Postural control in gymnasts: anisotropic fractal scaling reveals proprioceptive reintegration in vestibular perturbation

Dexterous postural control subtly complements movement variability with sensory correlations at many scales. The expressive poise of gymnasts exemplifies this lyrical punctuation of release with constraint, from coarse grain to fine scales. Dexterous postural control upon a 2D support surface might collapse the variation of center of pressure (CoP) to a relatively 1D orientation-a direction often oriented towards the focal point of a visual task. Sensory corrections in dexterous postural control might manifest in temporal correlations, specifically as fractional Brownian motions whose differences are more and less correlated with fractional Gaussian noises (fGns) with progressively larger and smaller Hurst exponent H. Traditional empirical work examines this arrangement of lower-dimensional compression of CoP along two orthogonal axes, anteroposterior (AP) and mediolateral (ML). Eyes-open and face-forward orientations cultivate greater variability along AP than ML axes, and the orthogonal distribution of spatial variability has so far gone hand in hand with an orthogonal distribution of H, for example, larger in AP and lower in ML. However, perturbing the orientation of task focus might destabilize the postural synergy away from its 1D distribution and homogenize the temporal correlations across the 2D support surface, resulting in narrower angles between the directions of the largest and smallest H. We used oriented fractal scaling component analysis (OFSCA) to investigate whether sensory corrections in postural control might thus become suborthogonal. OFSCA models raw 2D CoP trajectory by decomposing it in all directions along the 2D support surface and fits the directions with the largest and smallest H. We studied a sample of gymnasts in eyes-open and face-forward quiet posture, and results from OFSCA confirm that such posture exhibits the classic orthogonal distribution of temporal correlations. Head-turning resulted in a simultaneous decrease in this angle Δθ, which promptly reversed once gymnasts reoriented their heads forward. However, when vision was absent, there was only a discernible negative trend in Δθ, indicating a shift in the angle's direction but not a statistically significant one. Thus, the narrowing of Δθ may signify an adaptive strategy in postural control. The swift recovery of Δθ upon returning to a forward-facing posture suggests that the temporary reduction is specific to head-turning and does not impose a lasting burden on postural control. Turning the head reduced the angle between these two orientations, facilitating the release of postural degrees of freedom towards a more uniform spread of the CoP across both dimensions of the support surface. The innovative aspect of this work is that it shows how fractality might serve as a control parameter of adaptive mechanisms of dexterous postural control.

Nonlinear features of gaze behavior during joint attention in children with autism spectrum disorder

Since children with autism spectrum disorder (ASD) might exhibit a variety of aberrant response to joint attention (RJA) behaviors, there is growing interest in identifying robust, reliable and valid eye-tracking metrics for determining differences in RJA behaviors between typically developing (TD) children and those with ASD. Previous eye-tracking studies have not been deeply investigated nonlinear features of gaze time-series during RJA. As a main motivation, this study aimed to extract three nonlinear features (i.e., complexity, long-range correlation, and local instability) of gaze time-series during RJA in children with ASD, which can be measured by fractal dimension (FD), Hurst exponent (H), and largest Lyapunov exponent (LLE), respectively. To illustrate our idea, this study adopted a publicly accessible database, including eye-tracking data collected during RJA from 19 children with ASD (7.74 ± 2.73) and 30 TD children (8.02 ± 2.89), and conducted a battery of nonparametric analysis of covariance (ANCOVA), where gender was used as covariable. Findings showed that gaze time-series during RJA in autistic children may generally have greater FD but lower H than that in TD controls. This implies that children with ASD possess more complex and unpredictable gaze behaviors during RJA than TD children. Furthermore, nonlinear metrics outperformed traditional eye-tracking metrics in obtaining higher identification performance with an accuracy of 82% and an AUC value of 0.81, distinguishing the differences between successful and failed RJA trails, and predicting the severity of ASD symptoms. Findings might bring some new insights into the understanding of the impairments in RJA behaviors for children with ASD.

