TY - GEN
T1 - Automated Parcellation of the Cortex Using Structural Connectome Harmonics
AU - Taylor IV, Hoyt Patrick
AU - Wu, Zhengwang
AU - Wu, Ye
AU - Shen, Dinggang
AU - Zhang, Han
AU - Yap, Pew Thian
N1 - Funding Information:
Acknowledgment. This work was supported in part by NIH grants (NS093842, EB022880, MH108914, AG042599, and AG041721).
Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - Meaningful division of the human cortex into distinct regions is a longstanding goal in neuroscience. Many of the most widely cited parcellations utilize anatomical priors or depend on functional magnetic resonance imaging (MRI) data while there exists a relative dearth of parcellations that use only structural data based on diffusion MRI. In light of this, and the fact that structural connectivity represents the underlying substrates of functional connectivity, we employ a novel high-resolution, vertex-level graph model of the whole-brain structural connectome and show that the harmonic modes of this graph can be used to achieve parcellations that qualitatively agree with the widely accepted atlases in the literature. Further, we detail a multi-layer formulation of the structural connectome graph and demonstrate that hierarchical clustering of its harmonic modes yields subject-specific parcellations at varying resolutions with ensured and tunable group-level correspondence.
AB - Meaningful division of the human cortex into distinct regions is a longstanding goal in neuroscience. Many of the most widely cited parcellations utilize anatomical priors or depend on functional magnetic resonance imaging (MRI) data while there exists a relative dearth of parcellations that use only structural data based on diffusion MRI. In light of this, and the fact that structural connectivity represents the underlying substrates of functional connectivity, we employ a novel high-resolution, vertex-level graph model of the whole-brain structural connectome and show that the harmonic modes of this graph can be used to achieve parcellations that qualitatively agree with the widely accepted atlases in the literature. Further, we detail a multi-layer formulation of the structural connectome graph and demonstrate that hierarchical clustering of its harmonic modes yields subject-specific parcellations at varying resolutions with ensured and tunable group-level correspondence.
UR - http://www.scopus.com/inward/record.url?scp=85075699977&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-32248-9_53
DO - 10.1007/978-3-030-32248-9_53
M3 - Conference contribution
AN - SCOPUS:85075699977
SN - 9783030322472
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 475
EP - 483
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings
A2 - Shen, Dinggang
A2 - Yap, Pew-Thian
A2 - Liu, Tianming
A2 - Peters, Terry M.
A2 - Khan, Ali
A2 - Staib, Lawrence H.
A2 - Essert, Caroline
A2 - Zhou, Sean
PB - Springer Science and Business Media Deutschland GmbH
T2 - 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
Y2 - 13 October 2019 through 17 October 2019
ER -