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Martin del Campo Vera R, Sundaram S, Lee R, Lee Y, Leonor A, Chung RS, Shao A, Cavaleri J, Gilbert ZD, Zhang S, Kammen A, Mason X, Heck C, Liu CY, Kellis S, Lee B. Beta-band power classification of go/no-go arm-reaching responses in the human hippocampus. J Neural Eng 2024; 21:046017. [PMID: 38914073 PMCID: PMC11247508 DOI: 10.1088/1741-2552/ad5b19] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 05/25/2024] [Accepted: 06/24/2024] [Indexed: 06/26/2024]
Abstract
Objective.Can we classify movement execution and inhibition from hippocampal oscillations during arm-reaching tasks? Traditionally associated with memory encoding, spatial navigation, and motor sequence consolidation, the hippocampus has come under scrutiny for its potential role in movement processing. Stereotactic electroencephalography (SEEG) has provided a unique opportunity to study the neurophysiology of the human hippocampus during motor tasks. In this study, we assess the accuracy of discriminant functions, in combination with principal component analysis (PCA), in classifying between 'Go' and 'No-go' trials in a Go/No-go arm-reaching task.Approach.Our approach centers on capturing the modulation of beta-band (13-30 Hz) power from multiple SEEG contacts in the hippocampus and minimizing the dimensional complexity of channels and frequency bins. This study utilizes SEEG data from the human hippocampus of 10 participants diagnosed with epilepsy. Spectral power was computed during a 'center-out' Go/No-go arm-reaching task, where participants reached or withheld their hand based on a colored cue. PCA was used to reduce data dimension and isolate the highest-variance components within the beta band. The Silhouette score was employed to measure the quality of clustering between 'Go' and 'No-go' trials. The accuracy of five different discriminant functions was evaluated using cross-validation.Main results.The Diagonal-Quadratic model performed best of the 5 classification models, exhibiting the lowest error rate in all participants (median: 9.91%, average: 14.67%). PCA showed that the first two principal components collectively accounted for 54.83% of the total variance explained on average across all participants, ranging from 36.92% to 81.25% among participants.Significance.This study shows that PCA paired with a Diagonal-Quadratic model can be an effective method for classifying between Go/No-go trials from beta-band power in the hippocampus during arm-reaching responses. This emphasizes the significance of hippocampal beta-power modulation in motor control, unveiling its potential implications for brain-computer interface applications.
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Affiliation(s)
- Roberto Martin del Campo Vera
- Department of Neurological Surgery, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
| | - Shivani Sundaram
- Department of Neurological Surgery, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
| | - Richard Lee
- Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
| | - Yelim Lee
- Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
| | - Andrea Leonor
- Department of Neurological Surgery, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
| | - Ryan S Chung
- Department of Neurological Surgery, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
| | - Arthur Shao
- Department of Neurological Surgery, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
| | - Jonathon Cavaleri
- Department of Neurological Surgery, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
| | - Zachary D Gilbert
- Department of Neurological Surgery, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
| | - Selena Zhang
- Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States of America
| | - Alexandra Kammen
- Department of Neurological Surgery, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
| | - Xenos Mason
- Department of Neurological Surgery, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
- USC Neurorestoration Center, Keck School of Medicine of USC, Los Angeles, CA, United States of America
- Department of Neurology, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
- Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
| | - Christi Heck
- USC Neurorestoration Center, Keck School of Medicine of USC, Los Angeles, CA, United States of America
- Department of Neurology, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
- Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
| | - Charles Y Liu
- Department of Neurological Surgery, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
- USC Neurorestoration Center, Keck School of Medicine of USC, Los Angeles, CA, United States of America
- Department of Neurology, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
- Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
- Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States of America
| | - Spencer Kellis
- Department of Neurological Surgery, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
- USC Neurorestoration Center, Keck School of Medicine of USC, Los Angeles, CA, United States of America
- Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
| | - Brian Lee
- Department of Neurological Surgery, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
- USC Neurorestoration Center, Keck School of Medicine of USC, Los Angeles, CA, United States of America
- Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
- Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States of America
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Cabral HV, Cudicio A, Bonardi A, Del Vecchio A, Falciati L, Orizio C, Martinez-Valdes E, Negro F. Neural Filtering of Physiological Tremor Oscillations to Spinal Motor Neurons Mediates Short-Term Acquisition of a Skill Learning Task. eNeuro 2024; 11:ENEURO.0043-24.2024. [PMID: 38866498 DOI: 10.1523/eneuro.0043-24.2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 04/17/2024] [Accepted: 05/23/2024] [Indexed: 06/14/2024] Open
