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Cost-Reference Particle Filter-Based Method for Constructing Effective Brain Networks: Application in Optically

Yuyu Ma1,2, Xiaoyu Liang1,2, Huanqi Wu1,2

  • 1Key Laboratory of Ultra-Weak Magnetic Field Measurement Technology, Ministry of Education, School of Instrumentation and Optoelectronic Engineering, Beihang University, 37 Xueyuan Rd., Haidian District, Beijing 100083, China.

Bioengineering (Basel, Switzerland)
|January 8, 2025
PubMed
Summary
This summary is machine-generated.

A new Granger causality method using a cost-reference particle filter (CRPF) accurately maps brain networks from Optically Pumped Magnetometer Magnetoencephalography (OPM-MEG) data, even with unknown noise. This approach significantly reduces estimation errors compared to traditional methods.

Keywords:
Granger causalityOPM-MEGcost-reference particle filtereffective brain network

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Signal Processing

Background:

  • Optically Pumped Magnetometer Magnetoencephalography (OPM-MEG) offers novel brain signal recording.
  • Effective brain networks map causal relationships and information flow.
  • Traditional Granger causality methods assume Gaussian noise, which is often not true in experimental settings.

Purpose of the Study:

  • To propose a Granger causality method robust to unknown noise conditions for constructing effective brain networks.
  • To evaluate the performance of the proposed method against existing filters.

Main Methods:

  • Developed a Granger causality method based on a cost-reference particle filter (CRPF).
  • Assessed the method using simulations with Gaussian, alpha-stable, and pink noise.
  • Validated the method with experimental OPM-MEG data from somatosensory stimulation, finger movement, and auditory oddball paradigms.

Main Results:

  • CRPF significantly reduced MVAR model coefficient estimation errors compared to Kalman Filter (KF) and Maximum Correntropy Filter (MCF) under various noise conditions.
  • CRPF demonstrated error reductions of up to 88.1% compared to MCF and KF.
  • Experimental results showed CRPF accurately recovered known effective connectivity patterns.

Conclusions:

  • The proposed CRPF method is effective for constructing brain networks from OPM-MEG data under unknown noise.
  • CRPF offers improved accuracy and robustness over traditional methods for effective brain network analysis.
  • This work validates OPM-MEG as a tool for measuring complex brain connectivity.