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Session: Imaging - General
Session starts: Thursday 24 January, 10:40
Presentation starts: 11:25
Room: Lecture room 559

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Alle Meije Wink ()
Jan de Munck ()
Frederik Barkhof ()

Abstract:
Eigenvector centrality mapping (ECM) has recently emerged as a measure to spatially characterise connectivity in functional brain imaging by attributing network properties to voxels [1]. The main obstacle for widespread use of ECM in functional MRI (fMRI) is the cost of computing and storing the connectivity matrix. We present fast ECM (fECM), an efficient algorithm to estimate voxel-wise eigenvector centralities from fMRI time series [2]. Instead of explicitly storing the connectivity matrix, fECM computes matrix-vector products directly from the data, achieving high accelerations for computing voxel-wise centralities in fMRI at standard resolutions for multivariate analyses, and enabling high-resolution analyses performed on standard hardware. We demonstrate the validity of fECM at cluster and voxel levels, using synthetic and in vivo data. Results from synthetic data are compared to the theoretical 'gold standard', and local centrality changes in fMRI data are measured after experimental intervention. A simple scheme is presented to generate time series with prescribed covariances that represent a connectivity matrix. These time series are used to construct a 4D data set, whose volumes consist of separate regions with known intra- and interregional connectivities. The fECM method is tested and validated on these synthetic data. We demonstrate fECM on real fMRI data, first in healthy subjects scanned after real vs. sham repetitive transcranial magnetic stimulation (rTMS), an also in patients with multiple sclerosis (MS) patients compared to controls and in patients with Alzheimer's Disease (AD) compared to controls. Its computational speed and statistical sensitivity make fECM a good candidate for connectivity analyses of multi-modality and high-resolution functional neuroimaging data.