Direction Of Information Transfer Research Articles

Investigating the Propagation From Meteorological to Hydrological Drought by Introducing the Nonlinear Dependence With Directed Information Transfer Index

AbstractMeteorological drought is the fuse of hydrological drought, and understanding the propagation mechanisms from meteorological to hydrological drought is of great significance to the monitoring and prevention of hydrological drought. Besides the linear dependence, this study thoroughly investigated the propagation from meteorological to hydrological drought by introducing the nonlinear dependence with directed information transfer index (DITI) for the first time. In this study, the standardized precipitation index and the standardized runoff index were used to represent meteorological drought and hydrological drought, respectively. A new drought response time (DRT) evaluation system was constructed based on the maximum Pearson correlation coefficient and DITI, simultaneously considering the linear and nonlinear relationships between meteorological drought and hydrological drought. Moreover, the relationships of drought characteristics (duration and severity) between these two types of drought were established by using run theory and mathematical function, and the trigger thresholds from meteorological to hydrological drought were then determined. The results indicate that: (a) the effective drought propagation rate was mainly affected by the characteristics of meteorological drought events and the sensitivity of hydrological drought to meteorological drought; (b) the DRT in the Pearl River Basin (PRB) was mainly concentrated in 2–5 months, and the drought translation rate in the PRB was relatively large; and (c) the duration of hydrological drought events was longer in the sub‐regions with smaller meteorological drought trigger thresholds.

Cellular Crosstalk between Endothelial and Smooth Muscle Cells in Vascular Wall Remodeling.

Pathological vascular wall remodeling refers to the structural and functional changes of the vessel wall that occur in response to injury that eventually leads to cardiovascular disease (CVD). Vessel wall are composed of two major primary cells types, endothelial cells (EC) and vascular smooth muscle cells (VSMCs). The physiological communications between these two cell types (EC–VSMCs) are crucial in the development of the vasculature and in the homeostasis of mature vessels. Moreover, aberrant EC–VSMCs communication has been associated to the promotor of various disease states including vascular wall remodeling. Paracrine regulations by bioactive molecules, communication via direct contact (junctions) or information transfer via extracellular vesicles or extracellular matrix are main crosstalk mechanisms. Identification of the nature of this EC–VSMCs crosstalk may offer strategies to develop new insights for prevention and treatment of disease that curse with vascular remodeling. Here, we will review the molecular mechanisms underlying the interplay between EC and VSMCs. Additionally, we highlight the potential applicable methodologies of the co-culture systems to identify cellular and molecular mechanisms involved in pathological vascular wall remodeling, opening questions about the future research directions.

Effective connectivity in brain networks estimated using EEG signals is altered in children with ADHD

This study presents a methodology developed for estimating effective connectivity in brain networks (BNs) using multichannel scalp EEG recordings. The methodology uses transfer entropy as an information transfer measure to detect pair-wise directed information transfer between EEG signals within δ, θ, α, β and γ-bands. The developed methodology is then used to study the properties of directed BNs in children with attention-deficit hyperactivity disorder (ADHD) and compare them with that of the healthy controls using both statistical and receiver operating characteristic (ROC) analyses. The results indicate that directed information transfer between scalp EEG electrodes in the ADHD subjects differs significantly compared to the healthy ones. The results of the statistical and ROC analyses of frequency-specific graph measures demonstrate their highly discriminative ability between the two groups. Specifically, the graph measures extracted from the estimated directed BNs in the β-band show the highest discrimination between the ADHD and control groups. These findings are in line with the fact that β-band reflects active concentration, motor activity, and anxious mental states. The reported results show that the developed methodology has the capacity to be used for investigating patterns of directed BNs in neuropsychiatric disorders.