Combining Measures of Signal Complexity and Machine Learning for Time Series Analyis: A Review

Measures of signal complexity, such as the Hurst exponent, the fractal dimension, and the Spectrum of Lyapunov exponents, are used in time series analysis to give estimates on persistency, anti-persistency, fluctuations and predictability of the data under study. They have proven beneficial when doing time series prediction using machine and deep learning and tell what features may be relevant for predicting time-series and establishing complexity features. Further, the performance of machine learning approaches can be improved, taking into account the complexity of the data under study, e.g., adapting the employed algorithm to the inherent long-term memory of the data. In this article, we provide a review of complexity and entropy measures in combination with machine learning approaches. We give a comprehensive review of relevant publications, suggesting the use of fractal or complexity-measure concepts to improve existing machine or deep learning approaches. Additionally, we evaluate applications of these concepts and examine if they can be helpful in predicting and analyzing time series using machine and deep learning. Finally, we give a list of a total of six ways to combine machine learning and measures of signal complexity as found in the literature.

Variance and Scale-Free Properties of Resting-State Blood Oxygenation Level-Dependent Signal After Fear Memory Acquisition and Extinction

Recently, the dynamic properties of brain activity rather than its stationary values have attracted more interest in clinical applications. It has been shown that brain signals exhibit scale-free dynamics or long-range temporal correlations (LRTC) that differ between rest and cognitive tasks in healthy controls and clinical groups. Little is known about how fear-inducing tasks may influence dispersion and the LRTC of subsequent resting-state brain activity. In this study, we aimed to explore the changes in the variance and scale-free properties of the brain’s blood oxygenation level-dependent (BOLD) signal during the resting-state sessions before and after fear learning and fear memory extinction. During a 1-h break between magnetic resonance imaging (MRI) scanning, 23 healthy, right-handed volunteers experienced a fear extinction procedure, followed by Pavlovian fear conditioning that included partial reinforcement using mild electrical stimulation. We extracted the average time course of the BOLD signal from 245 regions of interest (ROIs) taken from the resting-state functional atlas. The variance of the BOLD signal and the Hurst exponent (H), which reflects the scale-free dynamic, were compared in the resting states before and after fear learning and fear memory extinction. After fear extinction, six ROIs showed a difference in H at the uncorrected level of significance, including areas associated with fear processing. H decreased during fear extinction but then became higher than before fear learning, specifically in areas related to the fear extinction network (FEN). However, activity in the other ROIs restored the H to its initial level. The variance of the BOLD signal in six ROIs demonstrated a significant increase from initial rest to the post-task rest. A limited number of ROIs showed changes in both H and variance. Our results imply that the variability and scale-free properties of the BOLD signal might serve as additional indicators of changes in spontaneous brain activity related to recent experience.

Differences in Gating Dynamics of BK Channels in Cellular and Mitochondrial Membranes from Human Glioblastoma Cells Unraveled by Short- and Long-Range Correlations Analysis

The large-conductance voltage- and Ca2+-activated K+ channels (BK) are encoded in humans by the Kcnma1 gene. Nevertheless, BK channel isoforms in different locations can exhibit functional heterogeneity mainly due to the alternative splicing during the Kcnma1 gene transcription. Here, we would like to examine the existence of dynamic diversity of BK channels from the inner mitochondrial and cellular membrane from human glioblastoma (U-87 MG). Not only the standard characteristics of the spontaneous switching between the functional states of the channel is discussed, but we put a special emphasis on the presence and strength of correlations within the signal describing the single-channel activity. The considered short- and long-range memory effects are here analyzed as they can be interpreted in terms of the complexity of the switching mechanism between stable conformational states of the channel. We calculate the dependencies of mean dwell-times of (conducting/non-conducting) states on the duration of the previous state, Hurst exponents by the rescaled range R/S method and detrended fluctuation analysis (DFA), and use the multifractal extension of the DFA (MFDFA) for the series describing single-channel activity. The obtained results unraveled statistically significant diversity in gating machinery between the mitochondrial and cellular BK channels.

