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Related Experiment Video

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Mixed Effects Models for Resampled Network Statistics Improves Statistical Power to Find Differences in Multi-Subject

Manjari Narayan1, Genevera I Allen2

  • 1Department of Electrical and Computer Engineering, Rice University Houston, TX, USA.

Frontiers in Neuroscience
|May 6, 2016
PubMed
Summary

This study introduces novel statistical methods to improve the analysis of brain connectivity in autism spectrum disorder (ASD). These methods enhance the detection of how brain network differences relate to ASD symptom severity.

Keywords:
Gaussian graphical modelsMarkov networkscovariatesfunctional connectivitylassomixed effects modelsnetwork statisticsresampling methods

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Area of Science:

  • Neuroscience
  • Statistics
  • Psychiatry

Background:

  • Autism spectrum disorder (ASD) presents diverse symptoms and functional impairments.
  • Understanding brain communication deficits in ASD is crucial for developing effective interventions.
  • Functional connectivity (FC) studies analyze brain network structure but are challenged by noisy neural data and imperfect network estimation.

Purpose of the Study:

  • To develop novel statistical models for analyzing functional connectivity in Gaussian graphical models.
  • To address the challenge of imperfectly estimated subject-level brain networks in detecting covariate effects.
  • To improve the statistical power of detecting relationships between brain network structure and symptom severity in neurodevelopmental disorders.

Main Methods:

  • Proposed novel two-level statistical models for functional connectivity analysis.
  • Introduced two approaches, R(2) and R(3), to account for imperfectly estimated subject networks.
  • R(2) utilizes resampling and random effects test statistics; R(3) additionally employs random adaptive penalization.
  • Conducted simulation studies with realistic graph structures to evaluate method performance.
  • Applied the novel methods to analyze functional connectivity in parts of the Autism Brain Imaging Data Exchange (ABIDE) dataset.

Main Results:

  • R(2) and R(3) demonstrated superior statistical power in detecting covariate effects compared to existing methods.
  • The proposed methods are particularly effective when the number of within-subject observations is similar to network size.
  • Analysis of the ABIDE dataset revealed evidence of hypoconnectivity associated with ASD symptom severity.
  • Specific brain regions implicated include the frontoparietal and limbic systems, and anterior and posterior cingulate cortices.

Conclusions:

  • The novel two-level models and associated methods (R(2), R(3)) offer a significant advancement in analyzing functional connectivity in complex brain disorders.
  • These methods improve the detection of covariate effects on brain network structure, even with noisy and imperfectly estimated data.
  • Findings provide insights into the neural underpinnings of autism spectrum disorder, highlighting specific patterns of brain hypoconnectivity related to symptom severity.