title: Automated iterative reclustering framework for determining hierarchical functional networks in resting state fMRI.
abstract: To spatially cluster resting state-functional magnetic resonance imaging (rs-fMRI) data into potential networks, there are only a few general approaches that determine the number of networks/clusters, despite a wide variety of techniques proposed for clustering. For individual subjects, extraction of a large number of spatially disjoint clusters results in multiple small networks that are spatio-temporally homogeneous but irreproducible across subjects. Alternatively, extraction of a small number of clusters creates spatially large networks that are temporally heterogeneous but spatially reproducible across subjects. We propose a fully automatic, iterative reclustering framework in which a small number of spatially large, heterogeneous networks are initially extracted to maximize spatial reproducibility. Subsequently, the large networks are iteratively subdivided to create spatially reproducible subnetworks until the overall within-network homogeneity does not increase substantially. The proposed approach discovers a rich network hierarchy in the brain while simultaneously optimizing spatial reproducibility of networks across subjects and individual network homogeneity. We also propose a novel metric to measure the connectivity of brain regions, and in a simulation study show that our connectivity metric and framework perform well in the face of low signal to noise and initial segmentation errors. Experimental results generated using real fMRI data show that the proposed metric improves stability of network clusters across subjects, and generates a meaningful pattern for spatially hierarchical structure of the brain.
authors.affiliation: Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.|Rotman Research Institute, Baycrest, Toronto, Ontario, Canada.
authors.forename: Seyed-Mohammad
authors.initials: SM
authors.lastname: Shams
authors.affiliation: Rotman Research Institute, Baycrest, Toronto, Ontario, Canada.
authors.forename: Babak
authors.initials: B
authors.lastname: Afshin-Pour
authors.affiliation: Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.|School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran.|Department of Radiology, Image Analysis Laboratory, Henry Ford Hospital, Detroit, Michigan.
authors.forename: Hamid
authors.initials: H
authors.lastname: Soltanian-Zadeh
authors.affiliation: Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.|School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran.
authors.forename: Gholam-Ali
authors.initials: GA
authors.lastname: Hossein-Zadeh
authors.affiliation: Rotman Research Institute, Baycrest, Toronto, Ontario, Canada.|Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.
authors.forename: Stephen C
authors.initials: SC
authors.lastname: Strother
mesh_terms: D000328:Adult; D000465:Algorithms; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D016000:Cluster Analysis; D003198:Computer Simulation; D066264:Datasets as Topic; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008959:Models, Neurological; D009434:Neural Pathways / Q000502:physiology; D010363:Pattern Recognition, Automated / Q000379:methods*; D012146:Rest; D012815:Signal Processing, Computer-Assisted; D055815:Young Adult
keywords: functional networks; hierarchical networks; iterative reclustering; resting state fMRI