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IPM
30
YEARS OLD

“School of Cognitive Sciences”

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Paper   IPM / Cognitive Sciences / 14224
School of Cognitive Sciences
  Title:   Automated iterative reclustering framework for determining hierarchical functional networks in resting state fMRI
  Author(s): 
1.  SM. Shams
2.  B. Afshin-Pour
3.  H. Soltanian-Zadeh
4.  GA. Hossein-Zadeh
5.  S. Strother
  Status:   Published
  Journal: Human Brain Mapping
  Vol.:  36
  Year:  2015
  Pages:   3303-3322
  Supported by:  IPM
  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.

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