Abstract
The acquisition of a motor skill involves adaptations of spinal and supraspinal pathways to alpha motoneurons. In this study, we estimated the shared synaptic contributions of these pathways to understand the neural mechanisms underlying the short-term acquisition of a new force-matching task. High-density surface electromyography (HDsEMG) was acquired from the first dorsal interosseous (FDI; 7 males and 6 females) and tibialis anterior (TA; 7 males and 4 females) during 15 trials of an isometric force-matching task. For two selected trials (pre- and post-skill acquisition), we decomposed the HDsEMG into motor unit spike trains, tracked motor units between trials, and calculated the mean discharge rate and the coefficient of variation of interspike interval (COVISI). We also quantified the post/pre ratio of motor units' coherence within delta, alpha, and beta bands. Force-matching improvements were accompanied by increased mean discharge rate and decreased COVISI for both muscles. Moreover, the area under the curve within alpha band decreased by ∼22% (TA) and ∼13% (FDI), with no delta or beta bands changes. These reductions correlated significantly with increased coupling between force/neural drive and target oscillations. These results suggest that short-term force-matching skill acquisition is mediated by attenuation of physiological tremor oscillations in the shared synaptic inputs. Supported by simulations, a plausible mechanism for alpha band reductions may involve spinal interneuron phase-cancelling descending oscillations. Therefore, during skill learning, the central nervous system acts as a matched filter, adjusting synaptic weights of shared inputs to suppress neural components unrelated to the specific task.
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Affiliation(s)
- Hélio V Cabral
- Department of Clinical and Experimental Sciences, Università degli Studi di Brescia, Brescia 25123, Italy
| | - Alessandro Cudicio
- Department of Clinical and Experimental Sciences, Università degli Studi di Brescia, Brescia 25123, Italy
| | - Alberto Bonardi
- Department of Clinical and Experimental Sciences, Università degli Studi di Brescia, Brescia 25123, Italy
| | - Alessandro Del Vecchio
- Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander University, Erlangen 91052, Germany
| | - Luca Falciati
- Department of Clinical and Experimental Sciences, Università degli Studi di Brescia, Brescia 25123, Italy
| | - Claudio Orizio
- Department of Clinical and Experimental Sciences, Università degli Studi di Brescia, Brescia 25123, Italy
| | - Eduardo Martinez-Valdes
- School of Sport, Exercise and Rehabilitation Sciences, College of Life and Environmental Sciences, University of Birmingham, Birmingham B152TT, United Kingdom
| | - Francesco Negro
- Department of Clinical and Experimental Sciences, Università degli Studi di Brescia, Brescia 25123, Italy
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Cabral HV, Inglis JG, Cudicio A, Cogliati M, Orizio C, Yavuz US, Negro F. Muscle contractile properties directly influence shared synaptic inputs to spinal motor neurons. J Physiol 2024; 602:2855-2872. [PMID: 38709959 DOI: 10.1113/jp286078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Accepted: 04/18/2024] [Indexed: 05/08/2024] Open
Abstract
Alpha band oscillations in shared synaptic inputs to the alpha motor neuron pool can be considered an involuntary source of noise that hinders precise voluntary force production. This study investigated the impact of changing muscle length on the shared synaptic oscillations to spinal motor neurons, particularly in the physiological tremor band. Fourteen healthy individuals performed low-level dorsiflexion contractions at ankle joint angles of 90° and 130°, while high-density surface electromyography (HDsEMG) was recorded from the tibialis anterior (TA). We decomposed the HDsEMG into motor units spike trains and calculated the motor units' coherence within the delta (1-5 Hz), alpha (5-15 Hz), and beta (15-35 Hz) bands. Additionally, force steadiness and force spectral power within the tremor band were quantified. Results showed no significant differences in force steadiness between 90° and 130°. In contrast, alpha band oscillations in both synaptic inputs and force output decreased as the length of the TA was moved from shorter (90°) to longer (130°), with no changes in delta and beta bands. In a second set of experiments (10 participants), evoked twitches were recorded with the ankle joint at 90° and 130°, revealing longer twitch durations in the longer TA muscle length condition compared to the shorter. These experimental results, supported by a simple computational simulation, suggest that increasing muscle length enhances the muscle's low-pass filtering properties, influencing the oscillations generated by the Ia afferent feedback loop. Therefore, this study provides valuable insights into the interplay between muscle biomechanics and neural oscillations. KEY POINTS: We investigated whether changes in muscle length, achieved by changing joint position, could influence common synaptic oscillations to spinal motor neurons, particularly in the tremor band (5-15 Hz). Our results demonstrate that changing muscle length from shorter to longer induces reductions in the magnitude of alpha band oscillations in common synaptic inputs. Importantly, these reductions were reflected in the oscillations of muscle force output within the alpha band. Longer twitch durations were observed in the longer muscle length condition compared to the shorter, suggesting that increasing muscle length enhances the muscle's low-pass filtering properties. Changes in the peripheral contractile properties of motor units due to changes in muscle length significantly influence the transmission of shared synaptic inputs into muscle force output. These findings prove the interplay between muscle mechanics and neural adaptations.
