AIM
In this work, we propose a novel use of adaptively constrained independent vector analysis (acIVA) to effectively capture the temporal and spatial properties of dynamic blood-oxygen-level-dependent (BOLD) activity (dBA), and efficiently quantify the spatial property of dBA (sdBA). We also propose to incorporate dBA into the study of brain dynamics to gain insight into activity-connectivity co-evolution patterns.
INTRODUCTION
Studies of the dynamics of human brain using functional magnetic resonance imaging (fMRI) has enabled the identification of unique functional network connectivity (FNC) states and provided new insights into mental disorders. There is evidence showing that both BOLD activity, which is captured by fMRI, and FNC are related to mental and cognitive processes. However, few studies have evaluated the inter-relationships of these two domains of function. Moreover, identification of subgroups of schizophrenia (SZ) has gained significant clinical importance due to a need to study the heterogeneity of SZ.
METHODS
We design a simulation study to verify the effectiveness of acIVA and apply acIVA to the dynamic study of resting-state fMRI data collected from individuals with SZ and healthy controls (HCs) in order to investigate the relationship between dBA and dynamic FNC (dFNC).
RESULTS
The simulation study demonstrates that acIVA accurately captures the spatial variability and provides an efficient quantification of sdBA. The fMRI analysis yields synchronized sdBA-tdBA patterns and shows that the dBA and dFNC are significantly correlated in the spatial domain. Using these dynamic features, we identify subgroups of SZ with significant differences in terms of their clinical symptoms.
CONCLUSION
We find that brain function is less efficiently organized in SZs compared with HCs since there are less synchronized sdBA-tdBA patterns in SZs and SZs prefer a component that merges multiple brain regions. The identification of subgroups of SZ using dynamic features inspires the use of neuroimaging in studying heterogeneity of disorders.