Abstract
As a simple representation of interactions among distributed brain regions, brain networks have been widely applied to automated diagnosis of brain diseases, such as Alzheimer’s disease (AD) and its early stage, i.e., mild cognitive impairment (MCI). In brain network analysis, a challenging task is how to measure the similarity between a pair of networks. Although many graph kernels (i.e., kernels defined on graphs) have been proposed for measuring the topological similarity of a pair of brain networks, most of them are defined using general graphs, thus ignoring the uniqueness of each node in brain networks. That is, each node in a brain network denotes a particular brain region, which is a specific characteristics of brain networks. Accordingly, in this paper, we construct a novel sub-network kernel for measuring the similarity between a pair of brain networks and then apply it to brain disease classification. Different from current graph kernels, our proposed sub-network kernel not only takes into account the inherent characteristic of brain networks, but also captures multi-level (from local to global) topological properties of nodes in brain networks, which are essential for defining the similarity measure of brain networks. To validate the efficacy of our method, we perform extensive experiments on subjects with baseline functional magnetic resonance imaging (fMRI) data obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Experimental results demonstrate that the proposed method outperforms several stateof- the-art graph-based methods in MCI classification.
| Original language | English |
|---|---|
| Journal | IEEE Transactions on Image Processing |
| DOIs | |
| Publication status | Accepted/In press - 2018 Jan 29 |
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Keywords
- Alzheimer’s disease (AD)
- Brain
- brain network
- classification
- Dementia
- Functional magnetic resonance imaging
- Graph kernel
- Graph theory
- Kernel
- Length measurement
- mild cognitive impairment (MCI)
ASJC Scopus subject areas
- Software
- Computer Graphics and Computer-Aided Design
Cite this
Sub-network Kernels for Measuring Similarity of Brain Connectivity Networks in Disease Diagnosis. / Jie, Biao; Liu, Mingxia; Zhang, Daoqiang; Shen, Dinggang.
In: IEEE Transactions on Image Processing, 29.01.2018.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Sub-network Kernels for Measuring Similarity of Brain Connectivity Networks in Disease Diagnosis
AU - Jie, Biao
AU - Liu, Mingxia
AU - Zhang, Daoqiang
AU - Shen, Dinggang
PY - 2018/1/29
Y1 - 2018/1/29
N2 - As a simple representation of interactions among distributed brain regions, brain networks have been widely applied to automated diagnosis of brain diseases, such as Alzheimer’s disease (AD) and its early stage, i.e., mild cognitive impairment (MCI). In brain network analysis, a challenging task is how to measure the similarity between a pair of networks. Although many graph kernels (i.e., kernels defined on graphs) have been proposed for measuring the topological similarity of a pair of brain networks, most of them are defined using general graphs, thus ignoring the uniqueness of each node in brain networks. That is, each node in a brain network denotes a particular brain region, which is a specific characteristics of brain networks. Accordingly, in this paper, we construct a novel sub-network kernel for measuring the similarity between a pair of brain networks and then apply it to brain disease classification. Different from current graph kernels, our proposed sub-network kernel not only takes into account the inherent characteristic of brain networks, but also captures multi-level (from local to global) topological properties of nodes in brain networks, which are essential for defining the similarity measure of brain networks. To validate the efficacy of our method, we perform extensive experiments on subjects with baseline functional magnetic resonance imaging (fMRI) data obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Experimental results demonstrate that the proposed method outperforms several stateof- the-art graph-based methods in MCI classification.
AB - As a simple representation of interactions among distributed brain regions, brain networks have been widely applied to automated diagnosis of brain diseases, such as Alzheimer’s disease (AD) and its early stage, i.e., mild cognitive impairment (MCI). In brain network analysis, a challenging task is how to measure the similarity between a pair of networks. Although many graph kernels (i.e., kernels defined on graphs) have been proposed for measuring the topological similarity of a pair of brain networks, most of them are defined using general graphs, thus ignoring the uniqueness of each node in brain networks. That is, each node in a brain network denotes a particular brain region, which is a specific characteristics of brain networks. Accordingly, in this paper, we construct a novel sub-network kernel for measuring the similarity between a pair of brain networks and then apply it to brain disease classification. Different from current graph kernels, our proposed sub-network kernel not only takes into account the inherent characteristic of brain networks, but also captures multi-level (from local to global) topological properties of nodes in brain networks, which are essential for defining the similarity measure of brain networks. To validate the efficacy of our method, we perform extensive experiments on subjects with baseline functional magnetic resonance imaging (fMRI) data obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Experimental results demonstrate that the proposed method outperforms several stateof- the-art graph-based methods in MCI classification.
KW - Alzheimer’s disease (AD)
KW - Brain
KW - brain network
KW - classification
KW - Dementia
KW - Functional magnetic resonance imaging
KW - Graph kernel
KW - Graph theory
KW - Kernel
KW - Length measurement
KW - mild cognitive impairment (MCI)
UR - http://www.scopus.com/inward/record.url?scp=85041391129&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85041391129&partnerID=8YFLogxK
U2 - 10.1109/TIP.2018.2799706
DO - 10.1109/TIP.2018.2799706
M3 - Article
C2 - 29470170
AN - SCOPUS:85041391129
JO - IEEE Transactions on Image Processing
JF - IEEE Transactions on Image Processing
SN - 1057-7149
ER -