Fractal Analysis of Human Gait Variability via Stride Interval Time Series

Fractal analysis of stride interval time series is a useful tool in human gait research which could be used as a marker for gait adaptability, gait disorder, and fall risk among patients with movement disorders. This study is designed to systematically and comprehensively investigate two practical aspects of fractal analysis which significantly affect the outcome: the series length and the parameters used in the algorithm. The Hurst exponent, scaling exponent, and/or fractal dimension are computed from both simulated and experimental data using three fractal methods, namely detrended fluctuation analysis, box-counting dimension, and Higuchi's fractal dimension. The advantages and drawbacks of each method are discussed, in terms of biases and variability. The results demonstrate that a careful selection of fractal analysis methods and their parameters is required, which is dependent on the aim of study (either analyzing differences between experimental groups or estimating an accurate determination of fractal features). A set of guidelines for the selection of the fractal methods and the length of stride interval time series is provided, along with the optimal parameters for a robust implementation for each method.

Descriptions of Entropy with Fractal Dynamics and Their Applications to the Flow Pressure of Centrifugal Compressor

In this study, some important intrinsic dynamics have been captured after analyzing the relationships between the dynamic pressure at an outlet of centrifugal compressor and fractal characteristics, which is one of powerful descriptions in entropy to measure the disorder or complexity in the nonlinear dynamic system. In particular, the fractal dynamics of dynamic pressure of the flow is studied, as the centrifugal compressor is in surge state, resulting in the dynamic pressure of flow and becoming a serious disorder and complex. First, the dynamic pressure at outlet of a centrifugal compressor with 800 kW is tested and then obtained by controlling the opening of the anti-surge valve at the outlet, and both the stable state and surge are initially tested and analyzed. Subsequently, the fractal dynamics is introduced to study the intrinsic dynamics of dynamic pressure under various working conditions, in order to identify surge, which is one typical flow instability in centrifugal compressor. Following fractal dynamics, the Hurst exponent, autocorrelation functions, and variance in measure theories of entropy are studied to obtain the mono-fractal characteristics of the centrifugal compressor. Further, the multi-fractal spectrums are investigated in some detail, and their physical meanings are consequently explained. At last, the statistical reliability of multi-fractal spectrum by modifying the original data has been studied. The results show that a distinct relationship between the dynamic pressure and fractal characteristics exists, including mono-fractal and multi-fractal, and such fractal dynamics are intrinsic. As the centrifugal compressor is working under normal condition, its autocorrelation function curve demonstrates apparent stochastic characteristics, and its Hurst exponent and variance are lower. However, its autocorrelation function curve demonstrates an apparent heavy tail distribution, and its Hurst exponent and variance are higher, as it is working in an unstable condition, namely, surge. In addition, the results show that the multi-fractal spectrum parameters are closely related to the dynamic pressure. With the state of centrifugal compressor being changed from stable to unstable states, some multi-fractal spectrum parameters Δα, Δf(α), α_max, and f(α_min) become larger, but α_min in the multi-fractal spectrum show the opposite trend, and consistent properties are graphically shown for the randomly shuffled data. As a conclusion, the proposed method, as one measure method for entropy, can be used to feasibly identify the incipient surge of a centrifugal compressor and design its surge controller.

A Fast DFA Algorithm for Multifractal Multiscale Analysis of Physiological Time Series

