Exploring transient neurophysiological states through local and time-varying measures of Information Dynamics DOI Creative Commons
Yuri Antonacci, Chiara Barà,

G. de Felice

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Июнь 24, 2024

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

Язык: Английский

Time-varying information measures: an adaptive estimation of information storage with application to brain-heart interactions DOI Creative Commons
Yuri Antonacci, Chiara Barà, Andrea Zaccaro

и другие.

Frontiers in Network Physiology, Год журнала: 2023, Номер 3

Опубликована: Окт. 18, 2023

Network Physiology is a rapidly growing field of study that aims to understand how physiological systems interact maintain health. Within the information theory framework storage (IS) allows measure regularity and predictability dynamic process under stationarity assumption. However, this assumption does not allow track over time transient pathways occurring in dynamical activity system. To address limitation, we propose time-varying approach based on recursive least squares algorithm (RLS) for estimating IS at each instant, non-stationary conditions. We tested simulated dynamics analysis electroencephalographic (EEG) signals recorded from healthy volunteers timed with heartbeat investigate brain-heart interactions. In simulations, show proposed both abrupt slow changes stored These are reflected its evolution variability time. The interactions reveals marked differences across cardiac cycle phases IS. On other hand, average values exhibit weak modulation parieto-occiptal areas scalp. Our highlights importance developing more advanced methods measuring account non-stationarity systems. RLS represents useful tool identifying spatio-temporal within neurocardiac system can contribute understanding

Язык: Английский

Процитировано

12
Intracortical brain‐heart interplay: An EEG model source study of sympathovagal changes DOI Creative Commons
Vincenzo Catrambone, Diego Candia‐Rivera, Gaetano Valenza

и другие.

Human Brain Mapping, Год журнала: 2024, Номер 45(6)

Опубликована: Апрель 15, 2024

Abstract The interplay between cerebral and cardiovascular activity, known as the functional brain‐heart (BHI), its temporal dynamics, have been linked to a plethora of physiological pathological processes. Various computational models axis proposed estimate BHI non‐invasively by taking advantage time resolution offered electroencephalograph (EEG) signals. However, investigations into specific intracortical sources responsible for this limited, which significantly hampers existing studies. This study proposes an analytical modeling framework estimating at source‐brain level. analysis relies on low‐resolution electromagnetic tomography localization from scalp electrophysiological recordings. is then quantified correlation dynamics. Using approach, we aimed evaluate reliability estimates derived source‐localized EEG signals compared with prior findings neuroimaging methods. approach validated using experimental dataset gathered 32 healthy individuals who underwent standard sympathovagal elicitation cold pressor test. Additional resting state data 34 has analysed assess robustness reproducibility methodology. Experimental results not only confirmed previous activation brain structures affecting cardiac dynamics (e.g., insula, amygdala, hippocampus, anterior mid‐cingulate cortices) but also provided insights anatomical bases axis. In particular, show that bidirectional activity pathways communication increases during pressure respect state, mainly targeting neural oscillations in , bands. offers new perspectives investigation could shed light various pathophysiological conditions.

Язык: Английский

Процитировано

4
Whole-body networks: a holistic approach for studying aging DOI
Orestis Stylianou, Johannes Meixner,

Tilman Schlick

и другие.

GeroScience, Год журнала: 2025, Номер unknown

Опубликована: Янв. 28, 2025

Язык: Английский

Процитировано

0
A feedback loop study of brain-heart interaction based on HEP and HRV DOI
Shanshan Wang, Xiaoni Wang, Yuxin Zhao

и другие.

Journal of Applied Biomedicine, Год журнала: 2025, Номер 45(2), С. 181 - 188

Опубликована: Фев. 28, 2025

Язык: Английский

Процитировано

0
Exploring transient neurophysiological states through local and time-varying measures of information dynamics DOI Creative Commons
Yuri Antonacci, Chiara Barà, Giulio de Felice

и другие.

Applied Mathematics and Computation, Год журнала: 2025, Номер 500, С. 129437 - 129437

Опубликована: Апрель 2, 2025

Язык: Английский

Процитировано

0
Cardio-respiratory interactions in interoceptive perception: The role of heartbeat-modulated cortical oscillations DOI Creative Commons
Andrea Zaccaro, Francesca della Penna, Francesco Bubbico

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2025, Номер unknown

Опубликована: Апрель 19, 2025

Abstract The cardiovascular and respiratory systems are anatomically functionally integrated within the cardio-respiratory system. This close connection suggests that breathing continuously shapes cardiac interoceptive perception. Previously, we demonstrated interactions in heartbeat-evoked potential, a neural marker of cortical processing signals. Specifically, observed enhanced late potential positivity greater accuracy during exhalation compared to inhalation participants engaged tasks. Here, extended these findings time-frequency domain by reanalysing our previous dataset. We investigated heartbeat-modulated oscillations, examining power, inter-trial coherence, functional connectivity across cycle at rest, task (heartbeat counting), an exteroceptive control (cardiac-tone counting). Results revealed heartbeat counting task, heartbeat-related increased inhalation, particularly alpha theta frequency bands. These effects were primarily localized right fronto-centro-parietal electrodes. Furthermore, identified interactive relationships between oscillations band predicted accuracy. independent physiology absent task. proposed model framework predictive coding, suggesting occur multiple levels hierarchy: peripheral, brainstem, cortical. Our interpretation highlights role alpha-band modulations enhancing precision-weighting prediction errors, thereby facilitating attentional allocation signals suppression task-irrelevant distractors, exhalation.

Язык: Английский

Процитировано

0
A method to assess linear self-predictability of physiologic processes in the frequency domain: application to beat-to-beat variability of arterial compliance DOI Creative Commons
Laura Sparacino, Yuri Antonacci, Chiara Barà

и другие.

Frontiers in Network Physiology, Год журнала: 2024, Номер 4

Опубликована: Апрель 4, 2024

The concept of self-predictability plays a key role for the analysis self-driven dynamics physiological processes displaying richness oscillatory rhythms. While time domain measures self-predictability, as well time-varying and local extensions, have already been proposed largely applied in different contexts, they still lack clear spectral description, which would be significantly useful interpretation frequency-specific content investigated processes. Herein, we propose novel approach to characterize linear (LSP) Gaussian frequency domain. LSP functions are related peaks power density (PSD) process, is represented sum components with specific through method decomposition. Remarkably, each profiles linked oscillation it returns when integrated along bands interest, measure meaning field information theory, corresponding well-known storage, whole axis. first illustrated theoretical simulation, showing that clearly reflects degree location predictability patterns analyzed process both domains. Then, beat-to-beat series arterial compliance obtained young healthy subjects. results evidence decomposition strategy PSD identifies responses postural stress low high oscillations cannot traced only, highlighting importance computing any physiologic process.

Язык: Английский

Процитировано

1
Exploring transient neurophysiological states through local and time-varying measures of Information Dynamics DOI Creative Commons
Yuri Antonacci, Chiara Barà,

G. de Felice

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Июнь 24, 2024

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

Язык: Английский

Процитировано

0