Quantifying the Information Flow between Ghana Stock Market Index and Its Constituents Using Transfer Entropy

We quantify the strength and the directionality of information transfer between the Ghana stock market index and its component stocks as well as observe the same among the individual stocks on the market using transfer entropy. The information flow between the market index and its components and among individual stocks is measured by the effective transfer entropy of the daily logarithm returns generated from the daily market index and stock prices of 32 stocks ranging from 2nd January 2009 to 16th February 2018. We find a bidirectional and unidirectional flow of information between the GSE index and its component stocks, and the stocks dominate the information exchange. Among the individual stocks, SCB is the most active stock in the information exchange as it is the stock that receives the highest amount of information, but the most informative source is EGL (an insurance company) that has the highest net information outflow while the most information sink is PBC that has the highest net information inflow. We further categorize the stocks into 9 stock market sectors and find the insurance sector to be the largest source of information which confirms our earlier findings. Surprisingly, the oil and gas sector is the information sink. Our results confirm the fact that other sectors including oil and gas mitigate their risk exposures through insurance companies and are always expectant of information originating from the insurance sector in relation to regulatory compliance issues. It is our firm conviction that this study would allow stakeholders of the market to make informed buy, sell, or hold decisions.

Improving on transfer entropy-based network reconstruction using time-delays: Approach and validation

Transfer entropy constitutes a viable model-free tool to infer causal relationships between two dynamical systems from their time-series. In an information-theoretic sense, transfer entropy associates a cause-and-effect relationship with directed information transfer, such that one may improve the prediction of the future of a dynamical system from the history of another system. Recent studies have proposed the use of transfer entropy to reconstruct networks, but the inherent dyadic nature of this metric challenges the development of a robust approach that can discriminate direct from indirect interactions between nodes. In this paper, we seek to fill this methodological gap through the cogent integration of time-delays in the transfer entropy computation. By recognizing that information transfer in the network is bound by a finite speed, we relate the value of the time-delayed transfer entropy between two nodes to the number of walks between them. Upon this premise, we lay out the foundation of an alternative framework for network reconstruction, which we illustrate through closed-form results on three-node networks and numerically validate on larger networks, using examples of Boolean models and chaotic maps.

Does right hemisphere superiority sufficiently explain the left visual field advantage in face recognition?

The tendency to perceive the identity of the left half of a centrally viewed face more strongly than that of the right half is associated with visual processing of faces in the right hemisphere (RH). Here we investigate conditions under which this well-known left visual field (LVF) half-face advantage fails to occur. Our findings challenge the sufficiency of its explanation as a function of RH specialization for face processing coupled with LVF-RH correspondence. In two experiments we show that the LVF half-face advantage occurs for normal faces and chimeric faces composed of different half-face identities. In a third experiment, we show that face inversion disrupts the LVF half-face advantage. In two additional experiments we show that half-faces viewed in isolation or paired with inverted half-faces fail to show the LVF advantage. Consistent with previous explanations of the LVF half-face advantage, our findings suggest that the LVF half-face advantage reflects RH superiority for processing faces and direct transfer of LVF face information to visual cortex in the RH. Critically, however, our findings also suggest the operation of a third factor, which involves the prioritization of face-processing resources to the LVF, but only when two upright face-halves compete for these resources. We therefore conclude that RH superiority alone does not suffice to explain the LVF advantage in face recognition. We also discuss the implications of our findings for specialized visual processing of faces by the right hemisphere, and we distinguish LVF advantages for faces viewed centrally and peripherally in divided field studies.

Evaluation of information transfer and data transfer models of rain-gauge network design based on information entropy

Rainfall is one of the most fundamental components of the water cycle and is one of the fundamental inputs of hydrological models. A well-designed network can not only depict the regional precipitation characteristics, but also economically yield maximum needed rainfall information. In regions where either there is limited data or data is not available, it is a common challenge to add stations. The entropy theory-based information transfer model and geostatistical interpolation techniques are two solutions to meet the challenge. In this study, we used a representative rain gauge network to do the network design. Two models, based on information transfer and data transfer, were compared for network design. Other rain gauges in the study area were used as reference (“true values”) for assessing the model. Results showed that the information transfer model estimated transinformation between station pairs better than did the data transfer model. Different representative gauges were evaluated separately by the directional information transfer index (DIT). The candidate gauges selected with least information redundancy were similar for both information transfer and data transfer models. Though both models captured some least information-redundant areas, other areas may be bypassed because of model errors or estimation errors.

