Assessment of Cardiorespiratory Interactions during Apneic Events in Sleep via Fuzzy Kernel Measures of Information Dynamics

Apnea and other breathing-related disorders have been linked to the development of hypertension or impairments of the cardiovascular, cognitive or metabolic systems. The combined assessment of multiple physiological signals acquired during sleep is of fundamental importance for providing additional insights about breathing disorder events and the associated impairments. In this work, we apply information-theoretic measures to describe the joint dynamics of cardiorespiratory physiological processes in a large group of patients reporting repeated episodes of hypopneas, apneas (central, obstructive, mixed) and respiratory effort related arousals (RERAs). We analyze the heart period as the target process and the airflow amplitude as the driver, computing the predictive information, the information storage, the information transfer, the internal information and the cross information, using a fuzzy kernel entropy estimator. The analyses were performed comparing the information measures among segments during, immediately before and after the respiratory event and with control segments. Results highlight a general tendency to decrease of predictive information and information storage of heart period, as well as of cross information and information transfer from respiration to heart period, during the breathing disordered events. The information-theoretic measures also vary according to the breathing disorder, and significant changes of information transfer can be detected during RERAs, suggesting that the latter could represent a risk factor for developing cardiovascular diseases. These findings reflect the impact of different sleep breathing disorders on respiratory sinus arrhythmia, suggesting overall higher complexity of the cardiac dynamics and weaker cardiorespiratory interactions which may have physiological and clinical relevance.

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Decomposing information into copying versus transformation

Journal of The Royal Society Interface ◽

10.1098/rsif.2019.0623 ◽

2020 ◽

Vol 17 (162) ◽

pp. 20190623 ◽

Cited By ~ 1

Author(s):

Artemy Kolchinsky ◽

Bernat Corominas-Murtra

Keyword(s):

Amino Acid ◽

Mutual Information ◽

Information Transfer ◽

World Systems ◽

Information Theoretic ◽

Versus Transformation ◽

Information Theoretic Measures ◽

Functional Consequences ◽

Standard Information ◽

Minimal Work

In many real-world systems, information can be transmitted in two qualitatively different ways: by copying or by transformation . Copying occurs when messages are transmitted without modification, e.g. when an offspring receives an unaltered copy of a gene from its parent. Transformation occurs when messages are modified systematically during transmission, e.g. when mutational biases occur during genetic replication. Standard information-theoretic measures do not distinguish these two modes of information transfer, although they may reflect different mechanisms and have different functional consequences. Starting from a few simple axioms, we derive a decomposition of mutual information into the information transmitted by copying versus the information transmitted by transformation. We begin with a decomposition that applies when the source and destination of the channel have the same set of messages and a notion of message identity exists. We then generalize our decomposition to other kinds of channels, which can involve different source and destination sets and broader notions of similarity. In addition, we show that copy information can be interpreted as the minimal work needed by a physical copying process, which is relevant for understanding the physics of replication. We use the proposed decomposition to explore a model of amino acid substitution rates. Our results apply to any system in which the fidelity of copying, rather than simple predictability, is of critical relevance.

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Information theoretic measures for quantifying sequence–ensemble relationships of intrinsically disordered proteins

Protein Engineering Design and Selection ◽

10.1093/protein/gzz014 ◽

2019 ◽

Vol 32 (4) ◽

pp. 191-202 ◽

Cited By ~ 1

Author(s):

Megan C Cohan ◽

Kiersten M Ruff ◽

Rohit V Pappu

Keyword(s):

Intrinsically Disordered Proteins ◽

De Novo ◽

Disordered Proteins ◽

Specific Sequence ◽

Information Theoretic ◽

Conformational Ensembles ◽

Intrinsically Disordered ◽

Information Theoretic Measures ◽

Modulating Functions ◽

The Impact

Abstract Intrinsically disordered proteins (IDPs) contribute to a multitude of functions. De novo design of IDPs should open the door to modulating functions and phenotypes controlled by these systems. Recent design efforts have focused on compositional biases and specific sequence patterns as the design features. Analysis of the impact of these designs on sequence-function relationships indicates that individual sequence/compositional parameters are insufficient for describing sequence-function relationships in IDPs. To remedy this problem, we have developed information theoretic measures for sequence–ensemble relationships (SERs) of IDPs. These measures rely on prior availability of statistically robust conformational ensembles derived from all atom simulations. We show that the measures we have developed are useful for comparing sequence-ensemble relationships even when sequence is poorly conserved. Based on our results, we propose that de novo designs of IDPs, guided by knowledge of their SERs, should provide improved insights into their sequence–ensemble–function relationships.

