies were extracted from each EMG dataset to identify underlying intermuscular coordination patterns in subacute stroke survivors. In Figure 2, three synergies were sufficient to reconstruct the original EMG signals in stroke and control groups. More specifically, 2.87 ± 0.64 3.14 ± 0.38, 3.17 ± 0.41, 2.85 ± 0.69, and 2.64 ± 0.57 synergies were identified from four stroke groups and the control group. Figure 3 shows that the combination of muscle synergies and corresponding coefficients can reconstruct the EMG signals excellently, providing high VAF values. Accordingly, three synergies were extracted from each subject for further analysis within and across groups. Figure 4 demonstrates the three muscle synergy patterns from each group of Brunnstrom Stage III to VI and the control, with group mean and standard deviation superposed on individual synergy patterns.

In all the five groups (Figure 4), the first synergy is dominated by the activation of DELA and DELM, which was referred to as shoulder flexor. Note that in the first synergy from Brunnstrom Stage III, IV, and V, the TRA is also co-activated while in the Brunnstrom VI, the synergy is quite similar to the control. In control group, the second synergy is dominated by activation of TRA, BIC, and BRAC. BIC and BRAC are elbow flexors, and TRA is used for keeping the back straight. The third synergy typically involves activation of TRI, which is the extensor of elbow. When healthy people performed the voluntary upward reaching, they might tend to bend their elbow and extend elbow while raising their hand to get a certain object, which is consistent with the change of elbow joint angle during the task (Figure 5). The other two synergies in Brunnstrom Stage III to VI are consisted of primary activation of shoulder flexor (PECM) or elbow flexors (BIC and BRAC). The increasing activation of PEMC, lack of activation of TRI and abnormality of activation of TRA in post-stroke Brunnstrom Stage III to VI are the most striking differences compared with control group. Since the group mean values in subpanel of Figure 4 are all relatively high, the synergy structure is consistent across subjects within a group. Figure 5 shows the elbow joint angle of subjects in each group. The lack of activation in elbow flexor of triceps in stroke patients (Figure 4) leads to larger elbow angle since they may bend their elbow as a compensation strategy during reaching. Stroke patients in Brunnstrom Stage III have the largest elbow joint angle and obvious perturbations in the holding phase. At Brunnstrom Stage IV and V, spastic muscle movement begins to decline while the voluntary movement becomes more complex, so the angles are relatively small and smooth compared to Stage III. The elbow angles in the control group are the smallest.

To identify alterations of individual synergies in each Brunnstrom Stage, we compute the scalar product between synergies for each subject (including individual control subjects) and corresponding synergy in control template, as summarized by group mean and standard deviation in Figure 6. The asterisks are used to denote those who have significant difference in similarity compared to the control template (two-sample -test, < 0.05). All the three synergies in Brunnstrom Stage III, two of three in Brunnstrom Stage IV and V, one in Brunnstrom VI are significantly different from the control template. This result suggests the alterations in muscle synergy structure of different Brunnstrom Stages. To further quantify the correlation of similarities and the Brunnstrom Stage, we calculate the Spearman correlation between them ( = 0.52, = 0.0014) and the similarities correlated significantly with Brunnstrom Stages. Overall, these results reflect the alterations in different Brunnstrom Stage, including the abnormal activation of TRA, the absence of muscle weights of TRI and the increase in PECM across the stroke groups.

Discussion

In this study, we investigated how hemiplegia stroke in different Brunnstrom Stages would affect the structure of muscle synergies and the recruitment patterns during voluntary reaching. For all subjects, three synergies were sufficient to interpret more than 95% of the total variance in EMG signals. We observed the co-activated muscles of trapezius and deltoids and the increased activations of pectoralis major muscle as well as the decreased activation of elbow extensor triceps in stroke groups. The similarity of muscle synergies between stroke and control group was correlated with Brunnstrom Stages. The synergy in post-stroke of each Brunnstrom Stage can be obtained by the merging of control template. What's more, the activation coefficients remained the same after stroke and irrespective of the motor recovery level. Overall, our results indicated that after stroke, different muscle synergies were recruited by similar modulation patterns to complete a movement and the alterations in the structure of muscle synergy in subacute stroke survivors may reflect a compensatory strategy after hemiplegia stroke.

