Investigating the Kappa Effect Elicited Through Concurrent Visual and Tactile Stimulation.

The experience of time and space in subjective perception is closely connected. The Kappa effect refers to the phenomenon where the perceived duration of the time interval between stimuli is influenced by the spatial distance between them. In this study, we aimed to explore the Kappa effect from a psychophysical perspective.

Impaired brain-heart axis in focal epilepsy: Alterations in information flow and implications for seizure dynamics.

This study delves into functional brain-heart interplay (BHI) dynamics during interictal periods before and after seizure events in focal epilepsy. Our analysis focuses on elucidating the causal interaction between cortical and autonomic nervous system (ANS) oscillations, employing electroencephalography and heart rate variability series. The dataset for this investigation comprises 47 seizure events from 14 independent subjects, obtained from the publicly available Siena Dataset.

Intracortical brain-heart interplay: An EEG model source study of sympathovagal changes.

The interplay between cerebral and cardiovascular activity, known as the functional brain-heart interplay (BHI), and its temporal dynamics, have been linked to a plethora of physiological and pathological processes. Various computational models of the brain-heart axis have been proposed to estimate BHI non-invasively by taking advantage of the time resolution offered by electroencephalograph (EEG) signals. However, investigations into the specific intracortical sources responsible for this interplay have been limited, which significantly hampers existing BHI studies.

Stochastic brain dynamics exhibits differential regional distribution and maturation-related changes.

Functional magnetic resonance imaging (fMRI) is a powerful non-invasive method for studying brain function by analyzing blood oxygenation level-dependent (BOLD) signals. These signals arise from intricate interplays of deterministic and stochastic biological elements. Quantifying the stochastic part is challenging due to its reliance on assumptions about the deterministic segment.

Restoring bottom-up communication in brain-heart interplay after trauma-focused psychotherapy in breast cancer patients with post-traumatic stress disorder.

Background: The psychological impact of breast cancer (BC) is substantial, with a significant number of patients (up to 32 %) experiencing post-traumatic stress disorder (PTSD). Exploring the emotional aspects of PTSD through the functional brain-heart interplay (BHI) offers valuable insights into the condition. BHI examines the functional interactions between cortical and sympathovagal dynamics.

A Unified Framework for Investigating Aperiodic and Periodic Components in the Hearbeat Dynamics Spectrum: a Feasibility Study.

Heart Rate Variability (HRV) series is a widely used, non-invasive, and easy-to-acquire time-resolved signal for evaluating autonomic control on cardiovascular activity. Despite the recognition that heartbeat dynamics contains both periodic and aperiodic components, the majority of HRV modeling studies concentrate on only one component. On the one hand, there are models based on self-similarity and 1/f behavior that focus on the aperiodic component; on the other hand, there is the conventional division of the spectral domain into narrow-band oscillations, which considers HRV as a combination of periodic components.

Characterization of Physiological Noise in Complex Cardiovascular Variability Series.

The cardiovascular system can be analyzed using spectral, nonlinear, and complexity metrics. Nevertheless, dynamical noise may significantly impact these quantifiers. To our knowledge, there has been no attempt to quantify the intrinsic cardiovascular system noise driving heartbeat dynamics.

Towards the definition of Microstates of the Cortical Brain-Heart Axis.

Brain microstates are defined as states with quasi-stable scalp activity topography and have been widely studied in literature. Whether those states are brain-specific or extend to the body level is unknown yet. We investigate the extension of cortical microstates to the peripheral autonomic nerve, specifically at the brain-heart axis level as a functional state of the central autonomic network.

Complex Brain-Heart Mapping in Mental and Physical Stress.

Objective: The central and autonomic nervous systems are deemed complex dynamic systems, wherein each system as a whole shows features that the individual system sub-components do not. They also continuously interact to maintain body homeostasis and appropriate react to endogenous and exogenous stimuli. Such interactions are comprehensively referred to functional brain-heart interplay (BHI).

Microstates of the cortical brain-heart axis.

Electroencephalographic (EEG) microstates are brain states with quasi-stable scalp topography. Whether such states extend to the body level, that is, the peripheral autonomic nerves, remains unknown. We hypothesized that microstates extend at the brain-heart axis level as a functional state of the central autonomic network.

Physiological Noise: Definition, Estimation, and Characterization in Complex Biomedical Signals.

Background: Nonlinear physiological systems exhibit complex dynamics driven by intrinsic dynamical noise. In cases where there is no specific knowledge or assumption about system dynamics, such as in physiological systems, it is not possible to formally estimate noise.

Aim: We introduce a formal method to estimate the power of dynamical noise, referred to as physiological noise, in a closed form, without specific knowledge of the system dynamics.

Altering Time Perception in Virtual Reality Through Multimodal Visual-Tactile Kappa Effect.

The perception of time is highly subjective and intertwined with space perception. In a well-known perceptual illusion, called Kappa effect, the distance between consecutive stimuli is modified to induce time distortions in the perceived inter-stimulus interval that are proportional to the distance between the stimuli. However, to the best of our knowledge, this effect has not been characterized and exploited in virtual reality (VR) within a multisensory elicitation framework.

