Insight into the Role of Gut Microbiota in Duchenne Muscular Dystrophy: An Age-Related Study in Mdx Mice.

Dystrophin deficiency alters the sarcolemma structure, leading to muscle dystrophy, muscle disuse, and ultimately death. Beyond limb muscle deficits, patients with Duchenne muscular dystrophy have numerous transit disorders. Many studies have highlighted the strong relationship between gut microbiota and skeletal muscle. The aims of this study were: i) to characterize the gut microbiota composition over time up to 1 year in dystrophin-deficient mdx mice, and ii) to analyze the intestine structure and function and expression of genes linked to bacterial-derived metabolites in ileum, blood, and skeletal muscles to study interorgan interactions. Mdx mice displayed a significant reduction in the overall number of different operational taxonomic units and their abundance (α-diversity). Mdx genotype predicted 20% of ß-diversity divergence, with a large taxonomic modification of Actinobacteria, Proteobacteria, Tenericutes, and Deferribacteres phyla and the included genera. Interestingly, mdx intestinal motility and gene expressions of tight junction and Ffar2 receptor were down-regulated in the ileum. Concomitantly, circulating inflammatory markers related to gut microbiota (tumor necrosis factor, IL-6, monocyte chemoattractant protein-1) and muscle inflammation Tlr4/Myd88 pathway (Toll-like receptor 4, which recognizes pathogen-associated molecular patterns) were up-regulated. Finally, in mdx mice, adiponectin was reduced in blood and its receptor modulated in muscles. This study highlights a specific gut microbiota composition and highlights interorgan interactions in mdx physiopathology with gut microbiota as the potential central metabolic organ.

Electrophysiological Mapping During Brain Tumor Surgery: Recording Cortical Potentials Evoked Locally, Subcortically and Remotely by Electrical Stimulation to Assess the Brain Connectivity On-line.

Direct electrical stimulation (DES) is used to perform functional brain mapping during awake surgery and in epileptic patients. DES may be coupled with the measurement of Evoked Potentials (EP) to study the conductive and integrative properties of activated neural ensembles and probe the spatiotemporal dynamics of short- and long-range networks. However, its electrophysiological effects remain by far unknown. We recorded ECoG signals on two patients undergoing awake brain surgery and measured EP on functional sites after cortical stimulations and were the firsts to record three different types of EP on the same patients. Using low-intensity (1-3 mA) to evoke electrogenesis we observed that: (i) "true" remote EPs are attenuated in amplitude and delayed in time due to the divergence of white matter pathways; (ii) "false" remote EPs are attenuated but not delayed: as they originate from the same electrical source; (iii) Singular but reproducible positive components in the EP can be generated when the DES is applied in the temporal lobe or the premotor cortex; and (iv) rare EP can be triggered when the DES is applied subcortically: these can be either negative, or surprisingly, positive. We proposed different activation and electrophysiological propagation mechanisms following DES, based on the nature of activated neural elements and discussed important methodological pitfalls when measuring EP in the brain. Altogether, these results pave the way to map the connectivity in real-time between the DES and the recording sites; to characterize the local electrophysiological states and to link electrophysiology and function. In the future, and in practice, this technique could be used to perform electrophysiological mapping in order to link (non)-functional to electrophysiological responses with DES and could be used to guide the surgical act itself.

Radius selection using kernel density estimation for the computation of nonlinear measures.

When nonlinear measures are estimated from sampled temporal signals with finite-length, a radius parameter must be carefully selected to avoid a poor estimation. These measures are generally derived from the correlation integral, which quantifies the probability of finding neighbors, i.e., pair of points spaced by less than the radius parameter. While each nonlinear measure comes with several specific empirical rules to select a radius value, we provide a systematic selection method. We show that the optimal radius for nonlinear measures can be approximated by the optimal bandwidth of a Kernel Density Estimator (KDE) related to the correlation sum. The KDE framework provides non-parametric tools to approximate a density function from finite samples (e.g., histograms) and optimal methods to select a smoothing parameter, the bandwidth (e.g., bin width in histograms). We use results from KDE to derive a closed-form expression for the optimal radius. The latter is used to compute the correlation dimension and to construct recurrence plots yielding an estimate of Kolmogorov-Sinai entropy. We assess our method through numerical experiments on signals generated by nonlinear systems and experimental electroencephalographic time series.