The Number of Synergies

NMF was applied to extract muscle synergies from EMG signals, where the identification of synergy numbers was one of the most important steps. However, the optimal number of synergies cannot be calculated automatically throughout all the decomposition methods proposed in the literature. A commonly used method was to choose the lowest number that satisfied the quality of the reconstructed data compared to original recorded EMG under the criterion of VAF/R² (d'Avella et al., 2006; Torres-Oviedo et al., 2006). In this study, the threshold of 95% in VAF was used to identify the number of synergies (Figure 2), according to which three synergies were typically sufficient for most of subjects. In the research of muscle synergy post-stroke during walking, paretic legs needed significantly fewer modules relative to the control (Clark et al., 2010). However, we found stroke patients at Brunnstrom Stage IV and V recruited no less synergies than the control group. This may due to the fact that stroke survivors in this two groups needed more synergies to assist with the reaching movement, such as shrugging their shoulders or bending their elbows. But at Brunnstrom Stage III, the number of synergy was smaller than the stage IV and V, since voluntary movements just started to emerge that they could not coordinate the movement well. Similar results were also observed by Hesam-Shariati et al. (2017) who found that patients with low motor-function needed fewer muscle synergies as the higher number of muscle synergies often reflected greater movement complexity. And at Stage VI, the synergy number was similar to the control, while movement ability was also similar to the control group.

Implications for Neurorehabilitation

Alterations of synergies in subacute stroke patients were observed and the similarities compared to the control group were correlated with Brunnstrom Stages. Similar results were observed in the studies of human locomotion and isometric hand tasks where alterations in muscle synergies were most prominent in severely impaired stroke survivors, and lesser in mild-to-moderate impaired subjects (Clark et al., 2010; Lee et al., 2013). Thus, muscle synergy analysis is a useful method to identify abnormalities in muscle coordination. The altered structure of muscle synergy could reflect changes in neural excitability and affect the muscle coordination patterns (Dietz and Sinkjaer, 2007). The study of muscle synergies may provide a basis for the development of training protocols addressing impaired motor coordination (Safavynia and Ting, 2012). Individualized therapeutic strategies can be developed by focusing on abnormal synergy patterns to accelerate the rehabilitation process. In addition, assistive approaches, such as robot-assisted technology and functional electrical stimulation can be beneficial to the restoration of muscle synergy structure and recruitment. Tracking of the development of abnormal muscle synergies during recovery may also provide a new perspective on stroke rehabilitation.

Limitations and Future Work

This study focuses on the alterations of muscle synergies at different Brunnstrom Stages, but there are limitations that need to be improved in further research. First, we would include more stroke patients in different impairment levels to provide a more convincing result and deeper understanding about the neurophysiological explanation of muscle synergies. Besides, the stroke duration of participants was no longer than 6 months, but the time since stroke onset was not considered in this study. Researches have proved that cortical reorganization occurred in stroke patients receiving a rehabilitation therapy (Sawaki et al., 2014; Shimamura et al., 2017) and the degree of reorganization was related to the duration of post-stroke. Overt and covert exercise of stroke patients can activate the sensorimotor cortices, which may influence the recruitment of muscle synergies (Szameitat et al., 2012). In the future, we should consider the role of timing on the alterations in muscle synergies and the merging process since cortical reorganization, which are common after stroke onset, can influence temporal processing.

Author Contributions

BP, YS, BX, ZhiH, JW, JH, YL, ZheH, and ZZ conceived and designed the experiments. BP and YL performed the experiments. BP analyzed the data and wrote the paper. YS, BX, ZhiH, JW, JH, YL, ZheH, and ZZ revised the paper.

Funding

This work was supported by National Natural Science Foundation of China, Grant No. 61431017 and 81272166.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

The authors would like to thank all the subjects from Peking University First Hospital for their assistances with data collection.

References

— Update: 11-02-2023 — cohaitungchi.com found an additional article How to Overcome Synergistic Movement After Stroke (When One Movement Leads to Many) from the website www.flintrehab.com for the keyword muscle synergy patterns of le after stroke.

When you move your arm, does your shoulder hike up? This is called a synergistic movement. During the early stages of stroke recovery, this type of movement is very common.

Read more  How to Overcome Synergistic Movement After Stroke (When One Movement Leads to Many)

The term synergy is derived from a Greek term meaning “working together.” Synergistic movement, therefore, refers to when multiple muscles work together to perform a specific movement. Following a stroke, synergistic movements occur when you intend to perform a specific movement, but you end up performing other movements as well. For example, trying to move your affected elbow might result in hand and shoulder movements, too. While movement is a great sign during stroke recovery, synergistic movement is less than ideal.