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Dynamic Formant Trajectories in German Read Speech: Impact of Predictability and Prominence

Frontiers in Communication ◽

10.3389/fcomm.2021.643528 ◽

2021 ◽

Vol 6 ◽

Author(s):

Erika Brandt ◽

Bernd Möbius ◽

Bistra Andreeva

Keyword(s):

Mixed Models ◽

Language Model ◽

Dynamic Structure ◽

Dynamic Features ◽

Information Theoretic ◽

Generalized Additive Mixed Models ◽

Additive Mixed Models ◽

Information Theoretic Measures ◽

Second Formant ◽

The Impact

Phonetic structures expand temporally and spectrally when they are difficult to predict from their context. To some extent, effects of predictability are modulated by prosodic structure. So far, studies on the impact of contextual predictability and prosody on phonetic structures have neglected the dynamic nature of the speech signal. This study investigates the impact of predictability and prominence on the dynamic structure of the first and second formants of German vowels. We expect to find differences in the formant movements between vowels standing in different predictability contexts and a modulation of this effect by prominence. First and second formant values are extracted from a large German corpus. Formant trajectories of peripheral vowels are modeled using generalized additive mixed models, which estimate nonlinear regressions between a dependent variable and predictors. Contextual predictability is measured as biphone and triphone surprisal based on a statistical German language model. We test for the effects of the information-theoretic measures surprisal and word frequency, as well as prominence, on formant movement, while controlling for vowel phonemes and duration. Primary lexical stress and vowel phonemes are significant predictors of first and second formant trajectory shape. We replicate previous findings that vowels are more dispersed in stressed syllables than in unstressed syllables. The interaction of stress and surprisal explains formant movement: unstressed vowels show more variability in their formant trajectory shape at different surprisal levels than stressed vowels. This work shows that effects of contextual predictability on fine phonetic detail can be observed not only in pointwise measures but also in dynamic features of phonetic segments.

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Information-theoretic measures for nonlinear causality detection: application to social media sentiment and cryptocurrency prices

Royal Society Open Science ◽

10.1098/rsos.200863 ◽

2020 ◽

Vol 7 (9) ◽

pp. 200863

Author(s):

Z. Keskin ◽

T. Aste

Keyword(s):

Time Series ◽

Information Transfer ◽

Synthetic Data ◽

Transfer Entropy ◽

Significant Information ◽

Information Theoretic ◽

Order Of Magnitude ◽

Information Theoretic Measures ◽

Media Sentiment ◽

Non Parametric

Information transfer between time series is calculated using the asymmetric information-theoretic measure known as transfer entropy. Geweke’s autoregressive formulation of Granger causality is used to compute linear transfer entropy, and Schreiber’s general, non-parametric, information-theoretic formulation is used to quantify nonlinear transfer entropy. We first validate these measures against synthetic data. Then we apply these measures to detect statistical causality between social sentiment changes and cryptocurrency returns. We validate results by performing permutation tests by shuffling the time series, and calculate the Z -score. We also investigate different approaches for partitioning in non-parametric density estimation which can improve the significance. Using these techniques on sentiment and price data over a 48-month period to August 2018, for four major cryptocurrencies, namely bitcoin (BTC), ripple (XRP), litecoin (LTC) and ethereum (ETH), we detect significant information transfer, on hourly timescales, with greater net information transfer from sentiment to price for XRP and LTC, and instead from price to sentiment for BTC and ETH. We report the scale of nonlinear statistical causality to be an order of magnitude larger than the linear case.

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Decomposing information into copying versus transformation

10.1101/584771 ◽

2019 ◽

Author(s):

Artemy Kolchinsky ◽

Bernat Corominas-Murtra

Keyword(s):

Amino Acid ◽

Information Transfer ◽

World Systems ◽

Information Theoretic ◽

Versus Transformation ◽

Information Theoretic Measures ◽

Functional Consequences ◽

Standard Information ◽

Biological Reproduction ◽

Minimal Work

In many real-world systems, information can be transmitted in two qualitatively different ways: by copying or by transformation. Copying occurs when messages are transmitted without modification, e.g., when an offspring receives an unaltered copy of a gene from its parent. Transformation occurs when messages are modified systematically during transmission, e.g., when non-random mutations occur during biological reproduction. Standard information-theoretic measures do not distinguish these two modes of information transfer, although they may reflect different mechanisms and have different functional consequences. Starting from a few simple axioms, we derive a decomposition of mutual information into the information transmitted by copying and by transformation. Our decomposition applies whenever the source and destination of the channel have the same set of outcomes, so that a notion of message identity exists, although generalizations to other kinds of channels and similarity notions are explored. Furthermore, copy information can be interpreted as the minimal work needed by a physical copying process, relevant to better understand the physics of replication. We use the proposed decomposition to explore a model of amino acid substitution rates. Our results apply to any system in which the fidelity of copying, rather than simple predictability, is of critical relevance.

