title: Connectivity-Based Brain Parcellation: A Connectivity-Based Atlas for Schizophrenia Research.
abstract: Defining brain structures of interest is an important preliminary step in brain-connectivity analysis. Researchers interested in connectivity patterns among brain structures typically employ manually delineated volumes of interest, or regions in a readily available atlas, to limit the scope of connectivity analysis to relevant regions. However, most structural brain atlases, and manually delineated volumes of interest, do not take voxel-wise connectivity patterns into consideration, and therefore may not be ideal for anatomic connectivity analysis. We herein propose a method to parcellate the brain into regions of interest based on connectivity. We formulate connectivity-based parcellation as a graph-cut problem, which we solve approximately using a novel multi-class Hopfield network algorithm. We demonstrate the application of this approach using diffusion tensor imaging data from an ongoing study of schizophrenia. Compared to a standard anatomic atlas, the connectivity-based atlas supports better classification performance when distinguishing schizophrenic from normal subjects. Comparing connectivity patterns averaged across the normal and schizophrenic subjects, we note significant systematic differences between the two atlases.
authors.affiliation: Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, 20742, USA. qiwang321@gmail.com.
authors.forename: Qi
authors.identifier: http://orcid.org/0000-0003-2142-3195
authors.initials: Q
authors.lastname: Wang
authors.affiliation: Department of Radiology, University of Maryland, 22 S Greene St, Baltimore, MD, 21201, USA.
authors.forename: Rong
authors.initials: R
authors.lastname: Chen
authors.affiliation: Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, 20742, USA. joseph@umiacs.umd.edu.
authors.forename: Joseph
authors.initials: J
authors.lastname: JaJa
authors.affiliation: Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, 20742, USA. yuj@umd.edu.
authors.forename: Yu
authors.initials: Y
authors.lastname: Jin
authors.affiliation: Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland, 55 Wade Avenue, Baltimore, MD, 21228, USA.
authors.forename: L Elliot
authors.initials: LE
authors.lastname: Hong
authors.affiliation: Department of Radiology, University of Maryland, 22 S Greene St, Baltimore, MD, 21201, USA.
authors.forename: Edward H
authors.initials: EH
authors.lastname: Herskovits
mesh_terms: D000465:Algorithms; D001271:Atlases as Topic; D001921:Brain / Q000473:pathology*; D056324:Diffusion Tensor Imaging / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008959:Models, Neurological; D016571:Neural Networks, Computer; D009434:Neural Pathways / Q000473:pathology; D012559:Schizophrenia / Q000473:pathology*; D012815:Signal Processing, Computer-Assisted
keywords: Connectivity-based parcellation; Diffusion tensor imaging; Graph-cut; Multi-class Hopfield network; Probabilistic tractography; Schizophrenia