Frontiers in network physiology最新文献

{"title":"A guide to Whittle maximum likelihood estimator in MATLAB","authors":"Clément Roume","doi":"10.3389/fnetp.2023.1204757","DOIUrl":"https://doi.org/10.3389/fnetp.2023.1204757","url":null,"abstract":"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.","PeriodicalId":73092,"journal":{"name":"Frontiers in network physiology","volume":"51 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135864001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cardiorespiratory dynamics during respiratory maneuver in athletes","authors":"Oleksandr Romanchuk","doi":"10.3389/fnetp.2023.1276899","DOIUrl":"https://doi.org/10.3389/fnetp.2023.1276899","url":null,"abstract":"Introduction: The modern practice of sports medicine and medical rehabilitation requires the search for subtle criteria for the development of conditions and recovery of the body after diseases, which would have a prognostic value for the prevention of negative effects of training and rehabilitation tools, and also testify to the development and course of mechanisms for counteracting pathogenetic processes in the body. The purpose of this study was to determine the informative directions of the cardiorespiratory system parameters dynamics during the performing a maneuver with a change in breathing rate, which may indicate the body functional state violation. Methods: The results of the study of 183 healthy men aged 21.2 ± 2.3 years who regularly engaged in various sports were analyzed. The procedure for studying the cardiorespiratory system included conducting combined measurements of indicators of activity of the respiratory and cardiovascular systems in a sitting position using a spiroarteriocardiograph device. The duration of the study was 6 min and involved the sequential registration of three measurements with a change in breathing rate (spontaneous breathing, breathing at 0.1 Hz and 0.25 Hz). Results: Performing a breathing maneuver at breathing 0.1 Hz and breathing 0.25 Hz in comparison with spontaneous breathing leads to multidirectional significant changes in heart rate variability indicators–TP (ms 2 ), LF (ms 2 ), LFHF (ms 2 /ms 2 ); of blood pressure variability indicators–TP DBP (mmHg 2 ), LF SBP (mmHg 2 ), LF DBP (mmHg 2 ), HF SBP (mmHg 2 ); of volume respiration variability indicators - LF R , (L×min -1 ) 2 ; HF R , (L×min -1 ) 2 ; LFHF R , (L×min -1 ) 2 /(L×min -1 ) 2 ; of arterial baroreflex sensitivity indicators - BR LF (ms×mmHg -1 ), BR HF (ms×mmHg -1 ). Differences in indicators of systemic hemodynamics and indicators of cardiovascular and respiratory systems synchronization were also informative. Conclusion: According to the results of the study, it is shown that during performing a breathing maneuver with a change in the rate of breathing, there are significant changes in cardiorespiratory parameters, the analysis of which the increments made it possible to determine of the changes directions dynamics, their absolute values and informative limits regarding the possible occurrence of the cardiorespiratory interactions dysregulation.","PeriodicalId":73092,"journal":{"name":"Frontiers in network physiology","volume":"181 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136068146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Phenotypic maps for precision medicine: a promising systems biology tool for assessing therapy response and resistance at a personalized level.","authors":"Sayantan Bhattacharyya, Shafqat F Ehsan, Loukia G Karacosta","doi":"10.3389/fnetp.2023.1256104","DOIUrl":"10.3389/fnetp.2023.1256104","url":null,"abstract":"<p><p>In this perspective we discuss how tumor heterogeneity and therapy resistance necessitate a focus on more personalized approaches, prompting a shift toward precision medicine. At the heart of the shift towards personalized medicine, omics-driven systems biology becomes a driving force as it leverages high-throughput technologies and novel bioinformatics tools. These enable the creation of systems-based maps, providing a comprehensive view of individual tumor's functional plasticity. We highlight the innovative PHENOSTAMP program, which leverages high-dimensional data to construct a visually intuitive and user-friendly map. This map was created to encapsulate complex transitional states in cancer cells, such as Epithelial-Mesenchymal Transition (EMT) and Mesenchymal-Epithelial Transition (MET), offering a visually intuitive way to understand disease progression and therapeutic responses at single-cell resolution in relation to EMT-related single-cell phenotypes. Most importantly, PHENOSTAMP functions as a reference map, which allows researchers and clinicians to assess one clinical specimen at a time in relation to their phenotypic heterogeneity, setting the foundation on constructing phenotypic maps for personalized medicine. This perspective argues that such dynamic predictive maps could also catalyze the development of personalized cancer treatment. They hold the potential to transform our understanding of cancer biology, providing a foundation for a future where therapy is tailored to each patient's unique molecular and cellular tumor profile. As our knowledge of cancer expands, these maps can be continually refined, ensuring they remain a valuable tool in precision oncology.