Our understanding of the functioning of the brain relies on minimally invasive imaging modalities, requiring advanced computational methods. New questions about brain functions pose computational challenges that need to be addressed by the imaging algorithms and by the data acquisition systems. The aim of the MS is to bring together mathematicians, computational scientists, and neuroscientists to discuss the most relevant advances in brain imaging. The main topics that will be addressed are brain modeling, signal and image processing, inversion methods, brain networks. The goal is to provide an interdisciplinary forum where scholars from different area will exchange ideas and discuss open.

Novel instrumentation and analysis approaches for brain imaging

Lauri Parkkonen (Aalto University)

Bayesian M/EEG brain mapping in the time and frequency domain

Alberto Sorrentino (University of Genoa)

"Hierarchical Bayesian Uncertainty Quantification for EEG/MEG Source Reconstruction"

Felix Lucka (CWI & UCL)

Phase Synchronization in MEG/EEG: methodological considerations and empirical evidence

Laura Marzetti (Università degli Studi G. d'Annunzio Chieti e Pescara)

Organizers:

Francesca Pitolli (Dept. of Basic and Applied Sciences for Engineering, University of Rome “La Sapienza”)

Erkki Somersalo (Case Western Reserve University)

Keywords:

bayesian methods, electroencephalography (eeg), image reconstruction, inverse problems, magnetoencepalogaphy (meg)