Detrended fluctuation analysis (DFA) is a popular tool in physiological and medical studies for estimating the self-similarity coefficient, α, of time series. Recent researches extended its use for evaluating multifractality (where α is a function of the multifractal parameter q) at different scales n. In this way, the multifractal-multiscale DFA provides a bidimensional surface α(q,n) to quantify the level of multifractality at each scale separately. We recently showed that scale resolution and estimation variability of α(q,n) can be improved at each scale n by splitting the series into maximally overlapped blocks. This, however, increases the computational load making DFA estimations unfeasible in most applications. Our aim is to provide a DFA algorithm sufficiently fast to evaluate the multifractal DFA with maximally overlapped blocks even on long time series, as usually recorded in physiological or clinical settings, therefore improving the quality of the α(q,n) estimate. For this aim, we revise the analytic formulas for multifractal DFA with first- and second-order detrending polynomials (i.e., DFA₁ and DFA₂) and propose a faster algorithm than the currently available codes. Applying it on synthesized fractal/multifractal series we demonstrate its numerical stability and a computational time about 1% that required by traditional codes. Analyzing long physiological signals (heart-rate tachograms from a 24-h Holter recording and electroencephalographic traces from a sleep study), we illustrate its capability to provide high-resolution α(q,n) surfaces that better describe the multifractal/multiscale properties of time series in physiology. The proposed fast algorithm might, therefore, make it easier deriving richer information on the complex dynamics of clinical signals, possibly improving risk stratification or the assessment of medical interventions and rehabilitation protocols.

Combining TEM, AFM, and Profilometry for Quantitative Topography Characterization Across All Scales

Surface roughness affects the functional properties of surfaces, including adhesion, friction, hydrophobicity, biological response, and electrical and thermal transport properties. However, experimental investigations to quantify these links are often inconclusive because surfaces are fractal-like, and the values of measured roughness parameters depend on measurement size. Here, we demonstrate the characterization of topography of an ultrananocrystalline diamond (UNCD) surface at the angstrom scale using transmission electron microscopy (TEM), as well as its combination with conventional techniques to achieve a comprehensive surface description spanning 8 orders of magnitude in size. We performed more than 100 individual measurements of the nanodiamond film using both TEM and conventional techniques (stylus profilometry and atomic force microscopy). While individual measurements of root-mean-square (RMS) height, RMS slope, and RMS curvature vary by orders of magnitude, we combine the various techniques using the power spectral density and use this to compute scale-independent parameters. This analysis reveals that "smooth" UNCD surfaces have an RMS slope greater than 1, even larger than the slope of the Austrian Alps when measured on the scale of a human step. This approach of comprehensive multiscale roughness characterization, measured with angstrom-scale detail, will enable the systematic evaluation and optimization of other technologically relevant surfaces, as well as systematic testing of the many analytical and numerical models for the behavior of rough surfaces.

Hurst Exponent Analysis of Resting-State fMRI Signal Complexity across the Adult Lifespan

Exploring functional information among various brain regions across time enables understanding of healthy aging process and holds great promise for age-related brain disease diagnosis. This paper proposed a method to explore fractal complexity of the resting-state functional magnetic resonance imaging (rs-fMRI) signal in the human brain across the adult lifespan using Hurst exponent (HE). We took advantage of the examined rs-fMRI data from 116 adults 19 to 85 years of age (44.3 ± 19.4 years, 49 females) from NKI/Rockland sample. Region-wise and voxel-wise analyses were performed to investigate the effects of age, gender, and their interaction on complexity. In region-wise analysis, we found that the healthy aging is accompanied by a loss of complexity in frontal and parietal lobe and increased complexity in insula, limbic, and temporal lobe. Meanwhile, differences in HE between genders were found to be significant in parietal lobe (p = 0.04, corrected). However, there was no interaction between gender and age. In voxel-wise analysis, the significant complexity decrease with aging was found in frontal and parietal lobe, and complexity increase was found in insula, limbic lobe, occipital lobe, and temporal lobe with aging. Meanwhile, differences in HE between genders were found to be significant in frontal, parietal, and limbic lobe. Furthermore, we found age and sex interaction in right parahippocampal gyrus (p = 0.04, corrected). Our findings reveal HE variations of the rs-fMRI signal across the human adult lifespan and show that HE may serve as a new parameter to assess healthy aging process.

