Seminar “Modelling the brain: resting-state MEG functional network analysis” given by Dr. Silvana Silva-Pereira

Abstract:

A popular way to analyze resting-state EEG and MEG data is to treat them as a functional network in which sensors are identified with nodes and the interaction between channel time-series with the network connections. Although conceptually appealing, the network-theoretical approach to sensor-level EEG and MEG data is challenged by the fact that EEG and MEG time-series are mixtures of source activity. It is therefore of interest to assess the relationship between functional networks of source activity and the ensuing sensor-level networks. We address the question of to what extent the network topology can be reconstructed from sensor-level functional connectivity measures in case of MEG data.

[1] Silva Pereira S, Hindriks R, Mühlberg S, Maris E, van Ede F, Griffa A, Hagmann P, Deco G. Effect of field spread on resting-state magneto encephalography functional network analysis: a computational modeling study. Brain Connectivity 2017; 7(9): 541-557.

Short Bio: Dr. Silvana Silva-Pereira studied Computer Science at Universitetet i Oslo, Norway, where she obtained her MSc. degree in mathematical modeling, and her PhD degree in Signal Theory and Communications from the Universitat Politècnica de Catalunya in 2012. She joined the Computational Neuroscience Group at the UPF in 2015, to apply her knowledge in signal and distributed information processing to contribute to the advances in neuroscience. Silvana is interested in understanding how the information in the human brain is processed and integrated to conform our particular experience of the world, conditioning our perception and behavior. Simultaneously, she works as a professor at the Department of Information and Communication Technologies (DTIC) of the UPF for undergraduate courses in engineering.

Looking forward to seeing you there.