Integration of network topological and connectivity properties for neuroimaging classification

Biao Jie, Daoqiang Zhang, Wei Gao, Qian Wang, Chong Yaw Wee, Dinggang Shen

Research output: Contribution to journalArticlepeer-review

114 Citations (Scopus)


Rapid advances in neuroimaging techniques have provided an efficient and noninvasive way for exploring the structural and functional connectivity of the human brain. Quantitative measurement of abnormality of brain connectivity in patients with neurodegenerative diseases, such as mild cognitive impairment (MCI) and Alzheimer's disease (AD), have also been widely reported, especially at a group level. Recently, machine learning techniques have been applied to the study of AD and MCI, i.e., to identify the individuals with AD/MCI from the healthy controls (HCs). However, most existing methods focus on using only a single property of a connectivity network, although multiple network properties, such as local connectivity and global topological properties, can potentially be used. In this paper, by employing multikernel based approach, we propose a novel connectivity based framework to integrate multiple properties of connectivity network for improving the classification performance. Specifically, two different types of kernels (i.e., vector-based kernel and graph kernel) are used to quantify two different yet complementary properties of the network, i.e., local connectivity and global topological properties. Then, multikernel learning (MKL) technique is adopted to fuse these heterogeneous kernels for neuroimaging classification. We test the performance of our proposed method on two different data sets. First, we test it on the functional connectivity networks of 12 MCI and 25 HC subjects. The results show that our method achieves significant performance improvement over those using only one type of network property. Specifically, our method achieves a classification accuracy of 91.9%, which is 10.8% better than those by single network-property-based methods. Then, we test our method for gender classification on a large set of functional connectivity networks with 133 infants scanned at birth, 1 year, and 2 years, also demonstrating very promising results.

Original languageEnglish
Article number6617721
Pages (from-to)576-589
Number of pages14
JournalIEEE Transactions on Biomedical Engineering
Issue number2
Publication statusPublished - 2014 Feb


  • Connectivity network
  • connectivity property
  • mild cognitive impairment (MCI)
  • multikernel learning (MKL)
  • topological property

ASJC Scopus subject areas

  • Biomedical Engineering


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