Search

Introduction: The right and left-hand motor imagery (MI) analysis based on the electroencephalogram (EEG) signal can directly link the central nervous system to a computer or a device. This study aims to identify a set of robust and nonlinear effective brain connectivity features quantified by transfer entropy (TE) to characterize the relationship between brain regions from EEG signals and create a hierarchical feature selection and classification for discrimination of right and lefthand MI tasks. Methods: TE is calculated among EEG channels as the distinctive, effective connectivity features. TE is a model-free method that can measure nonlinear effective connectivity and analyze multivariate dependent directed information flow among neural EEG channels. Then four feature subset selection methods namely relief-F, Fisher, Laplacian, and local learningbased clustering (LLCFS) algorithms are used to choose the most significant effective connectivity features and reduce redundant information. Finally, support vector machine (SVM) and linear discriminant analysis (LDA) methods are used for classification. Results: Results show that the best performance in 29 healthy subjects and 60 trials is achieved using the TE method via the Relief-F algorithm as feature selection and support vector machine (SVM) classification with 91.02% accuracy. Conclusion: The TE index and a hierarchical feature selection and classification can be useful for the discrimination of right- and left-hand MI tasks from multichannel EEG signals.

Inferring brain-effective connectivity networks from neuroimaging data has become a very hot topic in neuroinformatics and bioinformatics. In recent years, the search methods based on Bayesian network score have been greatly developed and become an emerging method for inferring effective connectivity. However, the previous score functions ignore the temporal information from functional magnetic resonance imaging (fMRI) series data and may not be able to determine all orientations in some cases. In this article, we propose a novel score function for inferring effective connectivity from fMRI data based on the conditional entropy and transfer entropy (TE) between brain regions. The new score employs the TE to capture the temporal information and can effectively infer connection directions between brain regions. Experimental results on both simulated and real-world data demonstrate the efficacy of our proposed score function.

Background and objective Based on magnetic resonance imaging (MRI), macroscopic structural and functional connectivity of human brain has been widely explored in the last decade. However, little work has been done on effective connectivity between individual brain parcels. In this preliminary study, we aim to investigate whole-brain effective connectivity networks from resting-state functional MRI (rs-fMRI) images. Material and methods After the functional connectivity networks of 26 healthy subjects (aged from 25 to 35 years old) from Human Connectome Project database were derived from rs-fMRI images with dynamic time warping, proportional thresholding (PT) was performed on the functional connectivity matrices by retaining the PT% strongest functional connections. PT% ranges from 40% to 10% in steps of 5%. Then, effective connections corresponding to the PT% strongest functional connections, both bi-directional and unidirectional, were estimated with Renyi's 2-order transfer entropy (TE) method. Topological metrics of the built functional and effective connectivity networks were further characterized, including clustering coefficient, transitivity, and modularity. Results It is found that the effective connectivity networks exhibit small world attributes, and that the networks contain a subset of highly interactive regions, including right frontal pole (in-degree 6), left middle frontal gyrus (in-degree 8, out-degree 1), right precentral gyrus (out-degree 9), left precentral gyrus (out-degree 7), right posterior division of supramarginal gyrus (in-degree 2, out-degree 3), left angular gyrus (out-degree 6), left inferior division of lateral occipital cortex (out-degree 6), right occipital pole (in-degree 5), right cerebellum 7b parcel (in-degree 15), and right cerebellum 8 parcel (in-degree 7, out-degree 1). Conclusions The observations in this study provide information about the casual interactions among brain parcels in resting state, helping reveal how different subregions of large-scale distributed neural networks are coupled together in performing cognitive functions.

In this article, power flow transfer entropy (PFTE) is proposed as a criterion to evaluate the power flow transfer characteristics of the multiterminal HVdc (MTdc) wind power integration grid. This proposed criterion can detect and locate the ac three-phase to ground (TPG) faults in the MTdc grid by its sensitivity to power flow change to avoid communication. The transient energy dissipation control strategy is then designed according to the variety of PFTE, which reflects the power flow reversal characteristic of the MTdc grid during ac faults. Experimental results show that the system based on the proposed communication-free method can ride through ac TPG faults without blocking any converter.

Variational autoencoders (VAEs) are generative models which combine deep learning and Bayesian machine learning. The VAEs are trained via minimizing the loss function, and the most popular loss function in VAEs is the evidence lower bound function in which the Kullback–Leibler divergence has been used. Motivated by the idea that information flow from VAEs input to output should be reflected in the loss function, we propose to incorporate transfer entropy (TE) to loss function to quantify the information flow. Subsequently, we apply our TE-based VAEs to data analytics in Internet of Things, including data compression and generative modeling. Simulation results show that our TE-based VAEs works much better than the Kullback–Leibler divergence-based VAE in terms of reconstruction error and label error. We further analyze the latent space to clarify why our TE-based VAE performs better. We compare our TE-based VAE against compressive sensing and demonstrate that our TE-based VAE could have 30 times stronger compression power than compressive sensing.

Advances in technology allowed the fast and easy creation and configuration of industrial alarms. The growth in the number of alarms, however, brought some problems to the alarm management systems, for example, alarm flooding. In this paper, it is propose a new method to detect the causal relationships between industrial alarm variables using Transfer Entropy theory and the structural learning of the Bayesian networks K2 Algorithm as its fundamental basis. This work argues that the detection of these relationships can help to reduce the number of alarms in the alarm management systems, avoiding the overloading of operators in case of plant failures. To validate the proposal, a case study using the well-known plant-wide simulator Tennessee Eastman Process was performed. In this study case the graph of the causal relationships obtained by the proposed method was compatible with the system functioning.

This article is devoted to solving the problem of quality-related root cause diagnosis for nonlinear process. First, an orthogonal kernel principal component regression model is constructed to achieve orthogonal decomposition of feature space, such that quality-related and quality-unrelated faults can be separately detected in the subspaces of opposite correlations to the output, without any effect on each other. Then, in view of the high complexity of traditional nonlinear fault diagnosis methods, an efficient method of kernel sample equivalence replacement is established to replace the partial differential operations of the kernel gradient algorithm, which can convert nonlinear fault detection indicators into the standard quadratic forms of the original variable sample, thereby making it possible to solve the nonlinear fault diagnosis problem by linear manners. Furthermore, a transfer entropy algorithm is utilized to the new model to analyze the causality between the diagnosed candidate faulty variables to find out the accurate root cause of the fault. Finally, comparative studies between the latest result and the proposed one are carried out in the Tennessee Eastman process to verify the effectiveness and superiority of the new method.

This paper presents a multiscale symbolic transfer entropy (MSTE) to extract the features of gas–liquid two-phase flow and distinguish flow patterns effectively. The role of the MSTE in typical chaotic time series is investigated. Then the characteristics of the flow patterns about three gas–liquid two-phase flows are analyzed from the perspective of causal analysis. The results show that the MSTE can identify different flow patterns and characterize the dynamic characteristics of flow patterns, providing a new method for identifying two-phase flow accurately. In addition, the MSTE reduces the influence of noise to a certain extent and preserves the dynamic characteristics based on simplifying the original sequence. Compared with traditional algorithm, the MSTE has fast calculation speed and anti-interference characteristics and can express the essential features well.

