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Edgewise and subgraph-level tests for brain networks.

Huaihou Chen1,2, Bingxin Zhao3, Eric C Porges4

  • 1Department of Biostatistics, University of Florida, Gainesville, FL, U.S.A.. huaihouchen@ufl.edu.

Statistics in Medicine
|July 12, 2016
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Summary

Researchers developed new flexible regression models to analyze resting-state functional connectivity differences due to age, gender, or disorders. These methods enhance brain network analysis for various conditions.

Keywords:
graphical structurenetwork-based statisticsresting-state brain functional connectivity

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Biostatistics

Background:

  • Resting-state functional magnetic resonance imaging (rs-fMRI) is crucial for understanding brain function at rest.
  • Identifying factors influencing functional connectivity, such as age, gender, and disorders, is vital for neuroscience research.
  • Existing methods may lack flexibility or power in detecting subtle changes in brain networks.

Purpose of the Study:

  • To develop novel, flexible regression models for analyzing resting-state functional connectivity.
  • To propose two complementary statistical methods for identifying changes in brain network edges and subgraphs.
  • To enhance the power and applicability of functional connectivity analysis, especially for low signal-to-noise ratio data.

Main Methods:

  • Development of flexible regression models for edge-wise analysis with contrast testing and familywise error rate control.
  • Application of network-based statistics (NBS) incorporating graph topological structure for subgraph analysis.
  • Proposal of stability criteria for NBS threshold selection and utilization of massive parallel processing for efficiency.

Main Results:

  • Simulation studies demonstrated that stability criteria for NBS thresholds outperform the traditional Bonferroni threshold.
  • The developed methods successfully identified effects of age, gender, and drug treatment in the Oxytocin and Aging Study.
  • The methods were also applied to the Autism Brain Imaging Data Exchange Study to detect effects of autism spectrum disorder.

Conclusions:

  • The proposed flexible regression models and NBS methods provide powerful tools for investigating resting-state functional connectivity.
  • These methods are applicable to diverse research questions, including the effects of demographic factors and neuropsychiatric disorders.
  • The stability criteria and efficient processing enhance the reliability and speed of functional connectivity analyses.