A Deep Learning Framework for Recognizing Both Static and Dynamic Gestures.

Intuitive user interfaces are indispensable to interact with the human centric smart environments. In this paper, we propose a unified framework that recognizes both static and dynamic gestures, using simple RGB vision (without depth sensing). This feature makes it suitable for inexpensive human-robot interaction in social or industrial settings. We employ a pose-driven spatial attention strategy, which guides our proposed Static and Dynamic gestures Network-StaDNet. From the image of the human upper body, we estimate his/her depth, along with the region-of-interest around his/her hands. The Convolutional Neural Network (CNN) in StaDNet is fine-tuned on a background-substituted hand gestures dataset. It is utilized to detect 10 static gestures for each hand as well as to obtain the hand image-embeddings. These are subsequently fused with the augmented pose vector and then passed to the stacked Long Short-Term Memory blocks. Thus, human-centred frame-wise information from the augmented pose vector and from the left/right hands image-embeddings are aggregated in time to predict the dynamic gestures of the performing person. In a number of experiments, we show that the proposed approach surpasses the state-of-the-art results on the large-scale Chalearn 2016 dataset. Moreover, we transfer the knowledge learned through the proposed methodology to the Praxis gestures dataset, and the obtained results also outscore the state-of-the-art on this dataset.

Attenuation and Delay of Remote Potentials Evoked by Direct Electrical Stimulation During Brain Surgery.

Direct electrical stimulation (DES) is used to perform functional brain mapping during awake surgery but its electrophysiological effects remain by far unknown. DES may be coupled with the measurement of evoked potentials (EPs) to study the conductive and integrative properties of activated neural ensembles and probe the spatiotemporal dynamics of short- and long-range networks. We recorded ECoG signals on two patients undergoing awake brain surgery and measured EPs on functional sites after cortical stimulations, using combinations of stimulation parameters. EPs were similar in shape but delayed in time and attenuated in amplitude when elicited from a different gyrus or remotely from the recording site. We were able to trigger remote EPs using low stimulation intensities. We propose different activation and electrophysiological propagation mechanisms following DES based on activated neural elements.

Alterations of EEG rhythms during motor preparation following awake brain surgery.

Slow-growing, infiltrative brain tumours may modify the electrophysiological balance between the two hemispheres. To determine whether and how asymmetry of EEG rhythms during motor preparation might occur following "awake brain surgery" for this type of tumour, we recorded electroencephalograms during a simple visuo-manual reaction time paradigm performed by the patients between 3 and 12 months after surgery and compared them to a control group of 8 healthy subjects. Frequency analyses revealed imbalances between the injured and healthy hemispheres. More particularly, we observed a power increase in the δ frequency band near the lesion site and a power increase in the α and ß frequency bands. Interestingly, these alterations seem to decrease for the two patients whose surgery were anterior to 9 months, independently of the size of the lesion. Reaction times did not reflect this pattern as they were clearly not inversely related to the anteriority of the surgery. Electrophysiology suggests here different processes of recovery compared to behavioral data and brings further insights for the understanding of EEG rhythms that should not be systematically confounded or assimilated with cognitive performances. EEG monitoring is rare for these patients, especially after awake brain surgery, however it is important.

Probabilistic analysis of recurrence plots generated by fractional Gaussian noise.

Recurrence plots of time series generated by discrete fractional Gaussian noise (fGn) processes are analyzed. We compute the probabilities of occurrence of consecutive recurrence points forming diagonals and verticals in the recurrence plot constructed without embedding. We focus on two recurrence quantification analysis measures related to these lines, respectively, the percent determinism and the laminarity ( LAM ). The behavior of these two measures as a function of the fGn's Hurst exponent H is investigated. We show that the dependence of the laminarity with respect to H is monotonic in contrast to the percent determinism. We also show that the length of the diagonal and vertical lines involved in the computation of percent determinism and laminarity has an influence on their dependence on H . Statistical tests performed on the LAM measure support its utility to discriminate fGn processes with respect to their H values. These results demonstrate that recurrence plots are suitable for the extraction of quantitative information on the correlation structure of these widespread stochastic processes.