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Affiliation(s)
- Hélio V Cabral
- Department of Clinical and Experimental Sciences, Università degli Studi di Brescia, Brescia, Italy
| | - J Greig Inglis
- Department of Clinical and Experimental Sciences, Università degli Studi di Brescia, Brescia, Italy
| | - Alessandro Cudicio
- Department of Clinical and Experimental Sciences, Università degli Studi di Brescia, Brescia, Italy
| | - Marta Cogliati
- Department of Clinical and Experimental Sciences, Università degli Studi di Brescia, Brescia, Italy
| | - Claudio Orizio
- Department of Clinical and Experimental Sciences, Università degli Studi di Brescia, Brescia, Italy
| | - Utku S Yavuz
- Biomedical Signals and Systems, University of Twente, Enschede, Netherlands
| | - Francesco Negro
- Department of Clinical and Experimental Sciences, Università degli Studi di Brescia, Brescia, Italy
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Mathieu E, Crémoux S, Duvivier D, Amarantini D, Pudlo P. Biomechanical modeling for the estimation of muscle forces: toward a common language in biomechanics, medical engineering, and neurosciences. J Neuroeng Rehabil 2023; 20:130. [PMID: 37752507 PMCID: PMC10521397 DOI: 10.1186/s12984-023-01253-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 09/18/2023] [Indexed: 09/28/2023] Open
Abstract
Different research fields, such as biomechanics, medical engineering or neurosciences take part in the development of biomechanical models allowing for the estimation of individual muscle forces involved in motor action. The heterogeneity of the terminology used to describe these models according to the research field is a source of confusion and can hamper collaboration between the different fields. This paper proposes a common language based on lexical disambiguation and a synthesis of the terms used in the literature in order to facilitate the understanding of the different elements of biomechanical modeling for force estimation, without questioning the relevance of the terms used in each field or the different model components or their interest. We suggest that the description should start with an indication of whether the muscle force estimation problem is solved following the physiological movement control (from the nervous drive to the muscle force production) or in the opposite direction. Next, the suitability of the model for force production estimation at a given time or for monitoring over time should be specified. Authors should pay particular attention to the method description used to find solutions, specifying whether this is done during or after data collection, with possible method adaptations during processing. Finally, the presence of additional data must be specified by indicating whether they are used to drive, assist, or calibrate the model. Describing and classifying models in this way will facilitate the use and application in all fields where the estimation of muscle forces is of real, direct, and concrete interest.
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Affiliation(s)
- Emilie Mathieu
- Univ. Polytechnique Hauts-de-France, LAMIH, CNRS, UMR 8201, Campus Mont Houy, 59313, Valenciennes, France
| | - Sylvain Crémoux
- Centre de Recherche Cerveau et Cognition (CerCO), UMR CNRS 5549, Paul Sabatier University, Toulouse, France
| | - David Duvivier
- Univ. Polytechnique Hauts-de-France, LAMIH, CNRS, UMR 8201, Campus Mont Houy, 59313, Valenciennes, France
| | - David Amarantini
- ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, Inserm, Paul Sabatier University, Toulouse, France.