Structural network inference from time-series data using a generative model and transfer entropy

In this paper we develop a novel framework for inferring a generative model of network structure representing the causal relations between data for a set of objects characterized in terms of time series. To do this we make use of transfer entropy as a means of inferring directed information transfer between the time-series data. Transfer entropy allows us to infer directed edges representing the causal relations between pairs of time series, and has thus been used to infer directed graph representations of causal networks for time-series data. We use the expectation maximization algorithm to learn a generative model which captures variations in the causal network over time. We conduct experiments on fMRI brain connectivity data for subjects in different stages of the development of Alzheimer’s disease (AD). Here we use the technique to learn class exemplars for different stages in the development of the disease, together with a normal control class, and demonstrate its utility in both graph multi-class and binary classifications. These experiments are showing the effectiveness of our proposed framework when the amounts of training data are relatively small.

Lateralization of directional brain-heart information transfer during visual emotional elicitation.

Previous studies have characterized the physiological interactions between central nervous system (brain) and peripheral cardiovascular system (heart) during affective elicitation in healthy subjects; however, questions related to the directionality of this functional interplay have been gaining less attention from the scientific community. Here, we explore brain-heart interactions during visual emotional elicitation in healthy subjects using measures of Granger causality (GC), a widely used descriptor of causal influences between two dynamical systems. The proposed approach inferences causality between instantaneous cardiovagal dynamics estimated from inhomogeneous point-process models of the heartbeat and high-density electroencephalogram (EEG) dynamics in 22 healthy subjects who underwent pleasant/unpleasant affective elicitation by watching pictures from the International Affective Picture System database. Particularly, we calculated the GC indexes between the EEG spectrogram in the canonical θ-, α-, β-, and γ-bands and both the instantaneous mean heart rate and its continuous parasympathetic modulations (i.e., the instantaneous HF power). Thus we looked for significant statistical differences among GC values estimated during the resting state, neutral elicitation, and pleasant/unpleasant arousing elicitation. As compared with resting state, coupling strength increases significantly in the left hemisphere during positive stimuli and in the right hemisphere during negative stimuli. Our results further reveal a correlation between emotional valence and lateralization of the dynamical information transfer going from brain-to-heart, mainly localized in the prefrontal, somatosensory, and posterior cortexes, and of the information transfer from heart-to-brain, mainly reflected into the fronto-parietal cortex oscillations in the γ-band (30-45 Hz).

FEDRO: a software tool for the automatic discovery of candidate ORFs in plants with c \u2192u RNA editing

BackgroundRNA editing is an important mechanism for gene expression in plants organelles. It alters the direct transfer of genetic information from DNA to proteins, due to the introduction of differences between RNAs and the corresponding coding DNA sequences. Software tools successful for the search of genes in other organisms not always are able to correctly perform this task in plants organellar genomes. Moreover, the available software tools predicting RNA editing events utilise algorithms that do not account for events which may generate a novel start codon.ResultsWe present Fedro, a Java software tool implementing a novel strategy to generate candidate Open Reading Frames (ORFs) resulting from Cytidine to Uridine (c→u) editing substitutions which occur in the mitochondrial genome (mtDNA) of a given input plant. The goal is to predict putative proteins of plants mitochondria that have not been yet annotated. In order to validate the generated ORFs, a screening is performed by checking for sequence similarity or presence in active transcripts of the same or similar organisms. We illustrate the functionalities of our framework on a model organism.ConclusionsThe proposed tool may be used also on other organisms and genomes. Fedro is publicly available at http://math.unipa.it/rombo/FEDRO.

