Non-Parametric Functional Muscle Network as a Robust Biomarker of Fatigue

O'Keeffe, Rory; Shirazi, Seyed Yahya; Yang, Jingxiang; Mehrdad, Sarmad; Rao, Smita; Atashzar, S. Farokh

doi:10.1109/jbhi.2023.3234960

Cited by 5 publications

(7 citation statements)

References 102 publications

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“…These results emphasize the significance of muscle networks in evaluating functional tasks that involve complex coordination of multiple muscles. Furthermore, the high sensitivity of muscle network to neurophysiological and functional changes recently shown in our work [15], [16], [17], [18] in combination with the high reliability observed in this study, endorses the use of topographical measures of muscle network as a comprehensive neurophysiological biomarker. Given the high clinical relevance of the tasks chosen in this study, the strong reliability across sessions suggests a potential for use in rehabilitation contexts.…”

Section: Discussionsupporting

confidence: 79%

“…For example, the results of this study support the extrapolation of recent successes of muscle networks in detecting neurophysiological changes caused by the following conditions to go beyond observational assessment and approach development of novel biomarkers: (a) stroke and sensory stimulation (see our recent work [15]), (b) fatigue in healthy subjects (see our recent work [18]), or (c) underlying muscle hypertension and various functional tasks (see our recent work [17], [16]). At the node/muscle level, the excellent reliability for the network metrics of targeted muscles during the dynamic tasks (for example, VL bilaterally, Fig.…”

Section: Discussionsupporting

confidence: 75%

“…It should be highlighted that using a similar formulation of muscle network as the one used in this current paper, in our recent observational study, we investigated the behavior of network degree and weighted clustering coefficient when compared to classic spectrotemporal measures [18]. In [18] we showed that the network metrics can detect significant changes before to after fatigue protocol in a consistent manner, at the group, individual, and muscle levels while spectrotemporal metrics failed to detect consistently. The superior between-session reliability of the network over spectrotemporal metrics displayed here (Figs.…”

Section: Discussionmentioning

confidence: 98%

“…In this paper, the non-parametric Spearman power correlation network was constructed for each epoch of the sEMG signals that were bandpass-filtered between 20 and 50 Hz [18]. In short, the Spearman power correlation quantifies the monotonic trends between the power of two signals, even if they are not linearly related.…”

Section: Methodsmentioning

confidence: 99%

“…Our recent studies have shed light on the clinical benefits of various formulations of muscle networks. In this regard (a) we have recently shown that nonlinear informationtheory-based temporal-domain functional muscle networks of skeletal muscles provide excellent conformity for the active muscles across stroke subjects and show high sensitivity to sensorimotor integration [15]; (b) we have shown that linear frequency-domain functional muscle network of perilaryngeal muscles can discriminate subtle changes in vocal tasks and be a potential biomarker of vocal hyperfunction [16], [17]; (c) we have also shown that non-parametric muscle network can be a sensitive biomarker of lower-limb fatigue with superior sensitivity when compared with conventional spectrotemporal biomarkers of fatigue [18]. As the next significant step, in this paper and for the first time, we investigate whether non-parametric functional muscle networks can secure test-retest reliability for within- and between-session settings during lightly-controlled lower limb tasks, which would be needed for any biomarker tracking the performance.…”

Section: Introductionmentioning

confidence: 99%

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Lower-limb Nonparametric Functional Muscle Network: Test-retest Reliability Analysis

O'Keeffe

Yang

Mehrdad

et al. 2023

Preprint

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{Objective:} Functional muscle network analysis has attracted a great deal of interest in recent years, promising high sensitivity to changes of intermuscular synchronicity, studied mostly for healthy subjects and recently for patients living with neurological conditions (e.g., those caused by stroke). Despite the promising results, the between- and within-session reliability of the functional muscle network measures are yet to be established. Here, for the first time, we question and evaluate the test-retest reliability of non-parametric lower-limb functional muscle networks for controlled and lightly-controlled tasks, i.e., sit-to-stand, and over-the-ground walking, respectively, in healthy subjects. {Method:} Fifteen subjects (eight females) were included over two sessions on two different days. The muscle activity was recorded using 14 surface electromyography (sEMG) sensors. The intraclass correlation coefficient (ICC) of the within-session and between-session trials was quantified for the various network metrics, including degree and weighted clustering coefficient. In order to compare with common classical sEMG measures, the reliabilities of the root mean square (RMS) of sEMG and the median frequency (MDF) of sEMG were also calculated. {Results:} The ICC analysis revealed superior between-session reliability for muscle networks, with statistically significant differences when compared to classic measures. {Conclusion and Significance:} This paper proposed that the topographical metrics generated from functional muscle network can be reliably used for multi-session observations securing high reliability for quantifying the distribution of synergistic intermuscular synchronicities of both controlled and lightly controlled lower limb tasks. In addition, the low number of sessions required by the topographical network metrics to reach reliable measurements indicates the potential as biomarkers during rehabilitation.

