title: Discovering dense and consistent landmarks in the brain.
abstract: The lack of consistent and reliable functionally meaningful landmarks in the brain has significantly hampered the advancement of brain imaging studies. In this paper, we use white matter fiber connectivity patterns, obtained from diffusion tensor imaging (DTI) data, as predictors of brain function, and to discover a dense, reliable and consistent map of brain landmarks within and across individuals. The general principles and our strategies are as follows. 1) Each brain landmark should have consistent structural fiber connectivity pattern across a group of subjects. We will quantitatively measure the similarity of the fiber bundles emanating from the corresponding landmarks via a novel trace-map approach, and then optimize the locations of these landmarks by maximizing the group-wise consistency of the shape patterns of emanating fiber bundles. 2) The landmark map should be dense and distributed all over major functional brain regions. We will initialize a dense and regular grid map of approximately 2000 landmarks that cover the whole brains in different subjects via linear brain image registration. 3) The dense map of brain landmarks should be reproducible and predictable in different datasets of various subject populations. The approaches and results in the above two steps are evaluated and validated via reproducibility studies. The dense map of brain landmarks can be reliably and accurately replicated in a new DTI dataset such that the landmark map can be used as a predictive model. Our experiments show promising results, and a subset of the discovered landmarks are validated via task-based fMRI.
authors.affiliation: University of Georgia, Dept. of Computer Science, 415 Boyd GSRC Building, Athens, GA, USA. dajiang.zhu@gmail.com
authors.forename: Dajiang
authors.initials: D
authors.lastname: Zhu
authors.forename: Degang
authors.initials: D
authors.lastname: Zhang
authors.forename: Carlos
authors.initials: C
authors.lastname: Faraco
authors.forename: Kaiming
authors.initials: K
authors.lastname: Li
authors.forename: Fan
authors.initials: F
authors.lastname: Deng
authors.forename: Hanbo
authors.initials: H
authors.lastname: Chen
authors.forename: Xi
authors.initials: X
authors.lastname: Jiang
authors.forename: Lei
authors.initials: L
authors.lastname: Guo
authors.forename: L Stephen
authors.initials: LS
authors.lastname: Miller
authors.forename: Tianming
authors.initials: T
authors.lastname: Liu
mesh_terms: D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology*; D003198:Computer Simulation; D056324:Diffusion Tensor Imaging / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008954:Models, Biological; D015233:Models, Statistical; D009413:Nerve Fibers, Myelinated / Q000648:ultrastructure*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity