Comparing directed functional connectivity between groups with confirmatory subgrouping GIMME

Teague Rhine Henry, Eric Feczko, Michaela Cordova, Eric Earl, Sandra Williams, Joel T. Nigg, Damien A. Fair, Kathleen M. Gates

Research output: Contribution to journalArticlepeer-review

24 Scopus citations


Connectivity modeling in functional neuroimaging has become widely used method of analysis for understanding functional architecture. One method for deriving directed connectivity models is Group Iterative Multiple Model Estimation (GIMME; Gates and Molenaar, 2012). GIMME looks for commonalities across the sample to detect signal from noise and arrive at edges that exist across the majority in the group (“group-level edges”) and individual-level edges. In this way, GIMME obtains generalizable results via the group-level edges while also allowing for between subject heterogeneity in connectivity, moving the field closer to obtaining reliable personalized connectivity maps. In this article, we present a novel extension of GIMME, confirmatory subgrouping GIMME, which estimates subgroup-level edges for a priori known groups (e.g. typically developing controls vs. clinical group). Detecting edges that consistently exist for individuals within predefined subgroups aids in interpretation of the heterogeneity in connectivity maps and allows for subgroup-specific inferences. We describe this algorithm, as well as several methods to examine the results. We present an empirical example that finds similarities and differences in resting state functional connectivity among four groups of children: typically developing controls (TDC), children with autism spectrum disorder (ASD), children with Inattentive (ADHD-I) and Combined (ADHD-C) Type ADHD. Findings from this study suggest common involvement of the left Broca's area in all the clinical groups, as well as several unique patterns of functional connectivity specific to a given disorder. Overall, the current approach and proof of principle findings highlight a novel and reliable tool for capturing heterogeneity in complex mental health disorders.

Original languageEnglish (US)
Pages (from-to)642-653
Number of pages12
StatePublished - Mar 2019
Externally publishedYes

Bibliographical note

Funding Information:
We gratefully acknowledge support from NIH National Institute of Biomedical Imaging and Bioengineering (Award Number: 1-R01-EB022904-01 ; PI Gates) and NIH National Institute of Mental Health ( R01 MH115357 PIs: Fair, Nigg; R01 MH096773 PI: Fair; R01 MH086654 PI: Nigg).

Publisher Copyright:
© 2018 Elsevier Inc.


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