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        <title>Frontiers in Network Physiology | Fractal Physiology section | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/network-physiology/sections/fractal-physiology</link>
        <description>RSS Feed for Fractal Physiology section in the Frontiers in Network Physiology journal | New and Recent Articles</description>
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        <pubDate>2026-05-14T22:05:42.476+00:00</pubDate>
        <ttl>60</ttl>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnetp.2025.1532700</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnetp.2025.1532700</link>
        <title><![CDATA[Restoring the complexity of walking in the elderly and its impact on clinical measures around the risk of falls]]></title>
        <pubdate>2025-04-02T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Samar Ezzina</author><author>Simon Pla</author><author>Didier Delignières</author>
        <description><![CDATA[Introduction: The hypothesis of the loss of complexity with aging and disease has received strong attention. Especially, the decrease of complexity of stride interval series in older people, during walking, was shown to correlate with falling propensity. However, recent experiments showed that a restoration of walking complexity in older people could occur through the prolonged experience of synchronized walking with a younger companion. This result was interpreted as the consequence of a complexity matching effect.Experiment: The aim of the present study was to analyze the link between the restoration of walking complexity in older people and clinical measures usually used in the context of rehabilitation or follow-up of older people.Results: We evidenced a link between restoring complexity, improving overall health and reducing fear of falling. In addition, we showed that 3 weeks of complexity matching training can have a positive effect on complexity up to 2 months post-protocol. Finally, we showed that the restoration of walking complexity obtained in the previous works is not guide-dependent.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnetp.2024.1379892</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnetp.2024.1379892</link>
        <title><![CDATA[Complexity synchronization in living matter: a mini review]]></title>
        <pubdate>2024-05-20T00:00:00Z</pubdate>
        <category>Mini Review</category>
        <author>Bruce J. West</author>
        <description><![CDATA[Fractal time series have been argued to be ubiquitous in human physiology and some of the implications of that ubiquity are quite remarkable. One consequence of the omnipresent fractality is complexity synchronization (CS) observed in the interactions among simultaneously recorded physiologic time series discussed herein. This new kind of synchronization has been revealed in the interaction triad of organ-networks (ONs) consisting of the mutually interacting time series generated by the brain (electroencephalograms, EEGs), heart (electrocardiograms, ECGs), and lungs (Respiration). The scaled time series from each member of the triad look nothing like one another and yet they bear a deeply recorded synchronization invisible to the naked eye. The theory of scaling statistics is used to explain the source of the CS observed in the information exchange among these multifractal time series. The multifractal dimension (MFD) of each time series is a measure of the time-dependent complexity of that time series, and it is the matching of the MFD time series that provides the synchronization referred to as CS. The CS is one manifestation of the hypothesis given by a “Law of Multifractal Dimension Synchronization” (LMFDS) which is supported by data. Therefore, the review aspects of this paper are chosen to make the extended range of the LMFDS hypothesis sufficiently reasonable to warrant further empirical testing.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnetp.2024.1393171</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnetp.2024.1393171</link>
        <title><![CDATA[Postural control in gymnasts: anisotropic fractal scaling reveals proprioceptive reintegration in vestibular perturbation]]></title>
        <pubdate>2024-04-18T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Madhur Mangalam</author><author>Ivan Seleznov</author><author>Elena Kolosova</author><author>Anton Popov</author><author>Damian G. Kelty-Stephen</author><author>Ken Kiyono</author>
        <description><![CDATA[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.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnetp.2023.1204757</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnetp.2023.1204757</link>
        <title><![CDATA[A guide to Whittle maximum likelihood estimator in MATLAB]]></title>
        <pubdate>2023-10-31T00:00:00Z</pubdate>
        <category>Methods</category>
        <author>Clément Roume</author>
        <description><![CDATA[The assessment of physiological complexity via the estimation of monofractal exponents or multifractal spectra of biological signals is a recent field of research that allows detection of relevant and original information for health, learning, or autonomy preservation. This tutorial aims at introducing Whittle’s maximum likelihood estimator (MLE) that estimates the monofractal exponent of time series. After introducing Whittle’s maximum likelihood estimator and presenting each of the steps leading to the construction of the algorithm, this tutorial discusses the performance of this estimator by comparing it to the widely used detrended fluctuation analysis (DFA). The objective of this tutorial is to propose to the reader an alternative monofractal estimation method, which has the advantage of being simple to implement, and whose high accuracy allows the analysis of shorter time series than those classically used with other monofractal analysis methods.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnetp.2023.1294545</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnetp.2023.1294545</link>
        <title><![CDATA[Multifractality in stride-to-stride variations reveals that walking involves more movement tuning and adjusting than running]]></title>
        <pubdate>2023-10-19T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Taylor J. Wilson</author><author>Madhur Mangalam</author><author>Nick Stergiou</author><author>Aaron D. Likens</author>
