Annual Research Review: Growth connectomics – the organization and reorganization of brain networks during normal and abnormal development. (1st December 2014)

• Record Type: Journal Article
• Authors: Vértes, Petra E.; Bullmore, Edward T.
• Is Part Of: Journal of child psychology and psychiatry and allied disciplines. Volume 56:Number 3(2015:Mar.)
• Page Start: 299
• Page End: 320
• Publication Date: 2014-12-01
• Subjects: Child psychology -- Periodicals; Child psychiatry -- Periodicals; 155.4
• DOI: 10.1111/jcpp.12365
• Languages: English
• ISSNs: 0021-9630

Abstract

<abstract abstract-type="main" id="jcpp12365-abs-0001"> <title> <x xml:space="preserve">Abstract</x> </title> <sec id="jcpp12365-sec-0001" sec-type="section"> <title>Background</title> <p>We first give a brief introduction to graph theoretical analysis and its application to the study of brain network topology or connectomics. Within this framework, we review the existing empirical data on developmental changes in brain network organization across a range of experimental modalities (including structural and functional MRI, diffusion tensor imaging, magnetoencephalography and electroencephalography in humans).</p> </sec> <sec id="jcpp12365-sec-0002" sec-type="section"> <title>Synthesis</title> <p>We discuss preliminary evidence and current hypotheses for how the emergence of network properties correlates with concomitant cognitive and behavioural changes associated with development. We highlight some of the technical and conceptual challenges to be addressed by future developments in this rapidly moving field. Given the parallels previously discovered between neural systems across species and over a range of spatial scales, we also review some recent advances in developmental network studies at the cellular scale. We highlight the opportunities presented by such studies and how they may complement neuroimaging in advancing our understanding of brain development. Finally, we note that many brain and mind disorders are thought to be neurodevelopmental in origin and that charting the trajectory of brain network changes associated with healthy development also sets the stage for understanding abnormal network development.</p> </sec> <sec id="jcpp12365-sec-0003" sec-type="section"> <title>Conclusions</title> <p>We therefore briefly review the clinical relevance of network metrics as potential diagnostic markers and some recent efforts in computational modelling of brain networks which might contribute to a more mechanistic understanding of neurodevelopmental disorders in future.</p> </sec> </abstract>