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Extended nonnegative matrix factorization for dynamic functional connectivity analysis of fMRI data.

Zhiying Long1, Yuanhang Xu2, Wenyan Zou2

  • 1School of Artificial Intelligence, Beijing Normal University, Beijing, 100875 China.

Cognitive Neurodynamics
|November 18, 2024
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Summary

Extended nonnegative matrix factorization (eNMF) enhances dynamic functional connectivity (DFC) analysis in functional magnetic resonance imaging (fMRI). This novel method improves brain state pattern extraction and temporal property estimation compared to traditional approaches.

Keywords:
Brain statesDynamic functional connectivityNMFfMRI

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Brain Dynamics Analysis

Background:

  • Dynamic functional connectivity (DFC) analysis using functional magnetic resonance imaging (fMRI) is crucial for understanding brain dynamics.
  • Traditional nonnegative matrix factorization (NMF) has limitations in DFC analysis due to its nonnegative constraint on input data.

Purpose of the Study:

  • To introduce an extended NMF (eNMF) method that accommodates negative values in input and decomposed matrices for DFC analysis.
  • To evaluate the performance of eNMF in analyzing simulated and real resting-state fMRI data compared to K-means.

Main Methods:

  • Development and application of the extended nonnegative matrix factorization (eNMF) algorithm.
  • Analysis of simulated and real resting-state fMRI data.
  • Comparison of eNMF with K-means clustering for DFC analysis.

Main Results:

  • eNMF successfully decomposed mixed-sign matrices into positive and mixed-sign components in simulated data.
  • eNMF extracted more accurate brain state patterns and estimated superior DFC temporal properties than K-means.
  • Real fMRI data analysis showed eNMF provided richer temporal DFC measures and greater sensitivity to intergroup differences.

Conclusions:

  • The proposed eNMF method offers significant improvements for DFC analysis, overcoming limitations of traditional NMF.
  • eNMF is more effective than K-means in identifying brain states and their temporal dynamics from fMRI data.
  • Preliminary findings suggest potential sex-based differences in DFC, with females exhibiting altered relaxation and cognitive process patterns.