Identifying current and remitted major depressive disorder with the Hurst exponent: a comparative study on two automated anatomical labeling atlases

Major depressive disorder (MDD) is a leading world-wide psychiatric disorder with high recurrence rate, therefore, it is desirable to identify current MDD (cMDD) and remitted MDD (rMDD) for their appropriate therapeutic interventions. In the study, 19 cMDD, 19 rMDD and 19 well-matched healthy controls (HC) were enrolled and scanned with the resting-state functional magnetic resonance imaging (rs-fMRI). The Hurst exponent (HE) of rs-fMRI in AAL-90 and AAL-1024 atlases were calculated and compared between groups. Then, a radial basis function (RBF) based support vector machine was proposed to identify every pair of the cMDD, rMDD and HC groups using the abnormal HE features, and a leave-one-out cross-validation was used to evaluate the classification performance. Applying the proposed method with AAL-1024 and AAL-90 atlas respectively, 87% and 84% subjects were correctly identified between cMDD and HC, 84% and 71% between rMDD and HC, and 89% and 74% between cMDD and rMDD. Our results indicated that the HE was an effective feature to distinguish cMDD and rMDD from HC, and the recognition performances with AAL-1024 parcellation were better than that with the conventional AAL-90 parcellation.

Self-similarity in NMR Spectra: An Application in Assessing the Level of Cysteine

High resolution of NMR spectroscopic data of biosamples are a rich source of information on the metabolic response to physiological variation or pathological events. There are many advantages of NMR techniques such as the sample preparation is fast, simple and non-invasive. Statistical analysis of NMR spectra usually focuses on differential expression of large resonance intensity corresponding to abundant metabolites and involves several data preprocessing steps. In this paper we estimate functional components of spectra and test their significance using multiscale techniques. We also explore scaling in NMR spectra and use the systematic variability of scaling descriptors to predict the level of cysteine, an important precursor of glutathione, a control antioxidant in human body. This is motivated by high cost (in time and resources) of traditional methods for assessing cysteine level by high performance liquid chromatograph (HPLC).

Components of variance in a multicentre functional MRI study and implications for calculation of statistical power

This article firstly presents a theoretical analysis of the statistical power of a parallel-group, repeated-measures (two-session) and two-centre design suitable for a placebo-controlled pharmacological MRI study. For arbitrary effect size, power is determined by the pooled between-session error, the pooled measurement error, the ratio of centre measurement errors, the total number of subjects and the proportion of subjects studied at the centre with greatest measurement error. Secondly, an experiment is described to obtain empirical estimates of variance components in task-related and resting state functional magnetic resonance imaging. Twelve healthy volunteers were scanned at two centres during performance of blocked and event-related versions of an affect processing task (each repeated twice per session) and rest. In activated regions, variance components were estimated: between-subject (23% of total), between-centre (2%), between-paradigm (4%), within-session occasion (paradigm repeat; 2%) and residual (measurement) error (69%). The between-centre ratio of measurement errors was 0.8. A similar analysis for the Hurst exponent estimated in resting data showed negligible contributions of between-subject and between-centre variability; measurement error accounted for 99% of total variance. Substituting these estimates in the theoretical expression for power, incorporation of two centres in the design necessitates a modest (10%) increase in the total number of subjects compared with a single-centre study. Furthermore, considerable improvements in power can be attained by repetition of the task within each scanning session. Thus, theoretical models of power and empirical data indicate that between-centre variability can be small enough to encourage multicentre designs without major compensatory increases in sample size.

Endogenous brain fluctuations and diagnostic imaging

Much of the rising health care costs in aging populations can be attributed to congenital disease and psychiatric and neurologic disorders. Early detection of changes related to these diseases can promote the development of new therapeutic strategies and effective treatments. Changes in tissue, such as damage resulting from continued functional abnormality, often exhibit a time-delay before detection is possible. Methods for detecting functional alterations in endogenous brain fluctuations allow for an early diagnosis before tissue damage occurs, enabling early treatment and a more likely positive outcome. A literature review and comprehensive overview of the current state of knowledge about endogenous brain fluctuations is presented here. Recent findings of the association between various pathological conditions and endogenous fluctuations are discussed. A particular emphasis is placed on research showing the relationship between clinical measures and pathological findings to the dynamics of endogenous fluctuations of the brain. Recent discoveries of methods for detecting abnormal functional connectivity are discussed and future research directions explored.