Biology subjects

Antonacci, Y.

Publications and source records attributed to Antonacci, Y..

6 recordsLinked to original sources

bioRxiv2026

Information-Theoretic Functional Connectivity Characterizes Multiscale Network Reorganization in Postoperative Cognitive Decline

Postoperative cognitive decline (POCD) after coronary artery bypass grafting (CABG) is increasingly conceptualized as a system-level disturbance of large-scale brain coordination rather than focal dysfunction. Here, we propose a multiscale neural engineering framework that combines static and dynamic information-theoretic connectivity with graph-theoretical analysis to characterize postoperative network vulnerability and its association with cognitive outcome. Resting-state fMRI was acquired in 14 male CABG patients at an early postoperative baseline (BL) and at 3-month follow-up (FU). Cognitive outcome at follow-up was assessed with the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS), classifying 7 patients as POCD (RBANS < 80) and 7 as NO POCD. Functional connectivity between 32 brain regions, grouped in 8 resting-state networks (RSN), was estimated using mutual information (MI; static dependence) and mutual information rate (MIR; dynamic information exchange), each computed with parametric Gaussian (linear) and model-free k-nearest neighbor estimators. Pairwise connections were validated via surrogate testing, and group differences in longitudinal connectivity change ({Delta} = FU-BL) were assessed with permutation tests at global, intra- and inter-RSN scales. Graph metrics were computed on statistically thresholded weighted networks and related to RBANS using permutation-based Spearman correlations. POCD was not associated with a uniform reduction in connectivity but with a structured pattern of network reorganization. Static connectivity showed widespread alterations, particularly within higher-order associative systems, including salience, dorsal attention, and default mode networks. Dynamic connectivity did not exhibit global group differences but revealed selective, network-specific alterations in temporal information exchange. Longitudinal analyses showed that better cognitive outcomes were associated with increased global efficiency and density and reduced modularity and small-worldness, indicating a greater brain integration. In contrast, poorer outcomes were associated with increased segregation and higher betweenness centrality, suggesting greater reliance on hub-mediated communication. Linear measures captured more widespread connectivity changes, whereas nonlinear estimators revealed more selective alterations in dynamic information flow. Combining static and dynamic information measures with complementary estimators and surrogate-validated graph analysis reveals dissociable signatures of postoperative network dysfunction. POCD is characterized by impaired restoration of distributed integration and a progressive shift toward hub-dependent communication, suggesting that large-scale integrative vulnerability may constitute a candidate biomarker of cognitive resilience after cardiac surgery.

bioengineering↗

bioRxiv2024

Exploring transient neurophysiological states through local and time-varying measures of Information Dynamics

Studying the temporal evolution of complex systems requires tools able to detect the presence and quantify the strength of predictable dynamics within their output signals. Information theory aids in such a description, particularly through information storage (IS), which reflects the regularity of system dynamics by measuring the information shared between the present and the past system states. While the conventional IS computation provides an overall measure of predictable information, transient behaviors of predictability occurring during system transitions can be assessed by time-resolved measures such as the local information storage (L-IS), assuming stationarity, and the time-varying information storage (TV-IS), without stationarity assumptions. In this work, through a comparative analysis in simulated and real contexts, we aim to demonstrate how these methods complement each other and reveal dynamic changes of the system behavior associated to state transitions. In simulations, we show that the TV-IS can effectively track sudden changes of the information stored in the system, which is reflected in its average value computed over specific time intervals; on the other hand, the surprise originated by the emergence of a change in the predictability of the system is reflected in the variance of the L-IS computed within specific time intervals. In neurophysiological applications, the distinct phenomena of respiratory activity in sleep apnea and brain activity during somatosensory stimulation both reveal a significant decrease of IS evoked by state transitions, highlighting how such transitions can inject new information in physiological systems, affecting significantly their internal dynamics. Overall, TV-IS and L-IS appear to provide different and complementary information about the behavior of the systems under investigation, thereby offering valuable tools for the study of complex physiological systems where both stationary and non-stationary conditions may be present.

bioengineering↗

bioRxiv2023

Measuring Connectivity in Linear Multivariate Processes with Penalized Regression Techniques

