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A Neurophysiological Pattern as a Precursor of Work-Related Musculoskeletal Disorders Using EEG Combined with EMG. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18042001. [PMID: 33669544 PMCID: PMC7921951 DOI: 10.3390/ijerph18042001] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Revised: 02/12/2021] [Accepted: 02/14/2021] [Indexed: 12/13/2022]
Abstract
We aimed to determine the neurophysiological pattern that is associated with the development of musculoskeletal pain that is induced by biomechanical constraints. Twelve (12) young healthy volunteers (two females) performed two experimental realistic manual tasks for 30 min each: (1) with the high risk of musculoskeletal pain development and (2) with low risk for pain development. During the tasks, synchronized electroencephalographic (EEG) and electromyography (EMG) signals data were collected, as well as pain scores. Subsequently, two main variables were computed from neurophysiological signals: (1) cortical inhibition as Task-Related Power Increase (TRPI) in beta EEG frequency band (β.TRPI) and (2) muscle variability as Coefficient of Variation (CoV) from EMG signals. A strong effect size was observed for pain measurement under the high risk condition during the last 5 min of the task execution; with muscle fatigue, because the CoV has decreased below 18%. An increase in cortical inhibition (β.TRPI >50%) was observed after the 5th min of the task in both experimental conditions. These results suggest the following neurophysiological pattern—β.TRPI ≥ 50% and CoV ≤ 18%—as a possible indicator to monitor the development of musculoskeletal pain in the shoulder in the context of repeated and prolonged exposure to manual tasks.
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Nasseroleslami B, Dukic S, Broderick M, Mohr K, Schuster C, Gavin B, McLaughlin R, Heverin M, Vajda A, Iyer PM, Pender N, Bede P, Lalor EC, Hardiman O. Characteristic Increases in EEG Connectivity Correlate With Changes of Structural MRI in Amyotrophic Lateral Sclerosis. Cereb Cortex 2020; 29:27-41. [PMID: 29136131 DOI: 10.1093/cercor/bhx301] [Citation(s) in RCA: 66] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2017] [Indexed: 12/11/2022] Open
Abstract
Amyotrophic lateral sclerosis (ALS) is a terminal progressive adult-onset neurodegeneration of the motor system. Although originally considered a pure motor degeneration, there is increasing evidence of disease heterogeneity with varying degrees of extra-motor involvement. How the combined motor and nonmotor degeneration occurs in the context of broader disruption in neural communication across brain networks has not been well characterized. Here, we have performed high-density crossectional and longitudinal resting-state electroencephalography (EEG) recordings on 100 ALS patients and 34 matched controls, and have identified characteristic patterns of altered EEG connectivity that have persisted in longitudinal analyses. These include strongly increased EEG coherence between parietal-frontal scalp regions (in γ-band) and between bilateral regions over motor areas (in θ-band). Correlation with structural MRI from the same patients shows that disease-specific structural degeneration in motor areas and corticospinal tracts parallels a decrease in neural activity over scalp motor areas, while the EEG over the scalp regions associated with less extensively involved extra-motor regions on MRI exhibit significantly increased neural communication. Our findings demonstrate that EEG-based connectivity mapping can provide novel insights into progressive network decline in ALS. These data pave the way for development of validated cost-effective spectral EEG-based biomarkers that parallel changes in structural imaging.
