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

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dc.citation.articleNumber108en_US
dc.citation.journalTitleFrontiers in Neuroscienceen_US
dc.citation.volumeNumber10en_US
dc.contributor.authorNarayan, Manjarien_US
dc.contributor.authorAllen, Genevera I.en_US
dc.date.accessioned2016-11-10T22:23:40Zen_US
dc.date.available2016-11-10T22:23:40Zen_US
dc.date.issued2016en_US
dc.description.abstractMany 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 understand individual differences in brain network structure in terms of covariates that measure symptom severity. 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 address this problem in the case of Gaussian graphical models of functional connectivity, by proposing 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 two related approaches-R (2) based on resampling and random effects test statistics, and R (3) that additionally employs random adaptive penalization. Simulation studies using realistic graph structures reveal that R (2) and R (3) have superior statistical power to detect covariate effects compared to existing approaches, particularly when the number of within subject observations is comparable to the size of subject networks. Using our novel models and methods to study parts of the ABIDE dataset, we find evidence of hypoconnectivity associated with symptom severity in autism spectrum disorders, in frontoparietal and limbic systems as well as in anterior and posterior cingulate cortices.en_US
dc.identifier.citationNarayan, Manjari and Allen, Genevera I.. "Mixed Effects Models for Resampled Network Statistics Improves Statistical Power to Find Differences in Multi-Subject Functional Connectivity." <i>Frontiers in Neuroscience,</i> 10, (2016) Frontiers: http://dx.doi.org/10.3389/fnins.2016.00108.en_US
dc.identifier.doihttp://dx.doi.org/10.3389/fnins.2016.00108en_US
dc.identifier.urihttps://hdl.handle.net/1911/92699en_US
dc.language.isoengen_US
dc.publisherFrontiersen_US
dc.rightsThis is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.en_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0en_US
dc.subject.keywordGaussian graphical modelsen_US
dc.subject.keywordMarkov networksen_US
dc.subject.keywordcovariatesen_US
dc.subject.keywordfunctional connectivityen_US
dc.subject.keywordlassoen_US
dc.subject.keywordmixed effects modelsen_US
dc.subject.keywordnetwork statisticsen_US
dc.subject.keywordresampling methodsen_US
dc.titleMixed Effects Models for Resampled Network Statistics Improves Statistical Power to Find Differences in Multi-Subject Functional Connectivityen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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