Information Transfer During Food Choice in the Slime Mold Physarum polycephalum

Throughout evolution, living systems have developed mechanisms to make adaptive decisions in the face of complex and changing environmental conditions. Most organisms make such decisions despite lacking a neural architecture. This is the case of the acellular slime mold Physarum polycephalum that has demonstrated remarkable information processing and problem-solving abilities. Previous studies suggest that the membrane of P. polycephalum plays an important role in integrating and processing information leading to the selection of a resource to exploit. The cyclical contraction-relaxation pattern of the membrane changes with the local quality of the environment, and individual contractile regions within a P. polycephalum can entrain neighboring regions, providing a potential mechanism for information processing and propagation. In this study, we use an information-theoretic tool, transfer entropy, to study the flow of information in single tubule segments of P. polycephalum in a binary choice between two food sources. We test P. polycephalum tubules in two food choice conditions, where the two available options are either symmetric in their nutrient concentrations or with one more concentrated in nutrients than the other (i.e., asymmetric). We measure the contractile pattern of the P. polycephalum membrane and use these data to explore the direction and amount of information transfer along the tubule as a function of the cell’s final decision. We find that the direction of information transfer is different in the two experimental conditions, and the amount of information transferred is inversely proportional to the distance between different contractile regions. Our results show that regions playing a leading role in information transfer changes with the decision-making challenges faced by P. polycephalum.

Using Information Theory to Evaluate Directional Precipitation Interactions Over the West Sahel Region in Observations and Models

AbstractWater availability has historically been one of the most significant threats to African regional social and economic well‐being. Over the Sahel region, a megadrought during the 1960s and 1970s induced by an abrupt and substantial rainfall reduction caused widespread famine and death. The postdrought recovery, which is still ongoing, has been characterized by gradual increases in rainfall, but with dramatic fluctuations. The large negative human impacts, slow recovery, and variability raise important questions of why and how rainfall dynamics evolve and interact with other components of the regional climate system. Here we provide an observational assessment of rainfall interactions mechanisms (informed by directional transfer of information entropy) that regulate Sahel rainfall. We quantitatively demonstrate that (1) sea surface temperature over the Gulf of Guinea controls moisture advection and transport to the West Sahel region and (2) strong bidirectional interactions exist between local vegetation dynamics and rainfall patterns. Then we assess the directional interaction patterns from nine state‐of‐the‐art Earth System Models (ESMs). We find that most ESMs are able to represent either the unidirectional control of sea surface temperature on precipitation or the bidirectional interaction between vegetation and precipitation. However, none of the ESMs represents both interactive patterns. The GFDL and IPSL‐CM5A‐LR models successfully reproduced observed patterns over ~50% of the West Sahel region but were not accurate in reproducing observed precipitation regional trends or interannual variation. We propose that the directional information transfer is a powerful mechanistic benchmark to assess model fidelity at the process level.

Infrastructure construction without information exchange: the trail clearing mechanism in Atta leafcutter ants.

A wide range of group-living animals construct tangible infrastructure networks, often of remarkable size and complexity. In ant colonies, infrastructure construction may require tens of thousands of work hours distributed among many thousand individuals. What are the individual behaviours involved in the construction and what level of complexity in inter-individual interaction is required to organize this effort? We investigate this question in one of the most sophisticated trail builders in the animal world: the leafcutter ants, which remove leaf litter, cut through overhangs and shift soil to level the path of trail networks that may cumulatively extend for kilometres. Based on obstruction experiments in the field and the laboratory, we identify and quantify different individual trail clearing behaviours. Via a computational model, we further investigate the presence of recruitment, which-through direct or indirect information transfer between individuals-is one of the main organizing mechanisms of many collective behaviours in ants. We show that large-scale transport networks can emerge purely from the stochastic process of workers encountering obstructions and subsequently engaging in removal behaviour with a fixed probability. In addition to such incidental removal, we describe a dedicated clearing behaviour in which workers remove additional obstructions independent of chance encounters. We show that to explain the dynamics observed in the experiments, no information exchange (e.g. via recruitment) is required, and propose that large-scale infrastructure construction of this type can be achieved without coordination between individuals.

