Time-dependent identification of networks via zero-delay cross-correlation analysis.
Master students in Physics, Psychology and Cognitive Science, Mathematics. (2022)
Monday 07 February 2022

The study of connectivity is receiving growing attention in the recent years, for example in brain research and in climate investigations.

Networks of interconnected regions within a given system are identified by assessing correlations between signals produced by different nodes. In neuroscience, to retrieve those signals, two main techniques are employed: functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG).

The goal of the thesis work is to apply a new approach developed in our lab for the assessment of nodes and network structures in brain regions by analyzing MEG time series.

Students interested in climate investigations can apply the same methods to time series generated by meteorological stations.