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Article
A variance components model for statistical inference on functional connectivity networks
NeuroImage (2017)
  • Mark Fiecas, University of Minnesota
  • Ivor Cribben, University of Alberta
  • Reyhaneh Bahktiari, University of Alberta
  • Jacqueline Cummine, University of Alberta
Abstract
We propose a variance components linear modeling framework for statistical inference on functional connectivity networks that accounts for the temporal autocorrelation inherent in functional magnetic resonance imaging (fMRI) time series data and for the heterogeneity across subjects.  The novel method estimates the former in a nonparametric and subject-specific manner, and estimates the latter using iterative least squares and residual maximum likelihood.  We apply the new model to a resting-state fMRI study to compare the functional connectivity networks in both typical and reading impaired young adults in order to characterize the resting state networks that are related to reading processes. We also compare the performance of the model to other methods that do not account for the temporal autocorrelation or heterogeneity across the subjects using an extensive simulation study. 
Keywords
  • Functional connectivity networks,
  • Temporal autocorrelation,
  • Subject heterogeneity,
  • Resting-state fMRI,
  • Dyslexia
Publication Date
2017
DOI
http://dx.doi.org/10.1016/j.neuroimage.2017.01.051
Citation Information
Fiecas, M., Cribben, I., Bahktiari, R., and Cummine, J. (2017). A variance components model for statistical inference on functional connectivity networks. NeuroImage, 149, 256-266. http://dx.doi.org/10.1016/j.neuroimage.2017.01.051