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Hierarchical High-Order Functional Connectivity Networks and Selective Feature Fusion for MCI Classification

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Conventional Functional connectivity (FC) analysis focuses on characterizing the correlation between two brain regions, whereas the high-order FC can model the correlation between two brain region pairs. To reduce the number of brain region pairs, clustering is applied to group all the brain region pairs into a small number of clusters. Then, a high-order FC network can be constructed based on the clustering result. By varying the number of clusters, multiple high-order FC networks can be generated and the one with the best overall performance can be finally selected. However, the important information contained in other networks may be simply discarded. To address this issue, in this paper, we propose to make full use of the information contained in all high-order FC networks. First, an agglomerative hierarchical clustering technique is applied such that the clustering result in one layer always depends on the previous layer, thus making the high-order FC networks in the two consecutive layers highly correlated. As a result, the features extracted from high-order FC network in each layer can be decomposed into two parts (blocks), i.e., one is redundant while the other might be informative or complementary, with respect to its previous layer. Then, a selective feature fusion method, which combines sequential forward selection and sparse regression, is developed to select a feature set from those informative feature blocks for classification. Experimental results confirm that our novel method outperforms the best single high-order FC network in diagnosis of mild cognitive impairment (MCI) subjects.

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Authors and Affiliations

  1. Department of Radiology and BRIC, University of North Carolina at Chapel Hill, 130 Mason Farm Road, Chapel Hill, NC, 27599, USA

    Xiaobo Chen, Han Zhang & Dinggang Shen

  2. Department of Brain and Cognitive Engineering, Korea University, Seoul, 02841, Republic of Korea

    Seong-Whan Lee & Dinggang Shen

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  1. Xiaobo Chen
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  2. Han Zhang
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the Alzheimer’s Disease Neuroimaging Initiative

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Correspondence to Dinggang Shen.

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Funding

This work was supported in part by National Institutes of Health (EB006733, EB008374, AG041721, AG049371, AG042599, AG053867, and EB022880). Xiaobo Chen was also supported by National Natural Science Foundation of China (Grand No: 61203244), Fujian Provincial Key Laboratory of Information Processing and Intelligent Control (Minjiang University)(MJUKF201724), and Talent Foundation of Jiangsu University (14JDG066).

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All the authors declare no conflicts of interest.

Ethical Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed Consent

Informed consent was obtained from all patients before the scan.

Additional information

Alzheimer’s Disease Neuroimaging Initiative (ADNI) is a Group/Institutional Author.

Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (www.loni.ucla.edu/ADNI). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: www.loni.ucla.edu\ADNI\Collaboration\ADNI_Authorship_list.pdf.

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Chen, X., Zhang, H., Lee, SW. et al. Hierarchical High-Order Functional Connectivity Networks and Selective Feature Fusion for MCI Classification. Neuroinform 15, 271–284 (2017). https://doi.org/10.1007/s12021-017-9330-4

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