Posted By: NITRC ADMIN - Sep 30, 2012
Tool/Resource: Neuroinformatics - The Journal
 

Abstract  
DICCCOL (Dense Individualized and Common Connectivity-based Cortical Landmarks) is a recently published system composed of 358 cortical landmarks that possess consistent correspondences across individuals and populations. Meanwhile, each DICCCOL landmark is localized in an individual brain’s unique morphological profile, and therefore the DICCCOL system offers a universal and individualized brain reference and localization framework. However, in current 358 diffusion tensor imaging (DTI)-derived DICCCOLs, only 95 of them have been functionally annotated via task-based or resting-state fMRI datasets and the functional roles of other DICCCOLs are unknown yet. This work aims to take the advantage of existing literature fMRI studies (1110 publications) reported and aggregated in the BrainMap database to examine the possible functional roles of 358 DICCCOLs via meta-analysis. Our experimental results demonstrate that a majority of 358 DICCCOLs can be functionally annotated by the BrainMap database, and many DICCCOLs have rich and diverse functional roles in multiple behavior domains. This study provides novel insights into the functional regularity and diversity of 358 DICCCOLs, and offers a starting point for future elucidation of fine-grained functional roles of cortical landmarks.

  • Content Type Journal Article
  • Category Original Article
  • Pages 1-17
  • DOI 10.1007/s12021-012-9165-y
  • Authors
    • Yixuan Yuan, School of Automation, Northwestern Polytechnical University, Xi’an, China
    • Xi Jiang, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Boyd GSRC 420, Athens, GA 30602, USA
    • Dajiang Zhu, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Boyd GSRC 420, Athens, GA 30602, USA
    • Hanbo Chen, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Boyd GSRC 420, Athens, GA 30602, USA
    • Kaiming Li, School of Automation, Northwestern Polytechnical University, Xi’an, China
    • Peili Lv, School of Automation, Northwestern Polytechnical University, Xi’an, China
    • Xiang Yu, School of Automation, Northwestern Polytechnical University, Xi’an, China
    • Xiaojin Li, School of Automation, Northwestern Polytechnical University, Xi’an, China
    • Shu Zhang, School of Automation, Northwestern Polytechnical University, Xi’an, China
    • Tuo Zhang, School of Automation, Northwestern Polytechnical University, Xi’an, China
    • Xintao Hu, School of Automation, Northwestern Polytechnical University, Xi’an, China
    • Junwei Han, School of Automation, Northwestern Polytechnical University, Xi’an, China
    • Lei Guo, School of Automation, Northwestern Polytechnical University, Xi’an, China
    • Tianming Liu, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Boyd GSRC 420, Athens, GA 30602, USA


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