title: The Functional Segregation and Integration Model: Mixture Model Representations of Consistent and Variable Group-Level Connectivity in fMRI.
abstract: The brain consists of specialized cortical regions that exchange information between each other, reflecting a combination of segregated (local) and integrated (distributed) processes that define brain function. Functional magnetic resonance imaging (fMRI) is widely used to characterize these functional relationships, although it is an ongoing challenge to develop robust, interpretable models for high-dimensional fMRI data. Gaussian mixture models (GMMs) are a powerful tool for parcellating the brain, based on the similarity of voxel time series. However, conventional GMMs have limited parametric flexibility: they only estimate segregated structure and do not model interregional functional connectivity, nor do they account for network variability across voxels or between subjects. To address these issues, this letter develops the functional segregation and integration model (FSIM). This extension of the GMM framework simultaneously estimates spatial clustering and the most consistent group functional connectivity structure. It also explicitly models network variability, based on voxel- and subject-specific network scaling profiles. We compared the FSIM to standard GMM in a predictive cross-validation framework and examined the importance of different model parameters, using both simulated and experimental resting-state data. The reliability of parcellations is not significantly altered by flexibility of the FSIM, whereas voxel- and subject-specific network scaling profiles significantly improve the ability to predict functional connectivity in independent test data. Moreover, the FSIM provides a set of interpretable parameters to characterize both consistent and variable aspects functional connectivity structure. As an example of its utility, we use subject-specific network profiles to identify brain regions where network expression predicts subject age in the experimental data. Thus, the FSIM is effective at summarizing functional connectivity structure in group-level fMRI, with applications in modeling the relationships between network variability and behavioral/demographic variables.
authors.affiliation: Section for Cognitive Systems, DTU Compute, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark, and Keenan Research Centre of the Li Ka Shing Knowledge Institute at St. Michael's Hospital, Toronto ON, Canada M5B 1MB nchurchill.research@gmail.com.
authors.forename: Nathan W
authors.initials: NW
authors.lastname: Churchill
authors.affiliation: Section for Cognitive Systems, DTU Compute, Technical University of Denmark, DK-2800, Kgs. Lyngby, Denmark, and Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, DK-2650 Hvidovre, Denmark kristoffer.madsen@gmail.com.
authors.forename: Kristoffer
authors.initials: K
authors.lastname: Madsen
authors.affiliation: Section for Cognitive Systems, DTU Compute, Technical University of Denmark, DK-2800, Kgs. Lyngby, Denmark mmor@dtu.dk.
authors.forename: Morten
authors.initials: M
authors.lastname: Mørup