TY - GEN
T1 - Graph-Kernel-Based Multi-task Structured Feature Selection on Multi-level Functional Connectivity Networks for Brain Disease Classification
AU - Wang, Zhengdong
AU - Jie, Biao
AU - Wang, Mi
AU - Feng, Chunxiang
AU - Zhou, Wen
AU - Shen, Dinggang
AU - Liu, Mingxia
N1 - Funding Information:
Acknowledgment. This study was supported by NSFC (Nos. 61573023, 61976006, 61703301, and 61902003), Anhui-NSFC (Nos. 1708085MF145 and 1808085MF171), and AHNU-FOYHE (No. xyqZD2017010).
Funding Information:
This study was supported by NSFC (Nos. 61573023, 61976006, 61703301, and 61902003), Anhui-NSFC (Nos. 1708085MF145 and 1808085MF171), and AHNU-FOYHE (No. xyqZD2017010).
Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - Function connectivity networks (FCNs) based on resting-state functional magnetic resonance imaging (rs-fMRI) have been used for analysis of brain diseases, such as Alzheimer’s disease (AD) and Attention Deficit Hyperactivity Disorder (ADHD). However, existing studies usually extract meaningful measures (e.g., local clustering coefficients) from FCNs as a feature vector for brain disease classification, and perform vector-based feature selection methods (e.g., t-test) to improve the performance of learning model, thus ignoring important structural information of FCNs. To address this problem, we propose a graph-kernel-based structured feature selection (gk-MTSFS) method for brain disease classification using rs-fMRI data. Different with existing method that focus on vector-based feature selection, our proposed gk-MTSFS method adopts the graph kernel (i.e., kernel constructed on graphs) to preserve the structural information of FCNs, and uses the multi-task learning to explore the complementary information of multi-level thresholded FCNs (i.e., thresholded FCNs with different thresholds). Specifically, in the proposed gk-MTSFS model, we first develop a novel graph-kernel based Laplacian regularizer to preserve the structural information of FCNs. Then, we employ an 2,1-norm based group sparsity regularizer to joint select a small amount of discriminative features from multi-level FCNs for brain disease classification. Experimental results on both ADNI and ADHD-200 datasets with rs-fMRI data demonstrate the effectiveness of our proposed gk-MTSFS method in rs-fMRI-based brain disease diagnosis.
AB - Function connectivity networks (FCNs) based on resting-state functional magnetic resonance imaging (rs-fMRI) have been used for analysis of brain diseases, such as Alzheimer’s disease (AD) and Attention Deficit Hyperactivity Disorder (ADHD). However, existing studies usually extract meaningful measures (e.g., local clustering coefficients) from FCNs as a feature vector for brain disease classification, and perform vector-based feature selection methods (e.g., t-test) to improve the performance of learning model, thus ignoring important structural information of FCNs. To address this problem, we propose a graph-kernel-based structured feature selection (gk-MTSFS) method for brain disease classification using rs-fMRI data. Different with existing method that focus on vector-based feature selection, our proposed gk-MTSFS method adopts the graph kernel (i.e., kernel constructed on graphs) to preserve the structural information of FCNs, and uses the multi-task learning to explore the complementary information of multi-level thresholded FCNs (i.e., thresholded FCNs with different thresholds). Specifically, in the proposed gk-MTSFS model, we first develop a novel graph-kernel based Laplacian regularizer to preserve the structural information of FCNs. Then, we employ an 2,1-norm based group sparsity regularizer to joint select a small amount of discriminative features from multi-level FCNs for brain disease classification. Experimental results on both ADNI and ADHD-200 datasets with rs-fMRI data demonstrate the effectiveness of our proposed gk-MTSFS method in rs-fMRI-based brain disease diagnosis.
UR - http://www.scopus.com/inward/record.url?scp=85076322122&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-35817-4_4
DO - 10.1007/978-3-030-35817-4_4
M3 - Conference contribution
AN - SCOPUS:85076322122
SN - 9783030358167
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 27
EP - 35
BT - Graph Learning in Medical Imaging - 1st International Workshop, GLMI 2019, held in Conjunction with MICCAI 2019, Proceedings
A2 - Zhang, Daoqiang
A2 - Zhou, Luping
A2 - Jie, Biao
A2 - Liu, Mingxia
PB - Springer
T2 - 1st International Workshop on Graph Learning in Medical Imaging, GLMI 2019 held in conjunction with the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
Y2 - 17 October 2019 through 17 October 2019
ER -