Neural Modeling and Simulation

This research activity is focused on

1) Modeling in-vitro neural cultures

2) Modeling Neuron-Glia dynamics and interaction

3) Modeling Neuron-Glia dynamics at a network level

This activity is carried out in collaboration with Prof. Enzo Wanke (University of Milano Bicocca, Milan, Italy)


Modeling in-vitro neural cultures

Computational Psychiatry

“Depression is the leading cause of disability around the world” reports the World Health Organization, an affirmation that attributes immediate gravitas to a condition that affects at least 322 million people, of which 40 million reside in Europe. Even more alarming is the data revealing that 10 million people suffering from depression have seriously considered killing themselves, and that 3 million have made an actual suicide plan.

Point-Process models

This research activity is focused on

  1. Developing Inhomogeneous Point-Process Models of human heartbeat dynamics
  2. Extend the framework including nonlinear dynamics
  3. Extend the framework including multivariate dynamics (e.g., respiration, blood pressure, etc.)

This activity is carried out in strict collaboration with Prof. Riccardo Barbieri (Politecnico di Milano, Milan, Italy, and Mass. General Hospital, Boston, USA), and Prof. Luca Citi (University of Essex, Colchester, UK)

Nonlinear Signal Analysis

It is widely recognized how physiological signals have nonlinear and non-stationary behaviour due to their complex functionalities and the several functional, anatomical, electro-chemical interactions that continuously exist among different systems and organs (e.g., brain-heart interplay, cardio-respiratory coupling, sympatho-vagal balance). Consequently, physiological signals have been recently successfully modelled by exploiting nonlinear and complex methodologies.

Cardio-Respiratory coupling

Cardiovascular structure and functions, including vascular anatomy, electrical conduction, heart rate and blood-pressure variability, as well as cardio-respiratory dynamics, are associated with complex spatial and temporal patterns that can be quantified through methodological approaches derived from the theory of complex dynamical systems. These approaches go beyond standard time and frequency domain analyses, as they account for the nonlinear relationship between the magnitude of physiological system responses and the strength/amplitude of the system input.

Biomedical Signal Processing

Our bodies are constantly communicating information about our health. This information can be captured through physiological instruments that measure heart rate, blood pressure, oxygen saturation levels, blood glucose, nerve conduction, brain activity and so forth. Traditionally, such measurements are taken at specific points in time and noted on a patient’s chart. Physicians actually see less than one percent of these values as they make their rounds, and treatment decisions are made based upon these isolated readings.

Biomedical Image Processing

Several imaging techniques including Magnetic Resonance Imaging (anatomical or functional) radiography, thermography, ultrasound, nuclear medicine, CT, Positron Emission Tomography may be exploited to study the anatomical and functional features of the neuro-cardio-vascular system. In our research group, we study how the dynamical information gathered from image processing might be of help in the understanding of the nervous systems activity.

Autonomic Nervous System

Heartbeat dynamics and its spontaneous fluctuations are directly controlled by autonomic nervous system (ANS) outflow to the heart. Specifically, the multipath feedback system for neural control of the heart is manifested by the complex interaction between the sympathetic and parasympathetic (vagal) limbs of the ANS.

We are developing novel metrics of Sympathetic and Parasympathetic Nervous System activity from heartbeat dynamics.