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Statistical and Machine Learning Link Selection Methods for Brain Functional Networks: Review and Comparison.

Ilinka Ivanoska1, Kire Trivodaliev1, Slobodan Kalajdziski1

  • 1Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University, 1000 Skopje, North Macedonia.

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|June 2, 2021
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Summary
This summary is machine-generated.

This study evaluates link selection methods for high-dimensional brain networks. Network Based Statistics (NBS), AnovaNet, and Extra Trees (ExT) show distinct advantages in information retention, stability, and computational cost, respectively.

Keywords:
brain functional networkslink selectionmachine learningstatistics

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Area of Science:

  • Neuroscience
  • Network Science
  • Data Science

Background:

  • Network-based representations revolutionize neuroscience by focusing on brain region interactions.
  • High dimensionality of these networks poses challenges for data analysis and understanding pathologies.
  • Link selection methods are crucial for simplifying network representations and improving data utilization.

Purpose of the Study:

  • To review and evaluate statistical and machine learning link selection methods for functional brain networks.
  • To assess the performance of different methods in terms of information retention, stability, and computational cost.
  • To discuss the implications of these findings for neuroscience research.

Main Methods:

  • A comprehensive review of statistical and machine learning link selection techniques.
  • Empirical evaluation of selected methods on real brain functional network data.
  • Comparative analysis based on quantitative and qualitative performance metrics.

Main Results:

  • Most link selection methods exhibit similar qualitative performance.
  • Network Based Statistics (NBS) excels in retaining information.
  • AnovaNet demonstrates superior stability, while Extra Trees (ExT) offer lower computational costs.
  • Machine learning methods do not present a clear advantage over statistical approaches despite higher complexity.
  • Significant heterogeneity in retained links suggests complementary data views across methods.

Conclusions:

  • Link selection is vital for managing high-dimensional neuroimaging data.
  • NBS, AnovaNet, and ExT offer distinct benefits for network analysis.
  • The complementary nature of different methods highlights the need for tailored approaches in neuroscience.
  • Further research should explore the integration of these methods for enhanced brain network analysis.