        <description><![CDATA[Introduction: The seemingly periodic human gait exhibits stride-to-stride variations as it adapts to the changing task constraints. The optimal movement variability hypothesis (OMVH) states that healthy stride-to-stride variations exhibit “fractality”—a specific temporal structure in consecutive strides that are ordered, stable but also variable, and adaptable. Previous research has primarily focused on a single fractality measure, “monofractality.” However, this measure can vary across time; strideto-stride variations can show “multifractality.” Greater multifractality in stride-tostride variations would highlight the ability to tune and adjust movements more.Methods: We investigated monofractality and multifractality in a cohort of eight healthy adults during self-paced walking and running trials, both on a treadmill and overground. Footfall data were collected through force-sensitive sensors positioned on their heels and feet. We examined the effects of self-paced walking vs. running and treadmill vs. overground locomotion on the measure of monofractality, α-DFA, in addition to the multifractal spectrum width, W, and the asymmetry in the multifractal spectrum, WAsym, of stride interval time series.Results: While the α-DFA was larger than 0.50 for almost all conditions, α-DFA was higher in running and locomoting overground than walking and locomoting on a treadmill. Similarly, W was greater while locomoting overground than on a treadmill, but an opposite trend indicated that W was greater in walking than running. Larger WAsym values in the negative direction suggest that walking exhibits more variation in the persistence of shorter stride intervals than running. However, the ability to tune and adjust movements does not differ between treadmill and overground, although both exhibit more variation in the persistence of shorter stride intervals.Discussion: Hence, greater heterogeneity in shorter than longer stride intervals contributed to greater multifractality in walking compared to running, indicated by larger negative WAsym values. Our results highlight the need to incorporate multifractal methods to test the predictions of the OMVH.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnetp.2023.1233894</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnetp.2023.1233894</link>
        <title><![CDATA[DFA as a window into postural dynamics supporting task performance: does choice of step size matter?]]></title>
        <pubdate>2023-08-07T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Patric C. Nordbeck</author><author>Valéria Andrade</author><author>Paula L. Silva</author><author>Nikita A. Kuznetsov</author>
        <description><![CDATA[Introduction: Detrended Fluctuation Analysis (DFA) has been used to investigate self-similarity in center of pressure (CoP) time series. For fractional gaussian noise (fGn) signals, the analysis returns a scaling exponent, DFA-α, whose value characterizes the temporal correlations as persistent, random, or anti-persistent. In the study of postural control, DFA has revealed two time scaling regions, one at the short-term and one at the long-term scaling regions in the diffusion plots, suggesting different types of postural dynamics. Much attention has been given to the selection of minimum and maximum scales, but the choice of spacing (step size) between the window sizes at which the fluctuation function is evaluated may also affect the estimates of scaling exponents. The aim of this study is twofold. First, to determine whether DFA can reveal postural adjustments supporting performance of an upper limb task under variable demands. Second, to compare evenly-spaced DFA with two different step sizes, 0.5 and 1.0 in log2 units, applied to CoP time series.Methods: We analyzed time series of anterior-posterior (AP) and medial-lateral (ML) CoP displacement from healthy participants performing a sequential upper limb task under variable demand.Results: DFA diffusion plots revealed two scaling regions in the AP and ML CoP time series. The short-term scaling region generally showed hyper-diffusive dynamics and long-term scaling revealed mildly persistent dynamics in the ML direction and random-like dynamics in the AP direction. There was a systematic tendency for higher estimates of DFA-α and lower estimates for crossover points for the 0.5-unit step size vs. 1.0-unit size.Discussion: Results provide evidence that DFA-α captures task-related differences between postural adjustments in the AP and ML directions. Results also showed that DFA-α estimates and crossover points are sensitive to step size. A step size of 0.5 led to less variable DFA-α for the long-term scaling region, higher estimation for the short-term scaling region, lower estimate for crossover points, and revealed anomalous estimates at the very short range that had implications for choice of minimum window size. We, therefore, recommend the use of 0.5 step size in evenly spaced DFAs for CoP time series similar to ours.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnetp.2023.1072815</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnetp.2023.1072815</link>
        <title><![CDATA[Neuron arbor geometry is sensitive to the limited-range fractal properties of their dendrites]]></title>
        <pubdate>2023-01-25T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Conor Rowland</author><author>Julian H. Smith</author><author>Saba Moslehi</author><author>Bruce Harland</author><author>John Dalrymple-Alford</author><author>Richard P. Taylor</author>
        <description><![CDATA[Fractal geometry is a well-known model for capturing the multi-scaled complexity of many natural objects. By analyzing three-dimensional images of pyramidal neurons in the rat hippocampus CA1 region, we examine how the individual dendrites within the neuron arbor relate to the fractal properties of the arbor as a whole. We find that the dendrites reveal unexpectedly mild fractal characteristics quantified by a low fractal dimension. This is confirmed by comparing two fractal methods—a traditional “coastline” method and a novel method that examines the dendrites’ tortuosity across multiple scales. This comparison also allows the dendrites’ fractal geometry to be related to more traditional measures of their complexity. In contrast, the arbor’s fractal characteristics are quantified by a much higher fractal dimension. Employing distorted neuron models that modify the dendritic patterns, deviations from natural dendrite behavior are found to induce large systematic changes in the arbor’s structure and its connectivity within a neural network. We discuss how this sensitivity to dendrite fractality impacts neuron functionality in terms of balancing neuron connectivity with its operating costs. We also consider implications for applications focusing on deviations from natural behavior, including pathological conditions and investigations of neuron interactions with artificial surfaces in human implants.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnetp.2022.1054439</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnetp.2022.1054439</link>
        <title><![CDATA[Editorial: Fractals in the diagnosis and treatment of the retina and brain diseases]]></title>
        <pubdate>2022-10-18T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Marina V. Zueva</author><author>Antonio Di Ieva</author><author>Svetlana D. Pyankova</author>
        <description></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fnetp.2022.1038239</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fnetp.2022.1038239</link>
        <title><![CDATA[Editorial: Advancing our understanding of the impact of dynamics at different spatiotemporal scales and structure on brain synchronous activity]]></title>
        <pubdate>2022-10-06T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Thanos Manos</author><author>Chris G. Antonopoulos</author><author>Antonio M. Batista</author><author>Kelly C. Iarosz</author>
        <description></description>
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