DICCCOL : Dense individualized and common connectivity-based cortical landmarks

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

105 Scopus Citations
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Author(s)

  • Dajiang Zhu
  • Kaiming Li
  • Lei Guo
  • Xi Jiang
  • Tuo Zhang
  • Degang Zhang
  • Hanbo Chen
  • Fan Deng
  • Carlos Faraco
  • Changfeng Jin
  • Chong-Yaw Wee
  • Peili Lv
  • Yan Yin
  • Xiaolei Hu
  • Lian Duan
  • Xintao Hu
  • Junwei Han
  • Lihong Wang
  • Dinggang Shen
  • L. Stephen Miller
  • Lingjiang Li
  • Tianming Liu

Detail(s)

Original languageEnglish
Pages (from-to)786-800
Journal / PublicationCerebral Cortex
Volume23
Issue number4
Online published5 Apr 2012
Publication statusPublished - 1 Apr 2013
Externally publishedYes

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.

Research Area(s)

  • cortical architecture, cortical landmark, diffusion tensor imaging, fMRI

Citation Format(s)

DICCCOL : Dense individualized and common connectivity-based cortical landmarks. / Zhu, Dajiang; Li, Kaiming; Guo, Lei et al.

In: Cerebral Cortex, Vol. 23, No. 4, 01.04.2013, p. 786-800.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review