TY - JOUR
T1 - Hyper-connectivity of functional networks for brain disease diagnosis
AU - Jie, Biao
AU - Wee, Chong Yaw
AU - Shen, Dinggang
AU - Zhang, Daoqiang
N1 - Funding Information:
The author acknowledges support from National Natural Science Foundation of China (Nos. 61573023 , 61422204 , 61473149 , 61473190 ), Natural Science Foundation of Anhui Province (No. 1508085MF125), the Jiangsu Natural Science Foundation for Distinguished Young Scholar (No. BK20130034), the Open Projects Program of National Laboratory of Pattern Recognition (No. 201407361), the Specialized Research Fund for the Doctoral Program of Higher Education (No. 20123218110009), the NUAA Fundamental Research Funds (No. NE2013105), and NIH grants (EB006733, EB008374, EB009634, MH100217, AG041721, AG049371, and AG042599).
PY - 2016/8/1
Y1 - 2016/8/1
N2 - Exploring structural and functional interactions among various brain regions enables better understanding of pathological underpinnings of neurological disorders. Brain connectivity network, as a simplified representation of those structural and functional interactions, has been widely used for diagnosis and classification of neurodegenerative diseases, especially for Alzheimer's disease (AD) and its early stage - mild cognitive impairment (MCI). However, the conventional functional connectivity network is usually constructed based on the pairwise correlation among different brain regions and thus ignores their higher-order relationships. Such loss of high-order information could be important for disease diagnosis, since neurologically a brain region predominantly interacts with more than one other brain regions. Accordingly, in this paper, we propose a novel framework for estimating the hyper-connectivity network of brain functions and then use this hyper-network for brain disease diagnosis. Here, the functional connectivity hyper-network denotes a network where each of its edges representing the interactions among multiple brain regions (i.e., an edge can connect with more than two brain regions), which can be naturally represented by a hyper-graph. Specifically, we first construct connectivity hyper-networks from the resting-state fMRI (R-fMRI) time series by using sparse representation. Then, we extract three sets of brain-region specific features from the connectivity hyper-networks, and further exploit a manifold regularized multi-task feature selection method to jointly select the most discriminative features. Finally, we use multi-kernel support vector machine (SVM) for classification. The experimental results on both MCI dataset and attention deficit hyperactivity disorder (ADHD) dataset demonstrate that, compared with the conventional connectivity network-based methods, the proposed method can not only improve the classification performance, but also help discover disease-related biomarkers important for disease diagnosis.
AB - Exploring structural and functional interactions among various brain regions enables better understanding of pathological underpinnings of neurological disorders. Brain connectivity network, as a simplified representation of those structural and functional interactions, has been widely used for diagnosis and classification of neurodegenerative diseases, especially for Alzheimer's disease (AD) and its early stage - mild cognitive impairment (MCI). However, the conventional functional connectivity network is usually constructed based on the pairwise correlation among different brain regions and thus ignores their higher-order relationships. Such loss of high-order information could be important for disease diagnosis, since neurologically a brain region predominantly interacts with more than one other brain regions. Accordingly, in this paper, we propose a novel framework for estimating the hyper-connectivity network of brain functions and then use this hyper-network for brain disease diagnosis. Here, the functional connectivity hyper-network denotes a network where each of its edges representing the interactions among multiple brain regions (i.e., an edge can connect with more than two brain regions), which can be naturally represented by a hyper-graph. Specifically, we first construct connectivity hyper-networks from the resting-state fMRI (R-fMRI) time series by using sparse representation. Then, we extract three sets of brain-region specific features from the connectivity hyper-networks, and further exploit a manifold regularized multi-task feature selection method to jointly select the most discriminative features. Finally, we use multi-kernel support vector machine (SVM) for classification. The experimental results on both MCI dataset and attention deficit hyperactivity disorder (ADHD) dataset demonstrate that, compared with the conventional connectivity network-based methods, the proposed method can not only improve the classification performance, but also help discover disease-related biomarkers important for disease diagnosis.
KW - Alzheimer's disease
KW - Classification
KW - Functional MR imaging
KW - Hyper-network
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U2 - 10.1016/j.media.2016.03.003
DO - 10.1016/j.media.2016.03.003
M3 - Article
C2 - 27060621
AN - SCOPUS:84962434232
VL - 32
SP - 84
EP - 100
JO - Medical Image Analysis
JF - Medical Image Analysis
SN - 1361-8415
ER -