Projects of the BrainetLab
Projects going-on
Community detection of functional networks
The brain is thought to consist of a network of interconnected, interacting components whose architecture is critical for the emergence of adaptive behaviors and cognition. Graph theory provides a powerful means to assess topology and organization of brain connectivity networks, like those derived from MRI and other neurimaging methods. Within this framework, the brain is represented as a network of n nodes interconnected by m links. Typically, the nodes correspond to anatomically defined brain regions and the links to a measure of inter-regional interaction or similarity. For resting state functional connectivity networks, edge weights are defined as interregional temporal correlations in the fluctuations of the BOLD signals, and the resulting graph can be represented by a correlation adjacency matrix. The arcs of structural connectivity networks (or connectomes), conversely, reflect the number of white matter tracts connecting any two regions. We apply Asymptotic Surprise to the study of benchmark resting state fMRI functional connectivity networks, and we assess its ability to resolve subcortical nuclei and structures that are not accessible to conventional, resolution-limited graph-partitioning methods.
Functional Connectivy of newborns
The functional organization in the adult brain has been widely investigated using neuroimaging techniques, like fMRI and MEG. Recent progress in this direction has allowed to explore at the brain’s intrinsic architecture and to widely investigate its functional organization. However, the study of the inception and development of functional connectivity at an early stage of life still represents a challenge. Indeed, many of the techniques used in adult neuroimaging studies cannot be straightforwardly applied to infants, due to the high level of subject compliance. To this end, Functional Near Infrared Spectroscopy (fNIRS) represents a powerful alternative, considering its good signal to noise ratio, a consequence of the incomplete skull development in the newborn, and its acceptable spatial and temporal resolution trade-off. This project aims to study the emergence of resting state networks in 1-3 days old infants with fNIRS measurements. To investigate the dynamics of functional connectivity development, we will analyze data using graph theoretical approach. Our scientific goal is to shed a light on this research field in order to give insights into the inceptions of neurodevelopmental disorders.
Neuroimaging of addiction
This division focuses on the application of functional Magnetic Resonance Imaging methods to map and investigate brain circuits involved in drug and alcohol addiction. Specifically, we pursue a translational, systems-based approach to understand the alterations in brain function, structure and connectivity in patients, and in animal models of drug dependence. Moreover, we apply neuroimaging methods, dubbed phMRI, to probe the effects of approved and new pharmacological treatments of addiction. This research effort is funded by the EC within the H2020 framework through the project System Biology of Alcohol Addiction (Sybil-AA).