| | - Philippe Pudlo
- Univ. Polytechnique Hauts-de-France, LAMIH, CNRS, UMR 8201, Campus Mont Houy, 59313, Valenciennes, France
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Olmos AA, Sterczala AJ, Parra ME, Dimmick HL, Miller JD, Deckert JA, Sontag SA, Gallagher PM, Fry AC, Herda TJ, Trevino MA. Sex-related differences in motor unit behavior are influenced by myosin heavy chain during high- but not moderate-intensity contractions. Acta Physiol (Oxf) 2023; 239:e14024. [PMID: 37551144 DOI: 10.1111/apha.14024] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 07/18/2023] [Accepted: 07/19/2023] [Indexed: 08/09/2023]
Abstract
AIMS Motor unit recruitment and firing rate patterns of the vastus lateralis (VL) have not been compared between sexes during moderate- and high-intensity contraction intensities. Additionally, the influence of fiber composition on potential sex-related differences remains unquantified. METHODS Eleven males and 11 females performed 40% and 70% maximal voluntary contractions (MVCs). Surface electromyographic (EMG) signals recorded from the VL were decomposed. Recruitment thresholds (RTs), MU action potential amplitudes (MUAPAMP ), initial firing rates (IFRs), mean firing rates (MFRs), and normalized EMG amplitude (N-EMGRMS ) at steady torque were analyzed. Y-intercepts and slopes were calculated for MUAPAMP , IFR, and MFR versus RT relationships. Type I myosin heavy chain isoform (MHC) was determined with muscle biopsies. RESULTS There were no sex-related differences in MU characteristics at 40% MVC. At 70% MVC, males exhibited greater slopes (p = 0.002) for the MUAPAMP , whereas females displayed greater slopes (p = 0.001-0.007) for the IFR and MFR versus RT relationships. N-EMGRMS at 70% MVC was greater for females (p < 0.001). Type I %MHC was greater for females (p = 0.006), and was correlated (p = 0.018-0.031) with the slopes for the MUAPAMP , IFR, and MFR versus RT relationships at 70% MVC (r = -0.599-0.585). CONCLUSION Both sexes exhibited an inverse relationship between MU firing rates and recruitment thresholds. However, the sex-related differences in MU recruitment and firing rate patterns and N-EMGRMS at 70% MVC were likely due to greater type I% MHC and smaller twitch forces of the higher threshold MUs for the females. Evidence is provided that muscle fiber composition may explain divergent MU behavior between sexes.
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Affiliation(s)
- Alex A Olmos
- Applied Neuromuscular Physiology Lab, Department of Kinesiology, Applied Health, and Recreation, Oklahoma State University, Stillwater, Oklahoma, USA
| | - Adam J Sterczala
- Neuromuscular Research Laboratory, Department of Sports Medicine and Nutrition, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Mandy E Parra
- School of Exercise and Sport Science, University of Mary Hardin-Baylor, Belton, Texas, USA
| | - Hannah L Dimmick
- Faculty of Kinesiology, University of Calgary, Calgary, Alberta, Canada
| | - Jonathan D Miller
- Department of Chemical and Petroleum Engineering, University of Kansas, Lawrence, Kansas, USA
| | - Jake A Deckert
- Department of Human Physiology, Gonzaga University, Spokane, Washington, USA
| | - Stephanie A Sontag
- Applied Neuromuscular Physiology Lab, Department of Kinesiology, Applied Health, and Recreation, Oklahoma State University, Stillwater, Oklahoma, USA
| | - Philip M Gallagher
- Applied Physiology Laboratory, Department of Health, Sport, and Exercise Sciences, University of Kansas, Lawrence, Kansas, USA
| | - Andrew C Fry
- Jayhawk Athletic Performance Laboratory - Wu Tsai Human Performance Alliance, University of Kansas, Lawrence, Kansas, USA
| | - Trent J Herda
- Neuromechanics Laboratory, Department of Health, Sport, and Exercise Sciences, University of Kansas, Lawrence, Kansas, USA
| | - Michael A Trevino
- Applied Neuromuscular Physiology Lab, Department of Kinesiology, Applied Health, and Recreation, Oklahoma State University, Stillwater, Oklahoma, USA
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Wang X, Luo Z, Zhang M, Zhao W, Xie S, Wong SF, Hu H, Li L. The interaction between changes of muscle activation and cortical network dynamics during isometric elbow contraction: a sEMG and fNIRS study. Front Bioeng Biotechnol 2023; 11:1176054. [PMID: 37180038 PMCID: PMC10167054 DOI: 10.3389/fbioe.2023.1176054] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 04/14/2023] [Indexed: 05/15/2023] Open
Abstract
Objective: The relationship between muscle activation during motor tasks and cerebral cortical activity remains poorly understood. The aim of this study was to investigate the correlation between brain network connectivity and the non-linear characteristics of muscle activation changes during different levels of isometric contractions. Methods: Twenty-one healthy subjects were recruited and were asked to perform isometric elbow contractions in both dominant and non-dominant sides. Blood oxygen concentrations in brain from functional Near-infrared Spectroscopy (fNIRS) and surface electromyography (sEMG) signals in the biceps brachii (BIC) and triceps brachii (TRI) muscles were recorded simultaneously and compared during 80% and 20% of maximum voluntary contraction (MVC). Functional connectivity, effective connectivity, and graph theory indicators were used to measure information interaction in brain activity during motor tasks. The non-linear characteristics of sEMG signals, fuzzy approximate entropy (fApEn), were used to evaluate the signal complexity changes in motor tasks. Pearson correlation analysis was used to examine the correlation between brain network characteristic values and sEMG parameters under different task conditions. Results: The effective connectivity between brain regions in motor tasks in dominant side was significantly higher than that in non-dominant side under different contractions (p < 0.05). The results of graph theory analysis showed that the clustering coefficient and node-local efficiency of the contralateral motor cortex were significantly varied under different contractions (p < 0.01). fApEn and co-contraction index (CCI) of sEMG under 80% MVC condition were significantly higher than that under 20% MVC condition (p < 0.05). There was a significant positive correlation between the fApEn and the blood oxygen value in the contralateral brain regions in both dominant or non-dominant sides (p < 0.001). The node-local efficiency of the contralateral motor cortex in the dominant side was positively correlated with the fApEn of the EMG signals (p < 0.05). Conclusion: In this study, the mapping relationship between brain network related indicators and non-linear characteristic of sEMG in different motor tasks was verified. These findings provide evidence for further exploration of the interaction between the brain activity and the execution of motor tasks, and the parameters might be useful in evaluation of rehabilitation intervention.
