A mixed-modeling framework for whole-brain dynamic network analysis

Abstract

The emerging area of dynamic brain network analysis has gained considerable attraction in recent years. However, development of multivariate statistical frameworks that allow for examining the associations between phenotypic traits and dynamic patterns of system-level properties of the brain, and drawing statistical inference about such associations, has largely lagged behind. To address this need we developed a mixed-modeling framework that allows for assessing the relationship between any desired phenotype and dynamic patterns of whole-brain connectivity and topology. This novel framework also allows for simulating dynamic brain networks with respect to desired covariates. Unlike current tools which largely use data-driven methods, our model-based method enables aligning neuroscientific hypotheses with the analytic approach. We demonstrate the utility of this model in identifying the relationship between fluid intelligence and dynamic brain networks using resting-state fMRI (rfMRI) data from 200 participants in the Human Connectome Project (HCP) study. We also demonstrate the utility of this model to simulate dynamic brain networks at both group and individual levels. To our knowledge, this approach provides the first model-based statistical method for examining dynamic patterns of system-level properties of the brain and their relationships to phenotypic traits as well as simulating dynamic brain networks.

Adaptive spatial-temporal neural network for ADHD identification using functional fMRI

“…These changes can help us understand how cognitive states evolve over time, which is critical for better understanding the pathology of brain diseases. For this reason, there has been a shift toward dynamic connectivity analysis in recent efforts (Bahrami et al, 2021 ; Wang Z. et al, 2022 ; Yang et al, 2022 ).…”

On null models for temporal small-worldness in brain dynamics

“…To test certain hypotheses about observed functional connectivity, after modeling brain activity as a complex temporal network ( Bahrami, Laurienti, Shappell, Dagenbach, & Simpson, 2022 ; Sizemore & Bassett, 2018 ; Thompson et al, 2017 ), it is important to compare it with an appropriate null model ( Lurie et al, 2020 ; Váša & Mišić, 2022 ). A null model is a random statistical object that has certain properties in common with the empirical data under consideration and is used to evaluate whether the latter has noteworthy features or properties that cannot be attributed to randomness or other constraints.…”