If you’re frustrated by this phenomenon, don’t worry. This article will guide you through the cause of synergistic movement and how to overcome this unwanted movement pattern.

Use the links below to jump directly to any section of this article.

• What is synergistic movement?
• Types of synergistic movement after stroke
• Synergistic movement in early stroke recovery
• Unlinking synergistic movement
• How to overcome synergistic movement patterns

What is Synergistic Movement?

Synergistic movement refers to coordinated movements that occur when multiple muscle groups are activated simultaneously. As such, we are utilizing synergistic movements all the time. For example, in healthy individuals, four specific muscle synergies are used when walking. Each of these occurs at a different stage of one’s gait (walking) pattern and is essential for coordinating the movements required for walking.

However, most of the time, synergistic movements after a stroke refers not to healthy motor synergies, but rather to abnormal synergies which contribute to poor movement patterns. When a stroke affects the part of the brain that controls muscle movement, your brain cannot correctly send signals to your muscles. Thus, motor difficulties such as abnormal synergistic movement may occur.

For example, a stroke survivor displaying synergy may hike their shoulder when attempting to raise only their arm. This synergistic movement can lead to poor movement patterns and further complications such as chronic pain unless it is addressed.

Types of Synergistic Movement after Stroke

There are two main categories of synergistic movement after stroke, referred to as the flexor and extensor synergies.

In the arms, these synergies link the shoulder, elbow, wrist, and finger muscles together. This can cause difficulties with completing activities of daily living, such as dressing and eating, as the abnormal synergistic movement interferes with healthy muscle synergies typically used for these tasks.

In the arms, flexor synergy refers to:

• shoulder abduction (raising the arm to the side)
• elbow flexion
• supination (palm facing upwards)
• wrist and finger flexion

The extensor synergy of the arm involves many of the opposite movements, including:

• shoulder adduction (reaching inward)
• elbow extension
• pronation (palm facing downward)
• wrist extension and finger flexion (these postures may vary)

Flexor and extensor synergies are also seen in the legs. These synergies can interfere with walking and standing balance. Flexor and extensor synergies are only experienced on the affected side of the body, thus individuals may walk with a limp or other abnormal gait.

The flexor synergy of the leg includes:

• external rotation, abduction, and flexion of the hip
• knee flexion
• ankle dorsiflexion and eversion (foot pointed upwards and outward)

Conversely, the extensor synergy of the leg consists of:

• internal rotation, adduction, and extension of the hip
• knee extension
• ankle extension and inversion (foot pointed downward and inward)

While synergistic movement after stroke may pose many challenges, it can also signify the beginning of a survivor’s journey to recovery.

Synergistic Movement in the Early Stages of Stroke Recovery

Although synergistic movement might seem like a frustrating problem, it’s actually a sign of potential improvement. The Brunnstrom stages of stroke recovery explains this. There are seven stages in this framework:

• Stage 1: Flaccidity
• Stage 2: Spasticity appears
• Stage 3: Increased spasticity
• Stage 4: Decreased spasticity
• Stage 5: Complex movement returns
• Stage 6: Spasticity disappears
• Stage 7: Normal function returns

Depending on the severity of their stroke, survivors can start at any stroke recovery stage. Synergistic movement tends to display in the second stage of the Brunnstrom framework, along with spasticity (a condition that causes stiff, tight muscles). The first stage, flaccidity, occurs when there is zero innervation in the affected muscles — otherwise known as post-stroke paralysis.

When a survivor progresses from stage 1 to stage 2 of the Brunnstrom framework, movement begins to return to the affected muscles — specifically, synergistic movement returns. Although this unintentional movement pattern requires further rehabilitation, it is a sign of progress. It means the affected muscles are starting to “wake up” and the survivor can continue to work through the stages of recovery.

Synergistic movement persists until stage 5 of the Brunnstrom framework, where survivors regain the ability to perform more complex, coordinated movements.

Now, let’s discuss how to progress past synergistic movement to a fuller recovery after stroke.

Unlinking Synergistic Movement

When synergistic movement occurs, physical and occupational therapy can help retrain your brain to move your muscles smoothly – without making other unnecessary movements. The best way to retrain your brain is with consistent practice of stroke rehabilitation exercises.

