Paper Info
Title
Identifying Autism Biomarkers In Default Mode Network Using Sparse Representation Of Resting-State Fmri Data
Abstract
Exploring abnormal functional connectivities in neurodevelopmental disorders have received great attention in recent years. However, identifying functionally homogeneous brain regions as nodes in functional brain connectivity analysis is still challenging. In this paper, we adopt an effective data-driven framework of sparse representation to identify brain network nodes inspired by the nature of sparse population coding of the human brain. Using the default mode network (DMN) in patients with Autism as a test-bed, we evaluate sparse coding method and compared it with widely used group-wise independent components analysis (group-ICA). The experimental results demonstrate that the network nodes identified by sparse representation are more functionally homogeneous, which may explain the superiority of sparse representation in differentiating Autism and healthy controls by brain connectivity biomarkers.
Year
DOI
Venue
2016
10.1109/ISBI.2016.7493500
2016 IEEE 13TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI)
Keywords
Field
DocType
resting-state fMRI, default mode network, functional connectivity, sparse coding, autism
Autism,Default mode network,Pattern recognition,Neural coding,Computer science,Sparse approximation,Resting state fMRI,Node (networking),Human brain,Independent component analysis,Artificial intelligence
Conference
ISSN
Citations 
PageRank 
1945-7928
1
0.35
References 
Authors
6
6
Name
Order
Citations
PageRank
Yudan Ren110.35
Xintao Hu211813.53
Jinglei Lv320526.70
Lei Quo410.69
Junwei Han53501194.57
Tianming Liu61033112.95