A connectivity-based method for defining regions-of-interest in fMRI data.
In this paper, we describe a new methodology for defining brain regions-of-interset (ROIs) in functional magnetic resonance imaging (fMRI) data. The ROIs are defined based on their functional connectivity to other ROIs, i.e., ROIs are defined as sets of voxels with similar connectivity patterns to other ROIs. The method relies on 1) a spatially regularized canonical correlation analysis for identifying maximally correlated signals, which are not due to correlated noise; 2) a test for merging ROIs which have similar connectivity patterns to the other ROIs; and 3) a graph-cuts optimization for assigning voxels to ROIs. Since our method is fully connectivity-based, the extracted ROIs and their corresponding time signals are ideally suited for a subsequent brain connectivity analysis.
Laboratorium Neuro- en Psychofysiologie, Campus Gasthuisberg, O&N 2, Herestraat 49, Bus 1021, 3000 Leuven, Belgium. filip.deleus@med.kuleuven.be
Filip
F
Deleus
Marc M
MM
Van Hulle
D000465:Algorithms; D000818:Animals; D001921:Brain / Q000502:physiology; D001931:Brain Mapping / Q000379:methods; D016000:Cluster Analysis; D000882:Haplorhini; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015233:Models, Statistical; D015999:Multivariate Analysis