Repetition is the best treatment for mobility issues after stroke. Whenever you consistently practice repetitive movements (referred to as massed practice), you reinforce the neural pathways in your brain responsible for that task. That’s why habits become second nature – the neural pathways have been strengthened through repetition. This process of creating and strengthening neural connections, referred to as neuroplasticity, is how the brain is able to rewire itself to recover after stroke.

When you practice arm exercises repetitively, you start to strengthen the neural connections that control your affected arm. However, it can be difficult to practice these exercises accurately when synergistic movement makes unintended muscles move, such as your shoulder hiking up.

This might cause you to worry about learning improper movement patterns. Next, we’ll discuss why you don’t need to worry about that when you try your best each time you exercise.

How to Overcome Synergistic Movement Patterns

Most therapists will agree that it is not ideal to practice rehab exercises incorrectly (i.e. with synergistic movement patterns), as it could reinforce these poor movement patterns.

However, when severe spasticity and synergistic movement prevent a stroke survivor from moving at all, it’s clear that any type of movement is better than no movement when you’re trying your best and focusing on good form every time.

As long as you are trying your best to use good form every time you exercise, you will continue to promote neuroplasticity and get better and better.

At this stage of recovery, you will get the most out of your exercises by doing them directly with your therapists because they can use manual therapy techniques to guide your limb(s) with proper form during each repetition. This will help to reinforce proper movement patterns rather than abnormal synergistic patterns.

Understanding Synergistic Movement After Stroke

Synergistic movements happen when you try to move one body part (like your arm) and end up moving multiple parts (like your arm, hand, and shoulder). Although this can be a frustrating pattern, it’s actually a possible sign of recovery as long as rehabilitation is being pursued.

You can minimize synergistic movement patterns by practicing therapeutic rehab exercises. Repetition of these movements helps rewire the brain and ‘separate’ your muscle movements. With consistent practice, you will work towards being able to perform accurate, coordinated movements.

— Update: 11-02-2023 — cohaitungchi.com found an additional article What Flexor Synergy Patterns After Stroke Mean For Recovery from the website www.flintrehab.com for the keyword muscle synergy patterns of le after stroke.

Although flexor synergy patterns after stroke can be frustrating to deal with, they may be a sign of improvement. Also called flexion synergy patterns, these synergistic movements result from multiple muscle contractions that are triggered at once. For example, if you try to move your shoulder, your elbow and wrist might contract as well.

While flexor synergy can be a sign of recovery, this can also inhibit movements and daily activities such as reaching and self-care. However, there is hope to improve overall function through consistent rehabilitation. To help you better understand flexor synergy patterns, this article will explain the cause of synergistic movements and how to work through them. 

What Are Synergy Patterns?

Coordinated muscle movements are a result of different muscle groups working together. These movement patterns are called synergies, and are responsible for muscle contraction and motions that appear smooth and controlled.

To complete a successful movement, two things must happen at once:

• The agonist muscles (the muscles that initiate the movement) must contract.
• The antagonist muscles (the muscles that inhibit the movement) must relax.

The brain is in charge of coordinating these movements, making sure the muscle groups do not accidentally conflict with each other. It does this by sending inhibitory or excitatory signals to the right muscle groups so they contract in a way that is synchronized and efficient.

For example, to pick up a fork, the triceps must activate to extend your arm, which means your bicep muscle must relax while this is happening. Otherwise, your elbow would bend at the wrong time and potentially drop the fork. The brain, therefore, will send signals to your bicep, telling it to relax so you can extend your arm with ease and complete the desired motion.  

After a stroke, however, your brain’s ability to send the correct signals to the muscle groups may be inhibited. This creates difficulty activating single muscle groups, meaning multiple muscle groups may fire at once instead of individually. As a result, these synergies become mixed up and strange or frustrating patterns can occur.

What Flexor Synergy Patterns After Stroke Mean for Recovery

Flexor synergy patterns after stroke are closely related to spasticity, or involuntary muscle firing. Spasticity occurs when there is a misfiring of signals between the brain and muscles, causing muscles to contract involuntarily, or spasm. As spasticity increases, so may the presence of flexor synergy patterns.