From local counterfactuals to global feature importance: efficient, robust, and model-agnostic explanations for brain connectivity networks.

Background: Explainable artificial intelligence (XAI) is a technology that can enhance trust in mental state classifications by providing explanations for the reasoning behind artificial intelligence (AI) models outputs, especially for high-dimensional and highly-correlated brain signals. Feature importance and counterfactual explanations are two common approaches to generate these explanations, but both have drawbacks. While feature importance methods, such as shapley additive explanations (SHAP), can be computationally expensive and sensitive to feature correlation, counterfactual explanations only explain a single outcome instead of the entire model.

Causal Symbolic Information Transfer for the Assessment of functional Brain-Heart Interplay through EEG Microstates Occurrences: a proof-of-concept study.

Electroencephalography (EEG) microstates analysis provides a sequence of topographies representing the scalp-related electric field over time, and each microstate is synthetically represented by a symbol. Despite recent advances on functional brain-heart interplay (BHI) assessment, to our knowledge no methodology takes EEG microstates into account to relate the causal heartbeat dynamics. Moreover, standard BHI methods are tailored to a single EEG-channel analysis, neglecting the comprehensive information associated with a multichannel cluster or a whole-brain activity.

Multiscale partition-based Kolmogorov-Sinai Entropy: a preliminary HRV study on Heart Failure vs. Atrial Fibrillation.

Several approaches for estimating complexity in physiological time series at various time scales have recently been developed, with a special focus on heart rate variability (HRV) series. While numerous multiscale complexity quantifiers have been investigated, a multiscale Kolmogorov-Sinai (K-S) entropy for the characterization of cardiovascular dynamics still has to be properly assessed. In this pilot study, we investigate the Algorithmic Information Content, which is calculated using an effective compression algorithm, to quantify multiscale partition- based K-S entropy on experimental HRV series.

Complexity Modulation in functional Brain-Heart Interplay series driven by Emotional Stimuli: an early study using Fuzzy Entropy.

Increasing attention has recently been devoted to the multidisciplinary investigation of functional brain-heart interplay (BHI), which has provided meaningful insights in neuroscience and clinical domains including cardiology, neurology, clinical psychology, and psychiatry. While neural (brain) and heartbeat series show high nonlinear and complex dynamics, a complexity analysis on BHI series has not been performed yet. To this end, in this preliminary study, we investigate BHI complexity modulation in 17 healthy subjects undergoing a 3-minute resting state and emotional elicitation through standardized image slideshow.

Cardiac sympathetic-vagal activity initiates a functional brain-body response to emotional arousal.

A century-long debate on bodily states and emotions persists. While the involvement of bodily activity in emotion physiology is widely recognized, the specificity and causal role of such activity related to brain dynamics has not yet been demonstrated. We hypothesize that the peripheral neural control on cardiovascular activity prompts and sustains brain dynamics during an emotional experience, so these afferent inputs are processed by the brain by triggering a concurrent efferent information transfer to the body.

A Multiscale Partition-Based Kolmogorov-Sinai Entropy for the Complexity Assessment of Heartbeat Dynamics.

Background: Several methods have been proposed to estimate complexity in physiological time series observed at different time scales, with a particular focus on heart rate variability (HRV) series. In this frame, while several complexity quantifiers defined in the multiscale domain have already been investigated, the effectiveness of a multiscale Kolmogorov-Sinai (K-S) entropy has not been evaluated yet for the characterization of heartbeat dynamics.

Methods: The use of the algorithmic information content, which is estimated through an effective compression algorithm, is investigated to quantify multiscale partition-based K-S entropy on publicly available experimental HRV series gathered from young and elderly subjects undergoing a visual elicitation task (Fantasia).

Characterization of upper limb movement-related EEG dynamics through fractional integrated autoregressive modeling.

The analysis of electroencephalographic (EEG) series associated with movement performance is important for understanding the cortical neural control on motor tasks. While the existence of long-range correlations in physiological dynamics has been reported in previous studies, such a characterization in EEG series gathered during upper-limb movements has not been performed yet. To this end, here we report on a fractional integrated autoregressive analysis of EEG series during different functional classes of motor actions and resting phase, and data were gathered from 33 healthy volunteers.

Recognizing motor imagery tasks from EEG oscillations through a novel ensemble-based neural network architecture.

Brain-Computer Interfaces (BCI) provide effective tools aimed at recognizing different brain activities, translate them into actions, and enable humans to directly communicate through them. In this context, the need for strong recognition performances results in increasingly sophisticated machine learning (ML) techniques, which may result in poor performance in a real application (e.g.

Quantifying partition-based Kolmogorov-Sinai Entropy on Heart Rate Variability: a young vs. elderly study.

In the last decades, a considerable effort has been devoted to quantify complexity in physiological time series, with a particular focus on heart rate variability (HRV). To this end, exemplary quantifiers including Approximate Entropy and Sample Entropy have successfully been applied by leveraging on statistical approximation and further parametrization through the definition of tolerance and embedding dimension, among others. In this study, we investigate the use of the Algorithmic Information Content, which is estimated through an effective compression algorithm, to quantify partition-based Kolmogorov-Sinai (K-S) entropy on HRV series.