BackgroundBrain network can be well used in emotion analysis to analyze the brain state of subjects. A novel dynamic brain network in arousal is proposed to analyze brain states and emotion with Electroencephalography (EEG) signals. New MethodTime factors is integrated to construct a dynamic brain network under high and low arousal conditions. The transfer entropy is adopted in the dynamic brain network. In order to ensure the authenticity of dynamics and connections, surrogate data are used for testing and analysis. Channel norm information features are proposed to optimize the data and evaluate the level of activity of the brain. ResultsThe frontal lobe, temporal lobe, and parietal lobe provide the most information about emotion arousal. The corresponding stimulation state is not maintained at all times. The number of active brain networks under high arousal conditions is generally higher than those under low arousal conditions. More consecutive networks show high activity under high arousal conditions among these active brain networks. The results of the significance analysis of the features indicates that there is a significant difference between high and low arousal. Comparison with Existing Method(s)Compared with traditional methods, the method proposed in this paper can analyze the changes of subjects' brain state over time in more detail. The proposed features can be used to quantify the brain network for accurate analysis. ConclusionsThe proposed dynamic brain network bridges the research gaps in lacking time resolution and arousal conditions in emotion analysis. We can clearly get the dynamic changes of the overall and local details of the brain under high and low arousal conditions. Furthermore, the active segments and brain regions of the subjects were quantified and evaluated by channel norm information.This method can be used to realize the feature extraction and dynamic analysis of the arousal dimension of emotional EEG, further explore the emotional dimension model, and also play an auxiliary role in emotional analysis.

Transfer entropy methods provide an approach to understanding asymmetric information flow in coupled systems, with particular application to understanding allosteric interactions in biomolecular systems. Transfer entropy analysis holds the potential to reveal pathways or networks of residues that are coupled in their information flow and thus give new insights into folding and binding dynamics. Most current methods for calculating transfer entropy require very long simulations and almost equally long calculations of joint probability histograms to compute the information transfer that make these methods either functionally intractable or statistically unreliable. Available approximate methods based on graph and network theory approaches are rapid but lose sensitivity to the chemical nature of the biomolecules and thus are not applicable in mutation studies. We show that reliable estimates of the transfer entropy can be obtained from the variance-covariance matrix of atomic fluctuations, which converges quickly and retains sensitivity to the full chemical profile of the biomolecular system. We validate our method on ERK2, a well-studied kinase involved in the MAPK signaling cascade for which considerable computational, experimental, and mutation data are available. We present the results of transfer entropy analysis on data obtained from molecular dynamics simulations of wild type active and inactive ERK2, along with mutants Q103A, I84A, L73P, and G83A. We show that our method is consistent with the results of computational and experimental studies on ERK2, and we provide a method for interpreting networks of interconnected residues in the protein from a perspective of allosteric coupling. We introduce new insights about possible allosteric activity of the extreme N-terminal region of the kinase, which to date has been under-explored in the literature and may provide an important new direction for kinase studies. We also describe evidence that suggests activation may occur by different paths or routes in different mutants. Our results highlight systematic advantages and disadvantages of each method for calculating transfer entropy and show the important role of transfer entropy analysis for understanding allosteric behavior in biomolecular systems.

Aim: The research aimed to study and model the emotion-related activity of functional networks within the human brain cortex using power spectrum density and detrended phase transfer entropy methods. Attention was focused on revealing alterations in cognitive mechanisms, caused by presentation of neutral human faces as rare stimuli among faces with either negative or positive expression. Methods: EEG-data was recorded during the perception and processing of neutral human facial expressions, presented among positive and negative faces in two series of images, alongside with resting state with open and closed eyes, which was further analyzed using power spectrum density and detrended phase transfer entropy methods. Results: Specific EEG-bands (θ and β) were chosen for the analysis based on their prominent role in memory- and emotion-related mechanisms. The topography of the spectral power density corresponded to the generally accepted ideas describing perception and visual stimuli processing mechanisms. The phase transfer entropy method was not sufficient to analyze resting state data. The results of the analysis performed using the phase transfer entropy method revealed the problems of neutral faces differentiation when presented in a positive emotional context. Simultaneously, enhanced processes of motivational coding and self-reflection were observed during the presentation of neutral faces in a negative emotional context. These results corresponded with the data obtained in our previous ERP-based study. Conclusions: Phase transfer entropy and spectral power density have demonstrated their effectiveness in analyzing the mechanisms of emotional visual stimuli processing mediated in different cortical areas.

The purpose of this article is to propose a methodology involving various methods that can be used to predict the data incompleteness of a dataset. Here the investigators have presented data incompleteness as both continuous and discrete random variables. In addition the investigators used transfer entropy for the purpose of advancing the science associated with the analysis of data incompleteness of electronic health records. The underlying methodology has been coined as “Machine Learning Analysis for Data Incompleteness” (MADI) with the intention of developing a possible solution to data incompleteness in electronic health records. MADI advances the analysis of data incompleteness with the use of Kolomogorov Smirnov goodness of fit, mielke distribution, and beta distributions for a holistic analysis. Alongside the methodology presented, the investigators explored stochastic gradient descent, generalized additive models, and support vector machines for comparison. Overall, the investigators have presented a complete set of methods and algorithms to help predict data incompleteness in a medical setting and provided suggestions for practical applications into the prediction of data incompleteness.

It is difficult for existing deep learning-based satellite on-orbit anomaly detection methods to define the residual-based detection threshold and identify false anomalies. To solve the above problems, this paper proposes both a detection threshold determination and dynamic correction method and a causality-based false anomaly identification and pruning method. We use the GRU (Gated Recurrent Unit) to model and predict the telemetry parameters to obtain the residual vector; determine and dynamically correct the threshold according to the prescribed false positive rate; propose an improved multivariate transfer entropy method to identify the causal relationships between the telemetry parameters; and, based on the causality, determine whether the detected parameter anomalies are false. Experiments show that the precision, recall, and F1-score of the method proposed in this paper are superior to the current typical method, and the false positive rate is significantly reduced, demonstrating the effectiveness of the proposed method.

Pain is an alert state of the human body that can be conveyed to the external world through different modalities. A possible communication channel for human pain is represented by facial expressions, whose role in social interactions has been well established. In this work, the link between pain and transfer entropy (TE), passing through facial expressions, is investigated. A new approach to the vision-based measurement (VBM) of pain is presented, which is based on TE among the time-series of facial landmarks positions. The system is composed of three main blocks: A VBM block for the automatic landmarking and the generation of the time-series from the video-sequences; a second block for the evaluation of TE; and finally a classification model based on machine learning algorithms for pain assessment. A public database of video sequences of patients experiencing pain in a controlled scenario was used for the characterization of the system in terms of accuracy and precision. Different uncertainty contributions related to realistic signals interruptions and fluctuations were modeled and propagated to provide a comprehensive evaluation of the proposed measurement system. The obtained results indicate that TE-based approaches can provide great benefits in automatic pain assessment, opening new perspectives for remote management of patients.

