Adaptive Cortical Parcellations for Source Reconstructed EEG/MEG Connectomes

Vancouver Convention Centre 

There is growing interest in the rich temporal and spectral properties of Electro- and Magnetoencephalography (E/MEG) signals in order to study the functional connectome of the brain [1, 2]. However, the spatial resolution of E/MEG data is limited, because several thousand sources of activation in the brain must be estimated from maximally a few hundred recording sites. This limited spatial resolution causes the so-called leakage problem: activity estimated in one region of interest (ROI) can be affected by leakage from locations outside this ROI [3, 4]. E/MEG studies typically adopt parcellations from structural or fMRI research for whole-brain connectivity analysis [5]. However, considering the spatial resolution of E/MEG, these parcellations are unlikely to be optimal [6]. Here, we utilise Cross-Talk Functions (CTFs) as a direct measure of spatial leakage [7] and utilise two CTF-informed image segmentation algorithms in order to parcellate the cortical surface into the maximum number of distinguishable ROIs.