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Optimized Multiscale Entropy Model Based on Resting-State fMRI for Appraising Cognitive Performance in Healthy

Fan Yang1,2, Fuyi Zhang1,2, Abdelkader Nasreddine Belkacem3

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Computational and Mathematical Methods in Medicine
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Summary

An optimized multiscale entropy (MSE) model objectively analyzes resting-state functional MRI (rfMRI) data. This approach identifies brain biomarkers for cognitive performance in healthy elderly individuals, achieving 80.05% classification accuracy.

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Area of Science:

  • Neuroimaging
  • Biostatistics
  • Machine Learning

Background:

  • Entropy models capture dynamic characteristics of resting-state functional MRI (rfMRI) signals.
  • Current entropy models for rfMRI analysis suffer from subjective parameter selection and lack of standardized methods.
  • Objective parameter selection is crucial for reliable analysis of rfMRI data.

Purpose of the Study:

  • To propose an optimized multiscale entropy (MSE) model for objective parameter selection in rfMRI analysis.
  • To identify brain regions with significant entropy differences as potential biomarkers for cognitive performance.
  • To utilize these biomarkers for classifying cognitive scores in healthy elderly individuals using machine learning.

Main Methods:

  • Healthy elderly volunteers were divided into 'excellent' and 'poor' cognitive groups based on scale tests.
  • The MSE model parameters were optimized using sensitivity analyses, including receiver operating characteristic (ROC) and area under the ROC curve (AUC).
  • Brain regions with significant entropy differences were identified as biomarkers, and their entropy values served as feature vectors for a probabilistic neural network classifier.

Main Results:

  • The optimized MSE model successfully identified brain regions sensitive to cognitive performance.
  • Machine learning classification of cognitive scores achieved an accuracy of 80.05%.
  • Objective and fixed parameters for MSE were established, enhancing model reliability.

Conclusions:

  • The optimized MSE model offers an objective approach to analyzing rfMRI data and identifying cognitive biomarkers.
  • This method provides a reliable basis for using entropy measures to assess cognitive function in the elderly.
  • The findings support the potential of optimized MSE for future cognitive health assessments.