Functional network dynamics in a neurodevelopmental disorder of known genetic origin.

Erin Hawkins,Duncan Astle,Mark Woolrich,Mengya Zhang,Danyal Akarca,Diandra Brkić,Kate Baker

doi:10.1002/hbm.24820

Abstract

Dynamic connectivity in functional brain networks is a fundamental aspect of cognitive development, but we have little understanding of the mechanisms driving variability in these networks. Genes are likely to influence the emergence of fast network connectivity via their regulation of neuronal processes, but novel methods to capture these rapid dynamics have rarely been used in genetic populations. The current study redressed this by investigating brain network dynamics in a neurodevelopmental disorder of known genetic origin, by comparing individuals with a ZDHHC9‐associated intellectual disability to individuals with no known impairment. We characterised transient network dynamics using a Hidden Markov Model (HMM) on magnetoencephalography (MEG) data, at rest and during auditory oddball stimulation. The HMM is a data‐driven method that captures rapid patterns of coordinated brain activity recurring over time. Resting‐state network dynamics distinguished the groups, with ZDHHC9 participants showing longer state activation and, crucially, ZDHHC9 gene expression levels predicted the group differences in dynamic connectivity across networks. In contrast, network dynamics during auditory oddball stimulation did not show this association. We demonstrate a link between regional gene expression and brain network dynamics, and present the new application of a powerful method for understanding the neural mechanisms linking genetic variation to cognitive difficulties.

Highlights

In recent years, whole-brain imaging methods have advanced our ability to characterise functional brain connectivity, and have revealed that distributed neural systems support cognition and behaviourErin Hawkins and Danyal Akarca contributed to this study.(Astle, Barnes, Baker, Colclough, & Woolrich, 2015; Barnes, Woolrich, Baker, Colclough, & Astle, 2016; Smith et al, 2015; Vidaurre et al, 2017)
We sought to characterise the dynamics of functional connectivity networks in individuals with the ZDHHC9 gene mutation, a single-gene developmental disorder associated with a homogenous phenotype of intellectual, language, and attentional impairments
We examined network dynamics using a Hidden Markov Model (HMM), a data-driven method that captures functional networks on a millisecond timescale and quantifies their dynamics

Summary

Introduction

Whole-brain imaging methods have advanced our ability to characterise functional brain connectivity, and have revealed that distributed neural systems support cognition and behaviourErin Hawkins and Danyal Akarca contributed to this study.(Astle, Barnes, Baker, Colclough, & Woolrich, 2015; Barnes, Woolrich, Baker, Colclough, & Astle, 2016; Smith et al, 2015; Vidaurre et al, 2017). Understanding of the rapid synchronisation and integration of functional connectivity networks at fast time-scales, in the range of 100–200 milliseconds, is limited. This is partly because there is a scarcity of methods capable of characterising the fast dynamics of brain networks. There is currently little understanding of the genetic effects on these networks and the cellular mechanisms that could drive their developmental variability or the consequences of perturbations to these network dynamics for cognition. The aim of this study is to redress this by exploring dynamic transient brain connectivity in a group of individuals with a neurodevelopmental disorder of known genetic origin

Objectives

Methods

Findings

Conclusion