Ricerc@Sapienza

Methods based on the use of multivariate
autoregressive models (MVAR) have proved to be an accurate
tool for the estimation of functional links between the activity
originated in different brain regions. A well-established method
for the parameters estimation is the Ordinary Least Square
(OLS) approach, followed by an assessment procedure that can
be performed by means of Asymptotic Statistic (AS). However,
the performances of both procedures are strongly influenced by
the number of data samples available, thus limiting the

Among different methods available for estimating brain connectivity from electroencephalographic signals (EEG), those based on MVAR models have proved to be flexible and accurate. They rely on the solution of linear equations that can be pursued through artificial neural networks (ANNs) used as MVAR model. However, when few data samples are available, there is a lack of accuracy in estimating MVAR parameters due to the collinearity between regressors. Moreover, the assessment procedure is also affected by the lack of data points.

Community detection plays a key role in the study of brain networks, as mechanisms of modular integration and segregation are known to characterize the brain functioning. Moreover, brain networks are intrinsically multilayer: they can vary across time, frequency, subjects, conditions, and meaning, according to different definitions of connectivity. Several algorithms for the multilayer community detection were defined to identify communities in time-varying networks.