</p>","PeriodicalId":73092,"journal":{"name":"Frontiers in network physiology","volume":"3 ","pages":"1256104"},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10642209/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"107593033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editorial: Circadian rhythms of mental health.","authors":"Kneginja Richter, Thomas Penzel","doi":"10.3389/fnetp.2023.1279911","DOIUrl":"10.3389/fnetp.2023.1279911","url":null,"abstract":"","PeriodicalId":73092,"journal":{"name":"Frontiers in network physiology","volume":"3 ","pages":"1279911"},"PeriodicalIF":0.0,"publicationDate":"2023-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10628730/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71523574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multifractality in stride-to-stride variations reveals that walking involves more movement tuning and adjusting than running.","authors":"Taylor J Wilson, Madhur Mangalam, Nick Stergiou, Aaron D Likens","doi":"10.3389/fnetp.2023.1294545","DOIUrl":"10.3389/fnetp.2023.1294545","url":null,"abstract":"<p><p><b>Introduction:</b> 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. <b>Methods:</b> 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, W<i>_Asym</i>, of stride interval time series. <b>Results:</b> 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 W<i>_Asym</i> 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. <b>Discussion:</b> Hence, greater heterogeneity in shorter than longer stride intervals contributed to greater multifractality in walking compared to running, indicated by larger negative W<i>_Asym</i> values. Our results highlight the need to incorporate multifractal methods to test the predictions of the OMVH.</p>","PeriodicalId":73092,"journal":{"name":"Frontiers in network physiology","volume":"3 ","pages":"1294545"},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10621042/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71489610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interacting information streams on the nephron arterial network.","authors":"Donald J Marsh,&nbsp;Anthony S Wexler,&nbsp;Niels-Henrik Holstein-Rathlou","doi":"10.3389/fnetp.2023.1254964","DOIUrl":"https://doi.org/10.3389/fnetp.2023.1254964","url":null,"abstract":"Blood flow and glomerular filtration in the kidney are regulated by two mechanisms acting on the afferent arteriole of each nephron. The two mechanisms operate as limit cycle oscillators, each responding to a different signal. The myogenic mechanism is sensitive to a transmural pressure difference across the wall of the arteriole, and tubuloglomerular feedback (TGF) responds to the NaCl concentration in tubular fluid flowing into the nephron’s distal tubule,. The two mechanisms interact with each other, synchronize, cause oscillations in tubular flow and pressure, and form a bimodal electrical signal that propagates into the arterial network. The electrical signal enables nephrons adjacent to each other in the arterial network to synchronize, but non-adjacent nephrons do not synchronize. The arteries supplying the nephrons have the morphologic characteristics of a rooted tree network, with 3 motifs characterizing nephron distribution. We developed a model of 10 nephrons and their afferent arterioles in an arterial network that reproduced these structural characteristics, with half of its components on the renal surface, where experimental data suitable for model validation is available, and the other half below the surface, from which no experimental data has been reported. The model simulated several interactions: TGF-myogenic in each nephron with TGF modulating amplitude and frequency of the myogenic oscillation; adjacent nephron-nephron with strong coupling; non-adjacent nephron-nephron, with weak coupling because of electrical signal transmission through electrically conductive arterial walls; and coupling involving arterial nodal pressure at the ends of each arterial segment, and between arterial nodes and the afferent arterioles originating at the nodes. The model predicted full synchronization between adjacent nephrons pairs and partial synchronization among weakly coupled nephrons, reproducing experimental findings. The model also predicted aperiodic fluctuations of tubular and arterial pressures lasting longer than TGF oscillations in nephrons, again confirming experimental observations. The model did not predict complete synchronization of all nephrons.","PeriodicalId":73092,"journal":{"name":"Frontiers in network physiology","volume":"3 ","pages":"1254964"},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10620968/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71489609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analyzing physiological signals recorded with a wearable sensor across the menstrual cycle using circular statistics.","authors":"Krystal Sides, Grentina Kilungeja, Matthew Tapia, Patrick Kreidl, Benjamin H Brinkmann, Mona Nasseri","doi":"10.3389/fnetp.2023.1227228","DOIUrl":"10.3389/fnetp.2023.1227228","url":null,"abstract":"<p><p>This