Developmental Changes in Dynamic Functional Connectivity From Childhood Into Adolescence

Mónica López-Vicente, Oktay Agcaoglu, Laura Pérez-Crespo, Fernando Estévez-López, José María Heredia-Genestar, Rosa H. Mulder, John C. Flournoy, Anna C.K. van Duijvenvoorde, Berna Güroğlu, Tonya White, Vince Calhoun, Henning Tiemeier*, Ryan L. Muetzel

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

13 Citations (Scopus)
24 Downloads (Pure)

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.

Original languageEnglish
Article number724805
JournalFrontiers in Systems Neuroscience
Volume15
DOIs
Publication statusPublished - 22 Nov 2021

Bibliographical note

Funding Information:
This project was funded by the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Skłodowska-Curie grant agreement no 707404 (ML-V and FE-L). The opinions expressed in this document reflect only the author’s view. The European Commission is not responsible for any use that may be made of the information it contains. Neuroimaging acquisition, image analysis and/or infrastructure were supported by the Sophia Foundation (S18-20, RLM), the Erasmus University Fellowship (RLM), the Netherlands Organization for Health Research and Development (ZonMw) TOP project number 91211021 (TW), and by NIH grants R01MH118695 (VC) and R01EB020407 (VC). Supercomputing resources were provided by the Dutch Scientific Organization (NWO) and SurfSara (Cartesius compute system). RHM was supported by The Netherlands Organization for Scientific Research (NWA Startimpuls 400.17.602). LP-C was supported

Funding Information:
This project was funded by the European Union?s Horizon 2020 Research and Innovation Programme under the Marie Sk?odowska-Curie grant agreement no 707404 (ML-V and FE-L). The opinions expressed in this document reflect only the author?s view. The European Commission is not responsible for any use that may be made of the information it contains. Neuroimaging acquisition, image analysis and/or infrastructure were supported by the Sophia Foundation (S18-20, RLM), the Erasmus University Fellowship (RLM), the Netherlands Organization for Health Research and Development (ZonMw) TOP project number 91211021 (TW), and by NIH grants R01MH118695 (VC) and R01EB020407 (VC). Supercomputing resources were provided by the Dutch Scientific Organization (NWO) and SurfSara (Cartesius compute system). RHM was supported by The Netherlands Organization for Scientific Research (NWA Startimpuls 400.17.602). LP-C was supported by the Spanish Ministry of Science, Innovation and Universities through the ?Centro de Excelencia Severo Ochoa 2019?2023? Program (CEX2018-000806-S), and the Generalitat de Catalunya through the CERCA Program.

Publisher Copyright:
Copyright © 2021 López-Vicente, Agcaoglu, Pérez-Crespo, Estévez-López, Heredia-Genestar, Mulder, Flournoy, van Duijvenvoorde, Güroğlu, White, Calhoun, Tiemeier and Muetzel.

Fingerprint

Dive into the research topics of 'Developmental Changes in Dynamic Functional Connectivity From Childhood Into Adolescence'. Together they form a unique fingerprint.

Cite this