Identification of differential brain regions in MCI progression via clustering-evolutionary weighted SVM ensemble algorithm

Frontiers of Computer Science volume 15, Article number: 156903 (2021) Cite this article

Abstract

Mild cognitive impairment (MCI) as the potential sign of serious cognitive decline could be divided into two stages, i.e., late MCI (LMCI) and early MCI (EMCI). Although the different cognitive states in the MCI progression have been clinically defined, effective and accurate identification of differences in neuroimaging data between these stages still needs to be further studied. In this paper, a new method of clustering-evolutionary weighted support vector machine ensemble (CEWSVME) is presented to investigate the alterations from cognitively normal (CN) to EMCI to LMCI. The CEWSVME mainly includes two steps. The first step is to build multiple SVM classifiers by randomly selecting samples and features. The second step is to introduce the idea of clustering evolution to eliminate inefficient and highly similar SVMs, thereby improving the final classification performances. Additionally, we extracted the optimal features to detect the differential brain regions in MCI progression, and confirmed that these differential brain regions changed dynamically with the development of MCI. More exactly, this study found that some brain regions only have durative effects on MCI progression, such as parahippocampal gyrus, posterior cingulate gyrus and amygdala, while the superior temporal gyrus and the middle temporal gyrus have periodic effects on the progression. Our work contributes to understanding the pathogenesis of MCI and provide the guidance for its timely diagnosis.

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Acknowledgements

This work was supported by the Hunan Provincial Science and Technology Project Foundation (2018TP1018), the National Science Foundation of China (61502167).

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Affiliations

  1. Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Hunan Normal University, Changsha, 410081, China

    Xia-an Bi, Yiming Xie & Hao Wu

  2. College of Information Science and Engineering, Hunan Normal University, Changsha, 410081, China

    Xia-an Bi, Yiming Xie & Hao Wu

  3. Business School of Hunan Normal University, Changsha, 410081, China

    Luyun Xu

Authors
  1. Xia-an Bi
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  2. Yiming Xie
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  3. Hao Wu
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  4. Luyun Xu
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Correspondence to Xia-an Bi or Luyun Xu.

Additional information

Xia-an Bi is currently an associate professor in the College of Information Science and Engineering in Hunan Normal University, China. He received the PhD degree in computer science and technology from the College of Information Science and Engineering in Hunan University, China in 2012. His current research interests include machine learning, brain science and artificial intelligence.

Yiming Xie, received the BE degree in Network Engineering from Chengdu Technological University, China in 2019. He is currently pursuing the MS degree in College of Information Science and Engineering, Hunan Normal University, China. The major of him is computer science and technology. His main research fields include data mining, brain science and artificial intelligence.

Hao Wu, received the BE degree in Computer Science and Technology from Jinggangshan University, China in 2019. He is currently pursuing the MS degree in College of Information Science and Engineering, Hunan Normal University, China. The major of him is computer technology. His main research fields include data mining, brain science and artificial intelligence.

Luyun Xu, received the PhD degree in business administration from Hunan University, China in 2018. She is currently an assistant professor in Business School in Hunan Normal University, China. Her research interests focus on knowledge management, data mining and machine learning. She has published in the Journal of Technology Transfer, Technological Analysis & Strategic Management, Computational and Mathematical Organization Theory.

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Bi, Xa., Xie, Y., Wu, H. et al. Identification of differential brain regions in MCI progression via clustering-evolutionary weighted SVM ensemble algorithm. Front. Comput. Sci. 15, 156903 (2021). https://doi.org/10.1007/s11704-020-9520-3

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Keywords

  • machine learning
  • MCI progression
  • optimal feature extraction
  • differential brain regions
  • functional magnetic resonance imaging