Flexor synergy patterns of the upper extremity after stroke commonly involve these main movements:

• External rotation and abduction of the shoulder
• Flexion of the elbow
• Supination or pronation of the forearm
• Flexion of the wrist and fingers

In other words, whenever you try to move your affected arm, your shoulder will raise, your elbow will bend, and your wrist may turn until your palm faces up while your fingers curl into a fist. This can also occur as a reaction to sudden, unplanned movements like when you cough or sneeze.

In addition to the upper extremity, the legs can also be affected by synergistic movement after stroke. For example, the flexor synergy pattern for the lower extremity generally involves hip flexion and external rotation, knee flexion, and ankle dorsiflexion. This can negatively affect activities like walking, dressing, and getting in and out of bed.

While flexor synergy movements can be irritating, they may represent a sign that you are making progress in your stroke recovery. Following a stroke, many individuals experience hemiparesis or hemiplegia, which refers to weakness or paralysis of one side of the body. Transitioning from a state of minimal muscle tone to the presence of synergy patterns after stroke indicates an increase in neural firing and recovery.

The different stages and transition periods after a stroke are described in the Brunnstrom stages of stroke recovery. Although each survivor experiences a unique recovery journey, these stages can provide a guide for what you may expect during stroke rehabilitation.

The Brunnstrom Stages of Stroke Recovery and Flexor Synergy Patterns

The Brunnstrom framework involves seven main stages in the stroke recovery process, with flexion synergy patterns appearing in stages 2 and 3. In the first Brunnstrom stage, the muscles are in a state of flaccidity. This means that messages from the brain are not connecting to your muscles, leaving them temporarily paralyzed.

As you enter stages 2 and 3, however, the brain has begun to re-establish a connection to the muscles, and the muscles start to finally “wake up.”  That is when synergy patterns can emerge. Flexor synergy patterns are your brain’s way of relearning how to control your muscles again.

It is important to note that while flexor synergy patterns can indicate recovery and be used in some compensation strategies, it is critical to continue pursuing dedicated rehabilitation to progress through this stage. If unaddressed, flexor synergies can worsen over time and lead to muscle shortening in the flexed position, called muscle contractures. The road to recovery is slow, but it is possible to help it along with consistent rehab techniques, which we will review next.

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Treating Flexor Synergy Patterns After Stroke

The best way to overcome flexor synergy patterns after stroke is through repetitive and meaningful practice of stroke rehabilitation exercises. This helps activate neuroplasticity and rewire the brain, which can encourage and promote recovery after a stroke.

The more you move your affected muscles, the more your brain can create new neural pathways that will reestablish communication with muscle groups. As these pathways become stronger, muscle coordination will improve and normal muscle synergy can be restored. This highlights the importance of high repetition or massed practice. The more a skill is practiced, the more efficient that skill will become.

Of course, this can be hard to do, especially when movements are unnatural or difficult. That’s why your best option is to work with your physical therapist and occupational therapist to find the ideal approach. They will create a treatment plan to address your individual goals and maximize your independence.

Here are some examples of exercises you can do to overcome flexor synergy patterns:

1. Passive Exercises and Stretching

Passive range-of-motion exercises can help you maintain range of motion and may assist in regaining muscle control. During passive exercises, you can use your unaffected limbs to move your affected limbs through their full range of motion. This stretches the muscles, helps maintain joint mobility, and prevents development of contractures or a clenched hand.

If performing passive range of motion independently isn’t possible for you, a therapist or trained caregiver can help you perform these exercises. Even though you technically aren’t performing the motion yourself, passive range of motion will still stimulate the brain and rekindle the neural networks that help you move. 

2. Sensory Exercises

Sensory stimulus plays a crucial role in synergistic movements. This sensory input is what allows your muscles to know how and where to move, which is why it is vital for stroke recovery and improving flexor synergy.

For example, the receptors in the muscles that send proprioceptive information help the brain determine where your joints are in space. This lets the brain choose which muscles it needs to activate to complete a movement. After a stroke, sensation can be diminished. Therefore, sensory exercises can help you restore normal sensation and improve your proprioception and movement as a result.

To help you get started, here are two helpful sensory exercises you can try at home:

• Joint sensation. Sit blindfolded on a chair and have a caregiver move your arm to several different positions. Try to identify where your arm is without looking.
• Fingertip touch. While still blindfolded, have someone touch each of your fingertips separately. Your goal is to correctly name which finger they touch. Then take off the blindfold and see if you were right.

