Review Article | Published:

The connectomics of brain disorders

Nature Reviews Neuroscience volume 16, pages 159172 (2015) | Download Citation

Abstract

Pathological perturbations of the brain are rarely confined to a single locus; instead, they often spread via axonal pathways to influence other regions. Patterns of such disease propagation are constrained by the extraordinarily complex, yet highly organized, topology of the underlying neural architecture; the so-called connectome. Thus, network organization fundamentally influences brain disease, and a connectomic approach grounded in network science is integral to understanding neuropathology. Here, we consider how brain-network topology shapes neural responses to damage, highlighting key maladaptive processes (such as diaschisis, transneuronal degeneration and dedifferentiation), and the resources (including degeneracy and reserve) and processes (such as compensation) that enable adaptation. We then show how knowledge of network topology allows us not only to describe pathological processes but also to generate predictive models of the spread and functional consequences of brain disease.

Key points

  • The brain is a complex, interconnected network; the connection topology of the brain thus fundamentally shapes the onset, expression and progression of brain disease.

  • To understand disorders of the brain requires knowledge of how brain networks respond — either adaptively or maladaptively — to pathological perturbation.

  • The burgeoning field of connectomics is providing new tools for describing and modelling these responses.

  • The effects of brain disease depend critically on the topological centrality and degeneracy of the affected regions; pathology of central regions exacerbates maladaptive responses, whereas degeneracy facilitates adaptive responses

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Acknowledgements

A.F., A.Z. and M.B. are supported by the Australian National Health and Medical Research Council (grant identifiers: 1050504, 1066779, 1047648 and 1037196) and the Australian Research Council (ID: FT130100589). M.B. acknowledges the support of a Queensland Health Fellowship and the James S. McDonnell Foundation (Brain Network Recovery Group JSMF22002082). The authors thank B. Fulcher for assistance in developing some of the images in this article, and O. Sporns for technical assistance and for generously providing the data used in Fig. 3.

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Affiliations

  1. Monash Clinical and Imaging Neuroscience, School of Psychological Sciences and Monash Biomedical Imaging, Monash University, Clayton, Victoria, Australia 3168.

    • Alex Fornito
  2. Melbourne Neuropsychiatry Centre and Melbourne School of Engineering, The University of Melbourne, Parkville, Victoria, Australia 3053.

    • Andrew Zalesky
  3. Systems Neuroscience Group, QIMR Berghofer Medical Research Institute, Herston, Queensland, Australia 4029.

    • Michael Breakspear
  4. Metro North Mental Health Service, The Royal Brisbane and Women's Hospital, Herston, Queensland, Australia 4029.

    • Michael Breakspear

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Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Alex Fornito.

Glossary

Diffusion tractography

An MRI technique for reconstructing large-scale white-matter fibres based on the preferential diffusion of water along the axes of these fibres.

Hierarchical modularity

The nested organization of highly interconnected subsets, or modules, of nodes within a network, such that modules are contained within modules and so on, across multiple scales of organization.

Graph theory

A branch of mathematics concerned with studying networks of connected elements. With graph theory, a brain network can be modelled as a graph of nodes (depicting single neurons, neuronal populations or macroscopic brain regions) linked by edges (depicting inter-regional structural or functional interactions).

Network topology

The way in which the connections of a network are organized with respect to each other.

Functional connectivity

A statistical dependence (such as a correlation) between neurophysiological recordings acquired from distinct brain regions.

Effective connectivity

The causal influence that one neuronal system exerts on another. Its measurement often requires a model of the neuronal dynamics causing variations in measured neural signals.

Neuromodulation

The regulation of neuronal activity by ascending neurotransmitter systems.

Degree distribution

The distribution of degree values obtained across network nodes.

Network fragmentation

The splitting of a network into disconnected subsets of nodes. The lack of connectivity between these subsets precludes any communication between them, meaning the nodes no longer function as an integrated system.

Structural connectivity

The physical connections (that is, axonal fibres) between brain regions.

Motifs

Simple, recurring patterns or subgraphs that represent building blocks of a larger network.

Non-stationarity

The tendency of some time series to show fluctuations in their mean, covariance and other descriptive measures over time. Non-stationary activity in the brain means that long-term temporal averages of neural activity may not accurately summarize dynamics over shorter timescales.

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Published

DOI

https://doi.org/10.1038/nrn3901

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