Recently, there has been an explosive interest in the literature about modeling and forecasting volatility in financial markets. Many researches have focused on energy markets and oil volatility index (OVX). In this article, we aim first at showing if there is an exchange of information between two stock time series, and then at evaluating what is the direction of this information flow. In particular, we propose an entropy-based approach that exploits two objective metrics, namely Mutual Information (MI) and Transfer Entropy (TE), that does not require a parametric model and is directly applicable on the data. The experimental outcomes, applied on Brent and WTI crude oil prices and their volatility index for the period from May 10, 2007 till July 03, 2018, demonstrate the effectiveness of the proposed method.

It is a challenging task to perform the non-linear system state recognition and safety monitoring under strong noise and complex excitation, to tackle this problem, we innovatively propose the variational mode decomposition multiscale holographic transfer entropy statistics (VMTES) method based on the energy transfer relationship between non-linear system signals. The VMTES is a method that measures the information flow direction and coupling degree between non-linear systems, which can precisely measure the slight changes of energy transfer of a mechanical system, accurately assess the slight mutation of dynamical behaviors and status change of a mechanical system and therefore realize fault location and quantification of the non-linear rotating machinery system. The damage severity and direction of the measure point can be precisely described with the VMTES damage assessment indicator, providing the reliable basis for the structural health monitoring and fault diagnosis of the mechanical system. By applying the result to the state recognition of a chaotic system and the structural health monitoring of a rotating machinery system, we can see from the experimental results that the VMTES can effectively detect the fault of gears and rolling bearings under several working conditions. Based on the experiment, we also explain how this method simultaneously locate and quantify the non-linear vibration caused by the fault.

EEG emotion recognition is one of the interesting and challenging tasks in the research based emotion human–computer interface system. In this paper, a multi-reservoirs feature coding continuous label fusion semi-supervised Generative Adversarial Networks (MCLFS-GAN) is proposed by using permutation phase transfer entropy as the EEG signal feature. Firstly, the obtained features are encapsulated in time series, and then the features are sent into multi-reservoirs according to the division of brain intra, brain interval or frequency band. After convolution optimization, the feature expression with time sequence relationship is obtained. The generic representation between the features and pseudo effective feature expression are iteratively learned in encoder E and generator G in the generative adversarial way. In addition, the continuous fusion for class intra tags can help to form continuous differences between classes. The experimental results show that the accuracy for the four classification is 81.32% and 54.87% respectively by using SAP and LOSO in DEAP database. Compared with other models, this algorithm can effectively improve the recognition performance.

Clozapine is an anti-psychotic drug that is known to be effective in the treatment of patients with chronic treatment-resistant schizophrenia (TRS-SCZ), commonly estimated to be around one third of all cases. However, clinicians sometimes delay the initiation of this drug because of its adverse side-effects. Therefore, identification of predictive biological markers of clozapine response are extremely valuable to aid on-time initiation of treatment. In this study, we develop a machine learning (ML) algorithm based on pre-treatment electroencephalogram (EEG) data sets to predict response to clozapine treatment in 57 TRS-SCZs, where the treatment outcome, after at least one-year follow-up is determined using the positive and negative syndrome scale (PANSS). The ML algorithm has three steps: 1) a brain source localization (BSL) procedure using the linearly constrained minimum variance (LCMV) beamforming approach is employed on the EEG signals to extract source waveforms from 30 specified brain regions. 2) An effective connectivity measure named symbolic transfer entropy (STE) is applied to the source waveforms. 3) A ML algorithm is applied to the STE matrix to determine whether a set of features can be found to discriminate most-responder (MR) SCZ patients from least-responder (LR) ones. The findings of this study reveal that STE features can achieve an accuracy of 95.83%. This finding implies that analysis of pre-treatment EEG could contribute to our ability to distinguish MR from LR SCZs, and that the source STE matrix may prove to be a promising tool for the prediction of the clinical response to clozapine.

This paper proposes a general framework for autonomous root-cause fault diagnosis in a complex process. In this framework, as a prerequisite step after conducting fault detection, the potential root-cause candidates are selected using a contribution score-based method (e.g. accumulative rate contribution scores). Then a fully automated procedure is proposed to determine the root-cause(s) of the detected fault amongst potential candidates without a priori knowledge of the base-line model or intervention of an expert. To locate the root-cause variable(s), firstly symbolic dynamic-based normalized transfer entropy (SDNTE) is proposed to generate an initial causal graph of root-cause candidates. Then symbolic dynamic-based normalized direct transfer entropy (SDNDTE) is proposed and utilized for pruning the initial graph (i.e. discard indirect and spurious causal edges). To this aim, explicit definitions of immediate intermediate variables (IIV) and source intermediate variables (SIV) are given and systematic algorithms are developed to find them efficiently. At last, a topological approach is proposed to locate the root-cause variables according to the pruned causal graph. The proposed generalized SDNDTE approach overcomes the computational complexity in the calculation of normalized direct transfer entropy (NDTE) using conventional multi-dimensional kernel probability density functions (PDFs) fitting, which enables real-time root-cause fault diagnosis. To demonstrate the effectiveness and applicability, the proposed autonomous scheme is tested on a numerical example at first, and its computational complexity is analysed and compared to other existing works. Finally, it is validated on the Tennessee Eastman process (TEP) benchmark model.

Effective connectivity analysis has been widely applied to noninvasive recordings such as functional magnetic resonance imaging and electroencephalograms (EEGs). Previous studies have aimed to extract the causal relations between brain regions, but the validity of the derived connectivity has not yet been fully determined. This is because it is generally difficult to identify causality in the usual experimental framework based on observations alone. Transcranial magnetic stimulation (TMS) provides a framework in which a controllable perturbation is applied to a local brain region and the effect is examined by comparing the neural activity with and without this stimulation. This study evaluates two methods for effective connectivity analysis, symbolic transfer entropy (STE) and vector autoregression (VAR), by applying them to TMS-EEG data. In terms of the consistency of results from different experimental sessions, STE is found to yield robust results irrespective of sessions, whereas VAR produces less correlation between sessions. Furthermore, STE preferentially detects the directional information flow from the TMS target. Taken together, our results suggest that STE is a reliable method for detecting the effect of TMS, implying that it would also be useful for identifying neural activity during cognitive tasks and resting states.

