title: STRUCTURAL CONNECTIVITY VIA THE TENSOR-BASED MORPHOMETRY.
abstract: The tensor-based morphometry (TBM) has been widely used in characterizing tissue volume difference between populations at voxel level. We present a novel computational framework for investigating the white matter connectivity using TBM. Unlike other diffusion tensor imaging (DTI) based white matter connectivity studies, we do not use DTI but only T1-weighted magnetic resonance imaging (MRI). To construct brain network graphs, we have developed a new data-driven approach called theε-neighbor method that does not need any predetermined parcellation. The proposed pipeline is applied in detecting the topological alteration of the white matter connectivity in maltreated children.
authors.affiliation: Department of Brain and Cognitive Sciences, Seoul National University, Korea.
authors.forename: Seung-Goo
authors.initials: SG
authors.lastname: Kim
authors.forename: Moo K
authors.initials: MK
authors.lastname: Chung
authors.forename: Jamie L
authors.initials: JL
authors.lastname: Hanson
authors.forename: Brian B
authors.initials: BB
authors.lastname: Avants
authors.forename: James C
authors.initials: JC
authors.lastname: Gee
authors.forename: Richard J
authors.initials: RJ
authors.lastname: Davidson
authors.forename: Seth D
authors.initials: SD
authors.lastname: Pollak
keywords: Jacobian determinant; brain network; maltreatment; structural connectivity; tensor-based morphometry