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Functional Neuro-Imaging and Brain Networks

M. Bucolo, M. Rance, A. Muscarello, A. Spampinato, M. Ruttorf and H. Flor

International Journal of Bioelectromagnetism, 14 (2), pp.100-107

The aim of this research was to establish a methodological framework, based on data-driven identification methods coupled with both graph analysis and statistics to investigate the functional and effective brain connectivity in neuroimaging data recorded from different modalities. As proof of concept functional and effective connectivity coupled with graph analysis is presented on fMRI datasets recorded during a Brain-Computer Interface (BCI) protocol. Particularly the functional analysis has been based on coherence, while effective connectivity has been evaluated using the multivariate autoregressive model (MVAR) in the frequency domain, referred to as directed transfer function (DTF). An automatic procedure to investigate the frequency values leading to a greater variability in the construction of brain networks based on the statistical analysis of the connectivity matrices has been investigated. The comparison of the graphs related to the brain networks highlights clearly the sensitivity of the results to the frequency and the analysis method, underlining the need and the potential of the proposed approach.

Contact: Dr. Frank Zöllner last modified: 20.03.2019
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