Due to the brain’s neuroplasticity, the more consistently you stimulate your senses, the faster your brain will relearn how to interpret sensation and improve synergy patterns. Your physical and occupational therapists can give you more ideas on how to improve sensation and proprioception after stroke.

3. Active Range-of-Motion Exercises

Active exercises are the best way to increase proper synergy patterns and regain voluntary movement. Even if active motion is difficult and inefficient at first, persistence and daily practice is necessary to regain function and help resolve flexor synergy patterns after stroke.

Examples of active exercises include:

• Hand to opposite knee. Sit on a chair and lean against the chair’s back, holding your head up high. Move your affected hand from your lap to your opposite knee.
• Hand to chin. While sitting in the same position, move your hand from your lap to your chin and back down. This gives you a chance to practice elbow flexion and extension.
• Diagonal arm raise. In sitting or standing, start with your arm straight and positioned at the opposite hip. Then raise the arm high while bringing it diagonally across your body to open up your chest and shoulder.

Again, when you first start these motions, you may not be able to do them correctly or efficiently. However, you are still activating neuroplasticity and promoting rewiring of important motor pathways, so do what you can and keep practicing.

Over time, these active range of motion exercises may become less challenging, and you will be able to progress to more involved strengthening activities with the guidance of your rehab team. Strengthening the opposing muscle groups can help inhibit flexor synergy patterns and improve function with daily activities.

See all active stroke rehab exercises »

4. Weight-Bearing Exercises

One excellent way to improve proprioception and decrease spasticity is to perform weight-bearing exercises with the affected limb. Placing some of your body’s weight through your upper extremity can help provide a low-load, long-duration stretch to the muscles.

Additionally, weight-bearing provides proprioceptive input to the receptors within your joints and can also reduce neuron excitability, decreasing the presence of spasticity or a flexor synergy over time.

Some examples of weight-bearing exercises for the upper extremity include:

• Seated weight-bearing through the wrist. While sitting, place your hands flat on the chair or bed next you. This can also be done with help from a caregiver. Slowly shift weight from side to side, gradually increasing the weight placed through your affected arm.
• Prone weight-bearing on elbows. With assistance if needed, lay on your stomach with your forearms on the surface below you for support. Try to press your chest up tall, keeping your weight on your forearms. If able, slowly shift your weight from side to side.

5. Manual Techniques and Modalities

In addition to passive and active exercises, your therapist may use other techniques to help address your flexor synergy pattern after stroke. For example, they may use manual strategies to help engage the desired muscle groups, such as tapping the muscle or providing other sensory feedback.

They can also perform manual techniques to help decrease tone or spasticity. This might include low-intensity passive range of motion, prolonged stretching, and joint approximation or compression. Additionally, your therapist can help you achieve weightbearing positions to help reduce flexor synergy patterns and encourage proprioceptive feedback while giving you the support you need to complete the activity safely.

Another common technique used in therapy, electrical stimulation, can be a valuable tool to combat flexor synergy patterns after stroke. This modality is applied via electrodes placed on the skin, sending electrical impulses to the desired nerves and muscles. This electrical stimulation can elicit muscle contraction to encourage appropriate muscle synergy, decrease spasticity, and improve arm function.

Flexor Synergy Patterns After Stroke: Key points

Flexor synergy patterns are common after stroke and cause multiple muscle groups to fire at once. Although this can be a sign of improving communication between your brain and muscles, flexor synergies can be uncomfortable and can lead to complications if not addressed.

Fortunately, you can overcome flexor synergy patterns with consistent practice of therapeutic rehab exercises. These movements help rewire the brain and allow you to isolate the correct muscle groups, improving coordination and fluidity of movement. These improvements take time, high repetition, and consistency, but your therapy team is there to help support you during your recovery journey. 

With enough practice, you can regain voluntary control of your muscles and move your arm smoothly again. We hope this article has helped explain flexor synergy patterns after stroke and the different treatments and techniques available to help you maximize your recovery.

— Update: 11-02-2023 — cohaitungchi.com found an additional article A Neuroanatomical Framework for Upper Limb Synergies after Stroke from the website www.ncbi.nlm.nih.gov for the keyword muscle synergy patterns of le after stroke.