Influence of repeated effort induced by a 6-min walk test on postural response in older sedentary women.

According to the latest recommendations, adults should exercise regularly at moderate intensity to improve aerobic fitness and body composition. However, it is unknown whether aerobic exercise at submaximal intensity has detrimental effects on balance in older sedentary adults. We explored the effects of two 6-min walk tests (6MWTs) on the postural responses in 49 sedentary women between 60 and 76 years old. We assumed that an increase in the center of pressure (COP) fluctuations or a loss in the complexity of the COP time series would be a sign of a deleterious effect on balance. We used kinematic stabilometric parameters, recurrence quantification analysis (RQA) and the central tendency measure (CTM). We refer to the measures obtained through RQA and CTM methods by dynamical measures. Repeated-measures analysis of variance showed no significant differences between the three sets of postural kinematic measures (before vs. after the first vs. after the second 6MWT). However, we observed significant differences between the three sets for the CTM measure in the antero-posterior direction (p < 0.002), RQA determinism in the medio-lateral (ML) direction (p < 0.0001), and RQA entropy in the ML direction (F = 5.93; p < 0.004).Our results indicate that the effects of moderate-intensity walking exercise on posture are not revealed by classical postural kinematic measures but only by dynamical measures. The loss of complexity in the COP time series observed after both the first and second 6MWTs may indicate presymptomatic deterioration in the postural adaptive capabilities of sedentary older women.

Integration of human walking gyroscopic data using empirical mode decomposition.

The present study was aimed at evaluating the Empirical Mode Decomposition (EMD) method to estimate the 3D orientation of the lower trunk during walking using the angular velocity signals generated by a wearable inertial measurement unit (IMU) and notably flawed by drift. The IMU was mounted on the lower trunk (L4-L5) with its active axes aligned with the relevant anatomical axes. The proposed method performs an offline analysis, but has the advantage of not requiring any parameter tuning. The method was validated in two groups of 15 subjects, one during overground walking, with 180° turns, and the other during treadmill walking, both for steady-state and transient speeds, using stereophotogrammetric data. Comparative analysis of the results showed that the IMU/EMD method is able to successfully detrend the integrated angular velocities and estimate lateral bending, flexion-extension as well as axial rotations of the lower trunk during walking with RMS errors of 1 deg for straight walking and lower than 2.5 deg for walking with turns.

Effects of Nonstationarity on Muscle Force Signals Regularity During a Fatiguing Motor Task.

Physiological signals present fluctuations that can be assessed from their temporal structure, also termed complexity. The complexity of a physiological signal is usually quantified using entropy estimators, such as Sample Entropy. Recent studies have shown a loss of force signal complexity with the development of neuromuscular fatigue. However, these studies did not consider the stationarity of the force signals which is an important prerequisite of Sample Entropy measurements. Here, we investigated the effect of the potential nonstationarity of force signals on the kinetics of neuromuscular fatigue-induced change in force signal's complexity. Eleven men performed submaximal intermittent isometric contractions of knee extensors until exhaustion. Neuromuscular fatigue was assessed from changes in voluntary and electrically evoked contractions. Sample Entropy values were computed from submaximal force signals throughout the fatiguing task. The Dickey-Fuller test was used to statistically investigate the stationarity of force signals and the Empirical Mode Decomposition was applied to detrend these signals. Maximal voluntary force, central voluntary activation and muscle twitch decreased throughout the task (all ), indicating the development of global, central and peripheral fatigue, respectively. We found an increase in Sample Entropy with fatigue ( p = 0.024 ) when not considering the nonstationarity of force signals (i.e., 43% of nonstationary signals). After applying the Empirical Mode Decomposition, we found a decrease in Sample Entropy with fatigue ( p = 0.002 ). These findings confirm the presence of nonstationarity in force signals during submaximal isometric contractions which influences the kinetics of Sample Entropy with neuromuscular fatigue.

Recurrence quantification analysis of surface electromyographic signal: sensitivity to potentiation and neuromuscular fatigue.

