Detailed Information

Cited 0 time in webofscience Cited 0 time in scopus
Metadata Downloads

Enhancing the representation of functional connectivity networks by fusing multi-view information for autism spectrum disorder diagnosis

Authors
Huang, HuifangLiu, XingdanJin, YanLee, Seong-WhanWee, Chong-YawShen, Dinggang
Issue Date
15-2월-2019
Publisher
WILEY
Keywords
computer-aided diagnosis; functional connectivity network; multi-kernel fusion; multi-view group-sparse network; multi-view learning; resting-state functional magnetic resonance imaging (R-fMRI)
Citation
HUMAN BRAIN MAPPING, v.40, no.3, pp.833 - 854
Indexed
SCIE
SCOPUS
Journal Title
HUMAN BRAIN MAPPING
Volume
40
Number
3
Start Page
833
End Page
854
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/67620
DOI
10.1002/hbm.24415
ISSN
1065-9471
Abstract
Functional connectivity network provides novel insights on how distributed brain regions are functionally integrated, and its deviations from healthy brain have recently been employed to identify biomarkers for neuropsychiatric disorders. However, most of brain network analysis methods utilized features extracted only from one functional connectivity network for brain disease detection and cannot provide a comprehensive representation on the subtle disruptions of brain functional organization induced by neuropsychiatric disorders. Inspired by the principles of multi-view learning which utilizes information from multiple views to enhance object representation, we propose a novel multiple network based framework to enhance the representation of functional connectivity networks by fusing the common and complementary information conveyed in multiple networks. Specifically, four functional connectivity networks corresponding to the four adjacent values of regularization parameter are generated via a sparse regression model with group constraint (l(2,1)-norm), to enhance the common intrinsic topological structure and limit the error rate caused by different views. To obtain a set of more meaningful and discriminative features, we propose using a modified version of weighted clustering coefficients to quantify the subtle differences of each group-sparse network at local level. We then linearly fuse the selected features from each individual network via a multi-kernel support vector machine for autism spectrum disorder (ASD) diagnosis. The proposed framework achieves an accuracy of 79.35%, outperforming all the compared single network methods for at least 7% improvement. Moreover, compared with other multiple network methods, our method also achieves the best performance, that is, with at least 11% improvement in accuracy.
Files in This Item
There are no files associated with this item.
Appears in
Collections
Graduate School > Department of Artificial Intelligence > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Researcher Lee, Seong Whan photo

Lee, Seong Whan
인공지능학과
Read more

Altmetrics

Total Views & Downloads

BROWSE