Longitudinal Changes of Resting-State Networks in Children With Attention-Deficit/Hyperactivity Disorder and Typically Developing Children

Shania Mereen Soman; Nandita Vijayakumar; Gareth Ball; Christian Hyde; Timothy J Silk

doi:10.1016/j.bpsc.2022.01.001

Longitudinal Changes of Resting-State Networks in Children With Attention-Deficit/Hyperactivity Disorder and Typically Developing Children

Biol Psychiatry Cogn Neurosci Neuroimaging. 2023 May;8(5):514-521. doi: 10.1016/j.bpsc.2022.01.001. Epub 2022 Jan 14.

Authors

Shania Mereen Soman¹, Nandita Vijayakumar², Gareth Ball³, Christian Hyde², Timothy J Silk⁴

Affiliations

¹ School of Psychology, Deakin University, Geelong, Victoria, Australia. Electronic address: [email protected].
² School of Psychology, Deakin University, Geelong, Victoria, Australia.
³ Developmental Imaging, Murdoch Children's Research Institute, Parkville, Victoria, Australia; Department of Paediatrics, University of Melbourne, Parkville, Victoria, Australia.
⁴ School of Psychology, Deakin University, Geelong, Victoria, Australia; Developmental Imaging, Murdoch Children's Research Institute, Parkville, Victoria, Australia. Electronic address: [email protected].

PMID: 35033687
DOI: 10.1016/j.bpsc.2022.01.001

Abstract

Background: Attention-deficit/hyperactivity disorder (ADHD) is a prevalent childhood neurodevelopmental disorder. Given the profound brain changes that occur across childhood and adolescence, it is important to identify functional networks that exhibit differential developmental patterns in children with ADHD. This study sought to examine whether children with ADHD exhibit differential developmental trajectories in functional connectivity compared with typically developing children using a network-based approach.

Methods: This longitudinal neuroimaging study included 175 participants (91 children with ADHD and 84 control children without ADHD) between ages 9 and 14 and up to 3 waves (173 total resting-state scans in children with ADHD and 197 scans in control children). We adopted network-based statistics to identify connected components with trajectories of development that differed between groups.

Results: Children with ADHD exhibited differential developmental trajectories compared with typically developing control children in networks connecting cortical and limbic regions as well as between visual and higher-order cognitive regions. A pattern of reduction in functional connectivity between corticolimbic networks was seen across development in the control group that was not present in the ADHD group. Conversely, the ADHD group showed a significant decrease in connectivity between predominantly visual and higher-order cognitive networks that was not displayed in the control group.

Conclusions: Our findings show that the developmental trajectories in children with ADHD are characterized by a subnetwork involving different trajectories predominantly between corticolimbic regions and between visual and higher-order cognitive network connections. These findings highlight the importance of examining the longitudinal maturational course to understand the development of functional connectivity networks in children with ADHD.

Keywords: Attention-deficit/hyperactivity disorder; Development; Multilevel modeling; Network-Based R-Statistics; Resting-state functional magnetic resonance imaging; Resting-state networks.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Adolescent
Attention Deficit Disorder with Hyperactivity*
Brain
Child
Connectome* / methods
Humans
Magnetic Resonance Imaging / methods
Neuroimaging