Simultaneous estimation of low- and high-order functional connectivity for identifying mild cognitive impairment

Yueying Zhou, Lishan Qiao, Weikai Li, Limei Zhang, Dinggang Shen

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

Functional connectivity (FC) network has been becoming an increasingly useful tool for understanding the cerebral working mechanism and mining sensitive biomarkers for neural/mental disease diagnosis. Currently, Pearson’s Correlation (PC) is the simplest and most commonly used scheme in FC estimation. Despite its empirical effectiveness, PC only encodes the low-order (i.e., second-order) statistics by calculating the pairwise correlations between network nodes (brain regions), which fails to capture the high-order information involved in FC (e.g., the correlations among different edges in a network). To address this issue, we propose a novel FC estimation method based on Matrix Variate Normal Distribution (MVND), which can capture both low- and high-order correlations simultaneously with a clear mathematical interpretability. Specifically, we first generate a set of BOLD subseries by the sliding window scheme, and for each subseries we construct a temporal FC network by PC. Then, we employ the constructed FC networks as samples to estimate the final low- and high-order FC networks by maximizing the likelihood of MVND. To illustrate the effectiveness of the proposed method, we conduct experiments to identify subjects with Mild Cognitive Impairment (MCI) from Normal Controls (NCs). Experimental results show that the fusion of low- and high-order FCs can generally help to improve the final classification performance, even though the high-order FCmay contain less discriminative information than its low-order counterpart. Importantly, the proposed method for simultaneous estimation of low- and high-order FCs can achieve better classification performance than the two baseline methods, i.e., the original PC method and a recent high-order FC estimation method.

Original languageEnglish
Article number3
JournalFrontiers in Neuroinformatics
Volume12
DOIs
Publication statusPublished - 2018 Feb 6

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Normal distribution
Normal Distribution
Correlation methods
Biomarkers
Brain
Fusion reactions
Statistics
Cognitive Dysfunction
Experiments

Keywords

  • Disease diagnosis
  • Functional connectivity
  • High-order network
  • Matrix variate normal distribution
  • Mild cognitive impairment

ASJC Scopus subject areas

  • Neuroscience (miscellaneous)
  • Biomedical Engineering
  • Computer Science Applications

Cite this

Simultaneous estimation of low- and high-order functional connectivity for identifying mild cognitive impairment. / Zhou, Yueying; Qiao, Lishan; Li, Weikai; Zhang, Limei; Shen, Dinggang.

In: Frontiers in Neuroinformatics, Vol. 12, 3, 06.02.2018.

Research output: Contribution to journalArticle

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