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Functional Brain Network Classification for Alzheimer’s Disease Detection with Deep Features and Extreme Learning Machine

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Abstract

The human brain can be inherently modeled as a brain network, where nodes denote billions of neurons and edges denote massive connections between neurons. Analysis on functional brain networks provides powerful abilities to discover potential mechanisms of human brain, and to aid brain disease detection, such as AD (Alzheimer’s disease). Effective discrimination of patients of AD and MCI (mild cognitive impairment) from NC (normal control) is important for the early diagnosis of AD. Therefore, this paper explores the problem of brain network classification for AD detection. Two deep learning methods of functional brain network classification are designed. The convolutional learning method learns the deep regional-connectivity features, while the recurrent learning method learns the deep adjacent positional features. The ELM (extreme learning machine)-boosted structure is also implemented to further improve the learning ability. Extensive experiments are conducted to evaluate and compare the AUC (area under curve), accuracy, recall, and training time of the proposed methods on a real-world dataset. Results indicate that (1) the proposed methods which learn deep features directly from brain networks outperform shallow learning methods and (2) models with the ELM-boosted structure achieve a higher performance. This paper explores the brain networks learning with deep features and ELM. The results demonstrate that the proposed methods provide a satisfactory learning ability in the application of AD detection.

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Funding

This research is partially supported by the National Natural Science Foundation of China (Grant Nos. 61702086, 61572121), the China Postdoctoral Science Foundation (Grant No. 2018M631806), the Natural Science Foundation of Liaoning Province (Grant No. 20170520164), the Fundamental Research Funds for the Central Universities of China (Grant Nos. N171904007, N171604007), and the Open Program of Neusoft Research of Intelligent Healthcare Technology, Co. Ltd. (Grant No. NRIHTOP1803).

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Authors and Affiliations

  1. College of Medicine and Biomedical Information Engineering, Northeastern University, Shenyang, Liaoning, China

    Xin Bi

  2. Neusoft Research of Intelligent Healthcare Technology, Co. Ltd., Shenyang, Liaoning, China

    Xin Bi

  3. School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning, China

    Xiangguo Zhao, Hong Huang, Deyang Chen & Yuliang Ma

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  1. Xin Bi
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  2. Xiangguo Zhao
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  3. Hong Huang
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Correspondence to Xin Bi.

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Bi, X., Zhao, X., Huang, H. et al. Functional Brain Network Classification for Alzheimer’s Disease Detection with Deep Features and Extreme Learning Machine. Cogn Comput 12, 513–527 (2020). https://doi.org/10.1007/s12559-019-09688-2

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  • DOI: https://doi.org/10.1007/s12559-019-09688-2

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