Causality analysis techniques, such as transfer entropy, have been proven to be useful tools for oscillation diagnosis in a variety of industrial processing applications. However, these tools have not gained widespread acceptance by industry. Causality analysis application procedures are complicated and time-consuming, requiring a large number of parameters to be selected. In this paper, a workflow for the application of transfer entropy for oscillation diagnosis is developed, emphasizing guidelines for the selection of parameters. Current parameter selection techniques require the user to search iteratively over a range of parameter values to find the optimum. With a computationally expensive technique, such as transfer entropy, this can be time-consuming. Alternatively, recommended default values are used, which may not be optimal. The process dynamics, characterized by time delay, time constants, and oscillation period, can have a strong influence on the optimal parameter selection. The relationship between process dynamics and the optimal transfer entropy parameters is investigated. These relationships are used to suggest guidelines for parameter selection. A strong relationship was found between the oscillation period and time delay and the optimal prediction horizon and time-interval parameters. The developed workflow for oscillation diagnosis was tested on an industrial case study. The workflow provided a robust systematic procedure for accurately identifying the propagation path of the oscillation.

Alarm flooding is a serious safety problem in the chemical process industries. Bayesian Networks are a set of powerful tools that can be used to trace the root-cause of alarms. For highly integrate...

As birds migrate in a linear formation, the frequent exchange of leader-follower in the Ibises can extend the formation flight time. In this paper, we utilize the information theory to analyze the underlying mechanism of the coordination and cooperation within the Ibises. To this end, we examine and quantify the dynamic characteristics of the information transfer in a group of Ibises at the individual level. Firstly, the local transfer entropy is employed to quantify the local dynamic information transfer during the migration of the Ibises. Therefore, we provide lucid explanations of how Ibis individually influences the group decision through information transfer to generate consistent collective behavior. The local active information storage is used to represent the memory of the Ibis. The relationship between the consistency of the collective behavior and the memory of the Ibis is obtained by calculating the mutual information between the historical state sequences and the future state of the Ibis. Finally, based on the dynamic information transfer, the relationship between the group memory and the consistent collective behavior, we effectively captured the relationship between the excitation and convergence of the Ibis’ collective behavior, and the variance of entropy is obtained.

In this paper, we provide a novel approach to capture causal interaction in a dynamical system from time series data. In Sinha and Vaidya (in: IEEE conference on decision and control, pp 7329–7334, 2016), we have shown that the existing measures of information transfer, namely directed information, Granger causality and transfer entropy, fail to capture the causal interaction in a dynamical system and proposed a new definition of information transfer that captures direct causal interactions. The novelty of the information transfer definition used in this paper is the fact that it can differentiate between direct and indirect influences Sinha and Vaidya (2016). The main contribution of this paper is to show that the proposed definition of information transfers in Sinha and Vaidya (2016) and Sinha and Vaidya (in: Indian control conference, pp 303–308, 2017) can be computed from time series data, and thus, the direct influences in a dynamical system can be identified from time series data. We use transfer operator theoretic framework, involving Perron–Frobenius and Koopman operators for the data-driven approximation of the system dynamics and computation of information transfer. Several examples, involving linear and nonlinear system dynamics, are presented to verify the efficiency of the developed algorithm.

Discovering causal relationships is a fundamental task in investigating the dynamics of complex systems (Pearl in Stat Surv 3:96–146, 2009). Traditional approaches like Granger causality or transfer entropy fail to capture all the interdependence of the statistic moments, which might lead to wrong causal conclusions. In the previous papers (Chen et al. in 25th international conference, ICONIP 2018, Siem Reap, Cambodia, proceedings, Part II, 2018), the authors proposed a novel definition of Wiener causality for measuring the intervene between time series based on relative entropy, providing an integrated description of statistic causal intervene. In this work, we show that relative entropy is a special case of an existing more general divergence estimation. We argue that any Bregman divergences can be used for detecting the causal relations and in theory remedies the information dropout problem. We discuss the benefits of various choices of divergence functions on causal inferring and the quality of the obtained causal models. As a byproduct, we also obtain the robustness analysis and elucidate that RE causality achieves a faster convergence rate among BD causalities. To substantiate our claims, we provide experimental evidence on how BD causalities improve detection accuracy.

To understand the mechanism of delay propagation from the perspective of multiple airports, constructing delay propagation relation (DPR) networks among airports is a novel analysis method. The latest method is to use transfer entropy to mine the delay propagation relation among airports. However, the transfer entropy will produce bias due to estimating high-dimensional conditional mutual information (CMI) in the delay propagation scenario. In this paper, we propose the low-dimensional approximation of CMI for transfer entropy (LTE) to address the above issue. By applying this improved algorithm, the delay propagation relation among airports can be more accurately explored and a more accurate DPR network can be obtained. For this network, we apply the complex network theory and its related indicators to provide systematic analysis about delay propagation. The results of case analysis show that in the delay propagation interactions among airports, large airports always receive delays and small airports propagate delays outward. Meanwhile, delays propagate more efficiently in the aviation system of smaller airlines. These results can provide some theoretical supports for making measures to reduce delay propagation.

Causality inference is the use of computational techniques to predict possible causal relationships for a set of variables, thereby forming a directed network. Causality inference in Gene Regulatory Networks (GRNs) is an important, yet challenging task due to the limits of available data and lack of efficiency in existing causality inference techniques. A number of techniques have been proposed and applied to infer causal relationships in various domains, although they are not specific to regulatory network inference. In this paper, we assess the effectiveness of methods for inferring causal GRNs. We introduce seven different inference methods and apply them to infer directed edges in GRNs. We use time-series expression data from the DREAM challenges to assess the methods in terms of quality of inference and rank them based on performance. The best method is applied to Breast Cancer data to infer a causal network. Experimental results show that Causation Entropy is best, however, highly time-consuming and not feasible to use in a relatively large network. We infer Breast Cancer GRN with the second-best method, Transfer Entropy. The topological analysis of the network reveals that top out-degree genes such as SLC39A5 which are considered central genes, play important role in cancer progression.

In this work, we consider the problem of inferring links in a communication network, using limited, passive observations of network traffic. Our approach leverages transfer entropy (TE) as a metric for quantifying the strength of the automatic repeat request (ARQ) mechanisms present in next-hop routing links. In contrast with existing approaches, TE provides an information-theoretic, model-free approach that operates on externally available packet arrival times. We show, using discrete event simulation of a wireless sensor network, that the TE based topology inference approach described here is robust to varying degrees of connection quality in the underlying network. Compared to an existing approach which uses the linear regression based formulation of Granger Causality for network topology inference, our approach has better asymptotic time complexity, and shows significant improvement in network topology reconstruction performance. Our approach, though sub-optimal, also has better time complexity, while still retaining reasonable performance, compared to a causation entropy based optimal algorithm proposed in the literature.

