This article was published online on 13 September 2013. An error was subsequently identified. This notice is included in the online and print versions to indicate that both have been corrected 20 March 2014.
Topological graph kernel on multiple thresholded functional connectivity networks for mild cognitive impairment classification
Article first published online: 13 SEP 2013
© 2013 Wiley Periodicals, Inc.
Human Brain Mapping
Volume 35, Issue 7, pages 2876–2897, July 2014
How to Cite
Jie, B., Zhang, D., Wee, C.-Y. and Shen, D. (2014), Topological graph kernel on multiple thresholded functional connectivity networks for mild cognitive impairment classification. Hum. Brain Mapp., 35: 2876–2897. doi: 10.1002/hbm.22353
- Issue published online: 11 JUN 2014
- Article first published online: 13 SEP 2013
- Manuscript Accepted: 3 JUN 2013
- Manuscript Revised: 21 MAY 2013
- Manuscript Received: 16 AUG 2012
- NIH. Grant Numbers: EB006733, EB008374, EB009634, AG041721
- Specialized Research Fund for the Doctoral Program of Higher Education. Grant Number: 20123218110009
- NUAA Fundamental Research. Grant Number: NE2013105
- Jiangsu Natural Science Foundation for Distinguished Young Scholar. Grant Number: BK20130034
- University Natural Science Foundation of Anhui. Grant Number: KJ2013Z095
- mild cognitive impairment;
- Alzheimer's disease;
- functional connectivity network;
- graph kernel;
- multiple thresholds
Recently, brain connectivity networks have been used for classification of Alzheimer's disease and mild cognitive impairment (MCI) from normal controls (NC). In typical connectivity-networks-based classification approaches, local measures of connectivity networks are first extracted from each region-of-interest as network features, which are then concatenated into a vector for subsequent feature selection and classification. However, some useful structural information of network, especially global topological information, may be lost in this type of approaches. To address this issue, in this article, we propose a connectivity-networks-based classification framework to identify accurately the MCI patients from NC. The core of the proposed method involves the use of a new graph-kernel-based approach to measure directly the topological similarity between connectivity networks. We evaluate our method on functional connectivity networks of 12 MCI and 25 NC subjects. The experimental results show that our proposed method achieves a classification accuracy of 91.9%, a sensitivity of 100.0%, a balanced accuracy of 94.0%, and an area under receiver operating characteristic curve of 0.94, demonstrating a great potential in MCI classification, based on connectivity networks. Further connectivity analysis indicates that the connectivity of the selected brain regions is different between MCI patients and NC, that is, MCI patients show reduced functional connectivity compared with NC, in line with the findings reported in the existing studies. Hum Brain Mapp 35:2876–2897, 2014. © 2013 Wiley Periodicals, Inc.