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Mukherjee S, Babadi B. Adaptive modeling and inference of higher-order coordination in neuronal assemblies: A dynamic greedy estimation approach. PLoS Comput Biol 2024; 20:e1011605. [PMID: 38805569 PMCID: PMC11161120 DOI: 10.1371/journal.pcbi.1011605] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 06/07/2024] [Accepted: 05/20/2024] [Indexed: 05/30/2024] Open
Abstract
Central in the study of population codes, coordinated ensemble spiking activity is widely observable in neural recordings with hypothesized roles in robust stimulus representation, interareal communication, and learning and memory formation. Model-free measures of synchrony characterize coherent pairwise activity but not higher-order interactions, a limitation transcended by statistical models of ensemble spiking activity. However, existing model-based analyses often impose assumptions about the relevance of higher-order interactions and require repeated trials to characterize dynamics in the correlational structure of ensemble activity. To address these shortcomings, we propose an adaptive greedy filtering algorithm based on a discretized mark point-process model of ensemble spiking and a corresponding statistical inference framework to identify significant higher-order coordination. In the course of developing a precise statistical test, we show that confidence intervals can be constructed for greedily estimated parameters. We demonstrate the utility of our proposed methods on simulated neuronal assemblies. Applied to multi-electrode recordings from human and rat cortical assemblies, our proposed methods provide new insights into the dynamics underlying localized population activity during transitions between brain states.
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Affiliation(s)
- Shoutik Mukherjee
- Department of Electrical and Computer Engineering, University of Maryland, College Park, Maryland, United States of America
- Institute for Systems Research, University of Maryland, College Park, Maryland, United States of America
| | - Behtash Babadi
- Department of Electrical and Computer Engineering, University of Maryland, College Park, Maryland, United States of America
- Institute for Systems Research, University of Maryland, College Park, Maryland, United States of America
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Mijatovic G, Bara C, Pernice R, Loncar-Turukalo T, Nollo G, Faes L. Exploring the Short-Term Memory of Heart Rate Variability through Model-Free Information Measures. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083690 DOI: 10.1109/embc40787.2023.10341158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
In this work, we perform a comparative analysis of discrete- and continuous-time estimators of information-theoretic measures quantifying the concept of memory utilization in short-term heart rate variability (HRV). Specifically, considering heartbeat intervals in discrete time we compute the measure of information storage (IS) and decompose it into immediate memory utilization (IMU) and longer memory utilization (MU) terms; considering the timings of heartbeats in continuous time we compute the measure of MU rate (MUR). All measures are computed through model-free approaches based on nearest neighbor entropy estimators applied to the HRV series of a group of 15 healthy subjects measured at rest and during postural stress. We find, moving from rest to stress, statistically significant increases of the IS and the IMU, as well as of the MUR. Our results suggest that both discrete-time and continuous-time approaches can detect the higher predictive capacity of HRV occurring with postural stress, and that such increased memory utilization is due to fast mechanisms likely related to sympathetic activation.
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Barà C, Sparacino L, Pernice R, Antonacci Y, Porta A, Kugiumtzis D, Faes L. Comparison of discretization strategies for the model-free information-theoretic assessment of short-term physiological interactions. CHAOS (WOODBURY, N.Y.) 2023; 33:033127. [PMID: 37003789 DOI: 10.1063/5.0140641] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Accepted: 02/17/2023] [Indexed: 06/19/2023]
Abstract
This work presents a comparison between different approaches for the model-free estimation of information-theoretic measures of the dynamic coupling between short realizations of random processes. The measures considered are the mutual information rate (MIR) between two random processes X and Y and the terms of its decomposition evidencing either the individual entropy rates of X and Y and their joint entropy rate, or the transfer entropies from X to Y and from Y to X and the instantaneous information shared by X and Y. All measures are estimated through discretization of the random variables forming the processes, performed either via uniform quantization (binning approach) or rank ordering (permutation approach). The binning and permutation approaches are compared on simulations of two coupled non-identical Hènon systems and on three datasets, including short realizations of cardiorespiratory (CR, heart period and respiration flow), cardiovascular (CV, heart period and systolic arterial pressure), and cerebrovascular (CB, mean arterial pressure and cerebral blood flow velocity) measured in different physiological conditions, i.e., spontaneous vs paced breathing or supine vs upright positions. Our results show that, with careful selection of the estimation parameters (i.e., the embedding dimension and the number of quantization levels for the binning approach), meaningful patterns of the MIR and of its components can be achieved in the analyzed systems. On physiological time series, we found that paced breathing at slow breathing rates induces less complex and more coupled CR dynamics, while postural stress leads to unbalancing of CV interactions with prevalent baroreflex coupling and to less complex pressure dynamics with preserved CB interactions. These results are better highlighted by the permutation approach, thanks to its more parsimonious representation of the discretized dynamic patterns, which allows one to explore interactions with longer memory while limiting the curse of dimensionality.