This study aimed to assess the capacity of recurrence quantification analysis (RQA) to detect potentiation and to determine the fatigue components to which RQA is sensitive. Fifteen men were divided in two groups [8 endurance-trained athletes (END) and 7 power-trained athletes (POW)]. They performed a 10-min intermittent (5s contraction, 5s rest) knee extension exercise at 50% of their maximal voluntary isometric contraction. Muscular fatigue and potentiation were evaluated with neurostimulation technique. Mechanical (peak torque, Pt) and electrophysiological (M-wave) responses following electrical stimulation of the femoral nerve were measured at rest and every 10s throughout exercise. Vastus lateralis muscle activity (root mean square, RMS) was recorded during each contraction, and RMS was normalized to M-wave area (RMS/M). During contraction, muscle activity was analyzed with RQA to obtain the percentage of determinism (%Det). At the beginning of exercise, a significant Pt increase (+52%, P<0.001) was observed in both groups, indicating potentiation. At this time, %Det remained constant in both groups, indicating that RQA did not detect potentiation. Thereafter, Pt decreased in POW from 5min 30s of exercise (-30%, P<0.001), reflecting impairment in excitation-contraction coupling, and %Det increased from 3min 30s (P<0.01). In END, Pt remained high and %Det was unchanged. These two results indicated that RQA detected the peripheral component of fatigue. Conversely, RQA did not detect central adaptation to fatigue since %Det remained constant when a significant increase in RMS/M (P<0.01) appeared in END.

Influence of noise on the sample entropy algorithm.

We study the effect of static additive noise on the sample entropy (SampEn) algorithm [J. S. Richman and J. R. Moorman, Am. J. Physiol. Heart Circ. Physiol. 278, 2039 (2000); R. B. Govindan et al., Physica A 376, 158 (2007)] for analyzing time series. Using surrogate data tests, we empirically investigate the ability of the SampEn index to detect nonlinearity in simulated time series corrupted by increased amounts of noise. Discrete and continuous chaotic and nonchaotic systems are included in the numerical experiments. Both Gaussian and uniformly distributed noises are considered. The results indicate that the SampEn statistic is a robust index for detecting nonlinearity in time series corrupted by observational noise.

Electrophysiological Activity Evoked by Direct Electrical Stimulation of the Human Brain: Interest of the P0 Component.

Direct electrical stimulation (DES) at 60 Hz is used clinically to perform real-time functional mapping of the brain and guide tumor resection during wide-awake neurosurgery. The electrophysiological effects of DES remain by far unknown, both locally and remotely. In this study, by lowering the DES frequency to 9 Hz and by using differential recording of electro-corticographic signals to improve the focality, we were able to observe that the amplitude of the initial P0 component of the direct cortical response increased when the inter-electrode distance was increased and the pulse width was decreased. This result strongly suggests that larger neural elements, including somas and axons of pyramidal neurons buried in deeper layers of the cortical column, are activated. Their activation produce the observed P0 component, which results from the synchronized summation of action potentials triggered by DES. Interestingly, the early P0 component was not observed during the usual 60 Hz DES. The study of the P0 component and subsequent evoked potentials may help decipher the effects of DES on the stimulated cortical column and identify the activation of underlying white matter fibers. This is crucial to better understand the electrophysiological diffusion of DES, especially at higher frequencies (e.g., 60 Hz).

Detecting determinism in short time series using a quantified averaged false nearest neighbors approach.

We propose a criterion to detect determinism in short time series. This criterion is based on the estimation of the parameter E2 defined by the averaged false neighbors method for analyzing time series [Cao, Physica D 110, 43 (1997)]. Using surrogate data testing with several chaotic and stochastic simulated time series, we show that the variation coefficient of E2 over a few values of the embedding dimension d defines a suitable statistic to detect determinism in short data sequences. This result holds for a time series generated by a high-dimensional chaotic system such as the Mackey-Glass one. Different decreasing lengths of the time series are included in the numerical experiments for both synthetic and real-world data. We also investigate the robustness of the criterion in the case of deterministic time series corrupted by additive noise.

Parameters Selection for Bivariate Multiscale Entropy Analysis of Postural Fluctuations in Fallers and Non-Fallers Older Adults.