The problem of inferring interactions from observations of individual behavior in networked dynamical systems is ubiquitous in science and engineering. From brain circuits to financial networks, there is a dire need for robust methodologies that can unveil network structures from individual time series. Originally formulated to identify asymmetries in pairs of coupled dynamical systems, transfer entropy has been proposed as a model-free, computationally-inexpensive framework for network inference. While previous studies have cataloged a library of pathological instances in which transfer entropy-based network reconstruction can fail, we presently lack analytical results that can help quantify the accuracy of the identification and pinpoint scenarios where false inferences results are more likely to be registered. Here, we present a detailed analytical study of a Boolean network model of policy diffusion. Through perturbation theory, we establish a closed-form expression for the transfer entropy between any pair of nodes in the network up to the third order in an expansion parameter that is associated with the spontaneous activity of the nodes. While for slowly-varying dynamics transfer entropy is successful in capturing the weight of any link, for faster dynamics the error in the inference is controlled by local topological features of the node pair. Specifically, the error in the inference of a weight between two nodes depends on the mismatch between their weighted indegrees that serves as a common uncertainty bath upon which we must tackle the inference problem. Interestingly, an equivalent result is discovered when numerically studying a network of coupled chaotic tent maps, suggesting that heterogeneity in the in-degree is a critical factor that can undermine the success of transfer entropy-based network inference.

This study aims to predict the direction of US stock prices by integrating time-varying effective transfer entropy (ETE) and various machine learning algorithms. At first, we explore that the ETE based on 3 and 6 months moving windows can be regarded as the market explanatory variable by analyzing the association between the financial crises and Granger-causal relationships among the stocks. Then, we discover that the prediction performance on the stock price direction can be improved when the ETE driven variable is integrated as a new feature in the logistic regression, multilayer perceptron, random forest, XGBoost, and long short-term memory network. Meanwhile, we suggest utilizing the adjusted accuracy derived from the risk-adjusted return in finance as a prediction performance measure. Lastly, we confirm that the multilayer perceptron and long short-term memory network are more suitable for stock price prediction. This study is the first attempt to predict the stock price direction using ETE, which can be conveniently applied to the practical field.

Retrieval of unintelligible speech is a basic need for speech impaired and is under research for several decades. But retrieval of random words from thoughts needs a substantial and consistent approach. This work focuses on the preliminary steps of retrieving vowels from Electroencephalography (EEG) signals acquired while speaking and imagining of speaking a consonant–vowel–consonant (CVC) word. The process, referred to as Speech imagery is imagining of speaking to oneself silently in the mind. Speech imagery is a form of mental imagery. Brain connectivity estimators such as EEG coherence, Partial Directed Coherence, Directed Transfer Function and Transfer Entropy have been used to estimate the concurrency and causal dependence (direction and strength) between different brain regions. From brain connectivity results it has been observed that the left frontal and left temporal electrodes were activated for speech and speech imagery processes. These brain connectivity estimators have been used for training Recurrent Neural Networks (RNN) and Deep Belief Networks (DBN) for identifying the vowel from the subject’s thought. Though the accuracy level was found to be varying for each vowel while speaking and imagining of speaking the CVC word, the overall classification accuracy was found to be 72% while using RNN whereas a classification accuracy of 80% was observed while using DBN. DBN was found to outperform RNN in both the speech and speech imagery processes. Thus, the combination of brain connectivity estimators and deep learning techniques appear to be effective in identifying the vowel from EEG signals of subjects’ thought.

Activity-dependent self-organization plays an important role in the formation of specific and stereotyped connectivity patterns in neural circuits. By combining neuronal cultures, tools with approaches from network neuroscience and information theory, we can study how complex network topology emerges from local neuronal interactions. We constructed effective connectivity networks using a transfer entropy analysis of electrophysiological signals recorded from rat embryo dissociated hippocampal neuron cultures between 6 and 35 days in vitro to investigate how the neuronal network topology evolves during maturation. The methodology for constructing the networks considered the synapse delay and addressed the influence of firing rate and population bursts as well as spurious effects on the inference of connections. We found that the number of links in the networks grew over the course of development, shifting from a segregated to a more integrated architecture. As part of this progression, three significant aspects of complex network topology emerged. In agreement with previous in silico and in vitro studies, a small-world architecture was detected, largely due to strong clustering among neurons. Additionally, the networks developed in a modular community topology, with most modules comprising nearby neurons. Finally, highly active neurons acquired topological characteristics that made them important nodes to the network and integrators of communities. These findings leverage new insights into how neuronal effective network topology relates to neuronal assembly self-organization mechanisms.

Cross-frequency interactions, a form of oscillatory neural activity, are thought to play an essential role in the integration of distributed information in the brain. Indeed, phase-amplitude interactions are believed to allow for the transfer of information from large-scale brain networks, oscillating at low frequencies, to local, rapidly oscillating neural assemblies. A promising approach to estimating such interactions is the use of transfer entropy (TE), a non-linear, information-theory-based effective connectivity measure. The conventional method involves feeding instantaneous phase and amplitude time series, extracted at the target frequencies, to a TE estimator. In this work, we propose that the problem of directed phase-amplitude interaction detection is recast as a phase TE estimation problem, under the hypothesis that estimating TE from data of the same nature, i.e., two phase time series, will improve the robustness to the common confounding factors that affect connectivity measures, such as the presence of high noise levels. We implement our proposal using a kernel-based TE estimator, defined in terms of Renyi’s α entropy, which has successfully been used to compute single-trial phase TE. We tested our approach on the synthetic data generated through a simulation model capable of producing a time series with directed phase-amplitude interactions at two given frequencies, and on EEG data from a cognitive task designed to activate working memory, a memory system whose underpinning mechanisms are thought to include phase–amplitude couplings. Our proposal detected statistically significant interactions between the simulated signals at the desired frequencies for the synthetic data, identifying the correct direction of the interaction. It also displayed higher robustness to noise than the alternative methods. The results attained for the working memory data showed that the proposed approach codes connectivity patterns based on directed phase–amplitude interactions, that allow for the different cognitive load levels of the working memory task to be differentiated.

Electroencephalography (EEG) signals, recorded from different channels, are used to study human brain activity in the context of emotion recognition and seizure detection. Most of the existing emotion recognition methods have focused on EEG characteristics at an electrode level and not on connectivity patterns. Causal connectivity refers to the understanding of the causal relationship between the channels. In this work, we have developed an emotion recognition model using EEG-based causal connectivity patterns. Granger causality is used to find the causal relationship of the EEG signals from different channels. The quantification of causal configurations between the channels is carried out using Transfer Entropy. The obtained Transfer Entropy values are used as features for the classification of emotions. The performance of the proposed method is validated using a publicly available SEED-IV dataset. The proposed technique achieves an average subject-specific classification accuracy of 90 % (using 18 channel signals). The proposed method achieves an improvement of 1 % over state-of-the-art techniques based on correlation using 62 channel signals and an improvement of 17 % compared to methods that use only 18 channel signals.

