4.5 Review

Annual Research Review: Growth connectomics - the organization and reorganization of brain networks during normal and abnormal development

Journal

JOURNAL OF CHILD PSYCHOLOGY AND PSYCHIATRY
Volume 56, Issue 3, Pages 299-320

Publisher

WILEY
DOI: 10.1111/jcpp.12365

Keywords

Brain networks; connectomics; development; cognitive change; neurodevelopmental disorders

Funding

  1. Medical Research Council [MR/K020706/1]
  2. Neuroscience in Psychiatry Network (NSPN) - Wellcome Trust Strategy Award
  3. MRC [MR/K020706/1] Funding Source: UKRI
  4. Medical Research Council [G0001354, MR/K020706/1, G0001354B, G1000183B] Funding Source: researchfish
  5. National Institute for Health Research [NF-SI-0513-10051] Funding Source: researchfish

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BackgroundWe 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). SynthesisWe 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. ConclusionsWe 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. Read the Commentary on this article at doi

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