The evaluation of time and frequency domain measures of coupling and causality relies on the parametric representation of linear multivariate processes. The study of temporal dependencies among time series is based on the identification of a Vector Autoregressive model. This procedure is pursued through the definition of a regression problem solved by means of Ordinary Least Squares (OLS) estimator. However, its accuracy is strongly influenced by the lack of data points and a stable solution is not always guaranteed. To overcome this issue, it is possible to use penalized regression techniques. The aim of this work is to compare the behavior of OLS with different penalized regression methods used for a connectivity analysis in different experimental conditions. Bias, accuracy in the reconstruction of network structure and computational time were used for this purpose. Different penalized regressions were tested by means of simulated data implementing different ground-truth networks under different amounts of data samples available. Then, the approaches were applied to real electroencephalographic signals (EEG) recorded from a healthy volunteer performing a motor imagery task. Penalized regressions outperform OLS in simulation settings when few data samples are available. The application on real EEG data showed how it is possible to use features extracted from brain networks for discriminating between two tasks even in conditions of data paucity. Penalized regression techniques can be used for brain connectivity estimation and can be exploited for the computation of all the connectivity estimators based on linearity assumption overcoming the limitations imposed by the classical OLS.

bioengineering↗

bioRxiv2023

Local and Global Measures of Information Storage for the Assessment of Heartbeat-Evoked Cortical Responses

ObjectiveBrain-heart interactions involve bidirectional effects produced by bottom-up input at each heartbeat, and top-down neural regulatory responses of the brain. While the cortical processing of the heartbeat is usually investigated through the analysis of the Heartbeat Evoked Potential, in this study we propose an alternative approach based on the variability in the predictability of the brain dynamics induced by the heartbeat. MethodsIn a group of eighteen subjects in whom simultaneous recording of the electroencephalogram (EEG) and electrocardiogram was performed in a resting-state, we analyzed the temporal profile of the local Information Storage (IS) to detect changes in the regularity of EEG signals in time windows associated with different phases of the cardiac cycle at rest. ResultsThe average values of the local IS were significantly higher in the parieto-occipital areas of the scalp, suggesting an activation of the Default Mode Network, regardless of the cardiac cycle phase. In contrast, the variability of the local IS showed marked differences across the cardiac cycle phases. ConclusionOur results suggest that cardiac activity influences the predictive information of EEG dynamics differently in the various phases of the cardiac cycle. SignificanceThe variability of local IS measures can represent a useful index to identify spatio-temporal dynamics within the neurocardiac system, which generally remain overlooked by the more widely employed global measures.

neuroscience↗

bioRxiv2022

Network plasticity in the honey bee antennal lobe during odor presentation

Odorant processing presents multiple parallels across animal species, and insects became relevant models for the study of olfactory coding because of the tractability of the underlying neural circuits. Within the insect brain, odorants are received by olfactory sensory neurons and processed by the antennal lobe network. Such a network comprises multiple nodes, named glomeruli, that receive sensory information and are interconnected by local interneurons participating in shaping the neural representation of an odorant. The study of functional connectivity between the nodes of a sensory network in vivo is a challenging task that requires simultaneous recording from multiple nodes at high temporal resolutions. Here, we used fast two-photon microscopy to follow the calcium dynamics of antennal lobe glomeruli and applied Granger causality analysis to assess the functional directed connectivity among network nodes during and after olfactory stimulation. Our findings show that there is a significant causal coupling between network nodes also in the absence of olfactory stimulation. However, connectivity patterns change upon odorant arrival, showing an increase in connection density and conveying odorant-specific information. Furthermore, we show that causal functional connectivity is unique in individual subjects, and cannot be detected upon artificial glomerular scrambling across individuals. This demonstrates that network states do not consist solely of correlations of mean glomerular responses, which are known to be conserved across bees.

neuroscience↗

bioRxiv2020

Information Decomposition in the Frequency Domain: a New Framework to Study Cardiovascular and Cardiorespiratory Oscillations

While cross-spectral and information-theoretic approaches are widely used for the multivariate analysis of physiological time series, their combined utilization is far less developed in the literature. This study introduces a framework for the spectral decomposition of multivariate information measures, which provides frequency-specific quantifications of the information shared between a target and two source time series and of its expansion into amounts related to how the sources contribute to the target dynamics with unique, redundant and synergistic information. The framework is illustrated in simulations of linearly interacting stochastic processes, showing how it allows to retrieve amounts of information shared by the processes within specific frequency bands which are otherwise not detectable by time-domain information measures, as well as coupling features which are not detectable by spectral measures. Then, it is applied to the time series of heart period, systolic and diastolic arterial pressure and respiration variability measured in healthy subjects monitored in the resting supine position and during head-up tilt. We show that the spectral measures of unique, redundant and synergistic information shared by these variability series, integrated within specific frequency bands of physiological interest, reflect the mechanisms of short term regulation of cardiovascular and cardiorespiratory oscillations and their alterations induced by the postural stress.

bioengineering↗