Cognitive workload is a crucial factor in tasks involving dynamic decision-making and other real-time and high-risk situations. Neuroimaging techniques have long been used for estimating cognitive workload. Given the portability, cost-effectiveness and high time-resolution of EEG as compared to fMRI and other neuroimaging modalities, an efficient method of estimating an individual’s workload using EEG is of paramount importance. Multiple cognitive, psychiatric and behavioral phenotypes have already been known to be linked with “functional connectivity”, i.e., correlations between different brain regions. In this work, we explored the possibility of using different model-free functional connectivity metrics along with deep learning in order to efficiently classify the cognitive workload of the participants. To this end, 64-channel EEG data of 19 participants were collected while they were doing the traditional n-back task. These data (after pre-processing) were used to extract the functional connectivity features, namely Phase Transfer Entropy (PTE), Mutual Information (MI) and Phase Locking Value (PLV). These three were chosen to do a comprehensive comparison of directed and non-directed model-free functional connectivity metrics (allows faster computations). Using these features, three deep learning classifiers, namely CNN, LSTM and Conv-LSTM were used for classifying the cognitive workload as low (1-back), medium (2-back) or high (3-back). With the high inter-subject variability in EEG and cognitive workload and recent research highlighting that EEG-based functional connectivity metrics are subject-specific, subject-specific classifiers were used. Results show the state-of-the-art multi-class classification accuracy with the combination of MI with CNN at 80.87%, followed by the combination of PLV with CNN (at 75.88%) and MI with LSTM (at 71.87%). The highest subject specific performance was achieved by the combinations of PLV with Conv-LSTM, and PLV with CNN with an accuracy of 97.92%, followed by the combination of MI with CNN (at 95.83%) and MI with Conv-LSTM (at 93.75%). The results highlight the efficacy of the combination of EEG-based model-free functional connectivity metrics and deep learning in order to classify cognitive workload. The work can further be extended to explore the possibility of classifying cognitive workload in real-time, dynamic and complex real-world scenarios.

Abnormalities in modern process industries are reported by alarms. Strong inter-connectivities within different units of a plant lead to annunciations of multiple alarms in a short period of time, which hinders a prompt operator response. A fundamental problem in such alarm floods is to identify the original source of abnormality by finding the directly affected alarm, known as the root cause. This is essentially a causal problem which has been tackled by many non-causal approaches, including transfer entropy and Bayesian network based methods, which despite their successful applications, have their own shortcomings. Causal identification requires intervention. That is, to “intervene” and manually change the distribution of a random variable A, and compare its conditional distribution with respect to another variable B before and during the intervention to examine whether A is caused by B. This acknowledged notion of causality, leads to causal Bayesian networks where unlike Bayesian networks, the edges are truly causal. Nevertheless, intervening the alarms during a fault event is implausible if not impossible, hindering the use of this rich notion of causality in alarm root cause analysis. We tackle this issue by treating abnormalities as uncertain interventions, because they, indeed, intervene the directly affected alarms and change their distributions. We then find the causal Bayesian network that best represents the alarm data. In addition to more-accurate root-cause identification, our causal Bayesian network based method has the ability to discover the root causes under multiple simultaneous abnormalities.

Over the years, cognitive research has been an active and evolving field, where non-invasive techniques like Electroencephalogram (EEG) play a dominant role in the study of brain functions. The electrical signals recorded from the brain using multi-channel EEG are used in a wide range of applications, making it possible to understand the concept of cognition better. It is crucial to study the non-linear and dynamic electrical signals generated from the brain in order to understand the behavior of each brain region. This study provides a better understanding of cognition by using page ranking (a widely used algorithm to rank a website in a network of sites using the principle of eigenvector centrality) of the EEG electrodes. Based on the performance in the short-term memory task called Corsi Block-tapping task (CBTT), the participants are classified into two groups, viz. good and poor performers. The directed Functional Brain Networks (FBNs) are constructed using Normalized Transfer Entropy (NTE) by considering EEG electrodes as nodes in the network, information flow between pairs of nodes as edges, and the NTE values of connectivity as edge weights. The NTE values computed during the performance of the CBTT task of the participants are compared with the baseline data for both good and poor performers. The weighted page rank algorithm is used to compute the ranks of the electrodes in terms of the cognitive load measured using NTE values of the different brain regions. The status of each electrode at the two groups is identified using the Reduction and Increase in Consistency (RIC) value. Therefore, the RIC value serves as an indicator of the decrease/increase/constant in the rank of an electrode during the performance of the CBTT task compared to that during the baseline. A user-defined Observation Phase Value (OPV) number of the top-ranked electrodes is used to analyze the cognitive processes within the groups (using three different arbitrary OPV values of 10, 20, and 30). Based on the ranks of the electrodes and an OPV value of 20, the common electrodes during the baseline and CBTT task activity are classified using four different categories of occurrences (100, 81–99, 61–80 and 50–60%) for all the good and poor performers of CBTT respectively. The empirical analysis helps characterize the EEG electrodes into different categories based on cognitive activities using efficient computational techniques. The inferences made from such an analysis play a significant role in understanding the cognitive behavior of human brain networks using the directional flow of information during cognitive load-based tasks such as short-term memory CBTT task.

The comprehension of neuronal network functioning, from most basic mechanisms of signal transmission to complex patterns of memory and decision making, is at the basis of the modern research in experimental and computational neurophysiology. While mechanistic knowledge of neurons and synapses structure increased, the study of functional and effective networks is more complex, involving emergent phenomena, nonlinear responses, collective waves, correlation and causal interactions. Refined data analysis may help in inferring functional/effective interactions and connectivity from neuronal activity. The Transfer Entropy (TE) technique is, among other things, well suited to predict structural interactions between neurons, and to infer both effective and structural connectivity in small- and large-scale networks. To efficiently disentangle the excitatory and inhibitory neural activities, in the article we present a revised version of TE, split in two contributions and characterized by a suited delay time. The method is tested on in silico small neuronal networks, built to simulate the calcium activity as measured via calcium imaging in two-dimensional neuronal cultures. The inhibitory connections are well characterized, still preserving a high accuracy for excitatory connections prediction. The method could be applied to study effective and structural interactions in systems of excitable cells, both in physiological and in pathological conditions.

One of the essential functions biological neural networks is the processing of information. This comprises processing sensory information to perceive the environment, up to processing motor information to interact with the environment. Due to methodological concerns, it has been historically unclear how information processing changes during different cognitive or behavioral states, and to what extent information is processed within or between the network of neurons in different brain areas. In this study, we leverage recent advances in the calculation of information dynamics to explore neural-level processing within and between the fronto-parietal areas AIP, F5 and M1 during a delayed grasping task performed by three macaque monkeys. While information processing was high within all areas during all cognitive and behavioral states of the task, inter-areal processing varied widely: during visuo-motor transformation, AIP and F5 formed a reciprocally connected processing unit, while no processing was present between areas during the memory period. Movement execution was processed globally across all areas with a predominance of processing in the feedback direction. Additionally, the fine-scale network structure re-configured at the neuron-level in response to different grasping conditions, despite of no differences in the overall amount of information present. These results suggest that areas dynamically form higher-order processing units according to the cognitive or behavioral demand, and that the information processing network is hierarchically organized at the neuron-level, with the coarse network structure determining the behavioral state and finer changes reflecting different conditions.Significance StatementWhat does it mean to say that the brain “processes information?” Scientists often discuss the brain in terms of information processing – animals take in information from their environment through their senses, and use it to make decisions about how to act in the world. In this work, we use a mathematical framework called information theory to explore how signals from the environment influence brain activity, and how brain activity in turn informs on behaviors. We found that different brain regions processed information in dynamic and flexible ways, with signals flowing up and down the hierarchy of sensory-motor depending on the demands of the moment. This shows how “computation” in the brain can reflect complex behaviors and cognitive states.