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Section: Discussionsupporting

confidence: 79%

Section: Discussionsupporting

confidence: 75%

Section: Discussionmentioning

confidence: 98%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Lower-limb Nonparametric Functional Muscle Network: Test-retest Reliability Analysis

O'Keeffe

Yang

Mehrdad

et al. 2023

Preprint

Self Cite

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ξ-π: a nonparametric model for neural power spectra decomposition

Hu,

Zhang,

Zhang

et al. 2023

Preprint

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The power spectra estimated from the iEEG, EEG, MEG recordings, and spike trains are the mixed representation of aperiodic transient activity and periodic oscillations, i.e., aperiodic component (AC) and periodic component (PC). Quantitative neurophysiology requires precise decomposition preceding parameterizing each component. However, the shape, statistical distribution, scale, and mixing mechanism of AC and PCs are unclear, challenging the effectiveness of current popular parametric models such as FOOOF, IRASA, BOSC, etc. Here,ξ-πwas proposed to decompose the neural spectra by embedding the nonparametric spectra estimation with penalized Whittle likelihood and the shape language modeling into the expectation maximization framework.ξ-πwas validated on the synthesized spectra with loss statistics and on the real spectra from the sleep EEG and the large sample iEEG with evaluation metrics and neurophysiological evidence. Compared to FOOOF, both the simulation presenting shape irregularities and the batch simulation with multiple isolated peaks indicated thatξ-πimproved the fit of AC and PCs with less loss and higher F1-score in recognizing the centering frequencies and the number of peaks; the sleep EEG revealed thatξ-πproduced more distinguishable AC exponents and improved the sleep state classification accuracy; the iEEG showed thatξ-πapproached the clinical findings in peak discovery. Overall,ξ-πoffered good performance in the spectra decomposition, which allows flexible parameterization using descriptive statistics or kernel functions.ξ-πmay be a promising tool for brain signal decoding in the fields such as cognitive neuroscience, brain-computer interface, neurofeedback, and brain diseases.

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$\bm{\xi}$-$\bm{\pi}$: A Nonparametric Model for Neural Power Spectra Decomposition

Hu,

Zhang,

Zhang

et al. 2024

IEEE J. Biomed. Health Inform.

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The power spectra estimated from the brain recordings are the mixed representation of aperiodic transient activity and periodic oscillations, i.e., aperiodic component (AC) and periodic component (PC). Quantitative neurophysiology requires precise decomposition preceding parameterizing each component. However, the shape, statistical distribution, scale, and mixing mechanism of AC and PCs are unclear, challenging the effectiveness of current popular parametric models such as FOOOF, IRASA, BOSC, etc. Here, ξ-π was proposed to decompose the neural spectra by embedding the nonparametric spectra estimation with penalized Whittle likelihood and the shape language modeling into the expectation maximization framework. ξ-π was validated on the synthesized spectra with loss statistics and on the sleep EEG and the large sample iEEG with evaluation metrics and neurophysiological evidence. Compared to FOOOF, both the simulation presenting shape irregularities and the batch simulation with multiple isolated peaks indicated that ξ-π improved the fit of AC and PCs with less loss and higher F1-score in recognizing the centering frequencies and the number of peaks; the sleep EEG revealed that ξ-π produced more distinguishable AC exponents and improved the sleep state classification accuracy; the iEEG showed that ξ-π approached the clinical findings in peak discovery. Overall, ξ-π offered good performance in the spectra decomposition, which allows flexible parameterization using descriptive statistics or kernel functions. ξ-π is a seminal tool for brain signal decoding in fields such as cognitive neuroscience, brain-computer interface, neurofeedback, and brain diseases.

show abstract

Non-Parametric Functional Muscle Network as a Robust Biomarker of Fatigue

Cited by 5 publications

References 102 publications

Lower-limb Nonparametric Functional Muscle Network: Test-retest Reliability Analysis

Lower-limb Nonparametric Functional Muscle Network: Test-retest Reliability Analysis

ξ-π: a nonparametric model for neural power spectra decomposition

$\bm{\xi}$-$\bm{\pi}$: A Nonparametric Model for Neural Power Spectra Decomposition

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