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Lack of association between heart period variability asymmetry and respiratory sinus arrhythmia in healthy and chronic heart failure individuals

PLoS ONE ◽

10.1371/journal.pone.0247145 ◽

2021 ◽

Vol 16 (2) ◽

pp. e0247145

Author(s):

Beatrice De Maria ◽

Laura Adelaide Dalla Vecchia ◽

Roberto Maestri ◽

Gian Domenico Pinna ◽

Monica Parati ◽

...

Keyword(s):

Heart Failure ◽

Chronic Heart Failure ◽

Respiratory Sinus Arrhythmia ◽

Nyha Class ◽

Respiratory Pattern ◽

Heart Period ◽

Middle Aged ◽

Experimental Conditions ◽

Sinus Arrhythmia ◽

The Impact

Temporal asymmetry is a peculiar aspect of heart period (HP) variability (HPV). HPV asymmetry (HPVA) is reduced with aging and pathology, but its origin is not fully elucidated. Given the impact of respiration on HPV resulting in the respiratory sinus arrhythmia (RSA) and the asymmetric shape of the respiratory pattern, a possible link between HPVA and RSA might be expected. In this study we tested the hypothesis that HPVA is significantly associated with RSA and asymmetry of the respiratory rhythm. We studied 42 middle-aged healthy (H) subjects, and 56 chronic heart failure (CHF) patients of whom 26 assigned to the New York Heart Association (NYHA) class II (CHF-II) and 30 to NYHA class III (CHF-III). Electrocardiogram and lung volume were monitored for 8 minutes during spontaneous breathing (SB) and controlled breathing (CB) at 15 breaths/minute. The ratio of inspiratory (INSP) to expiratory (EXP) phases, namely the I/E ratio, and RSA were calculated. HPVA was estimated as the percentage of negative HP variations, traditionally measured via the Porta’s index (PI). Departures of PI from 50% indicated HPVA and its significance was tested via surrogate data. We found that RSA increased during CB and I/E ratio was smaller than 1 in all groups and experimental conditions. In H subjects the PI was about 50% during SB and it increased significantly during CB. In both CHF-II and CHF-III groups the PI was about 50% during SB and remained unmodified during CB. The PI was uncorrelated with RSA and I/E ratio regardless of the experimental condition and group. Pooling together data of different experimental conditions did not affect conclusions. Therefore, we conclude that the HPVA cannot be explained by RSA and/or I/E ratio, thus representing a peculiar feature of the cardiac control that can be aroused in middle-aged H individuals via CB.

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Unified framework for information integration based on information geometry

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1603583113 ◽

2016 ◽

Vol 113 (51) ◽

pp. 14817-14822 ◽

Cited By ~ 48

Author(s):

Masafumi Oizumi ◽

Naotsugu Tsuchiya ◽

Shun-ichi Amari

Keyword(s):

Probability Distribution ◽

Information Integration ◽

Information Transfer ◽

Holistic Approach ◽

Information Geometry ◽

Unified Framework ◽

Information Theoretic ◽

Research Fields ◽

Information Theoretic Measures ◽

Integrated Information

Assessment of causal influences is a ubiquitous and important subject across diverse research fields. Drawn from consciousness studies, integrated information is a measure that defines integration as the degree of causal influences among elements. Whereas pairwise causal influences between elements can be quantified with existing methods, quantifying multiple influences among many elements poses two major mathematical difficulties. First, overestimation occurs due to interdependence among influences if each influence is separately quantified in a part-based manner and then simply summed over. Second, it is difficult to isolate causal influences while avoiding noncausal confounding influences. To resolve these difficulties, we propose a theoretical framework based on information geometry for the quantification of multiple causal influences with a holistic approach. We derive a measure of integrated information, which is geometrically interpreted as the divergence between the actual probability distribution of a system and an approximated probability distribution where causal influences among elements are statistically disconnected. This framework provides intuitive geometric interpretations harmonizing various information theoretic measures in a unified manner, including mutual information, transfer entropy, stochastic interaction, and integrated information, each of which is characterized by how causal influences are disconnected. In addition to the mathematical assessment of consciousness, our framework should help to analyze causal relationships in complex systems in a complete and hierarchical manner.

