CitationDuffy, Kelly Anne; Fisher, Zachary F.; Arizmendi, Cara A.; Molenaar, Peter C. M.; Hopfinger, Joseph; Cohen, Jessica R.; Beltz, Adriene; Lindquist, Martin A.; Hallquist, Michael N.; & Gates, Kathleen M. (Online ahead of print). Detecting Task-Dependent Functional Connectivity in GIMME with Person-Specific Hemodynamic Response Functions. Brain Connectivity. PMCID: PMC Journal - In Process
AbstractIntroduction: Group iterative multiple model estimation (GIMME) has proven to be a reliable data-driven method to arrive at functional connectivity maps that represent associations between brain regions across time in groups and individuals. However, to date, GIMME has not been able to model time-varying task-related effects. This paper introduces HRF-GIMME, an extension of GIMME that enables the modeling of the direct and modulatory effects of a task on fMRI data collected using event-related designs. Critically, HRF-GIMME incorporates person-specific modeling of the hemodynamic response function (HRF) to accommodate known variability in onset delay and shape.
Methods: Following an introduction of the technical aspects of HRF-GIMME, the performance of HRF-GIMME is evaluated via both a simulation study and application to empirical data. The simulation study assesses the sensitivity and specificity of HRF-GIMME using data simulated from one slow and two rapid event-related designs, and HRF-GIMME is then applied to two empirical data sets from similar designs to evaluate performance in recovering known neural circuitry.
Results: HRF-GIMME showed high sensitivity and specificity across all simulated conditions, and performed well in the recovery of expected relations between convolved task vectors and brain regions in both simulated and empirical data, particularly for the slow event-related design.
Conclusion: Results from simulated and empirical data indicate that HRF-GIMME is a powerful new tool for obtaining directed functional connectivity maps of intrinsic and task-related connections that is able to uncover what is common across the sample as well as crucial individual-level path connections and estimates.
Reference TypeJournal Article
Year PublishedOnline ahead of print
Journal TitleBrain Connectivity
Author(s)Duffy, Kelly Anne
Fisher, Zachary F.
Arizmendi, Cara A.
Molenaar, Peter C. M.
Cohen, Jessica R.
Lindquist, Martin A.
Hallquist, Michael N.
Gates, Kathleen M.