Recently, there is a growing interest in applying Transfer Entropy (TE) in quantifying the effective connectivity between artificial neurons. In a feedforward network, the TE can be used to quantify the relationships between neuron output pairs located in different layers. Our focus is on how to include the TE in the learning mechanisms of a Convolutional Neural Network (CNN) architecture. We introduce a novel training mechanism for CNN architectures which integrates the TE feedback connections. Adding the TE feedback parameter accelerates the training process, as fewer epochs are needed. On the flip side, it adds computational overhead to each epoch. According to our experiments on CNN classifiers, to achieve a reasonable computational overhead–accuracy trade-off, it is efficient to consider only the inter-neural information transfer of the neuron pairs between the last two fully connected layers. The TE acts as a smoothing factor, generating stability and becoming active only periodically, not after processing each input sample. Therefore, we can consider the TE is in our model a slowly changing meta-parameter.

In recent years, some studies brought the complex networks theory into neuroscience. Connectivity patterns are widely studied and vary from the data acquisition method: fMRI, EEG, and so on. Those connectivity data can be classified into three main types: anatomical, functional, and effective. The first one relies only on anatomical connections, the second relies on the correlations of signals recorded from different parts of the brain, and the latter on the direction of the information flow. In this work, it is proposed to study the variability of the parameters of complex networks, for two methods of effective connectivity (namely: Transfer Entropy and Phase Transfer Entropy) and EEG data, of 10 patients with absence epilepsy. It is known that the topology of the complex network of the epilepsy crisis varies substantially between patients. However, for the same patient, effective connectivity topologies may vary, but they should not. The variation of network parameters, for the same complex network (the same patient), can lead to misinterpretations of the analyzed phenomena. In this work, preliminary results indicate that such variability indeed occurs, for the same patient, even in strictly similar measures, such as Transfer Entropy and Phase Transfer Entropy.

Submitted by CVPortalegre Deposit (cv_ipportalegre@cienciavitae.pt) on 2021-11-12T23:12:58Z No. of bitstreams: 2 Transfer_Entropy_Approach_for_Portfolio_Optimization_An_Empirical_Approach_for_CESEE_Markets.pdf: 1895110 bytes, checksum: 543ffcab332d79847ba1a7e109e4346a (MD5) package.zip: 1488823 bytes, checksum: 82552a3b5fb0e4981f4ccd2023bc1684 (MD5) Approved for entry into archive by Catarina Dias (catarina.dias@ipportalegre.pt) on 2021-11-12T23:35:02Z (GMT) No. of bitstreams: 2 package.zip: 1488823 bytes, checksum: 82552a3b5fb0e4981f4ccd2023bc1684 (MD5) Transfer_Entropy_Approach_for_Portfolio_Optimization_An_Empirical_Approach_for_CESEE_Markets.pdf: 1895110 bytes, checksum: 543ffcab332d79847ba1a7e109e4346a (MD5) Approved for entry into archive by Catarina Dias (catarina.dias@ipportalegre.pt) on 2021-11-12T23:36:11Z (GMT) No. of bitstreams: 2 package.zip: 1488823 bytes, checksum: 82552a3b5fb0e4981f4ccd2023bc1684 (MD5) Transfer_Entropy_Approach_for_Portfolio_Optimization_An_Empirical_Approach_for_CESEE_Markets.pdf: 1895110 bytes, checksum: 543ffcab332d79847ba1a7e109e4346a (MD5) Made available in DSpace on 2021-11-12T23:36:11Z (GMT). No. of bitstreams: 2 package.zip: 1488823 bytes, checksum: 82552a3b5fb0e4981f4ccd2023bc1684 (MD5) Transfer_Entropy_Approach_for_Portfolio_Optimization_An_Empirical_Approach_for_CESEE_Markets.pdf: 1895110 bytes, checksum: 543ffcab332d79847ba1a7e109e4346a (MD5) Previous issue date: 2021-08 N/A

In this paper, we cast the problem of fault isolation in industrial robots as that of causal analysis within coupled dynamical processes and evaluate the related efficacy of the information-theoretic approach of transfer entropy. To create a realistic and exhaustive dataset, we simulate wear-induced failure by increasing friction coefficient on select axes within an in-house robotic simulation tool that incorporates an elastic gearbox model. The source axis of failure is identified as one which has the highest net transfer entropy across all pairs of axes. In an exhaustive simulation study, we vary the friction successively in each axis across three common industrial tasks: pick and place, spot welding, and arc welding. Our results show that transfer entropy-based approach is able to detect the axis of failure more than 80% of the time when the friction coefficient is 5% above the nominal value and always when friction coefficient is 10% above the nominal value. The transfer entropy approach is more than twice as accurate as cross-correlation, a classical time series analysis used to identify directional dependence among processes.

Neural oscillations are present in the brain at different spatial and temporal scales, and they are linked to several cognitive functions. Furthermore, the information carried by their phases is fundamental for the coordination of anatomically distributed processing in the brain. The concept of phase transfer entropy refers to an information theory-based measure of directed connectivity among neural oscillations that allows studying such distributed processes. Phase TE is commonly obtained from probability estimations carried out over data from multiple trials, which bars its use as a characterization strategy in brain–computer interfaces. In this work, we propose a novel methodology to estimate TE between single pairs of instantaneous phase time series. Our approach combines a kernel-based TE estimator defined in terms of Renyi’s α entropy, which sidesteps the need for probability distribution computation with phase time series obtained by complex filtering the neural signals. Besides, a kernel-alignment-based relevance analysis is added to highlight relevant features from effective connectivity-based representation supporting further classification stages in EEG-based brain–computer interface systems. Our proposal is tested on simulated coupled data and two publicly available databases containing EEG signals recorded under motor imagery and visual working memory paradigms. Attained results demonstrate how the introduced effective connectivity succeeds in detecting the interactions present in the data for the former, with statistically significant results around the frequencies of interest. It also reflects differences in coupling strength, is robust to realistic noise and signal mixing levels, and captures bidirectional interactions of localized frequency content. Obtained results for the motor imagery and working memory databases show that our approach, combined with the relevance analysis strategy, codes discriminant spatial and frequency-dependent patterns for the different conditions in each experimental paradigm, with classification performances that do well in comparison with those of alternative methods of similar nature.

