title: DICCCOL: dense individualized and common connectivity-based cortical landmarks.
abstract: Is there a common structural and functional cortical architecture that can be quantitatively encoded and precisely reproduced across individuals and populations? This question is still largely unanswered due to the vast complexity, variability, and nonlinearity of the cerebral cortex. Here, we hypothesize that the common cortical architecture can be effectively represented by group-wise consistent structural fiber connections and take a novel data-driven approach to explore the cortical architecture. We report a dense and consistent map of 358 cortical landmarks, named Dense Individualized and Common Connectivity-based Cortical Landmarks (DICCCOLs). Each DICCCOL is defined by group-wise consistent white-matter fiber connection patterns derived from diffusion tensor imaging (DTI) data. Our results have shown that these 358 landmarks are remarkably reproducible over more than one hundred human brains and possess accurate intrinsically established structural and functional cross-subject correspondences validated by large-scale functional magnetic resonance imaging data. In particular, these 358 cortical landmarks can be accurately and efficiently predicted in a new single brain with DTI data. Thus, this set of 358 DICCCOL landmarks comprehensively encodes the common structural and functional cortical architectures, providing opportunities for many applications in brain science including mapping human brain connectomes, as demonstrated in this work.
authors.affiliation: Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA 30602, USA.
authors.forename: Dajiang
authors.initials: D
authors.lastname: Zhu
authors.forename: Kaiming
authors.initials: K
authors.lastname: Li
authors.forename: Lei
authors.initials: L
authors.lastname: Guo
authors.forename: Xi
authors.initials: X
authors.lastname: Jiang
authors.forename: Tuo
authors.initials: T
authors.lastname: Zhang
authors.forename: Degang
authors.initials: D
authors.lastname: Zhang
authors.forename: Hanbo
authors.initials: H
authors.lastname: Chen
authors.forename: Fan
authors.initials: F
authors.lastname: Deng
authors.forename: Carlos
authors.initials: C
authors.lastname: Faraco
authors.forename: Changfeng
authors.initials: C
authors.lastname: Jin
authors.forename: Chong-Yaw
authors.initials: CY
authors.lastname: Wee
authors.forename: Yixuan
authors.initials: Y
authors.lastname: Yuan
authors.forename: Peili
authors.initials: P
authors.lastname: Lv
authors.forename: Yan
authors.initials: Y
authors.lastname: Yin
authors.forename: Xiaolei
authors.initials: X
authors.lastname: Hu
authors.forename: Lian
authors.initials: L
authors.lastname: Duan
authors.forename: Xintao
authors.initials: X
authors.lastname: Hu
authors.forename: Junwei
authors.initials: J
authors.lastname: Han
authors.forename: Lihong
authors.initials: L
authors.lastname: Wang
authors.forename: Dinggang
authors.initials: D
authors.lastname: Shen
authors.forename: L Stephen
authors.initials: LS
authors.lastname: Miller
authors.forename: Lingjiang
authors.initials: L
authors.lastname: Li
authors.forename: Tianming
authors.initials: T
authors.lastname: Liu
mesh_terms: D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000368:Aged; D000465:Algorithms; D001288:Attention / Q000502:physiology; D001931:Brain Mapping; D002540:Cerebral Cortex / Q000033:anatomy & histology / Q000098:blood supply / Q000502:physiology*; D038524:Diffusion Magnetic Resonance Imaging; D004644:Emotions / Q000502:physiology; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008297:Male; D009413:Nerve Fibers, Myelinated / Q000502:physiology*; D009434:Neural Pathways / Q000502:physiology*; D012660:Semantics