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Modeling Dynamic Functional Neuroimaging Data Using Structural Equation Modeling.

Larry R Price1, Angela R Laird, Peter T Fox

  • 1Texas State University-San Marcos.

Structural Equation Modeling : a Multidisciplinary Journal
|September 28, 2011
PubMed
Summary
This summary is machine-generated.

Developing a path analytic network model for brain imaging requires adequate sample sizes. Small sample sizes (less than 15 participants per group) in positron emission tomography studies can lead to biased results and insufficient statistical power.

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Area of Science:

  • Neuroimaging
  • Statistical Modeling

Background:

  • Positron emission tomography (PET) is a neuroimaging technique.
  • Path analytic network models are increasingly used to understand brain connectivity.

Purpose of the Study:

  • To present a method for developing a path analytic network model using PET data.
  • To evaluate the impact of sample size on the reliability of parameter estimates.

Main Methods:

  • Quantitative activation likelihood estimation meta-analysis was used to identify regions of interest.
  • Bayesian structural equation modeling was employed to construct a population path model.
  • A simulation study using Markov chain Monte Carlo methods assessed sample size effects.

Main Results:

  • Parameter estimates showed significant bias (>5%) with sample sizes below 15 per group.
  • Statistical power was below the conventional threshold of 0.80 for small sample sizes.
  • Larger sample sizes (N=50, N=100) improved parameter estimation and statistical power.

Conclusions:

  • Sample size is a critical factor in the validity of path analytic network models derived from PET data.
  • Researchers should ensure sufficient sample sizes (at least 15 per group) to avoid biased findings.
  • The proposed method provides a framework for robust neuroimaging network analysis.