In this paper, we discuss the statistical coherence between financial time series in terms of Renyi’s information measure or entropy. In particular, we tackle the issue of the directional information flow between bivariate time series in terms of Renyi’s transfer entropy. The latter represents a measure of information that is transferred only between certain parts of underlying distributions. This fact is particularly relevant in financial time series, where the knowledge of “black swan” events such as spikes or sudden jumps is of key importance. To put some flesh on the bare bones, we illustrate the essential features of Renyi’s information flow on two coupled GARCH(1,1) processes.

Neuromorphic systems comprised of self-assembled nanowires exhibit a range of neural-like dynamics arising from the interplay of their synapse-like electrical junctions and their complex network topology. Additionally, various information processing tasks have been demonstrated with neuromorphic nanowire networks. Here, we investigate the dynamics of how these unique systems process information through information-theoretic metrics. In particular, Transfer Entropy (TE) and Active Information Storage (AIS) are employed to investigate dynamical information flow and short-term memory in nanowire networks. In addition to finding that the topologically central parts of networks contribute the most to the information flow, our results also reveal TE and AIS are maximized when the networks transitions from a quiescent to an active state. The performance of neuromorphic networks in memory and learning tasks is demonstrated to be dependent on their internal dynamical states as well as topological structure. Optimal performance is found when these networks are pre-initialised to the transition state where TE and AIS are maximal. Furthermore, an optimal range of information processing resources (i.e. connectivity density) is identified for performance. Overall, our results demonstrate information dynamics is a valuable tool to study and benchmark neuromorphic systems.

Summary One of the most challenging frontiers in biological systems understanding is fluorescent label-free imaging. We present here the NeuriTES platform that revisits the standard paradigms of video analysis to detect unlabeled objects and adapt to the dynamic evolution of the phenomenon under observation. Object segmentation is reformulated using robust algorithms to assure regular cell detection and transfer entropy measures are used to study the inter-relationship among the parameters related to the evolving system. We applied the NeuriTES platform to the automatic analysis of neurites degeneration in presence of amyotrophic lateral sclerosis (ALS) and to the study of the effects of a chemotherapy drug on living prostate cancer cells (PC3) cultures. Control cells have been considered in both the two cases study. Accuracy values of 93% and of 92% are achieved, respectively. NeuriTES not only represents a tool for investigation in fluorescent label-free images but demonstrates to be adaptable to individual needs., Highlights • Monitoring of cell phenotype changes by fluorescence label-free time-lapse microscopy • Adaptive semantic segmentation for the robust detection of cell shape • TE to correlate morphological and textural soma descriptors along time • Directed TE graph for the representation of mutual relationship among descriptors, The bigger picture One of the most challenging frontiers for the automatic understanding of biological systems is fluorescent label-free imaging in which the behavior changes of living being are characterized without cell staining. To this aim, we present here the NeuriTES platform that revisits standard paradigms of video analysis to detect unlabeled objects and correlate the analysis to phenotype evolution of the mechanisms under observation. Through the exploitation of adaptive algorithms and of transfer entropy measures, the platform assures regular cell detection and the possibility to extract reliable parameters related to the evolving cell system. As a proof-of-concept, NeuriTES is applied to two fascinating phenotype investigation scenarios, amyotrophic lateral sclerosis (ALS) disease mechanism and the study of the effects of a chemotherapy drug on living prostate cancer cells (PC3) cultures. Directed graphs assist the biologists with a visual understanding of the mechanisms identified., Nowadays, observational traits (phenotype) of biological entities complement with genomics offering the possibility to monitor phenomenon evolutions and underlying mechanism (neurodegeneration, cancer cell replication, etc.). By complementing video, data analysis, and machine learning approaches with label-free time-lapse microscopy, NeuriTES tool allows automatically analyzing such traits and extracting as outcomes proof of concepts and visual graph-based representation of the phenotypical rationale behind.

In this work we report on a systematic study of the causal relations in information transfer mechanisms between brain regions under resting condition. The 1000 Functional Connectomes Beijing Zang dataset was used, which includes brain functional images of 180 healthy individuals. We first characterize the information transfer mechanisms by means of Transfer Entropy concepts and, on this basis, propose a set of indexes concerning the whole functional brain network in the frame of a multilayer description. By exploring the influence of a set of states in two given regions at time t (At; Bt.) over the state of one of them at a following time step (Bt+1), a series of time-dependent events can be observed pointing to four kinds of significant interactions, namely:- (de)activation in the same state (ActS); - (de)activation in the oppostive state (ActO);- turn off in the same state (TfS); - turn off in the opposite state (TfO).This leads to four specific rules and to a directional multilayer network based upon four interaction matrices, one for each rule. By hierarchical clustering methods the four rules can be reduced to two sharing some similarities with positive and negative functional connectivity. The global architecture of the four interactions and the features of single nodes were initially explored under stationary conditions. The information transfer mechanisms on the ensuing functional network were studied by specific indexes describing in a multilayer frame the effects of the network structure in several dynamical processes. The healthy subjects database was used to carefully calibrate and validate the proposed approach, whose final aim remains the detection of clinical differences among individuals, as well as among different cognitive states.

One of the main aims of current biology is to understand the origin of the molecular organization that underlies the complex dynamic architecture of cellular life. Here, we present an overview of the main sources of biomolecular order and complexity spanning from the most elementary levels of molecular activity to the emergence of cellular systemic behaviors. First, we have addressed the dissipative self-organization, the principal source of molecular order in the cell. Intensive studies over the last four decades have demonstrated that self-organization is central to understand enzyme activity under cellular conditions, functional coordination between enzymatic reactions, the emergence of dissipative metabolic networks (DMN), and molecular rhythms. The second fundamental source of order is molecular information processing. Studies on effective connectivity based on transfer entropy (TE) have made possible the quantification in bits of biomolecular information flows in DMN. This information processing enables efficient self-regulatory control of metabolism. As a consequence of both main sources of order, systemic functional structures emerge in the cell; in fact, quantitative analyses with DMN have revealed that the basic units of life display a global enzymatic structure that seems to be an essential characteristic of the systemic functional metabolism. This global metabolic structure has been verified experimentally in both prokaryotic and eukaryotic cells. Here, we also discuss how the study of systemic DMN, using Artificial Intelligence and advanced tools of Statistic Mechanics, has shown the emergence of Hopfield-like dynamics characterized by exhibiting associative memory. We have recently confirmed this thesis by testing associative conditioning behavior in individual amoeba cells. In these Pavlovian-like experiments, several hundreds of cells could learn new systemic migratory behaviors and remember them over long periods relative to their cell cycle, forgetting them later. Such associative process seems to correspond to an epigenetic memory. The cellular capacity of learning new adaptive systemic behaviors represents a fundamental evolutionary mechanism for cell adaptation., This work was supported by the University of Basque Country UPV/EHU and Basque Center of Applied Mathematics, grant US18/21