Connectivity strength-weighted sparse group representation-based brain network construction for MCI classification.
Brain functional network analysis has shown great potential in understanding brain functions and also in identifying biomarkers for brain diseases, such as Alzheimer's disease (AD) and its early stage, mild cognitive impairment (MCI). In these applications, accurate construction of biologically meaningful brain network is critical. Sparse learning has been widely used for brain network construction; however, its l1-norm penalty simply penalizes each edge of a brain network equally, without considering the original connectivity strength which is one of the most important inherent linkwise characters. Besides, based on the similarity of the linkwise connectivity, brain network shows prominent group structure (i.e., a set of edges sharing similar attributes). In this article, we propose a novel brain functional network modeling framework with a "connectivity strength-weighted sparse group constraint." In particular, the network modeling can be optimized by considering both raw connectivity strength and its group structure, without losing the merit of sparsity. Our proposed method is applied to MCI classification, a challenging task for early AD diagnosis. Experimental results based on the resting-state functional MRI, from 50 MCI patients and 49 healthy controls, show that our proposed method is more effective (i.e., achieving a significantly higher classification accuracy, 84.8%) than other competing methods (e.g., sparse representation, accuracy = 65.6%). Post hoc inspection of the informative features further shows more biologically meaningful brain functional connectivities obtained by our proposed method. Hum Brain Mapp 38:2370-2383, 2017. © 2017 Wiley Periodicals, Inc.
School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China.|IDEA Lab, Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
Renping
0000-0002-1348-5583
R
Yu
IDEA Lab, Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
Han
H
Zhang
IDEA Lab, Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
Le
L
An
IDEA Lab, Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
Xiaobo
X
Chen
School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China.
Zhihui
Z
Wei
IDEA Lab, Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.|Department of Brain and Cognitive Engineering, Korea University, Seoul, 02841, Republic of Korea.
Dinggang
D
Shen
D000465:Algorithms; D001921:Brain / Q000000981:diagnostic imaging / Q000503:physiopathology*; D001931:Brain Mapping; D060825:Cognitive Dysfunction / Q000145:classification / Q000473:pathology / Q000503:physiopathology; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008297:Male; D008959:Models, Neurological; D009434:Neural Pathways / Q000000981:diagnostic imaging / Q000503:physiopathology*; D010100:Oxygen / Q000097:blood; D012372:ROC Curve
D010100:Oxygen
brain network; disease classification; functional connectivity; mild cognitive impairment (MCI); sparse representation