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Affiliation(s)
- Xiaohan Wang
- Institute of Medical Research, Northwestern Polytechnical University, Xi’an, China
| | - Zichong Luo
- Faculty of Science and Technology, University of Macau, Taipa, China
| | - Mingxia Zhang
- Institute of Medical Research, Northwestern Polytechnical University, Xi’an, China
| | - Weihua Zhao
- Hospital of Northwestern Polytechnical University, Xi’an, China
| | - Songyun Xie
- School of Electronics and Information, Northwestern Polytechnical University, Xi’an, China
| | - Seng Fat Wong
- Faculty of Science and Technology, University of Macau, Taipa, China
| | - Huijing Hu
- Institute of Medical Research, Northwestern Polytechnical University, Xi’an, China
| | - Le Li
- Institute of Medical Research, Northwestern Polytechnical University, Xi’an, China
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Nijenhuis B, Tijssen MAJ, van Zutphen T, van der Eb J, Otten E, Elting JW. Inter-muscular coherence in speed skaters with skater's cramp. Parkinsonism Relat Disord 2023; 107:105250. [PMID: 36563538 DOI: 10.1016/j.parkreldis.2022.105250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Revised: 12/09/2022] [Accepted: 12/15/2022] [Indexed: 12/23/2022]
Abstract
INTRODUCTION Skater's cramp is a career-ending movement disorder in expert speed skaters noted to be a likely task-specific dystonia. In other movement disorders, including task-specific dystonia, studies have found evidence of central dysregulation expressed as higher inter-muscular coherence. We looked at whether inter-muscular coherence was higher in affected skaters as a possible indicator that it is centrally driven, and by extension further evidence it is a task-specific dystonia. METHODS In 14 affected and 14 control skaters we calculated inter-muscular coherence in the theta-band in a stationary task where tonic muscle activation was measured at 10%, 20% and 50% of maximum voluntary contraction. Additionally, we calculated wavelet coherence while skating at key moments in the stroke cycle. RESULTS Coherence did not differ in the stationary activation task. While skating, coherence was higher in the impacted leg of affected skaters compared to their non-impacted leg, p = .05, η2 = 0.031, and amplitude of electromyography correlated with coherence in the impacted leg, p = .009, R2adjusted = 0.41. A sub-group of severely affected skaters (n = 6) had higher coherence in the impacted leg compared to the left and right leg of controls, p = .02, Cohen's d = 1.59 and p = .01, Cohen's d = 1.63 respectively. Results were less clear across the entire affected cohort probably due to a diverse case-mix. CONCLUSION Our results of higher coherence in certain severe cases of skater's cramp is preliminary evidence of a central dysregulation, making the likelihood it is a task-specific dystonia higher.
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Affiliation(s)
- B Nijenhuis
- Department of Neurology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands; Expertise Center Movement Disorders Groningen, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands; University of Groningen, Faculty Campus Fryslân, Leeuwarden, the Netherlands.