Direct information transfer rate optimisation for SSVEP-based BCI

Objective. In this work, a classification method for steady-state visual evoked potential (SSVEP)-based brain–computer interface (BCI) is proposed. The method is based on information transfer rate (ITR) maximisation. Approach. The proposed classification method uses features extracted by traditional SSVEP-based BCI methods and finds optimal discrimination thresholds for each feature to classify the targets. Optimising the thresholds is formalised as a maximisation task of a performance measure of BCIs called information transfer rate. However, instead of the standard method of calculating ITR, which makes certain assumptions about the data, a more general formula is derived to avoid incorrect ITR calculation when the standard assumptions are not met. Main results. The proposed method shows good performance in classifying targets of a BCI, outperforming previously reported results on the same dataset by a factor of 2 in terms of ITR. The highest achieved ITR on the used dataset was 62 bit min-1. Significance. This approach allows the optimal discrimination thresholds to be automatically calculated and thus eliminates the need for manual parameter selection or performing computationally expensive grid searches. The proposed method also provides a way to reduce false classifications, which is important in real-world applications.

Multiset sparse redundancy analysis for high-dimensional omics data.

Redundancy Analysis (RDA) is a well‐known method used to describe the directional relationship between related data sets. Recently, we proposed sparse Redundancy Analysis (sRDA) for high‐dimensional genomic data analysis to find explanatory variables that explain the most variance of the response variables. As more and more biomolecular data become available from different biological levels, such as genotypic and phenotypic data from different omics domains, a natural research direction is to apply an integrated analysis approach in order to explore the underlying biological mechanism of certain phenotypes of the given organism. We show that the multiset sparse Redundancy Analysis (multi‐sRDA) framework is a prominent candidate for high‐dimensional omics data analysis since it accounts for the directional information transfer between omics sets, and, through its sparse solutions, the interpretability of the result is improved. In this paper, we also describe a software implementation for multi‐sRDA, based on the Partial Least Squares Path Modeling algorithm. We test our method through simulation and real omics data analysis with data sets of 364,134 methylation markers, 18,424 gene expression markers, and 47 cytokine markers measured on 37 patients with Marfan syndrome.

The Interaction Analysis between the Sympathetic and Parasympathetic Systems in CHF by Using Transfer Entropy Method.

Congestive heart failure (CHF) is a cardiovascular disease associated with autonomic dysfunction, where sympathovagal imbalance was reported in many studies using heart rate variability (HRV). To learn more about the dynamic interaction in the autonomic nervous system (ANS), we explored the directed interaction between the sympathetic nervous system (SNS) and the parasympathetic nervous system (PNS) with the help of transfer entropy (TE). This article included 24-h RR interval signals of 54 healthy subjects (31 males and 23 females, 61.38 ± 11.63 years old) and 44 CHF subjects (8 males and 2 females, 19 subjects’ gender were unknown, 55.51 ± 11.44 years old, 4 in class I, 8 in class II and 32 in class III~IV, according to the New York Heart Association Function Classification), obtained from the PhysioNet database and then segmented into 5-min non-overlapping epochs using cubic spline interpolation. For each segment in the normal group and CHF group, frequency-domain features included low-frequency (LF) power, high-frequency (HF) power and LF/HF ratio were extracted as classical estimators of autonomic activity. In the nonlinear domain, TE between LF and HF were calculated to quantify the information exchanging between SNS and PNS. Compared with the normal group, an extreme decrease in LF/HF ratio (p = 0.000) and extreme increases in both TE(LF→HF) (p = 0.000) and TE(HF→LF) (p = 0.000) in the CHF group were observed. Moreover, both in normal and CHF groups, TE(LF→HF) was a lot greater than TE(HF→LF) (p = 0.000), revealing that TE was able to distinguish the difference in the amount of directed information transfer among ANS. Extracted features were further applied in discriminating CHF using IBM SPSS Statistics discriminant analysis. The combination of the LF/HF ratio, TE(LF→HF) and TE(HF→LF) reached the highest screening accuracy (83.7%). Our results suggested that TE could serve as a complement to traditional index LF/HF in CHF screening.