Mechanisms of Synergy Formation

To make sense of the ways in which stroke can alter muscle synergies, we need first to appreciate the relationship between the anatomical and physiological basis for synergy formation, and the deficit caused by the stroke, remembering that both acute and chronic changes occur. Abstractly, synergies represent low-dimensional movement information expressed in a higher dimensional space of possible activations. Some synergies may arise purely from functional coordination of high-dimensional structures (“functional synergies”). These functional synergies could be considered “soft” in the sense there are not dedicated anatomical structures existing to subserve them. For example, the spatiotemporal dynamics of upper limb movement change markedly in the context of bimanual tasks, even though the anatomical substrate (for a single side) is identical between unimanual and bimanual conditions (Kelso et al., 1979). Alternatively, synergies may be constructed in synergy-specific anatomical structures and then at some subsequent point in the motor pathway that information would have to diverge to the different muscles. These “anatomical synergies” would be “hard,” in the sense that the combinations of muscles involved will be relatively fixed. Soft synergies resulting purely from functional co-activation are therefore potentially more dynamic and context-dependent than hard synergies.

In healthy humans, the corticospinal tract (CST) is the principal conveyor of voluntary drive to the upper limb (Lemon, 2008). Consequently, it is along this neural pathway that the source of synergies has been proposed. The least flexible hard synergies are presumably expressed by dedicated interneuron networks within the spinal cord. Microstimulation in the spinal cord of frogs [reviewed in Bizzi et al. (2008) or rats (Tresch and Bizzi, 1999)] activates combinations of muscles that depend on the precise stimulation location, generate directed movements, and can be combined to form natural behaviors like jumping and swimming. This result has been taken as evidence of the existence in the spinal cord of anatomical modules that construct hard muscle synergies. Overduin et al. (2012) found that microstimulation of the motor cortex activated combinations of very similar synergies to those observed in natural grasping. That cortical activation gives rise to multiple different synergies suggests that their site of generation lies downstream of the cortex, either in the brainstem or spinal cord.

Mapping studies have been used to identify regions of cerebral cortex connected to a particular muscle, either by direct anatomical tract tracing (Rathelot and Strick, 2006), single cell recording (Schieber and Hibbard, 1993), or assessing functional connectivity with transcranial magnetic stimulation (TMS; Devanne et al., 2006). Instead of the neat, somatotopic arrangement of muscles implied by the motor homunculus concept [which was actually an oversimplification of the reports of Penfield; see Penfield (1954)], maps derived using these methods show that muscle representations on the cortical surface have distributed, complex shapes that overlap with areas connected to other muscles. Overlapping maps are consistent with an anatomical basis for cortical control of hard synergies, since such an architecture means that activation at a single locus on the cortex results in activation of all of the muscles represented at that point, and as the region of activation increases in area, neighboring regions can be recruited in a systematic manner (Wickens et al., 1994; Rathelot and Strick, 2006; Capaday et al., 2013). Distributed muscle representations in primary motor cortex, along with extensive horizontal projections (Huntley and Jones, 1991) may provide a flexible network substrate for soft synergies. A cortical basis for synergies is further supported by the observation that discharge of single corticomotor neurons strongly correlates with activity in a functional set of muscles (Holdefer and Miller, 2002). These different mechanisms and sites of synergy formation, functional, spinal, and cortical, are not mutually exclusive, and it seems likely that all could have effects depending on the context.

Figure ​Figure11 shows a schematic of motor control structures and descending pathways from the cortex to muscles. C1–5 represent functionally differentiated cortical modules, capturing the repertoire of theorized modes of descending output. These need not correspond to specific anatomical structures, while their relative spatial arrangement is suggestive of the distributed arrangement seen in the cortex, where adjoining regions can represent non-contiguous muscles. C1 and C5 are connected via direct CST fibers to motor neuron pools in the spinal cord. Such individuated cortical connectivity is typical of distal muscles. C4 is similarly connected, but represents a cortical synergy, potentially distinct anatomical regions that are modulated as a unit by common inputs and producing correlated outputs. C2 and C3 connect in a one-to-one fashion to spinal synergy modules (S1 and S2) that each have branching, overlapping connectivity to motor neuron pools. A lateral connection between the descending pathways from C2 to the S1 module is latent (dashed) in the healthy condition. Finally, interhemispheric pathways exist from C4 and C5 to the contralateral motor cortex. The contralateral cortex contains, among others, connections to the brainstem and alternative descending pathways such as the cortico-reticulo-propriospinal pathway (CRPP), which divergently innervate multiple, primarily proximal motor neuron pools.