Measuring High-Order Interactions in Rhythmic Processes through Multivariate Spectral Information Decomposition

Many complex systems in physics, biology and engineering are modeled as dynamical networks and described using multivariate time series analysis. Recent developments have shown that the emergent dynamics of a network system are significantly affected by interactions involving multiple network nodes which cannot be described using pairwise links. While these higher-order interactions can be probed using information-theoretic measures, a rigorous framework to describe them in the frequency domain is still lacking. This work presents an approach for the spectral decomposition of multivariate information measures, capable of identifying higher-order synergistic and redundant interactions between oscillatory processes. We show theoretically that synergy and redundancy can coexist at different frequencies among the output signals of a network system and can be detected only using the proposed spectral method. To demonstrate the broad applicability of the framework, we provide parametric and non-parametric data-efficient estimators for the spectral information measures, and employ them to describe multivariate interactions in three complex systems producing rich oscillatory dynamics, namely the human brain, a ring of electronic oscillators, and the global climate system. In these systems, we show that the use of our framework for the spectral decomposition of information measures reveals multivariate and higher-order interactions not detectable in the time domain. Our results are exemplary of how the frequency-specific analysis of multivariate dynamics can aid the implementation of assessment and control strategies in realworld network systems

Estimation of Granger causality through Artificial Neural Networks: applications to physiological systems and chaotic electronic oscillators

One of the most challenging problems in the study of complex dynamical systems is to find the statistical interdependencies among the system components. Granger causality (GC) represents one of the most employed approaches, based on modeling the system dynamics with a linear vector autoregressive (VAR) model and on evaluating the information flow between two processes in terms of prediction error variances. In its most advanced setting, GC analysis is performed through a statespace (SS) representation of the VAR model that allows to compute both conditional and unconditional forms of GC by solving only one regression problem. While this problem is typically solved through Ordinary Least Square (OLS) estimation, a viable alternative is to use Artificial Neural Networks (ANNs) implemented in a simple structure with one input and one output layer and trained in a way such that the weights matrix corresponds to the matrix of VAR parameters. In this work, we introduce an ANN combined with SS models for the computation of GC. The ANN is trained through the Stochastic Gradient Descent L1 (SGD-L1) algorithm, and a cumulative penalty inspired from penalized regression is applied to the network weights to encourage sparsity. Simulating networks of coupled Gaussian systems, we show how the combination of ANNs and SGD-L1 allows to mitigate the strong reduction in accuracy of OLS identification in settings of low ratio between number of time series points and of VAR parameters. We also report how the performances in GC estimation are influenced by the number of iterations of gradient descent and by the learning rate used for training the ANN. We recommend using some specific combinations for these parameters to optimize the performance of GC estimation. Then, the performances of ANN and OLS are compared in terms of GC magnitude and statistical significance to highlight the potential of the new approach to reconstruct causal coupling strength and network topology even in challenging conditions of data paucity. The results highlight the importance of of a proper selection of regularization parameter which determines the degree of sparsity in the estimated network. Furthermore, we apply the two approaches to real data scenarios, to study the physiological network of brain and peripheral interactions in humans under different conditions of rest and mental stress, and the effects of the newly emerged concept of remote synchronization on the information exchanged in a ring of electronic oscillators. The results highlight how ANNs provide a mesoscopic description of the information exchanged in networks of multiple interacting physiological systems, preserving the most active causal interactions between cardiovascular, respiratory and brain systems. Moreover, ANNs can reconstruct the flow of directed information in a ring of oscillators whose statistical properties can be related to those of physiological network

Surface Engineering for Microsensing

In the last years, the unique electronic properties of carbon nanotubes have generated a great excitement due to the possibility to tune their electrical and optical properties for specific applications. The possibility to couple functionalized carbon nanotubes with biomolecules, quantum dots and metal nanoparticles has opened the possibility to produce functional materials with specific characteristics. In this work we describe how carbon nanotubes may be utilized to fabricate an engineered surface for sensing applications. Multi-walled carbon nanotubes were first chemically cut by acid treatments and then functionalized with short thiol chains (2-aminoethanethiol). The carbon nanotubes were successively deposited on a gold film by simple chemical adsorption. In an alternative route the acid-treated carbon nanotubes were deposited on a 2-aminoethanethiol modified gold film by means of an external electric field. Gold nanoparticles were then bonded to the nanotubes to exploit their plasmon resonances in Raman spectroscopy. Reaction yields as well as the final products were analyzed by X-Ray Photoelectron Spectroscopy, Infra-Red Spectroscopy, Scanning Electron Microscopy and Atomic Force Microscopy. The result of this surface engineering process leads to a high density of gold nanoparticles on a carbon nanotube carpet with a very efficient intensification of the Raman spectra. Gold nanoclusters were also directly synthesized on thiol functionalized carbon nanotubes film and characterized by means of X-ray photoelectron spectroscopy. Detailed analysis of the Au 4f core line is used to study the chemical modifications occurring on the substrate, as well as to obtain information on the nanocluster size distribution after each reducing treatment. Interestingly, a solution of carbon nanotubes decorated with gold nanoparticles show a drastic increase of the photoluminescence respect to the carbon nanotubes without the gold nanoparticles. This effect, investigated with the optical spectroscopy, may be explained as a consequence of an electromagnetic energy transfer from the gold nanoparticles to the emitting sites of the carbon nanotubes

Multi-modal interaction on Linux handheld devices

Recently, in the field of mobile computing, significant efforts are aimed at creating speech enabled handheld devices, to provide an easier input method to users. At ITC-irst, inside WILMA (Wireless Internet and Location Management Architecture, www.wilmaproject.org) project, we developed a MBS (Multi-modal Browsing System), based on a client server architecture using a wireless infrastructure (IEEE802.11b standard). The client is running on a GNU/Linux Compaq iPAQ H3870 PDA (Personal Digital Assistant); this device can access the remote SPINET (Speech Into Equivalent Text) speech recognizer, developed in ITC-irst[4.1.1]. An Intimate Linux distribution\cite [11], modified for this application, was installed on the PDA [1]. After a brief overview of the IEEE802.11b standard, we will describe the MBS architecture in details, and we will discuss some possible future improvements of the syste

A new Framework for the Spectral Information Decomposition of Multivariate Gaussian Processes

: Different information-theoretic measures are available in the literature for the study of pairwise and higher-order interactions in multivariate dynamical systems. While these measures operate in the time domain, several physiological and non-physiological systems exhibit a rich oscillatory content that is typically analyzed in the frequency domain through spectral and cross-spectral approaches. For Gaussian systems, the relation between information and spectral measures has been established considering coupling and causality measures, but not for higher-order interactions. To fill this gap, in this work we introduce an information-theoretic framework in the frequency domain to quantify the information shared between a target process and two sources, even multivariate, and to highlight the presence of redundancy and synergy in the analyzed dynamical system. Firstly, we simulate different linear interacting processes by showing the capability of the proposed framework to retrieve amounts of information shared by the processes in specific frequency bands which are not detectable by the related time-domain measures. Then, the framework is applied on EEG time series representative of the brain activity during a motor execution task in a group of healthy subjects