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Meta-connectomics: human brain network and connectivity meta-analyses

Published online by Cambridge University Press:  26 January 2016

N. A. Crossley*
Affiliation:
Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK Institute for Biological and Medical Engineering, Schools of Medicine, Biological Sciences and Engineering, P. Catholic University of Chile, Chile Department of Psychiatry, School of Medicine, P. Catholic University of Chile, Chile
P. T. Fox
Affiliation:
Research Imaging Institute and Department of Radiology, The University of Texas Health Science Center at San Antonio, San Antonio, TX, USA South Texas Veterans Health Care System, Research Service, San Antonio, TX, USA
E. T. Bullmore
Affiliation:
Behavioural & Clinical Neuroscience Institute, Department of Psychiatry, University of Cambridge, Cambridge, UK Cambridgeshire & Peterborough NHS Foundation Trust, Cambridge, UK GlaxoSmithKline, ImmunoPsychiatry, Alternative Discovery & Development, Cambridge, UK
*
* Address for correspondence: Dr N. A. Crossley, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK. (Email: nicolas.crossley@kcl.ac.uk)

Abstract

Abnormal brain connectivity or network dysfunction has been suggested as a paradigm to understand several psychiatric disorders. We here review the use of novel meta-analytic approaches in neuroscience that go beyond a summary description of existing results by applying network analysis methods to previously published studies and/or publicly accessible databases. We define this strategy of combining connectivity with other brain characteristics as ‘meta-connectomics’. For example, we show how network analysis of task-based neuroimaging studies has been used to infer functional co-activation from primary data on regional activations. This approach has been able to relate cognition to functional network topology, demonstrating that the brain is composed of cognitively specialized functional subnetworks or modules, linked by a rich club of cognitively generalized regions that mediate many inter-modular connections. Another major application of meta-connectomics has been efforts to link meta-analytic maps of disorder-related abnormalities or MRI ‘lesions’ to the complex topology of the normative connectome. This work has highlighted the general importance of network hubs as hotspots for concentration of cortical grey-matter deficits in schizophrenia, Alzheimer's disease and other disorders. Finally, we show how by incorporating cellular and transcriptional data on individual nodes with network models of the connectome, studies have begun to elucidate the microscopic mechanisms underpinning the macroscopic organization of whole-brain networks. We argue that meta-connectomics is an exciting field, providing robust and integrative insights into brain organization that will likely play an important future role in consolidating network models of psychiatric disorders.

Type
Review Article
Copyright
Copyright © Cambridge University Press 2016 

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