Multi-Scale Feature Combination of Brain Functional Network for eMCI Classification

Zhuqing Jiao*, Zhengwang Xia, Xuelian Ming, Chun Cheng, Shui Hua Wang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

25 Citations (Scopus)


People suffering from mild cognitive impairment (MCI) are at an increased risk of developing Alzheimer's disease (AD) or another dementia. High prevalence will possibly be reduced if early interventions could be applied to the stage of early MCI (eMCI). In network-based classification, brain functional networks are often constructed, relying on the entire time series. It can lead to the neglect of the complex and dynamic interaction relationships among brain regions. As a result, the features derived from this type of functional network may fail to serve as an effective disease biomarker. To address this problem, we proposed a multi-scale feature combination framework for the eMCI classification. In this framework, global static features, time-varying features, and more refined features could be able to flexibly extract from static functional networks, dynamic functional networks, and high-order functional networks, respectively. Then, they are utilized to train and test the classification model in the form of feature combination. The experimental results have verified that the proposed method achieves superior classification accuracy than other competed methods in the eMCI classification, indicating a great potential in understanding the dysfunction of the brain regions.

Original languageEnglish
Article number8731965
Pages (from-to)74263-74273
Number of pages11
JournalIEEE Access
Publication statusPublished - 2019
Externally publishedYes


  • Alzheimer's disease (AD)
  • Brain functional network
  • classification accuracy
  • early mild cognitive impairment (eMCI)
  • multi-scale feature combination


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