| | - M A J Tijssen
- Department of Neurology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands; Expertise Center Movement Disorders Groningen, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - T van Zutphen
- University of Groningen, Faculty Campus Fryslân, Leeuwarden, the Netherlands
| | - J van der Eb
- Leiden Institute of Advanced Computer Science, Leiden, the Netherlands
| | - E Otten
- University of Groningen, Department of Movement Sciences, Groningen, the Netherlands
| | - J W Elting
- Department of Neurology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands; Expertise Center Movement Disorders Groningen, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
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Lulic-Kuryllo T, Greig Inglis J. Sex differences in motor unit behaviour: A review. J Electromyogr Kinesiol 2022; 66:102689. [DOI: 10.1016/j.jelekin.2022.102689] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Revised: 07/28/2022] [Accepted: 07/29/2022] [Indexed: 10/15/2022] Open
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Simoneau M, Pialasse JP, Mercier P, Blouin JS. Adolescents with idiopathic scoliosis show decreased intermuscular coherence in lumbar paraspinal muscles: a new pathophysiological perspective. Clin Neurophysiol 2022; 138:38-51. [DOI: 10.1016/j.clinph.2022.03.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 02/18/2022] [Accepted: 03/02/2022] [Indexed: 11/03/2022]
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Flood MW, Grimm B. EntropyHub: An open-source toolkit for entropic time series analysis. PLoS One 2021; 16:e0259448. [PMID: 34735497 PMCID: PMC8568273 DOI: 10.1371/journal.pone.0259448] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Accepted: 10/18/2021] [Indexed: 11/24/2022] Open
Abstract
An increasing number of studies across many research fields from biomedical engineering to finance are employing measures of entropy to quantify the regularity, variability or randomness of time series and image data. Entropy, as it relates to information theory and dynamical systems theory, can be estimated in many ways, with newly developed methods being continuously introduced in the scientific literature. Despite the growing interest in entropic time series and image analysis, there is a shortage of validated, open-source software tools that enable researchers to apply these methods. To date, packages for performing entropy analysis are often run using graphical user interfaces, lack the necessary supporting documentation, or do not include functions for more advanced entropy methods, such as cross-entropy, multiscale cross-entropy or bidimensional entropy. In light of this, this paper introduces EntropyHub, an open-source toolkit for performing entropic time series analysis in MATLAB, Python and Julia. EntropyHub (version 0.1) provides an extensive range of more than forty functions for estimating cross-, multiscale, multiscale cross-, and bidimensional entropy, each including a number of keyword arguments that allows the user to specify multiple parameters in the entropy calculation. Instructions for installation, descriptions of function syntax, and examples of use are fully detailed in the supporting documentation, available on the EntropyHub website- www.EntropyHub.xyz. Compatible with Windows, Mac and Linux operating systems, EntropyHub is hosted on GitHub, as well as the native package repository for MATLAB, Python and Julia, respectively. The goal of EntropyHub is to integrate the many established entropy methods into one complete resource, providing tools that make advanced entropic time series analysis straightforward and reproducible.
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Affiliation(s)
- Matthew W. Flood
- Human Motion, Orthopaedics, Sports Medicine and Digital Methods (HOSD), Luxembourg Institute of Health (LIH), Eich, Luxembourg
| | - Bernd Grimm
- Human Motion, Orthopaedics, Sports Medicine and Digital Methods (HOSD), Luxembourg Institute of Health (LIH), Eich, Luxembourg
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Pethick J, Winter SL, Burnley M. Physiological complexity: influence of ageing, disease and neuromuscular fatigue on muscle force and torque fluctuations. Exp Physiol 2021; 106:2046-2059. [PMID: 34472160 DOI: 10.1113/ep089711] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Accepted: 08/26/2021] [Indexed: 11/08/2022]
Abstract
NEW FINDINGS What is the topic of this review? Physiological complexity in muscle force and torque fluctuations, specifically the quantification of complexity, how neuromuscular complexityis altered by perturbations and the potential mechanism underlying changes in neuromuscular complexity. What advances does it highlight? The necessity to calculate both magnitude- and complexity-based measures for the thorough evaluation of force/torque fluctuations. Also the need for further research on neuromuscular complexity, particularly how it relates to the performance of functional activities (e.g. manual dexterity, balance, locomotion). ABSTRACT Physiological time series produce inherently complex fluctuations. In the last 30 years, methods have been developed to characterise these fluctuations, and have revealed that they contain information about the function of the system producing them. Two broad classes of metrics are used: (1) those which quantify the regularity of the signal (e.g. entropy metrics); and (2) those which quantify the fractal properties of the signal (e.g. detrended fluctuation analysis). Using these techniques, it has been demonstrated that ageing results in a loss of complexity in the time series of a multitude of signals, including heart rate, respiration, gait and, crucially, muscle force or torque output. This suggests that as the body ages, physiological systems become less adaptable (i.e. the systems' ability to respond rapidly to a changing external environment is diminished). More recently, it has been shown that neuromuscular fatigue causes a substantial loss of muscle torque complexity, a process that can be observed in a few minutes, rather than the decades it requires for the same system to degrade with ageing. The loss of torque complexity with neuromuscular fatigue appears to occur exclusively above the critical torque (at least for tasks lasting up to 30 min). The loss of torque complexity can be exacerbated with previous exercise of the same limb, and reduced by the administration of caffeine, suggesting both peripheral and central mechanisms contribute to this loss. The mechanisms underpinning the loss of complexity are not known but may be related to altered motor unit behaviour as the muscle fatigues.
