Developmental Changes in Dynamic Functional Connectivity From Childhood Into Adolescence

• Authors: Mónica López-Vicente, Erasmus MC University; Oktay Agcaoglu, Georgia State University; Laura Pérez-Crespo, Barcelona Institute for Global Health (ISGlobal); Fernando Estévez-López, Erasmus MC University; José María Heredia-Genestar, Erasmus MC University; Rosa H Mulder, Erasmus MC University; John C Flournoy, Harvard University; Anna CK van Duijvenvoorde, Leiden University; Berna Güroğlu, Leiden University; Tonya White, Erasmus MC Univers; Viince D Calhoun, Emory University; Henning Tiemeier, Erasmus MC University; Ryan L Muetzel, Erasmus MC University
• Journal Title: Frontiers in Systems Neuroscience
• Volume: Volume 15
• Publisher: Frontiers Media | 2021-11-22
• Type of Work: Article | Final Publisher PDF
• Publisher DOI: http://doi.org/10.3389/fnsys.2021.724805
• Subjects: Biology, Genetics; Health Sciences, Radiology; Psychology, Behavioral
• Keywords: brain development; fMRI; longitudinal; resting state – fMRI; linear mixed effect model

Abstract

The longitudinal study of typical neurodevelopment is key for understanding deviations due to specific factors, such as psychopathology. However, research utilizing repeated measurements remains scarce. Resting-state functional magnetic resonance imaging (MRI) studies have traditionally examined connectivity as ‘static’ during the measurement period. In contrast, dynamic approaches offer a more comprehensive representation of functional connectivity by allowing for different connectivity configurations (time varying connectivity) throughout the scanning session. Our objective was to characterize the longitudinal developmental changes in dynamic functional connectivity in a population-based pediatric sample. Resting-state MRI data were acquired at the ages of 10 (range 8-to-12, n = 3,327) and 14 (range 13-to-15, n = 2,404) years old using a single, study-dedicated 3 Tesla scanner. A fully-automated spatially constrained group-independent component analysis (ICA) was applied to decompose multi-subject resting-state data into functionally homogeneous regions. Dynamic functional network connectivity (FNC) between all ICA time courses were computed using a tapered sliding window approach. We used a k-means algorithm to cluster the resulting dynamic FNC windows from each scan session into five dynamic states. We examined age and sex associations using linear mixed-effects models. First, independent from the dynamic states, we found a general increase in the temporal variability of the connections between intrinsic connectivity networks with increasing age. Second, when examining the clusters of dynamic FNC windows, we observed that the time spent in less modularized states, with low intra- and inter-network connectivity, decreased with age. Third, the number of transitions between states also decreased with age. Finally, compared to boys, girls showed a more mature pattern of dynamic brain connectivity, indicated by more time spent in a highly modularized state, less time spent in specific states that are frequently observed at a younger age, and a lower number of transitions between states. This longitudinal population-based study demonstrates age-related maturation in dynamic intrinsic neural activity from childhood into adolescence and offers a meaningful baseline for comparison with deviations from typical development. Given that several behavioral and cognitive processes also show marked changes through childhood and adolescence, dynamic functional connectivity should also be explored as a potential neurobiological determinant of such changes.