TY - GEN
T1 - Identification of cortical landmarks based on consistent connectivity to subcortical structures
AU - Zhang, Degang
AU - Guo, Lei
AU - Zhu, Dajiang
AU - Zhang, Tuo
AU - Hu, Xintao
AU - Li, Kaiming
AU - Jiang, Xi
AU - Chen, Hanbo
AU - Lv, Jinglei
AU - Deng, Fan
AU - Zhao, Qun
PY - 2011
Y1 - 2011
N2 - Quantitative assessment of structural connectivities between cortical and subcortical regions has been of increasing interest in recent years. This paper proposes an algorithmic pipeline for identification of reliable cortical landmarks based on the consistent structural connectivity between cortical and subcortical regions. First, twelve subcortical regions are segmented from MRI data, and cortical surface and white matter fibers are reconstructed and tracked from magnetic resonance diffusion tensor imaging (DTI) data. Second, given that subcortical structures are relatively consistent across individual subjects, the structural connectivity from cortical to subcortical regions is extracted as the connectional attribute for each cortical region. Third, the cortex is segmented into different regions based on their cortico-subcortical connection attributes, and regions with the most consistent connectivity patterns across different subjects are selected as cortical landmarks. Experimental results from eight healthy subjects show that our approaches can identify 22 reliable cortical landmarks, a portion of which are validated via task-based fMRI data.
AB - Quantitative assessment of structural connectivities between cortical and subcortical regions has been of increasing interest in recent years. This paper proposes an algorithmic pipeline for identification of reliable cortical landmarks based on the consistent structural connectivity between cortical and subcortical regions. First, twelve subcortical regions are segmented from MRI data, and cortical surface and white matter fibers are reconstructed and tracked from magnetic resonance diffusion tensor imaging (DTI) data. Second, given that subcortical structures are relatively consistent across individual subjects, the structural connectivity from cortical to subcortical regions is extracted as the connectional attribute for each cortical region. Third, the cortex is segmented into different regions based on their cortico-subcortical connection attributes, and regions with the most consistent connectivity patterns across different subjects are selected as cortical landmarks. Experimental results from eight healthy subjects show that our approaches can identify 22 reliable cortical landmarks, a portion of which are validated via task-based fMRI data.
KW - connectivity pattern
KW - cortical parcellation
KW - subcortical regions
UR - http://www.scopus.com/inward/record.url?scp=80053541068&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-24446-9_9
DO - 10.1007/978-3-642-24446-9_9
M3 - 会议稿件
AN - SCOPUS:80053541068
SN - 9783642244452
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 68
EP - 75
BT - Multimodal Brain Image Analysis - First International Workshop, MBIA 2011, Held in Conjunction with MICCAI 2011, Proceedings
T2 - 1st International Workshop on Multimodal Brain Image Analysis, MBIA 2011, in Conjunction with the 14th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2011
Y2 - 18 September 2011 through 18 September 2011
ER -