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Affiliation(s)
- Jamie Pethick
- School of Sport, Rehabilitation and Exercise Sciences, University of Essex, Colchester, UK
| | - Samantha L Winter
- School of Sport, Exercise and Health Sciences, Loughborough University, Loughborough, UK
| | - Mark Burnley
- Endurance Research Group, School of Sport and Exercise Sciences, University of Kent, Chatham Maritime, UK
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Deng L, Luo J, Lyu Y, Song R. Effects of Future Information and Trajectory Complexity on Kinematic Signal and Muscle Activation during Visual-Motor Tracking. ENTROPY 2021; 23:e23010111. [PMID: 33467619 PMCID: PMC7830702 DOI: 10.3390/e23010111] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Revised: 12/31/2020] [Accepted: 01/06/2021] [Indexed: 11/17/2022]
Abstract
Visual-motor tracking movement is a common and essential behavior in daily life. However, the contribution of future information to visual-motor tracking performance is not well understood in current research. In this study, the visual-motor tracking performance with and without future-trajectories was compared. Meanwhile, three task demands were designed to investigate their impact. Eighteen healthy young participants were recruited and instructed to track a target on a screen by stretching/flexing their elbow joint. The kinematic signals (elbow joint angle) and surface electromyographic (EMG) signals of biceps and triceps were recorded. The normalized integrated jerk (NIJ) and fuzzy approximate entropy (fApEn) of the joint trajectories, as well as the multiscale fuzzy approximate entropy (MSfApEn) values of the EMG signals, were calculated. Accordingly, the NIJ values with the future-trajectory were significantly lower than those without future-trajectory (p-value < 0.01). The smoother movement with future-trajectories might be related to the increasing reliance of feedforward control. When the task demands increased, the fApEn values of joint trajectories increased significantly, as well as the MSfApEn of EMG signals (p-value < 0.05). These findings enrich our understanding about visual-motor control with future information.
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Affiliation(s)
- Linchuan Deng
- Key Laboratory of Sensing Technology and Biomedical Instrument of Guangdong Province, Sun Yat-Sen University, Guangzhou 510006, China; (L.D.); (J.L.); (Y.L.)
| | - Jie Luo
- Key Laboratory of Sensing Technology and Biomedical Instrument of Guangdong Province, Sun Yat-Sen University, Guangzhou 510006, China; (L.D.); (J.L.); (Y.L.)
| | - Yueling Lyu
- Key Laboratory of Sensing Technology and Biomedical Instrument of Guangdong Province, Sun Yat-Sen University, Guangzhou 510006, China; (L.D.); (J.L.); (Y.L.)
| | - Rong Song
- Key Laboratory of Sensing Technology and Biomedical Instrument of Guangdong Province, Sun Yat-Sen University, Guangzhou 510006, China; (L.D.); (J.L.); (Y.L.)
- Shenzhen Research Institute, Sun Yat-Sen University, Shenzhen 518057, China
- Correspondence:
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McManus L, De Vito G, Lowery MM. Analysis and Biophysics of Surface EMG for Physiotherapists and Kinesiologists: Toward a Common Language With Rehabilitation Engineers. Front Neurol 2020; 11:576729. [PMID: 33178118 PMCID: PMC7594523 DOI: 10.3389/fneur.2020.576729] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Accepted: 09/07/2020] [Indexed: 12/31/2022] Open
Abstract
Recent decades have seen a move toward evidence-based medicine to inform the clinical decision-making process with reproducible findings from high-quality research studies. There is a need for objective, quantitative measurement tools to increase the reliability and reproducibility of studies evaluating the efficacy of healthcare interventions, particularly in the field of physical and rehabilitative medicine. Surface electromyography (sEMG) is a non-invasive measure of muscle activity that is widely used in research but is under-utilized as a clinical tool in rehabilitative medicine. Other types of electrophysiological signals (e.g., electrocardiography, electroencephalography, intramuscular EMG) are commonly recorded by healthcare practitioners, however, sEMG has yet to successfully transition to clinical practice. Surface EMG has clear clinical potential as an indicator of muscle activation, however reliable extraction of information requires knowledge of the appropriate methods for recording and analyzing sEMG and an understanding of the underlying biophysics. These concepts are generally not covered in sufficient depth in the standard curriculum for physiotherapists and kinesiologists to encourage a confident use of sEMG in clinical practice. In addition, the common perception of sEMG as a specialized topic means that the clinical potential of sEMG and the pathways to application in practice are often not apparent. The aim of this paper is to address barriers to the translation of sEMG by emphasizing its benefits as an objective clinical tool and by overcoming its perceived complexity. The many useful clinical applications of sEMG are highlighted and examples provided to illustrate how it can be implemented in practice. The paper outlines how fundamental biophysics and EMG signal processing concepts could be presented to a non-technical audience. An accompanying tutorial with sample data and code is provided which could be used as a tool for teaching or self-guided learning. The importance of observing sEMG in routine use in clinic is identified as an essential part of the effective communication of sEMG recording and signal analysis methods. Highlighting the advantages of sEMG as a clinical tool and reducing its perceived complexity could bridge the gap between theoretical knowledge and practical application and provide the impetus for the widespread use of sEMG in clinic.