Entropy measures are often used to quantify the regularity of postural sway time series. Recent methodological developments provided both multivariate and multiscale approaches allowing the extraction of complexity features from physiological signals; see "Dynamical complexity of human responses: A multivariate data-adaptive framework," in Bulletin of Polish Academy of Science and Technology, vol. 60, p. 433, 2012. The resulting entropy measures are good candidates for the analysis of bivariate postural sway signals exhibiting nonstationarity and multiscale properties. These methods are dependant on several input parameters such as embedding parameters. Using two data sets collected from institutionalized frail older adults, we numerically investigate the behavior of a recent multivariate and multiscale entropy estimator; see "Multivariate multiscale entropy: A tool for complexity analysis of multichannel data," Physics Review E, vol. 84, p. 061918, 2011. We propose criteria for the selection of the input parameters. Using these optimal parameters, we statistically compare the multivariate and multiscale entropy values of postural sway data of non-faller subjects to those of fallers. These two groups are discriminated by the resulting measures over multiple time scales. We also demonstrate that the typical parameter settings proposed in the literature lead to entropy measures that do not distinguish the two groups. This last result confirms the importance of the selection of appropriate input parameters.

Recurrence quantification analysis of human postural fluctuations in older fallers and non-fallers.

We investigate postural sway data dynamics in older adult fallers and non-fallers. Center of pressure (COP) signals were recorded during quiet standing in 28 older adults. The subjects were divided in two groups: with and without history of falls. COP time series were analyzed using recurrence quantification analysis (RQA) in both anteroposterior and mediolateral (ML) directions. Classical stabilometric variables (path length and range) were also computed. The results showed that RQA outputs quantifying predictability of COP fluctuations and Shannon entropy of recurrence plot diagonal line length distribution, were significantly higher in fallers, only for ML direction. In addition, the range of ML COP signals was also significantly higher in fallers. This result is in accordance with some findings of the literature and could be interpreted as an increased hip strategy in fallers. The RQA results seem coherent with the theory of loss of complexity with aging and disease. Our results suggest that RQA is a promising approach for the investigation of COP fluctuations in a frail population.

Dynamical and stabilometric measures are complementary for the characterization of postural fluctuations in older women.

We investigate the complementarities of several measures extracted from center of pressure (COP) recordings during quiet standing, in older women. The selected variables include classical stabilometric measures (SMs) and several dynamical measures (DMs). The computed DMs quantify various features of the temporal structure of COP signals, including predictability, regularity and smoothness of the trajectories. The postural fluctuations of a group of 101 healthy older women were recorded by means of a force platform. After estimating the SMs and DMs from the COP data, we used principal components analysis (PCA) to quantify the contribution of each measure. The results suggest that SMs and DMs are complementary. In addition, the different DMs are globally not redundant. This finding is a reinforcement argument in favor of the use of DMs as postural signatures.

A structurally optimal control model for predicting and analyzing human postural coordination.

This paper proposes a closed-loop optimal control model predicting changes between in-phase and anti-phase postural coordination during standing and related supra-postural activities. The model allows the evaluation of the influence of body dynamics and balance constraints onto the adoption of postural coordination. This model minimizes the instantaneous norm of the joint torques with a controller in the head space, in contrast with classical linear optimal models used in the postural literature and defined in joint space. The balance constraint is addressed with an adaptive ankle torque saturation. Numerical simulations showed that the model was able to predict changes between in-phase and anti-phase postural coordination modes and other non-linear transient dynamics phenomena.

Characterizing the dynamics of postural sway in humans using smoothness and regularity measures.

We investigate human postural sway velocity time series by computing two dynamical statistics quantifying the smoothness (the central tendency measure or CTM) and the regularity (the sample entropy or SampEn) of their underlying dynamics. The purpose of the study is to investigate the effect of aging and vision on the selected measures and to explore the nature of postural dynamics by performing surrogate data tests. A group of 14 young subjects was compared to a group of 11 older healthy subjects in two visual conditions: with eyes open (EO) and with eyes closed (EC). The results suggest that vision and age do not influence the two statistics of the velocity data in the same way. More specifically, the smoothness statistic is able to detect the aging effect. The regularity measure is sensitive to the visual feedback removal. In contrast with some findings in the literature, the results of the surrogate data tests indicate that the center of pressure velocity dynamics are stochastic and are not produced by a purely deterministic behavior. Finally, we discuss some potential implications of our results in terms of postural control mechanisms.