A5030899687- Heart Rate Variability and Autonomic Control
- EEG and Brain-Computer Interfaces
- Non-Invasive Vital Sign Monitoring
- Neural dynamics and brain function
- Functional Brain Connectivity Studies
- ECG Monitoring and Analysis
- Optical Imaging and Spectroscopy Techniques
- Vagus Nerve Stimulation Research
- Cardiovascular Syncope and Autonomic Disorders
- Hemodynamic Monitoring and Therapy
- Cardiovascular Health and Disease Prevention
- Complex Systems and Time Series Analysis
- Memory and Neural Mechanisms
- Mental Health Research Topics
- Neuroscience of respiration and sleep
- Anesthesia and Sedative Agents
- Advanced Chemical Sensor Technologies
- Circadian rhythm and melatonin
- Emotion and Mood Recognition
- Neuroscience and Neuropharmacology Research
- Blood Pressure and Hypertension Studies
- Particle physics theoretical and experimental studies
- Sepsis Diagnosis and Treatment
- Radiomics and Machine Learning in Medical Imaging
- Radiation Dose and Imaging
Politecnico di Milano
2016-2024
University of Florence
2015-2024
University of Pisa
1983-2023
University of Modena and Reggio Emilia
2022-2023
Polytechnic University of Turin
2023
Massachusetts General Hospital
2013-2022
Harvard University
2013-2022
Individual Differences
2022
Marion General Hospital
2022
Massachusetts Institute of Technology
2011-2021
Measuring agreement between a statistical model and spike train data series, that is, evaluating goodness of fit, is crucial for establishing the model's validity prior to using it make inferences about particular neural system. Assessing goodness-of-fit challenging problem point process models, especially histogram-based models such as perstimulus time histograms (PSTH) rate functions estimated by smoothing. The time-rescaling theorem well-known result in probability theory, which states...
Neural receptive fields are dynamic in that with experience, neurons change their spiking responses to relevant stimuli. To understand how neural systems adapt the irrepresentations of biological information, analyses field plasticity from experimental measurements crucial. Adaptive signal processing, well-established engineering discipline for characterizing temporal evolution system parameters, suggests a framework studying fields. We use Bayes' rule Chapman-Kolmogorov paradigm linear...
Emotion recognition through computational modeling and analysis of physiological signals has been widely investigated in the last decade. Most proposed emotion systems require relatively long-time series multivariate records do not provide accurate real-time characterizations using short-time series. To overcome these limitations, we propose a novel personalized probabilistic framework able to characterize emotional state subject heartbeat dynamics exclusively. The study includes thirty...
Understanding transient dynamics of the autonomic nervous system during fear learning remains a critical step to translate basic research into treatment fear-related disorders. In humans, it has been demonstrated that typically elicits heart rate deceleration. However, classical analyses variability (HRV) fail disentangle contribution parasympathetic and sympathetic systems, crucially, they are not able capture phasic changes learning. Here, gain deeper insight physiological underpinnings...
Heart rate is a vital sign, whereas heart variability an important quantitative measure of cardiovascular regulation by the autonomic nervous system. Although design algorithms to compute and assess active area research, none approaches considers natural point-process structure human heartbeats, gives instantaneous estimates variability. We model stochastic heartbeat intervals as history-dependent inverse Gaussian process derive from it explicit probability density that new definitions...
Objective Fibromyalgia (FM) is a chronic functional pain syndrome characterized by widespread pain, significant catastrophizing, sympathovagal dysfunction, and amplified temporal summation for evoked pain. While several studies have demonstrated altered resting brain connectivity in FM, not specifically probed the somatosensory system its role both somatic nonsomatic FM symptoms. Our objective was to evaluate primary cortex (S1) explore how sustained, deep tissue modulates this connectivity....
Abstract Migraine pathophysiology includes altered brainstem excitability, and recent neuromodulatory approaches aimed at controlling migraine episodes have targeted key relay modulatory nuclei. In this study, we evaluated the impact of respiratory-gated auricular vagal afferent nerve stimulation (RAVANS), a novel intervention based on an existing transcutaneous vagus approach, in modulation activity connectivity patients. We applied 3T–functional magnetic resonance imaging with improved...
The presence of recurring arrhythmic events (also known as cardiac dysrhythmia or irregular heartbeats), well erroneous beat detection due to low signal quality, significantly affect estimation both time and frequency domain indices heart rate variability (HRV).A reliable, real-time classification correction ECG-derived heartbeats is a necessary prerequisite for an accurate on-line monitoring HRV cardiovascular control.We have developed novel point process based method R-R interval error...
Abstract Standard functional assessment of autonomic nervous system (ANS) activity on cardiovascular control relies spectral analysis heart rate variability (HRV) series. However, difficulties in obtaining a reliable measure sympathetic from HRV spectra limits the exploitation sympatho-vagal metrics. On other hand, measures electrodermal (EDA) have been demonstrated to provide quantifier dynamics. In this study we propose novel indices phasic regulation mechanisms by combining and EDA...
BackgroundThe therapeutic potential of transcutaneous auricular VNS (taVNS) is currently being explored for numerous clinical applications. However, optimized response different indications may depend on specific neuromodulation parameters, and systematic assessments their influence are still needed to optimize this promising approach.HypothesisWe proposed that stimulation frequency would have a significant effect nucleus tractus solitarii (NTS) functional MRI (fMRI) respiratory-gated taVNS...
Reliable and effective noninvasive measures of sympathetic parasympathetic peripheral outflow are crucial importance in cardiovascular physiology. Although many techniques have been proposed to take up this long-lasting challenge, none has a satisfying discrimination the dynamics two separate branches. Spectral analysis heart rate variability is most currently used technique for such assessment. Despite its widespread use, it demonstrated that subdivision low-frequency (LF) high-frequency...
The study of functional Brain-Heart Interplay (BHI) from non-invasive recordings has gained much interest in recent years. Previous endeavors aimed at understanding how the two dynamical systems exchange information, providing novel holistic biomarkers and important insights on essential cognitive aspects neural system functioning. However, interplay between cardiac sympathovagal cortical oscillations still room for further investigation. In this study, we introduce a new computational...
Neural spike train decoding algorithms and techniques to compute Shan-non mutual information are important methods for analyzing how neural systems represent biological signals. Decoding also one of several strategies being used design controls brain-machine inter-faces. Developing optimal desig n therefore problems in com-putational neuroscience. We present a general recursive filter algorithm based on point process model individual neuron spiking activity linear stochastic state-space the...
In the last decades, mathematical modeling and signal processing techniques have played an important role in study of cardiovascular control physiology heartbeat nonlinear dynamics. particular, models been devised for assessment system by accounting short-memory second-order nonlinearities. this paper, we introduce a novel inverse Gaussian point process model with Laguerre expansion Volterra kernels. Within model, nonlinearities also account long-term information given past events...