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Affiliation(s)
- Chiara Barà
- Department of Engineering, University of Palermo, 90128 Palermo, Italy
| | - Laura Sparacino
- Department of Engineering, University of Palermo, 90128 Palermo, Italy
| | - Riccardo Pernice
- Department of Engineering, University of Palermo, 90128 Palermo, Italy
| | - Yuri Antonacci
- Department of Engineering, University of Palermo, 90128 Palermo, Italy
| | - Alberto Porta
- Department of Biomedical Sciences for Health, University of Milan, 20133 Milan, Italy
| | - Dimitris Kugiumtzis
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
| | - Luca Faes
- Department of Engineering, University of Palermo, 90128 Palermo, Italy
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Balagué N, Hristovski R, Almarcha M, Garcia-Retortillo S, Ivanov PC. Network Physiology of Exercise: Beyond Molecular and Omics Perspectives. SPORTS MEDICINE - OPEN 2022; 8:119. [PMID: 36138329 PMCID: PMC9500136 DOI: 10.1186/s40798-022-00512-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 08/27/2022] [Indexed: 11/17/2022]
Abstract
Molecular Exercise Physiology and Omics approaches represent an important step toward synthesis and integration, the original essence of Physiology. Despite the significant progress they have introduced in Exercise Physiology (EP), some of their theoretical and methodological assumptions are still limiting the understanding of the complexity of sport-related phenomena. Based on general principles of biological evolution and supported by complex network science, this paper aims to contrast theoretical and methodological aspects of molecular and network-based approaches to EP. After explaining the main EP challenges and why sport-related phenomena cannot be understood if reduced to the molecular level, the paper proposes some methodological research advances related to the type of studied variables and measures, the data acquisition techniques, the type of data analysis and the assumed relations among physiological levels. Inspired by Network Physiology, Network Physiology of Exercise provides a new paradigm and formalism to quantify cross-communication among diverse systems across levels and time scales to improve our understanding of exercise-related phenomena and opens new horizons for exercise testing in health and disease.
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Volpes G, Barà C, Busacca A, Stivala S, Javorka M, Faes L, Pernice R. Feasibility of Ultra-Short-Term Analysis of Heart Rate and Systolic Arterial Pressure Variability at Rest and during Stress via Time-Domain and Entropy-Based Measures. SENSORS (BASEL, SWITZERLAND) 2022; 22:9149. [PMID: 36501850 PMCID: PMC9739824 DOI: 10.3390/s22239149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 11/22/2022] [Accepted: 11/22/2022] [Indexed: 06/17/2023]
Abstract
Heart Rate Variability (HRV) and Blood Pressure Variability (BPV) are widely employed tools for characterizing the complex behavior of cardiovascular dynamics. Usually, HRV and BPV analyses are carried out through short-term (ST) measurements, which exploit ~five-minute-long recordings. Recent research efforts are focused on reducing the time series length, assessing whether and to what extent Ultra-Short-Term (UST) analysis is capable of extracting information about cardiovascular variability from very short recordings. In this work, we compare ST and UST measures computed on electrocardiographic R-R intervals and systolic arterial pressure time series obtained at rest and during both postural and mental stress. Standard time-domain indices are computed, together with entropy-based measures able to assess the regularity and complexity of cardiovascular dynamics, on time series lasting down to 60 samples, employing either a faster linear parametric estimator or a more reliable but time-consuming model-free method based on nearest neighbor estimates. Our results are evidence that shorter time series down to 120 samples still exhibit an acceptable agreement with the ST reference and can also be exploited to discriminate between stress and rest. Moreover, despite neglecting nonlinearities inherent to short-term cardiovascular dynamics, the faster linear estimator is still capable of detecting differences among the conditions, thus resulting in its suitability to be implemented on wearable devices.
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Affiliation(s)
- Gabriele Volpes
- Department of Engineering, University of Palermo, Viale delle Scienze, Building 9, 90128 Palermo, Italy
| | - Chiara Barà
- Department of Engineering, University of Palermo, Viale delle Scienze, Building 9, 90128 Palermo, Italy
| | - Alessandro Busacca
- Department of Engineering, University of Palermo, Viale delle Scienze, Building 9, 90128 Palermo, Italy
| | - Salvatore Stivala
- Department of Engineering, University of Palermo, Viale delle Scienze, Building 9, 90128 Palermo, Italy
| | - Michal Javorka
- Department of Physiology, Jessenius Faculty of Medicine, Comenius University, 036 01 Martin, Slovakia
| | - Luca Faes
- Department of Engineering, University of Palermo, Viale delle Scienze, Building 9, 90128 Palermo, Italy
| | - Riccardo Pernice
- Department of Engineering, University of Palermo, Viale delle Scienze, Building 9, 90128 Palermo, Italy
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Estimating Permutation Entropy Variability via Surrogate Time Series. ENTROPY 2022; 24:e24070853. [PMID: 35885077 PMCID: PMC9318716 DOI: 10.3390/e24070853] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 06/19/2022] [Accepted: 06/20/2022] [Indexed: 01/27/2023]
Abstract
In the last decade permutation entropy (PE) has become a popular tool to analyze the degree of randomness within a time series. In typical applications, changes in the dynamics of a source are inferred by observing changes of PE computed on different time series generated by that source. However, most works neglect the crucial question related to the statistical significance of these changes. The main reason probably lies in the difficulty of assessing, out of a single time series, not only the PE value, but also its uncertainty. In this paper we propose a method to overcome this issue by using generation of surrogate time series. The analysis conducted on both synthetic and experimental time series shows the reliability of the approach, which can be promptly implemented by means of widely available numerical tools. The method is computationally affordable for a broad range of users.
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