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Sources of predictive information in dynamical neural networks

10.1101/2019.12.23.887554 ◽

2019 ◽

Author(s):

Madhavun Candadai ◽

Eduardo J. Izquierdo

Keyword(s):

Adaptive Behavior ◽

Information Transfer ◽

Computational Models ◽

Natural Environments ◽

Predictive Information ◽

Information Theoretic ◽

Structure Changes ◽

Strong Indicator ◽

Flow Of Information ◽

Over Time

Behavior involves the ongoing interaction between an organism and its environment. One of the prevailing theories of adaptive behavior is that organisms are constantly making predictions about their future environmental stimuli. However, how they acquire that predictive information is still poorly understood. Two complementary mechanisms have been proposed: predictions are generated from an agent’s internal model of the world or predictions are extracted directly from the environmental stimulus. In this work, we demonstrate that predictive information, measured using mutual information, cannot distinguish between these two kinds of systems. Furthermore, we show that predictive information cannot distinguish between organisms that are adapted to their environments and random dynamical systems exposed to the same environment. To understand the role of predictive information in adaptive behavior, we need to be able to identify where it is generated. To do this, we decompose information transfer across the different components of the organism-environment system and track the flow of information in the system over time. To validate the proposed framework, we examined it on a set of computational models of idealized agent-environment systems. Analysis of the systems revealed three key insights. First, predictive information, when sourced from the environment, can be reflected in any agent irrespective of its ability to perform a task. Second, predictive information, when sourced from the nervous system, requires special dynamics acquired during the process of adapting to the environment. Third, the magnitude of predictive information in a system can be different for the same task if the environmental structure changes.Significance StatementAn organism’s ability to predict the consequences of its actions on future stimuli is considered a strong indicator of its environmental adaptation. However, in highly structured natural environments, to what extent does an agent have to develop specialized mechanisms to generate predictions? To study this, we present an information theoretic framework to infer the source of predictive information in an organism: extrinsically from the environment or intrinsically from the agent. We find that predictive information extracted from the environment can be reflected in any agent and is therefore not a good indicator of behavioral performance. Studying the flow of predictive information over time across the organism-environment system enables us to better understand its role in behavior.

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Information transfer and recovery for the sense of touch

10.1101/2020.12.08.415729 ◽

2020 ◽

Author(s):

Chao Huang ◽

Bernhard Englitz ◽

Andrey Reznik ◽

Fleur Zeldenrust ◽

Tansu Celikel

Keyword(s):

Information Transfer ◽

Sensory Information ◽

Single Neurons ◽

Information Theoretic ◽

Rate Limiting Step ◽

Information Theoretic Measures ◽

Sensory Cortices ◽

A Cell ◽

Trial Basis ◽

Sense Of Touch

Transformation of postsynaptic potentials (PSPs) into action potentials (APs) is the rate-limiting step of communication in neural networks. The efficiency of this intracellular information transfer also powerfully shapes stimulus representations in sensory cortices. Using whole-cell recordings and information-theoretic measures, we show herein that somatic PSPs accurately represent stimulus location on a trial-by-trial basis in single neurons even 4 synapses away from the sensory periphery in the whisker system. This information is largely lost during AP generation but can be rapidly (<20 ms) recovered using complementary information in local populations in a cell-type-specific manner. These results show that as sensory information is transferred from one neural locus to another, the circuits reconstruct the stimulus with high fidelity so that sensory representations of single neurons faithfully represent the stimulus in the periphery, but only in their PSPs, resulting in lossless information processing for the sense of touch in the primary somatosensory cortex.

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Transfer Entropy Analysis of Interactions between Bats Using Position and Echolocation Data

Entropy ◽

10.3390/e22101176 ◽

2020 ◽

Vol 22 (10) ◽

pp. 1176

Author(s):

Irena Shaffer ◽

Nicole Abaid

Keyword(s):

Information Transfer ◽

Animal Species ◽

Transfer Entropy ◽

Signal Interference ◽

Information Theoretic ◽

Echolocation Calls ◽

Primary Means ◽

Information Theoretic Measures ◽

Large Groups

Many animal species, including many species of bats, exhibit collective behavior where groups of individuals coordinate their motion. Bats are unique among these animals in that they use the active sensing mechanism of echolocation as their primary means of navigation. Due to their use of echolocation in large groups, bats run the risk of signal interference from sonar jamming. However, several species of bats have developed strategies to prevent interference, which may lead to different behavior when flying with conspecifics than when flying alone. This study seeks to explore the role of this acoustic sensing on the behavior of bat pairs flying together. Field data from a maternity colony of gray bats (Myotis grisescens) were collected using an array of cameras and microphones. These data were analyzed using the information theoretic measure of transfer entropy in order to quantify the interaction between pairs of bats and to determine the effect echolocation calls have on this interaction. This study expands on previous work that only computed information theoretic measures on the 3D position of bats without echolocation calls or that looked at the echolocation calls without using information theoretic analyses. Results show that there is evidence of information transfer between bats flying in pairs when time series for the speed of the bats and their turning behavior are used in the analysis. Unidirectional information transfer was found in some subsets of the data which could be evidence of a leader–follower interaction.

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