title: Anatomy-guided Dense Individualized and Common Connectivity-based Cortical Landmarks (A-DICCCOL).
abstract: Establishment of structural and functional correspondences of human brain that can be quantitatively encoded and reproduced across different subjects and populations is one of the key issues in brain mapping. As an attempt to address this challenge, our recently developed Dense Individualized and Common Connectivity-based Cortical Landmarks (DICCCOL) system reported 358 connectional landmarks, each of which possesses consistent DTI-derived white matter fiber connection pattern that is reproducible in over 240 healthy brains. However, the DICCCOL system can be substantially improved by integrating anatomical and morphological information during landmark initialization and optimization procedures. In this paper, we present a novel anatomy-guided landmark discovery framework that defines and optimizes landmarks via integrating rich anatomical, morphological, and fiber connectional information for landmark initialization, group-wise optimization and prediction, which are formulated and solved as an energy minimization problem. The framework finally determined 555 consistent connectional landmarks. Validation studies demonstrated that the 555 landmarks are reproducible, predictable, and exhibited reasonably accurate anatomical, connectional, and functional correspondences across individuals and populations and thus are named anatomy-guided DICCCOL or A-DICCCOL. This A-DICCCOL system represents common cortical architectures with anatomical, connectional, and functional correspondences across different subjects and would potentially provide opportunities for various applications in brain science.
authors.forename: Xi
authors.initials: X
authors.lastname: Jiang
authors.forename: Tuo
authors.initials: T
authors.lastname: Zhang
authors.forename: Dajiang
authors.initials: D
authors.lastname: Zhu
authors.forename: Kaiming
authors.initials: K
authors.lastname: Li
authors.forename: Hanbo
authors.initials: H
authors.lastname: Chen
authors.forename: Jinglei
authors.initials: J
authors.lastname: Lv
authors.forename: Xintao
authors.initials: X
authors.lastname: Hu
authors.forename: Junwei
authors.initials: J
authors.lastname: Han
authors.forename: Dinggang
authors.initials: D
authors.lastname: Shen
authors.forename: Lei
authors.initials: L
authors.lastname: Guo
authors.forename: Tianming
authors.initials: T
authors.lastname: Liu
mesh_terms: D001921:Brain / Q000033:anatomy & histology*; D001931:Brain Mapping / Q000379:methods*; D016208:Databases, Factual; D056324:Diffusion Tensor Imaging / Q000379:methods; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods; D009415:Nerve Net / Q000033:anatomy & histology