Synergy analysis as a tool to design and assess an effective stroke rehabilitation

Functional synergy recruitment index as a reliable biomarker of motor function and recovery in chronic stroke patients

Objective. Stroke affects the expression of muscle synergies underlying motor control, most notably in patients with poorer motor function. The majority of studies on muscle synergies have conventionally approached this analysis by assuming alterations in the inner structures of synergies after stroke. Although different synergy-based features based on this assumption have to some extent described pathological mechanisms in post-stroke neuromuscular control, a biomarker that reliably reflects motor function and recovery is still missing.Approach. Based on the theory of muscle synergies, we alternatively hypothesize that functional synergy structures are physically preserved and measure the temporal correlation between the recruitment profiles of healthy modules by paretic and healthy muscles, a feature hereafter reported as the FSRI. We measured clinical scores and extracted the muscle synergies of both ULs of 18 chronic stroke survivors from the electromyographic activity of 8 muscles during bilateral movements before and after 4 weeks of non-invasive BMI controlled robot therapy and physiotherapy. We computed the FSRI as well as features quantifying inter-limb structural differences and evaluated the correlation of these synergy-based measures with clinical scores.Main results. Correlation analysis revealed weak relationships between conventional features describing inter-limb synergy structural differences and motor function. In contrast, FSRI values during specific or combined movement data significantly correlated with UL motor function and recovery scores. Additionally, we observed that BMI-based training with contingent positive proprioceptive feedback led to improved FSRI values during the specific trained finger extension movement.Significance. We demonstrated that FSRI can be used as a reliable physiological biomarker of motor function and recovery in stroke, which can be targeted via BMI-based proprioceptive therapies and adjuvant physiotherapy to boost effective rehabilitation.

Design and effectiveness evaluation of mirror myoelectric interfaces: a novel method to restore movement in hemiplegic patients

The motor impairment occurring after a stroke is characterized by pathological muscle activation patterns or synergies. However, while robot-aided myoelectric interfaces have been proposed for stroke rehabilitation, they do not address this issue, which might result in inefficient interventions. Here, we present a novel paradigm that relies on the correction of the pathological muscle activity as a way to elicit rehabilitation, even in patients with complete paralysis. Previous studies demonstrated that there are no substantial inter-limb differences in the muscle synergy organization of healthy individuals. We propose building a subject-specific model of muscle activity from the healthy limb and mirroring it to use it as a learning tool for the patient to reproduce the same healthy myoelectric patterns on the paretic limb during functional task training. Here, we aim at understanding how this myoelectric model, which translates muscle activity into continuous movements of a 7-degree of freedom upper limb exoskeleton, could transfer between sessions, arms and tasks. The experiments with 8 healthy individuals and 2 chronic stroke patients proved the feasibility and effectiveness of such myoelectric interface. We anticipate the proposed method to become an efficient strategy for the correction of maladaptive muscle activity and the rehabilitation of stroke patients.

Evaluation of Functional Correlation of Task-Specific Muscle Synergies with Motor Performance in Patients Poststroke

The central nervous system produces movements by activating specifically programmed muscle synergies that are also altered with injuries in the brain, such as stroke. In this study, we hypothesize that there exists a positive correlation between task-specific muscle synergy and motor functions at joint and task levels in patients following stroke. The purpose here is to define and evaluate neurophysiological metrics based on task-specific muscle synergy for assessing motor functions in patients. A patient group of 10 subjects suffering from stroke and a control group of nine age-matched healthy subjects were recruited to participate in this study. Electromyography (EMG) signals and movement kinematics were recorded in patients and control subjects while performing arm reaching tasks. Muscle synergies of individual patients were extracted off-line from EMG records of each patient, and a baseline pattern of muscle synergy was obtained from the pooled EMG data of all nine control subjects. Peak velocities and movement durations of each reaching movement were computed from measured kinematics. Similarity indices of matching components to those of the baseline synergy were defined by synergy vectors and time profiles, respectively, as well as by a combined similarity of vector and time profile. Results showed that pathological synergies of patients were altered from the characteristics of baseline synergy with missing components, or varied vector patterns and time profiles. The kinematic performance measured by peak velocities and movement durations was significantly poorer for the patient group than the control group. In patients, all three similarity indices were found to correlate significantly to the kinematics of movements for the reaching tasks. The correlation to the Fugl-Meyer score of arm was the highest with the vector index, the lowest with the time profile index, and in between with the combined index. These findings illustrate that the analysis of task-specific muscle synergy can provide valuable insights into motor deficits for patients following stroke, and the task-specific similarity indices are useful neurophysiological metrics to predict the function of neuromuscular control at the joint and task levels for patients.

On identifying kinematic and muscle synergies: a comparison of matrix factorization methods using experimental data from the healthy population

Human motor behavior is highly goal directed, requiring the central nervous system to coordinate different aspects of motion generation to achieve the motion goals. The concept of motor synergies provides an approach to quantify the covariation of joint motions and of muscle activations, i.e., elemental variables, during a task. To analyze goal-directed movements, factorization methods can be used to reduce the high dimensionality of these variables while accounting for much of the variance in large data sets. Three factorization methods considered in this paper are principal component analysis (PCA), nonnegative matrix factorization (NNMF), and independent component analysis (ICA). Bilateral human reaching data sets are used to compare the methods, and advantages of each are presented and discussed. PCA and NNMF had a comparable performance on both EMG and joint motion data and both outperformed ICA. However, NNMF's nonnegativity condition for activation of basis vectors is a useful attribute in identifying physiologically meaningful synergies, making it a more appealing method for future studies. A simulated data set is introduced to clarify the approaches and interpretation of the synergy structures returned by the three factorization methods.

NEW & NOTEWORTHY

Literature on comparing factorization methods in identifying motor synergies using numerically generated, simulation, and muscle activation data from animal studies already exists. We present an empirical evaluation of the performance of three of these methods on muscle activation and joint angles data from human reaching motion: principal component analysis, nonnegative matrix factorization, and independent component analysis. Using numerical simulation, we also studied the meaning and differences in the synergy structures returned by each method. The results can be used to unify approaches in identifying and interpreting motor synergies.

The impact of visual feedback on the motor control of the upper-limb

Stroke is a leading cause of adult disability with upper-limb hemiparesis being one of the most frequent consequences. Given that stroke only affects the paretic arm's control structure (the set of synergies and activation vectors needed to perform a movement), we propose that the control structure of the non-affected arm can serve as a physiological reference to rehabilitate the paretic arm. However, it is unclear how rehabilitation can effectively tune the control structure of a patient. The use of Visual Feedback (VF) is recommended to boost stroke rehabilitation, as it is able to positively modify neural mechanisms and improve motor performance. Thus, in this study we investigate whether VF can effectively modify the control structure of the upper-limb. We asked six neurologically intact subjects to perform a complete upper-limb rehabilitation routine comprised of 12 movements in absence and presence of VF. Our results indicate that VF significantly increases interlimb similarity both in terms of synergies and activation coefficients. However, the magnitude of improvement depended upon each subject. In general, VF brings the control structure of the nondominant side closer to the control structure of dominant side, suggesting that VF modifies the control structure towards more optimized motor patterns. This is especially interesting because stroke mainly affects the activation coefficients of patients and because it has been shown that the control of the affected side resembles that of the nondominant side. In conclusion, VF may enhance motor performance by effectively tuning the control-structure. Notably, this finding offers new insights to design improved stroke rehabilitation.