Contatto di riferimento: Prof. Stefano Diciotti
Recapito telefonico per contatti: +39 0547 339121
About the speaker
Nicola Toschi is an Associate Professor in Medical Physics at the University of Rome "Tor Vergata" and a Visiting Professor at the Martinos Center for Biomedical Imaging (Harvard Medical School). His research is focused on MR physics and developing image processing and modelling techniques able to extract quantitative biomarkers of investigative, diagnostic and prognostic value in a clinical context. He is the author of over 100 peer-reviewed publications in these fields, which have been cited approximately 2500 times since year 2000, where the main clinical domains of application have been diagnostic imaging, neurology and psychiatry, neuroendocrinology, cardiology and anesthesiology. His current work is directed toward the design of novel MR contrasts at ultra-high field, the estimation of directional interactions in brain functional connectivity, the estimation of central correlates of autonomic nervous system activity, the application of non Gaussian diffusion imaging in neurodegenerative disease, and predictive estimation of intraoperative cardiovascular risk.
Abstract
Complexity science represents a convergence of numerous ideas and theories which address the nonlinearity and dynamics of real-world systems, often known as complex adaptive systems (CAS). Naturally occurring phenomena follow nonlinear power- and scaling laws which point towards a ubiquitous presence of CAS as underlying governing mechanisms of biological processes as well as their disease-related aberrations. Accordingly, in recent years concepts such as variability, fractality, scaling, entropy and degeneracy, have been successively applied to detecting and estimating the complexity of living systems as well as to producing powerful biomarkers of disease. This seminar will focus on the definition of complexity in general and CAS in particular, as well as on the mathematical approaches stemming from the fields of chaos, nonlinear dynamical systems and information theory which have been able to successfully detect and quantify complexity in biological systems.