Parkinson’s disease and Levodopa effects on muscle synergies in postural perturbation

Mileti, Ilaria; Zampogna, Alessandro; Taborri, Juri; Martelli, Francesca; Rossi, Stefano; Prete, Zaccaria Del; Paoloni, Marco; Suppa, Antonio; Palermo, Eduardo

doi:10.1109/memea.2019.8802229

Cited by 6 publications

(18 citation statements)

References 25 publications

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“…Kinematic analysis in laboratory settings during motor learning sequencing tasks has shown that PD patients after levodopa medication are able to better perform sequential movements 44,45 . When directly recorded at the muscle level, reduced muscle synergy has been observed in PD patients 46,47 . In agreement with these observations, our study showed a reduction of movements with disease severity and an improvement with levodopa medications.…”

Section: Discussionmentioning

confidence: 99%

Using an unbiased symbolic movement representation to characterize Parkinson’s disease states

Abrami

Heisig

Ramos

et al. 2020

Sci Rep

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Unconstrained human movement can be broken down into a series of stereotyped motifs or 'syllables' in an unsupervised fashion. Sequences of these syllables can be represented by symbols and characterized by a statistical grammar which varies with external situational context and internal neurological state. By first constructing a Markov chain from the transitions between these syllables then calculating the stationary distribution of this chain, we estimate the overall severity of parkinson's symptoms by capturing the increasingly disorganized transitions between syllables as motor impairment increases. comparing stationary distributions of movement syllables has several advantages over traditional neurologist administered in-clinic assessments. this technique can be used on unconstrained at-home behavior as well as scripted in-clinic exercises, it avoids differences across human evaluators, and can be used continuously without requiring scripted tasks be performed. We demonstrate the effectiveness of this technique using movement data captured with commercially available wrist worn sensors in 35 participants with Parkinson's disease in-clinic and 25 participants monitored at home.

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Section: Discussionmentioning

confidence: 99%

Using an unbiased symbolic movement representation to characterize Parkinson’s disease states

Abrami

Heisig

Ramos

et al. 2020

Sci Rep

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“…Ten research articles investigated muscle synergies in PD patients [71,83,[87][88][89][90][91][92][93][94]. According to the aim of the study, the research articles could be divided into three main groups: (i) six articles investigating muscle synergies during postural tasks with and without external perturbations (balance) [71,83,[89][90][91]93], (ii) three articles investigating muscle synergies during walking tasks (locomotion) [87,88,93], and (iii) two articles investigating muscle synergies during upper limb movements (resting tremor and reaching) [92,94]. Three out of the six articles investigating muscle synergies during postural tasks in PD were reported by a single research group [71,83,90].…”

Section: Muscle Synergies In Parkinson's Diseasementioning

confidence: 99%

“…Previous studies enrolled PD patients according to different inclusion criteria, including (i) diagnosis of idiopathic PD [71,83,[87][88][89][90][91][92][93][94], (ii) absence of clinically overt postural instability [71], (iii) absence of other neurological disorders [89,90] or any other comorbidity possibly affecting motor control, including polyneuropathies or significant visual, vestibular, or musculoskeletal disorders [91], (iv) absence of cognitive impairment [91,92], and (v) ability to independently perform the experimental motor tasks in any state of therapy, including active and inactive deep brain stimulation (respectively DBS-ON and DBS-OFF) [90]. Demographic and anthropometric data considered in most of the studies included: (i) age, (ii) body mass, (iii) height, (iv) sex, (v) disease onset side, and (vi) disease duration.…”

Section: Muscle Synergies In Parkinson's Diseasementioning

confidence: 99%

“…Most studies used the Unified Parkinson's Disease Rating Scale (UPDRS) part III [71,83,[88][89][90][91][92][93] and the H&Y [83,[91][92][93] to assess motor symptoms in PD patients. Two studies provided information about patients' clinical phenotype (i.e., tremor dominant or postural instability/gait difficulty dominant) [92,93].…”

Section: Muscle Synergies In Parkinson's Diseasementioning

confidence: 99%

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Muscle Synergies in Parkinson’s Disease

Mileti

Zampogna

Santuz

et al. 2020

Sensors

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Over the last two decades, experimental studies in humans and other vertebrates have increasingly used muscle synergy analysis as a computational tool to examine the physiological basis of motor control. The theoretical background of muscle synergies is based on the potential ability of the motor system to coordinate muscles groups as a single unit, thus reducing high-dimensional data to low-dimensional elements. Muscle synergy analysis may represent a new framework to examine the pathophysiological basis of specific motor symptoms in Parkinson’s disease (PD), including balance and gait disorders that are often unresponsive to treatment. The precise mechanisms contributing to these motor symptoms in PD remain largely unknown. A better understanding of the pathophysiology of balance and gait disorders in PD is necessary to develop new therapeutic strategies. This narrative review discusses muscle synergies in the evaluation of motor symptoms in PD. We first discuss the theoretical background and computational methods for muscle synergy extraction from physiological data. We then critically examine studies assessing muscle synergies in PD during different motor tasks including balance, gait and upper limb movements. Finally, we speculate about the prospects and challenges of muscle synergy analysis in order to promote future research protocols in PD.

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“…Muscle synergies are a valuable tool to understand the mechanisms behind motor control in a quantitative and non-invasive way. Applications range from the medical field (e.g., monitoring of patients suffering from neurological/neurodegenerative diseases [ 1 , 2 , 3 ] or joint disorders [ 4 ]), to the rehabilitation field (e.g., pre/post-treatment comparisons [ 5 , 6 ]), to the robotic field (e.g., control of robotic devices or exoskeletons [ 7 , 8 ]), to the sport field [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

An Algorithm for Choosing the Optimal Number of Muscle Synergies during Walking

Ballarini

Ghislieri

Knaflitz

et al. 2021

Sensors

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In motor control studies, the 90% thresholding of variance accounted for (VAF) is the classical way of selecting the number of muscle synergies expressed during a motor task. However, the adoption of an arbitrary cut-off has evident drawbacks. The aim of this work is to describe and validate an algorithm for choosing the optimal number of muscle synergies (ChoOSyn), which can overcome the limitations of VAF-based methods. The proposed algorithm is built considering the following principles: (1) muscle synergies should be highly consistent during the various motor task epochs (i.e., remaining stable in time), (2) muscle synergies should constitute a base with low intra-level similarity (i.e., to obtain information-rich synergies, avoiding redundancy). The algorithm performances were evaluated against traditional approaches (threshold-VAF at 90% and 95%, elbow-VAF and plateau-VAF), using both a simulated dataset and a real dataset of 20 subjects. The performance evaluation was carried out by analyzing muscle synergies extracted from surface electromyographic (sEMG) signals collected during walking tasks lasting 5 min. On the simulated dataset, ChoOSyn showed comparable performances compared to VAF-based methods, while, in the real dataset, it clearly outperformed the other methods, in terms of the fraction of correct classifications, mean error (ME), and root mean square error (RMSE). The proposed approach may be beneficial to standardize the selection of the number of muscle synergies between different research laboratories, independent of arbitrary thresholds.

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Parkinson’s disease and Levodopa effects on muscle synergies in postural perturbation

Cited by 6 publications

References 25 publications

Using an unbiased symbolic movement representation to characterize Parkinson’s disease states

Using an unbiased symbolic movement representation to characterize Parkinson’s disease states

Muscle Synergies in Parkinson’s Disease

An Algorithm for Choosing the Optimal Number of Muscle Synergies during Walking

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