study aims to identify the most significant features in physiological signals representing a biphasic pattern in the menstrual cycle using circular statistics which is an appropriate analytic method for the interpretation of data with a periodic nature. The results can be used empirically to determine menstrual phases. A non-uniform pattern was observed in ovulating subjects, with a significant periodicity (p<math><mo><</mo></math>0.05) in mean temperature, heart rate (HR), Inter-beat Interval (IBI), mean tonic component of Electrodermal Activity (EDA), and signal magnitude area (SMA) of the EDA phasic component in the frequency domain. In contrast, non-ovulating cycles displayed a more uniform distribution (p<math><mo>></mo></math>0.05). There was a significant difference between ovulating and non-ovulating cycles (p<math><mo><</mo></math>0.05) in temperature, IBI, and EDA but not in mean HR. Selected features were used in training an Autoregressive Integrated Moving Average (ARIMA) model, using data from at least one cycle of a subject, to predict the behavior of the signal in the last cycle. By iteratively retraining the algorithm on a per-day basis, the mean temperature, HR, IBI and EDA tonic values of the next day were predicted with root mean square error (RMSE) of 0.13 ± 0.07 (C°), 1.31 ± 0.34 (bpm), 0.016 ± 0.005 (s) and 0.17 ± 0.17 (<i>μ</i>S), respectively.</p>","PeriodicalId":73092,"journal":{"name":"Frontiers in network physiology","volume":"3 ","pages":"1227228"},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10621043/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71489608","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Time-varying information measures: an adaptive estimation of information storage with application to brain-heart interactions.","authors":"Yuri Antonacci,&nbsp;Chiara Barà,&nbsp;Andrea Zaccaro,&nbsp;Francesca Ferri,&nbsp;Riccardo Pernice,&nbsp;Luca Faes","doi":"10.3389/fnetp.2023.1242505","DOIUrl":"10.3389/fnetp.2023.1242505","url":null,"abstract":"Network Physiology is a rapidly growing field of study that aims to understand how physiological systems interact to maintain health. Within the information theory framework the information storage (IS) allows to measure the regularity and predictability of a dynamic process under stationarity assumption. However, this assumption does not allow to track over time the transient pathways occurring in the dynamical activity of a physiological system. To address this limitation, we propose a time-varying approach based on the recursive least squares algorithm (RLS) for estimating IS at each time instant, in non-stationary conditions. We tested this approach in simulated time-varying dynamics and in the analysis of electroencephalographic (EEG) signals recorded from healthy volunteers and timed with the heartbeat to investigate brain-heart interactions. In simulations, we show that the proposed approach allows to track both abrupt and slow changes in the information stored in a physiological system. These changes are reflected in its evolution and variability over time. The analysis of brain-heart interactions reveals marked differences across the cardiac cycle phases of the variability of the time-varying IS. On the other hand, the average IS values exhibit a weak modulation over parieto-occiptal areas of the scalp. Our study highlights the importance of developing more advanced methods for measuring IS that account for non-stationarity in physiological systems. The proposed time-varying approach based on RLS represents a useful tool for identifying spatio-temporal dynamics within the neurocardiac system and can contribute to the understanding of brain-heart interactions.","PeriodicalId":73092,"journal":{"name":"Frontiers in network physiology","volume":"3 ","pages":"1242505"},"PeriodicalIF":0.0,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10619917/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71429846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editorial: Granger causality and information transfer in physiological systems: basic research and applications.","authors":"Sonia Charleston-Villalobos,&nbsp;Michal Javorka,&nbsp;Luca Faes,&nbsp;Andreas Voss","doi":"10.3389/fnetp.2023.1284256","DOIUrl":"https://doi.org/10.3389/fnetp.2023.1284256","url":null,"abstract":"","PeriodicalId":73092,"journal":{"name":"Frontiers in network physiology","volume":"3 ","pages":"1284256"},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10613078/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71415768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Long-term exercise adaptation. Physical aging phenomena in biological networks.","authors":"Robert Hristovski,&nbsp;Natàlia Balagué,&nbsp;Marko Stevanovski","doi":"10.3389/fnetp.2023.1243736","DOIUrl":"https://doi.org/10.3389/fnetp.2023.1243736","url":null,"abstract":"","PeriodicalId":73092,"journal":{"name":"Frontiers in network physiology","volume":"3 ","pages":"1243736"},"PeriodicalIF":0.0,"publicationDate":"2023-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10582263/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49685753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}