Contatto di riferimento: Prof. Stefano Diciotti
About the speaker
Riccardo Barbieri received the M.S. degree in Electrical Engineering from the University of Rome “La Sapienza”, Rome, Italy, in 1992, and the Ph.D. in Biomedical Engineering from Boston University, Boston, MA, in 1998. He is now Associate Professor at the Politecnico di Milano, and Research Affiliate at the Massachusetts General Hospital.
His broad research interests are in the development of signal processing algorithms for the analysis of biological systems. He is currently focusing his studies on computational modeling of neural information encoding, and on application of nonlinear and multivariate statistical models to characterize heart rate variability and cardiovascular control dynamics. He is author of more than 100 peer-reviewed publications in these fields since 1994.
Dr. Barbieri is a Member of the American Association for the Advancement of Science, the European Society of Hypertension, the Society for Neuroscience, and Senior Member of IEEE and the Engineering in Medicine and Biology Society.
Abstract
This presentation focuses on our Point Process Framework as applied to encoding and decoding information from Neural Ensemble activity of Ca1 cells in the Hippocampus, as well as recently derived definitions of Heart Rate Variability (HRV) based on explicit point process Bayesian probability models. Our research has demonstrated that these models provide accurate descriptions of the local spiking properties of the neuron, and the most refined decoding paradigm was able to obtain position estimates with errors as small as 5 cm.
Point process models also give a physiologically sound representation of the stochastic structure generating the heartbeat, allowing for instantaneous assessment of fast, non-stationary dynamics. Our current work is focusing on incorporating the point process framework into nonlinear models of cardiovascular control and autonomic regulation, with inclusion of other cardiovascular variables such as arterial blood pressure and respiration, as well as combining HRV estimates with fMRI brain imaging in order to identify human brain correlates of autonomic modulation.
The presented dynamic statistical measures yield important implications for research studies of cardiovascular and autonomic regulation, and they provide the basis for potential real-time indicators for ambulatory monitoring and instantaneous assessment of autonomic control in clinical settings.