Paper Info
Title
Exploring Fiber Skeletons via Joint Representation of Functional Networks and Structural Connectivity.
Abstract
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.
Year
DOI
Venue
2018
10.1007/978-3-030-00931-1_41
Lecture Notes in Computer Science
Keywords
Field
DocType
Structural connectivity,Functional networks,Joint representation,Connectome
Modalities,Human Connectome Project,Dictionary learning,Pattern recognition,Computer science,Connectome,Functional networks,Human brain,Artificial intelligence
Conference
Volume
ISSN
Citations 
11072
0302-9743
0
PageRank 
References 
Authors
0.34
9
3
Name
Order
Citations
PageRank
Shu Zhang112613.64
Tianming Liu21033112.95
Dajiang Zhu332036.72