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Estimating coupling strength between multivariate neural series with multivariate permutation conditional mutual

Dong Wen1, Peilei Jia1, Sheng-Hsiou Hsu2

  • 1School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China; The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Yanshan University, Qinhuangdao 066004, China; The Key Laboratory for Software Engineering of Hebei Province, Yanshan University, Qinhuangdao 066004, China.

Neural Networks : the Official Journal of the International Neural Network Society
|December 19, 2018
PubMed
Summary
This summary is machine-generated.

A new method, multivariate permutation conditional mutual information (MPCMI), accurately estimates neural signal coupling strength. This approach shows promise as a clinical biomarker for conditions like mild cognitive impairment.

Keywords:
Amnestic mild cognitive impairmentCoupling strengthMulti-channel neural mass modelMultivariate neural seriesMultivariate permutation conditional mutual informationResting state EEG signals

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Biomarker Discovery

Background:

  • Understanding brain function relies on studying neural coupling.
  • Accurate quantification of coupling strength in multivariate neural signals is crucial.
  • Existing methods may not fully capture complex neural interactions.

Purpose of the Study:

  • To introduce a novel method, multivariate permutation conditional mutual information (MPCMI), for estimating neural signal coupling strength.
  • To validate the performance of MPCMI against established techniques.
  • To apply MPCMI for differentiating neural coupling in clinical populations.

Main Methods:

  • Developed and applied the multivariate permutation conditional mutual information (MPCMI) method.
  • Validated MPCMI using simulated multivariate neural signals (MNS) from a multi-channel neural mass model (MNMM).
  • Compared MPCMI performance against permutation conditional mutual information (PCMI), multivariate Granger causality (MVGC), and Granger causality analysis (GCA).
  • Applied MPCMI to resting-state electroencephalographic (rsEEG) signals from patients with amnestic mild cognitive impairment (aMCI) and normal controls (NC), both with type 2 diabetes mellitus (T2DM).

Main Results:

  • MPCMI demonstrated superior performance in extracting coupling strength from simulated MNS compared to PCMI, MVGC, and GCA.
  • MPCMI successfully identified significant differences in coupling strength between aMCI and NC groups in Alpha1 and Alpha2 frequency bands.
  • The method effectively captured coupling strength features in rsEEG data.

Conclusions:

  • MPCMI is an effective method for quantitatively estimating coupling strength in multivariate neural signals.
  • MPCMI shows potential as a valuable biomarker for clinical applications, particularly in distinguishing neurological conditions.
  • The findings highlight the utility of MPCMI in neuroscience research and clinical diagnostics.