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Brain Function Network: Higher Order vs. More Discrimination.

Tingting Guo1, Yining Zhang1, Yanfang Xue1

  • 1School of Mathematics Science, Liaocheng University, Liaocheng, China.

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|September 9, 2021
PubMed
Summary
This summary is machine-generated.

Higher-order brain functional networks (BFNs) using sequential Pearson correlations (PC^n) show decreased discriminative ability. Fusing PC^n (n>1) with PC^1 improves MCI identification sensitivity but not accuracy.

Keywords:
Pearson's correlationbrain functional networkgender predictionhigher-order correlationmild cognitive impairment

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Brain Imaging Analysis

Background:

  • Brain functional networks (BFNs) are crucial for understanding individual differences and neurological diseases.
  • Pearson's correlation (PC) is a common method for BFN estimation, capturing only low-order statistics.
  • Higher-order statistics in BFNs are being explored for enhanced discriminative power.

Purpose of the Study:

  • To investigate the impact of higher-order sequential Pearson correlations (PC^n) on BFN construction.
  • To evaluate the discriminative ability of PC^n-based BFNs for predicting individual differences and identifying mild cognitive impairment (MCI).
  • To explore the convergence properties of PC^n-based BFNs.

Main Methods:

  • Constructing BFNs using sequential Pearson correlations up to higher orders (PC^n, n>2).
  • Utilizing PC^n-based BFNs for predicting sex differences (Female vs. Male).
  • Applying PC^n-based BFNs to classify mild cognitive impairment (MCI) subjects from healthy controls (HCs).

Main Results:

  • The discriminative ability of PC^n-based BFNs generally decreases as the order 'n' increases.
  • Fusing PC^n-based BFNs (n>1) with PC^1-based BFNs improved sensitivity for MCI identification.
  • Classification accuracy for MCI did not improve when fusing higher-order BFNs.
  • PC^n-based BFNs converge to a binary matrix with elements of ±1 as 'n' increases.

Conclusions:

  • Higher-order sequential correlations do not necessarily enhance BFN discriminative ability for individual differences or disease classification.
  • Combining lower and higher-order BFNs shows potential for improving sensitivity in specific clinical applications like MCI detection.
  • The convergence of PC^n to a binary matrix suggests a fundamental property of this sequential correlation method.