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Affiliation(s)
- Lara McManus
- Neuromuscular Systems Laboratory, School of Electrical and Electronic Engineering, University College Dublin, Dublin, Ireland
| | - Giuseppe De Vito
- Neuromuscular Physiology Laboratory, Department of Biomedical Sciences, University of Padova, Padova, Italy
| | - Madeleine M Lowery
- Neuromuscular Systems Laboratory, School of Electrical and Electronic Engineering, University College Dublin, Dublin, Ireland
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Fleming JE, Orłowski J, Lowery MM, Chaillet A. Self-Tuning Deep Brain Stimulation Controller for Suppression of Beta Oscillations: Analytical Derivation and Numerical Validation. Front Neurosci 2020; 14:639. [PMID: 32694975 PMCID: PMC7339866 DOI: 10.3389/fnins.2020.00639] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Accepted: 05/25/2020] [Indexed: 01/06/2023] Open
Abstract
Closed-loop control strategies for deep brain stimulation (DBS) in Parkinson's disease offer the potential to provide more effective control of patient symptoms and fewer side effects than continuous stimulation, while reducing battery consumption. Most of the closed-loop methods proposed and tested to-date rely on controller parameters, such as controller gains, that remain constant over time. While the controller may operate effectively close to the operating point for which it is set, providing benefits when compared to conventional open-loop DBS, it may perform sub-optimally if the operating conditions evolve. Such changes may result from, for example, diurnal variation in symptoms, disease progression or changes in the properties of the electrode-tissue interface. In contrast, an adaptive or “self-tuning” control mechanism has the potential to accommodate slowly varying changes in system properties over a period of days, months, or years. Such an adaptive mechanism would automatically adjust the controller parameters to maintain the desired performance while limiting side effects, despite changes in the system operating point. In this paper, two neural modeling approaches are utilized to derive and test an adaptive control scheme for closed-loop DBS, whereby the gain of a feedback controller is continuously adjusted to sustain suppression of pathological beta-band oscillatory activity at a desired target level. First, the controller is derived based on a simplified firing-rate model of the reciprocally connected subthalamic nucleus (STN) and globus pallidus (GPe). Its efficacy is shown both when pathological oscillations are generated endogenously within the STN-GPe network and when they arise in response to exogenous cortical STN inputs. To account for more realistic biological features, the control scheme is then tested in a physiologically detailed model of the cortical basal ganglia network, comprised of individual conductance-based spiking neurons, and simulates the coupled DBS electric field and STN local field potential. Compared to proportional feedback methods without gain adaptation, the proposed adaptive controller was able to suppress beta-band oscillations with less power consumption, even as the properties of the controlled system evolve over time due to alterations in the target for beta suppression, beta fluctuations and variations in the electrode impedance.
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Affiliation(s)
- John E Fleming
- Neuromuscular Systems Laboratory, UCD School of Electrical & Electronic Engineering, University College Dublin, Dublin, Ireland
| | - Jakub Orłowski
- Laboratoire des Signaux et Systèmes, Université Paris-Saclay, CNRS, CentraleSupélec, Gif-sur-Yvette, France
| | - Madeleine M Lowery
- Neuromuscular Systems Laboratory, UCD School of Electrical & Electronic Engineering, University College Dublin, Dublin, Ireland
| | - Antoine Chaillet
- Laboratoire des Signaux et Systèmes, Université Paris-Saclay, CNRS, CentraleSupélec, Gif-sur-Yvette, France.,Institut Universitaire de France, Paris, France
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