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Abstract:
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Many complex brain disorders such as autism spectrum disorders exhibit a wide range of symptoms and disability. To understand how brain communication is impaired in such conditions, functional connectivity studies seek to study individual differences in brain network structure between groups and conditions. In practice, however, functional connectivity is not observed but estimated from complex and noisy neural activity measurements. Imperfect subject network estimates can compromise subsequent efforts to detect covariate effects on network structure. We consider Gaussian graphical models (GGMs), an important class of statistical models to understand network mechanisms of brain function. To find differences in populations of graphical models we propose novel two-level models that treat both subject level networks and population level covariate effects as unknown parameters. To account for imperfectly estimated subject level networks when fitting these models, we propose R^3, a procedure based on resampling, random adaptive penalization and random effects test statistics. We demonstrate the benefits of this approach on an fMRI study of autism and an interventional TMS-EEG study.
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