Cortical Network Topology in Prodromal and Mild Dementia Due to Alzheimer\u2019s Disease: Graph Theory Applied to Resting State EEG

Graph theory analysis on resting state electroencephalographic rhythms disclosed topological properties of cerebral network. In Alzheimer’s disease (AD) patients, this approach showed mixed results. Granger causality matrices were used as input to the graph theory allowing to estimate the strength and the direction of information transfer between electrode pairs. The number of edges (degree), the number of inward edges (in-degree), of outgoing edges (out-degree) were statistically compared among healthy controls, patients with mild cognitive impairment due to AD (AD-MCI) and AD patients with mild dementia (ADD) to evaluate if degree abnormality could involve low and/or high degree vertices, the so called hubs, in both prodromal and over dementia stage. Clustering coefficient and local efficiency were evaluated as measures of network segregation, path length and global efficiency as measures of integration, the assortativity coefficient as a measure of resilience. Degree, in-degree and out-degree values were lower in AD-MCI and ADD than the control group for non-hubs and hubs vertices. The number of edges was preserved for frontal electrodes, where patients’ groups showed an additional hub in F3. Clustering coefficient was lower in ADD compared with AD-MCI in the right occipital electrode, and it was positively correlated with mini mental state examination. Local and global efficiency values were lower in patients’ than control groups. Our results show that the topology of the network is altered in AD patients also in its prodromal stage, begins with the reduction of the number of edges and the loss of the local and global efficiency.

Differences in step characteristics and linear kinematics between rugby players and sprinters during initial sprint acceleration

The initial steps of a sprint are important in team sports, such as rugby, where there is an inherent requirement to maximally accelerate over short distances. Current understanding of sprint acceleration technique is primarily based on data from track and field sprinters, although whether this information is transferable to athletes such as rugby players is unclear, due to differing ecological constraints. Sagittal plane video data were collected (240 Hz) and manually digitised to calculate the kinematics of professional rugby forwards (n = 15) and backs (n = 15), and sprinters (n = 18; 100 m personal best range = 9.96–11.33 s) during the first three steps of three maximal sprint accelerations. Using a between-group research design, differences between groups were determined using magnitude-based inferences, and within-group relationships between technique variables and initial sprint acceleration performance were established using correlation. Substantial between-group differences were observed in multiple variables. Only one variable, toe-off distance, differed between groups (d = −0.42 to −2.62) and also demonstrated meaningful relationships with sprint performance within all three groups (r = −0.44 to −0.58), whereby a stance foot position more posterior relative to the centre of mass at toe-off was associated with better sprint performance. While toe-off distance appears to be an important technical feature for sprint acceleration performance in both sprinters and rugby players, caution should be applied to the direct transfer of other kinematic information from sprinters to inform the technical development of acceleration in team sports athletes.

Direction-dependent interaction rules enrich pattern formation in an individual-based model of collective behavior.

Direction-dependent interaction rules are incorporated into a one-dimensional discrete-time stochastic individual-based model (IBM) of collective behavior to compare pattern formation with an existing partial differential equation (PDE) model. The IBM is formulated in terms of three social interaction forces: repulsion, alignment, and attraction, and includes information regarding conspecifics’ direction of travel. The IBM produces a variety of spatial patterns which qualitatively match patterns observed in a PDE model. The addition of direction-dependent interaction rules exemplifies how directional information transfer within a group of individuals can result in enriched pattern formation. Our individual-based modelling framework reveals the influence that direction-dependent interaction rules such as biological communication can have upon individual movement trajectories and how these trajectories combine to form group patterns.

A new variant of restricted Boltzmann machine with horizontal connections

Restricted Boltzmann machines (RBMs) are successfully employed to construct deep architectures because their power of expression and the inference is tractable and easy. In this paper, we propose a model named self-connected restricted Boltzmann machine (SCRBM), which adds horizontal connections to the hidden layer to enable direct information transfer between hidden units. We present a simple and effective method based on greedy layer-wise procedure of deep learning algorithms to train the model. Under the algorithm, SCRBM has a three-layer architecture. The first hidden layer extracts features from the data, and the second hidden layer is used to stimulate various interactions between units in the layer. Specifically, to stimulate the lateral inhibition that exists in sensory systems, a log sparse item is introduced to the second hidden layer of SCRBM. Our experiments show that the features learned by our algorithm are more vivid and clean than those learned by basic RBM and SparseRBM. Further experiments show the performance of SCRBM outperforms basic RBM and SparseRBM on several widely used datasets in terms of accuracy.