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Affiliation(s)
- Bahman Nasseroleslami
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, the University of Dublin, 152-160 Pearse Street, Dublin, Ireland
| | - Stefan Dukic
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, the University of Dublin, 152-160 Pearse Street, Dublin, Ireland
| | - Michael Broderick
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, the University of Dublin, 152-160 Pearse Street, Dublin, Ireland
| | - Kieran Mohr
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, the University of Dublin, 152-160 Pearse Street, Dublin, Ireland
| | - Christina Schuster
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, the University of Dublin, 152-160 Pearse Street, Dublin, Ireland
| | - Brighid Gavin
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, the University of Dublin, 152-160 Pearse Street, Dublin, Ireland
| | - Russell McLaughlin
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, the University of Dublin, 152-160 Pearse Street, Dublin, Ireland.,Smurfit Institute of Genetics, Trinity College Dublin, the University of Dublin, College Street, Dublin, Ireland
| | - Mark Heverin
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, the University of Dublin, 152-160 Pearse Street, Dublin, Ireland
| | - Alice Vajda
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, the University of Dublin, 152-160 Pearse Street, Dublin, Ireland
| | - Parameswaran M Iyer
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, the University of Dublin, 152-160 Pearse Street, Dublin, Ireland
| | - Niall Pender
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, the University of Dublin, 152-160 Pearse Street, Dublin, Ireland.,Beaumont Hospital, Beaumont Road, Dublin, Ireland
| | - Peter Bede
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, the University of Dublin, 152-160 Pearse Street, Dublin, Ireland.,Beaumont Hospital, Beaumont Road, Dublin, Ireland
| | - Edmund C Lalor
- Trinity College Institute of Neuroscience, Trinity College Dublin, the University of Dublin, Lloyd Building, College Green, Dublin, Ireland.,Trinity Centre for Bioengineering, Trinity College Dublin, the University of Dublin, Trinity Biomedical Sciences Institute, 152-160 Pearse Street, Dublin, Ireland.,Department of Biomedical Engineering and Department of Neuroscience, University of Rochester, Rochester, NY, USA
| | - Orla Hardiman
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, the University of Dublin, 152-160 Pearse Street, Dublin, Ireland.,Beaumont Hospital, Beaumont Road, Dublin, Ireland.,Trinity College Institute of Neuroscience, Trinity College Dublin, the University of Dublin, Lloyd Building, College Green, Dublin, Ireland
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Savić AM, Lontis ER, Mrachacz‐Kersting N, Popović MB. Dynamics of movement‐related cortical potentials and sensorimotor oscillations during palmar grasp movements. Eur J Neurosci 2019; 51:1962-1970. [DOI: 10.1111/ejn.14629] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2018] [Revised: 09/17/2019] [Accepted: 11/18/2019] [Indexed: 11/29/2022]
Affiliation(s)
- Andrej M. Savić
- Signals and Systems Department School of Electrical Engineering University of Belgrade Belgrade Serbia
- Health Division Tecnalia Donostia‐San Sebastian Spain
| | - Eugen R. Lontis
- Department of Health Science and Technology Faculty of Medicine Aalborg University Aalborg Ø Denmark
| | - Natalie Mrachacz‐Kersting
- Fachbereich Informationstechnik Neurowissenschaften und Medizintechnik University of Applied Sciences and Arts Dortmund Germany
| | - Mirjana B. Popović
- Signals and Systems Department School of Electrical Engineering University of Belgrade Belgrade Serbia
- Institute for Medical Research University of Belgrade Belgrade Serbia
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Farjadian AB, Nabian M, Hartman A, Yen SC, Nasseroleslami B. Visuomotor control of ankle joint using position vs. force. Eur J Neurosci 2019; 50:3235-3250. [PMID: 31273853 DOI: 10.1111/ejn.14502] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2018] [Revised: 05/31/2019] [Accepted: 06/19/2019] [Indexed: 11/27/2022]
Abstract
Ankle joint plays a critical role in daily activities involving interactions with environment using force and position control. Neuromechanical dysfunctions (e.g., due to stroke or brain injury), therefore, have a major impact on individuals' quality of life. The effective design of neuro-rehabilitation protocols for robotic rehabilitation platforms relies on understanding the control characteristics of the ankle joint in interaction with external environment using force and position, as the findings in upper limb may not be generalizable to the lower limb. This study aimed to characterize the skilled performance of ankle joint in visuomotor position and force control. A two-degree-of-freedom (DOF) robotic footplate was used to measure individuals' force and position. Healthy individuals (n = 27) used ankle force or position for point-to-point and tracking control tasks in 1-DOF and 2-DOF virtual game environments. Subjects' performance was quantified as a function of accuracy and completion time. In contrast to comparable performance in 1-DOF control tasks, the performance in 2-DOF tasks was different and had characteristic patterns in the position and force conditions, with a significantly better performance for position. Subjective questionnaires on the perceived difficulty matched the objective experimental results, suggesting that the poor performance in force control was not due to experimental set-up or fatigue but can be attributed to the different levels of challenge needed in neural control. It is inferred that in visuomotor coordination, the neuromuscular specialization of ankle provides better control over position rather than force. These findings can inform the design of neuro-rehabilitation platforms, selection of effective tasks and therapeutic protocols.
