EMG Rectification Is Detrimental for Identifying Abnormalities in Corticomuscular and Intermuscular Coherence in Spinocerebellar Ataxia Type 2

Ruiz-Gonzalez, Yusely; Velázquez‐Pérez, Luis; Rodríguez‐Labrada, Roberto; Torres‐Vega, Reidenis; Ziemann, Ulf

doi:10.1007/s12311-020-01149-z

Cited by 11 publications

(9 citation statements)

References 35 publications

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“…Abnormal CMC was present even in the absence of clinical evidence of corticospinal tract involvement in the aforementioned study. Other studies from the same investigators demonstrated lower IMCβ in symptomatic SCA2 patients as well as those in the prodromal state of the disease (27,28).…”

Section: Discussionmentioning

confidence: 84%

“…The use of recti cation in calculating intermuscular coherence has been heavily debated (27,(31)(32)(33)(34), with most studies using recti ed EMG. We chose not to rectify EMG signals because a previous study showed a clear difference between neurotypical subjects and ALS patients with upper motor neuron dysfunction using IMC from unrecti ed EMG signals (20), and because recti cation has been shown to signi cantly attenuate IMC in the γ frequency band (27). We also have con rmed that recti cation abolishes the γ band coherence (data not shown), and thus recti cation could not be used to differentiate SCA patients from neurotypical subjects.…”

Section: Discussionmentioning

confidence: 99%

“…Coherence in neurophysiological signals measured from sensorimotor cortex, subcortical regions, and lower motor neurons represents synchronization of neuronal activity in these spatially distant structures involved in a coordinated movement (11). Corticomuscular coherence (CMC) in the β frequency range (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) is present between oscillatory activity of motor cortex and active motor units in the contralateral limbs (12)(13)(14)(15)(16). The cortically originated signals can also be assessed by measuring the coherence between co-activated muscles (intermuscular coherence; IMC) in a subset of the βγ frequency ranges (20-40 Hz) (12,14,17,18).…”

Section: Introductionmentioning

confidence: 99%

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Intermuscular coherence in spinocerebellar ataxias 3 and 6: a preliminary study

Issa

Aydın

Bhatnagar

et al. 2023

Preprint

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Objective: Spinocerebellar ataxias (SCAs) are familial neurodegenerative diseases involving the cerebellum and spinocerebellar tracts. While there is variable involvement of corticospinal tracts (CST), dorsal root ganglia, and motor neurons in SCA3, SCA6 is characterized by a pure, late-onset ataxia. Abnormal intermuscular coherence in the beta-gamma frequency range (IMCbg) implies lack of integrity of CST or the afferent input from the acting muscles. We test the hypothesis that IMCbg has the potential to be a biomarker of disease activity in SCA3 but not SCA6. Methods:Intermuscular coherence between biceps and brachioradialis muscles was measured from surface EMG waveforms in SCA3 (N=16) and SCA6 (N=20) patients, and in neurotypical subjects (N=23). Results: IMC peak frequencies were present in the b range in SCA patients and in the g range in neurotypical subjects. The difference between IMC amplitudes in the g and b ranges was significant when comparing neurotypical control subjects to SCA3 (p < 0.01) and SCA6 (p = 0.01) patients. IMCbg amplitude was smaller in SCA3 patients compared to neurotypical subjects (p<0.05), but not different between SCA3 and SCA6 patients or between SCA6 and neurotypical subjects. Conclusion/significance:IMC metrics can differentiate SCA patients from normal controls.

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Section: Discussionmentioning

confidence: 84%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Intermuscular coherence in spinocerebellar ataxias 3 and 6: a preliminary study

Issa

Aydın

Bhatnagar

et al. 2023

Preprint

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“…Note that there are studies using EEG and MEG to investigate physiological features, dynamics, and networks in brain regions, outside of the cerebellum in these movement disorders. We also identified these studies that have used common measures such as source localization, connectivity, and coherence in ataxia [85][86][87][88][89][90][91], tremor , and dystonia to study brain physiology but these studies do not include the cerebellum; thus, they are not reviewed in detail here. The applications of EEG and MEG based source analysis and connectivity analysis have a wide range of utility.…”

Section: Discussionmentioning

confidence: 99%

Physiological Recordings of the Cerebellum in Movement Disorders

et al. 2022

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The cerebellum plays an important role in movement disorders, specifically in symptoms of ataxia, tremor, and dystonia. Understanding the physiological signals of the cerebellum contributes to insights into the pathophysiology of these movement disorders and holds promise in advancing therapeutic development. Non-invasive techniques such as electroencephalogram and magnetoencephalogram can record neural signals with high temporal resolution at the millisecond level, which is uniquely suitable to interrogate cerebellar physiology. These techniques have recently been implemented to study cerebellar physiology in healthy subjects as well as individuals with movement disorders. In the present review, we focus on the current understanding of cerebellar physiology using these techniques to study movement disorders.

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“…[113,114] Y. Ruiz-Gonzalez et al found that rectified EMG signals descend the significance level of the lower beta-frequency band and distort the coherence estimation in high-frequency portions, which contributes to a seriously bad impact on corticomuscular coherence (CMC) and intermuscular coherence (IMC) estimation. [115]…”

Section: Rectificationmentioning

confidence: 99%

Use of Advanced Materials and Artificial Intelligence in Electromyography Signal Detection and Interpretation

Gao

Gong

Chen

et al. 2022

Advanced Intelligent Systems

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Electromyography (EMG) is an integral part of many biomedical and healthcare applications. It has been used as a metric for tracking rehabilitation progress and identifying diseases that affect muscle activation patterns. Although it is widely used in many disciplines, conventional EMG recording and interpretation techniques lack in providing precise signal detection and robust classification accuracy. In recent years, thanks to advances in both material science and artificial intelligence, EMG detection techniques are improving at a rapid pace. Materials that allow for enhanced biocompatibility have improved the quality of data recorded by electrodes. The use of machine learning algorithms has paved new ways to understand complex EMG signals, triggering diverse, novel, and improved application scenarios within the healthcare framework. To help readers establish a clear picture of the two most important components in EMG technology, i.e., electrodes and algorithms, while catching up with the latest research outcomes, this review article is composed. The article starts by introducing conventional EMG electrode materials and architectures, then explains how state‐of‐the‐art works have improved electrode utilization. Subsequently, EMG signal conditioning and interpretation algorithms are investigated. Finally, current challenges in the research domain and authors’ perspectives are discussed.

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EMG Rectification Is Detrimental for Identifying Abnormalities in Corticomuscular and Intermuscular Coherence in Spinocerebellar Ataxia Type 2

Cited by 11 publications

References 35 publications

Intermuscular coherence in spinocerebellar ataxias 3 and 6: a preliminary study

Intermuscular coherence in spinocerebellar ataxias 3 and 6: a preliminary study

Physiological Recordings of the Cerebellum in Movement Disorders

Use of Advanced Materials and Artificial Intelligence in Electromyography Signal Detection and Interpretation

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