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Cross-Modal Multivariate Pattern Analysis
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Quantifying statistical interdependence by message passing on graphs-part II: multidimensional point processes.

J Dauwels1, F Vialatte, T Weber

  • 1Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. jdauwels@mit.edu

Neural Computation
|May 5, 2009
PubMed
Summary
This summary is machine-generated.

Stochastic event synchrony (SES) quantifies signal similarity by aligning multidimensional events. This advanced technique proves more sensitive than classical methods for detecting anomalies in EEG synchrony, particularly in mild cognitive impairment (MCI) patients.

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Area of Science:

  • Neuroscience
  • Signal Processing
  • Statistical Inference

Background:

  • Stochastic event synchrony (SES) is a method for quantifying signal similarity by aligning extracted events.
  • While one-dimensional SES is established, multidimensional analysis presents greater combinatorial challenges.

Purpose of the Study:

  • To extend the SES technique for analyzing multidimensional point processes.
  • To develop a robust statistical inference framework for joint alignment and parameter estimation in multidimensional SES.
  • To evaluate SES's sensitivity in detecting anomalies in electroencephalogram (EEG) synchrony, specifically in mild cognitive impairment (MCI).

Main Methods:

  • The study employs a coordinate descent algorithm, alternating between estimating SES parameters via maximum a posteriori (MAP) estimation and refining pairwise alignment using the max-product algorithm on a cyclic graphical model.
  • The max-product algorithm is utilized for pairwise alignment due to the intractability of dynamic programming in high-dimensional state spaces.
  • The SES method's reliability is assessed using surrogate data before application to clinical EEG data.

Main Results:

  • The proposed SES method effectively handles multidimensional point processes, overcoming combinatorial complexities.
  • Numerical results demonstrate that SES is significantly more sensitive to perturbations in EEG synchrony compared to various classical synchrony measures.
  • The method successfully detected anomalies in EEG synchrony among patients with mild cognitive impairment (MCI).

Conclusions:

  • The extended SES technique provides a powerful tool for analyzing complex, multidimensional signal synchrony.
  • SES exhibits superior sensitivity in detecting subtle changes in neural synchrony, offering potential for early diagnosis of neurological conditions like MCI.
  • The statistical inference approach offers a reliable framework for robustly quantifying signal similarity in intricate systems.