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Affiliation(s)
- Amir Bahador Farjadian
- Active Adaptive Control Laboratory, Massachusetts Institute of Technology, Boston, MA, USA
| | - Mohsen Nabian
- Department of Mechanical & Industrial Engineering, Northeastern University, Boston, MA, USA.,Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Amber Hartman
- Department of Physical Therapy, Movement & Rehabilitation Sciences, Northeastern University, Boston, MA, USA
| | - Sheng-Che Yen
- Department of Physical Therapy, Movement & Rehabilitation Sciences, Northeastern University, Boston, MA, USA
| | - Bahman Nasseroleslami
- Academic Unit of Neurology, Trinity College Dublin, The University of Dublin, Dublin, Ireland
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Iyer PM, Mohr K, Broderick M, Gavin B, Burke T, Bede P, Pinto-Grau M, Pender NP, McLaughlin R, Vajda A, Heverin M, Lalor EC, Hardiman O, Nasseroleslami B. Mismatch Negativity as an Indicator of Cognitive Sub-Domain Dysfunction in Amyotrophic Lateral Sclerosis. Front Neurol 2017; 8:395. [PMID: 28861032 PMCID: PMC5559463 DOI: 10.3389/fneur.2017.00395] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2017] [Accepted: 07/25/2017] [Indexed: 12/11/2022] Open
Abstract
Objective To evaluate the utility of mismatch negativity (MMN), a neurophysiologic marker of non-motor cognitive processing, in amyotrophic lateral sclerosis (ALS). Methods 89 patients, stratified into 4 different phenotypic presentations of ALS (67 spinal-onset, 15 bulbar-onset, 7 ALS-FTD, 7 C9ORF72 gene careers), and 19 matched controls underwent 128-channel EEG data recording. Subjects were presented with standard auditory tones interleaved with pitch-deviant tones in three recording blocks. The MMN response was quantified by peak amplitude, peak delay, average amplitude, and average delay, 100–300 ms after stimuli. 64 patients underwent cognitive screening using the Edinburgh Cognitive and Behavioural ALS Screen (ECAS), and 38 participants underwent contemporaneous cognitive assessment using the Stroop Color–Word Interference test (CWIT), which measures attention shift, inhibitory control, and error monitoring. Results The MMN response was observed in frontal and frontocentral regions of patient and control groups. Compared to controls, waveforms were attenuated in early onset, and the average delay was significantly increased in all of the ALS subgroups, with no significant difference between subgroups. Comparing with the control response, the ALS MMN response clustered into four new subgroups characterized by differences in response latency. The increased average delay correlated with changes in the Stroop CWIT; however, it did not show a direct relationship with age, gender, traditional phenotypes, revised ALS Functional Rating Scale, or ECAS scores. Conclusion and significance The MMN response in ALS patients reflects the cognitive dysfunction in specific sub-domains, as the new patient subgroups, identified by cluster analysis, do not segregate with existing clinical or cognitive classifications. Event-related potentials can provide additional quantitative neurophysiologic measures of impairment in specific cognitive sub-domains from which it may be possible to generate novel biologically relevant subgroups of ALS.
