NEW Phone: 617-724-1061
NEW e-mail: barbieri@neurostat.mit.edu

My research is devoted to mathematical modeling of neural and cardiovascular control systems by development and application of signal processing algorithms.

Cardiovascular Control. The specific aim is to investigate novel statistical models of cardiovascular control, providing new quantitative indices that could have important implications for research studies of cardiovascular and autonomic regulation and for heart rate monitoring in clinical settings. In my MA and PhD studies I have successfully investigated AR multivariate models applied to cardiovascular variables, and demonstrated their usefulness in providing new insights into cardiovascular regulation pathophysiology (Theses 1992, 1998; IEEE-EMB, 1997; IEEE-EMB, 2001; Am J Physiol RICP, 2002). More recently, I have derived new definitions of heart rate and heart rate variability under the assumption that the stochastic properties of the R-R intervals are governed by an inverse Gaussian renewal model (Am J Physiol: Heart and Circ Physiol, 2005). This framework gives a more physiologically sound representation of the stochastic structure in heart rate than those provided by current definitions and analysis methods. I further implemented an adaptive point process procedure, and demonstrated the ability of this method to track instantaneous dynamics in autonomic regulation of the cardiovascular system (IEEE-TBE, 2006). Recent work is focusing on more complex representations incorporating the point process framework into models of cardiovascular control and autonomic regulation, with inclusion of other cardiovascular variables such as arterial blood pressure, central venous pressure and respiration (IEEE-TBE, 2009, 2010). I am the Principal Investigator of NIH-NHLBI Grant HL084502 entitled “Point Process Models of Human Heart Beat Interval Dynamics”. The project is in collaboration with Dr. Emery Brown and Dr. Sara Lazar from Massachusetts General Hospital, Dr. Elizabeth B. Klerman from Brigham and Women's Hospital, Dr. Roger Mark from Massachusetts Institute of Technology, and Dr. J Philip Saul from Medical University of South Carolina.

Neural Systems. Studies focus on how individual and ensembles of neurons encode information about relevant biological stimuli, using point process models of spatial information representation in rat hippocampal neurons. This work is in collaboration with Dr. Matthew Wilson at MIT, and Dr. Loren Frank at UCSF. My initial research on modeling the single neuron by using non-Poisson stimulus-response models of neural spike train activity demonstrated that these models provide more accurate descriptions of the local spiking properties of the neuron (J Neurosci Meth. 2001). I further implemented Bayesian decoding algorithms for position estimation. Using only the recorded spiking activity of 30 cells, the most refined paradigm is now able to obtain position estimates with errors as small as 5 cm (Neural Comp, 2004). Future work will consider models with more complex history dependence, multivariate interactions among cells (SFN Conference, 2003), as well as application of point process adaptive filter algorithms for estimation of neural plasticity in ensemble neural spike train decoding (SFN Conference, 2005).

My long-term goal is to further bring together my experience in the cardiovascular field with the more recent knowledge in quantitative neurosciences, and to create a unique research environment specialized in studying the complex interrelations between brain and physiology of the human body by means of mathematical modeling and signal processing techniques. My primary teaching responsibilities concern the supervision of some of the undergraduates, graduate students, medical students and postdoctoral fellows pursuing their research projects at NSRL.

Please visit my main homepage for further details:
http://users.neurostat.mit.edu/barbieri/