Abstract
The analysis of time-varying connectivity by using functional MRI has gained momentum given its ability to complement traditional static methods by capturing additional patterns of variation in human brain function. Attention deficit hyperactivity disorder (ADHD) is a complex, common developmental neuropsychiatric disorder associated with heterogeneous connectivity differences that are challenging to disambiguate. However, dynamic connectivity has not been examined in ADHD, and surprisingly few whole-brain analyses of static functional network connectivity (FNC) using independent component analysis (ICA) exist. We present the first analyses of time-varying connectivity and whole-brain FNC using ICA in ADHD, introducing a novel framework for comparing local and global dynamic connectivity in a 44-network model. We demonstrate that dynamic connectivity analysis captures robust motifs associated with group effects consequent on the diagnosis of ADHD, implicating increased global dynamic range, but reduced fluidity and range localized to the default mode network system. These differentiate ADHD from other major neuropsychiatric disorders of development. In contrast, static FNC based on a whole-brain ICA decomposition revealed solely age effects, without evidence of group differences. Our analysis advances current methods in time-varying connectivity analysis, providing a structured example of integrating static and dynamic connectivity analysis to further investigation into functional brain differences during development.
Highlights
Connectivity analysis of resting-state functional MRI data has largely proceeded by computing mean correlations between networks of interest across time courses
Our analysis of static functional network connectivity (FNC) across all functional brain networks detected no significant difference in any pairwise correlation between Attention deficit hyperactivity disorder (ADHD) and typical development (TD) youth
An intriguing conceptual parallel is present in our earlier work in schizophrenia, where we identified that thalamico-cortical dynamic functional network connectivity (dFNC) differences with TD existed in states with the strongest cortical-subcortical anticorrelations (Damaraju et al, 2014)
Summary
Connectivity analysis of resting-state functional MRI (fMRI) data has largely proceeded by computing mean correlations between networks (or nodes) of interest across time courses. This provides an averaged snapshot of “static” functional network connectivity (sFNC), and superordinate approximation of underlying dynamic states (Ciric, Nomi, Uddin, & Satpute, 2017). Secondary measures of dynamism have intuitive biological appeal as potential corollaries of cognitive control and flexibility (Nomi et al, 2017) to examine how the brain switches among prototype connectivity states over the time course of fMRI observations. Initial investigations of the development of dynamism in youth, when cognitive control networks undergo considerable refinement entraining behavioral maturation, suggests increasing age is associated with increased variability in the time allocated to each state (Marusak et al, 2017) and greater switching fluidity in executive regions (Chai et al, 2017)
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