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Affiliation(s)
- Parameswaran Mahadeva Iyer
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland.,Department of Neurology, Beaumont Hospital, Dublin, Ireland
| | - Kieran Mohr
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland
| | - Michael Broderick
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland
| | - Brighid Gavin
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland
| | - Tom Burke
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland.,Department of Psychology, Beaumont Hospital, Dublin, Ireland
| | - Peter Bede
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland
| | - Marta Pinto-Grau
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland.,Department of Psychology, Beaumont Hospital, Dublin, Ireland
| | - Niall P Pender
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland.,Department of Psychology, Beaumont Hospital, Dublin, Ireland
| | - Russell McLaughlin
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland
| | - Alice Vajda
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland
| | - Mark Heverin
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland
| | - Edmund C Lalor
- Trinity College Institute of Neuroscience, Trinity College Dublin, The University of Dublin, Dublin, Ireland.,Trinity Centre for Bioengineering, Trinity College Dublin, The University of Dublin, Dublin, Ireland.,Department of Biomedical Engineering and Department of Neuroscience, University of Rochester, Rochester, NY, United States
| | - Orla Hardiman
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland.,Department of Neurology, Beaumont Hospital, Dublin, Ireland.,Trinity College Institute of Neuroscience, Trinity College Dublin, The University of Dublin, Dublin, Ireland
| | - Bahman Nasseroleslami
- Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland
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Li XP, Xia Q, Qu D, Wu TC, Yang DG, Hao WD, Jiang X, Li XM. The dynamic dielectric at a brain functional site and an EM wave approach to functional brain imaging. Sci Rep 2014; 4:6893. [PMID: 25367217 PMCID: PMC4219156 DOI: 10.1038/srep06893] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2014] [Accepted: 10/15/2014] [Indexed: 11/12/2022] Open
Abstract
Functional brain imaging has tremendous applications. The existing methods for functional brain imaging include functional Magnetic Resonant Imaging (fMRI), scalp electroencephalography (EEG), implanted EEG, magnetoencephalography (MEG) and Positron Emission Tomography (PET), which have been widely and successfully applied to various brain imaging studies. To develop a new method for functional brain imaging, here we show that the dielectric at a brain functional site has a dynamic nature, varying with local neuronal activation as the permittivity of the dielectric varies with the ion concentration of the extracellular fluid surrounding neurons in activation. Therefore, the neuronal activation can be sensed by a radiofrequency (RF) electromagnetic (EM) wave propagating through the site as the phase change of the EM wave varies with the permittivity. Such a dynamic nature of the dielectric at a brain functional site provides the basis for an RF EM wave approach to detecting and imaging neuronal activation at brain functional sites, leading to an RF EM wave approach to functional brain imaging.
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Affiliation(s)
- X P Li
- Neuroengineering Lab, National University of Singapore, 9 Engineering Drive 1, Singapore 117575
| | - Q Xia
- Newrocare Pte Ltd, 6 EU Tong Sen Street, #12-03, The Central, Singapore 059817
| | - D Qu
- Neuroengineering Lab, National University of Singapore, 9 Engineering Drive 1, Singapore 117575
| | - T C Wu
- Neuroengineering Lab, National University of Singapore, 9 Engineering Drive 1, Singapore 117575
| | - D G Yang
- Guilin University of Electronic Technology, No.1 Jinji Road, Guilin 541004, Guangxi, P.R. China
| | - W D Hao
- Guilin University of Electronic Technology, No.1 Jinji Road, Guilin 541004, Guangxi, P.R. China
| | - X Jiang
- Guilin University of Electronic Technology, No.1 Jinji Road, Guilin 541004, Guangxi, P.R. China
| | - X M Li
- Guilin University of Electronic Technology, No.1 Jinji Road, Guilin 541004, Guangxi, P.R. China
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Detection of Movement Intention from Movement-Related Cortical Potentials with Different Paradigms. BIOSYSTEMS & BIOROBOTICS 2014. [DOI: 10.1007/978-3-319-08072-7_42] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
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