TY - GEN
T1 - Exploring fiber skeletons via joint representation of functional networks and structural connectivity
AU - Zhang, Shu
AU - Liu, Tianming
AU - Zhu, Dajiang
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2018.
PY - 2018
Y1 - 2018
N2 - Studying human brain connectome has been an important, yet challenging problem due to the intrinsic complexity of the brain function and structure. Many studies have been done to map the brain connectome, like Human Connectome Project (HCP). However, multi-modality (DTI and fMRI) brain connectome analysis is still under-studied. One challenge is the lack of a framework to efficiently link different modalities together. In this paper, we integrate two research efforts including sparse dictionary learning derived functional networks and structural connectivity into a joint representation of brain connectome. This joint representation then guided the identification of the main skeletons of whole-brain fiber connections, which contributes to a better understanding of brain architecture of structural connectome and its local pathways. We applied our framework on the HCP multimodal DTI/fMRI data and successfully constructed the main skeleton of whole-brain fiber connections. We identified 14 local fiber skeletons that are functionally and structurally consistent across individual brains.
AB - Studying human brain connectome has been an important, yet challenging problem due to the intrinsic complexity of the brain function and structure. Many studies have been done to map the brain connectome, like Human Connectome Project (HCP). However, multi-modality (DTI and fMRI) brain connectome analysis is still under-studied. One challenge is the lack of a framework to efficiently link different modalities together. In this paper, we integrate two research efforts including sparse dictionary learning derived functional networks and structural connectivity into a joint representation of brain connectome. This joint representation then guided the identification of the main skeletons of whole-brain fiber connections, which contributes to a better understanding of brain architecture of structural connectome and its local pathways. We applied our framework on the HCP multimodal DTI/fMRI data and successfully constructed the main skeleton of whole-brain fiber connections. We identified 14 local fiber skeletons that are functionally and structurally consistent across individual brains.
KW - Connectome
KW - Functional networks
KW - Joint representation
KW - Structural connectivity
UR - http://www.scopus.com/inward/record.url?scp=85053865385&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-00931-1_41
DO - 10.1007/978-3-030-00931-1_41
M3 - 会议稿件
AN - SCOPUS:85053865385
SN - 9783030009304
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 357
EP - 366
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings
A2 - Frangi, Alejandro F.
A2 - Davatzikos, Christos
A2 - Fichtinger, Gabor
A2 - Alberola-López, Carlos
A2 - Schnabel, Julia A.
PB - Springer Verlag
T2 - 21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018
Y2 - 16 September 2018 through 20 September 2018
ER -