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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
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An information-theoretic approach to study spatial dependencies in small datasets.

Maurizio Porfiri1,2,3, Manuel Ruiz Marín3

  • 1Department of Mechanical and Aerospace Engineering, New York University Tandon School of Engineering, Brooklyn, NY, USA.

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This study introduces a network-based method to uncover spatial patterns using limited data. It quantifies spatial associations, offering insights into complex socioeconomic issues like migration and public health.

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Area of Science:

  • Spatial statistics
  • Network theory
  • Information theory

Background:

  • Identifying spatial patterns and their drivers is crucial across disciplines like epidemiology and economics.
  • Existing methods often require extensive data or lack statistical rigor for complex spatial processes.

Purpose of the Study:

  • To develop a non-parametric statistical framework for analyzing spatial associations from limited measurements.
  • To leverage network theory to link spatial patterning to underlying network structures.
  • To provide a principled approach for understanding mechanisms behind socioeconomic phenomena.

Main Methods:

  • Developed a non-parametric scheme based on network and information theory.
  • Related spatial patterning to network topology using mutual information statistics.
  • Derived closed-form expressions for mutual information based on network features for specific processes.

Main Results:

  • Demonstrated feasibility on synthetic datasets with 25-100 measurements from linear and nonlinear processes.
  • Successfully identified meaningful spatial patterns in real-world datasets of human migration and motor vehicle deaths.
  • Validated the approach's ability to provide statistically-principled insights into socioeconomic mechanisms.

Conclusions:

  • The proposed network-based mutual information approach effectively reveals spatial patterns from limited data.
  • This method offers a statistically sound way to investigate the mechanisms driving complex spatial phenomena.
  • Applicable to diverse fields, aiding in understanding critical socioeconomic challenges.