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The Connectomics of Brain Disorders

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The Connectomics of Brain Disorders REVIEWS The connectomics of brain disorders Alex Fornito1, Andrew Zalesky2 and Michael Breakspear3,4 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. The ancient Roman physician Galen was one of the first to From this perspective, the behavioural impairments propose that pathology in one part of the nervous system that arise from neural insult were thought to emerge could affect other regions when he posited that animal from damage to discrete and specialized brain regions, spirits could flow through interconnecting neural path- as exemplified by famous clinical case studies such as ways1,2. This hypothesis was revisited nearly two millen- Broca’s Leborgne12, Harlow’s Phineas Gage13, and Scoville nia later by Brown-Séquard, who suggested that the effects and Milner’s H.M.14. However, this view provides only a of focal brain damage on remote regions resulted from partial account of brain function. The brain is a highly actions at a distance3. von Monakow extended the concept, complex, interconnected network that balances regional and coined the term diaschisis (derived from Greek and segregation and specialization of function with strong meaning ‘shocked throughout’) to describe the depression integration15,16, a balance that gives rise to complex and 1Monash Clinical and Imaging of function that can arise in undamaged brain regions that precisely coordinated dynamics across multiple spatio­ 2,4 17 Neuroscience, School of are connected to a lesioned site (reviewed in REF. 5). At a temporal scales . A generic property of any network is Psychological Sciences and similar time, Wernicke proposed an associative theory of that dysfunction can spread easily between linked ele- Monash Biomedical Imaging, brain function, in which higher-order cognitive processes ments, leading to pathological cascades that can encom- Monash University, Clayton, 18 Victoria, Australia 3168. arose from the integration of multiple, spatially distrib- pass large swathes of the system . In the brain, axonal 2Melbourne Neuropsychiatry uted neural systems and in which disorders as diverse as and synaptic contacts can act as conduits for the propaga- Centre and Melbourne School aphasia and schizophrenia resulted from the disruption of tion of disease processes. This is exemplified by the rapid of Engineering, The University specific associative pathways6 (see also REF. 7). Wernicke’s spread of focal epileptogenic activity into generalized of Melbourne, Parkville, contemporaries, including Hughlings Jackson, Meynert, seizures19, the spatially distributed activation changes Victoria, Australia 3053. 20 3Systems Neuroscience Flechsig, Dejerine and Lichtheim, also viewed connectiv- resulting from regionally localized ischaemic insults Group, QIMR Berghofer ity to be central to any understanding of CNS pathology. and the gradual progression of pathology in degenera- Medical Research Institute, Their work paved the way for Geschwind’s introduction of tive diseases that are thought to have focal onset, such as Herston, Queensland, the ‘disconnexion syndrome’ and the concomitant expan- Huntington disease, Parkinson disease and other forms Australia 4029. 21,22 4Metro North Mental Health sion of the range of clinical symptoms that may now be of neurodegeneration . 8,9 Service, The Royal Brisbane attributed to disordered brain connectivity . These An important first step in understanding how neural and Women’s Hospital, develop­ments complemented the emergence of neural- network organization influences the onset, expression Herston, Queensland, systems-based accounts of perception, cognition and and course of disease is the generation of a compre- Australia 4029. emotion10,11 (for a detailed review, see REF. 7). hensive map — a connectome23 — of the connectivity Correspondence to A.F. (BOX 1) e‑mail: Despite these early insights, a major focus of twenti- architecture of the brain . This goal has driven [email protected] eth-century clinical neuroscience was the localization of several recent, large-scale collaborative endeavours that doi:10.1038/nrn3901 psychological processes to specific areas of the brain. are unprecedented in neuroscience24–26, and has led to NATURE REVIEWS | NEUROSCIENCE VOLUME 16 | MARCH 2015 | 159 © 2015 Macmillan Publishers Limited. All rights reserved REVIEWS Box 1 | Mapping the human connectome MRI is the tool most widely used to map structural and functional properties of the human connectome. MRI connectomics involves three main steps. First, network nodes are defined, commonly using one of many heuristic approaches (see REF. 148 for a discussion of these methods; see the brain below figure parts a, b on which the green spheres depict an example of a priori-defined regions of interest). Second measures of structural, functional or effective connectivity between nodes are quantified. Both functional connectivity and effective connectivity can be measured during active task performance119,151–153 or task-free (‘resting’) states142,154–156 using functional MRI (see the figure, part a). Structural connectivity can be measured using diffusion tractography (see REFS 148,150 for limitations; see the figure, part b149). The final step involves analysis of either network connectivity or topology77. Connectivity analyses examine variations in the type and strength of connectivity between brain regions, and can be carried out at the level of candidate neural systems (candidate systems analysis) or across the entire brain (connectome-wide analysis)157. In the latter case, whole-brain connectivity can be succinctly represented as a matrix in which each row and column represents a different region, and each matrix element [i, j] encodes the type and strength of connectivity between region pairs (see the figure, parts c, d). The network can also be represented as a graph that comprises nodes (which represent regions) connected by edges (which represent connections; see the figure, parts e, f, in which line thickness represents strength of connectivity). In MRI analyses, connectivity matrices and graphs are often undirected (that is, they represent the presence but not direction of a connection) and weighted to reflect variations of inter-regional connectivity strength (see the figure, parts c, e). Models of effective connectivity can resolve directionality (see the figure, parts d, f). Topological analyses are grounded in graph theory15 and have uncovered several non-trivial characteristic properties of the brain. These properties include a short average path-length between nodes, which enables efficient communication (that is, a parallel and integrated topology), coupled with low wiring cost158,159; a heavy-tailed distribution of regional connectivity, which implies the existence of highly connected hub regions27,111; strong interconnectivity of hub regions, defining a topologically central core, or rich club113,126; and high clustering and hierarchical modularity across multiple resolution scales, which supports functional specialization160. Part g of the figure illustrates some of these key topological properties of brain networks; namely, the shortest path length between two nodes at opposite ends of the network (left; path length shown in orange), clustered connectivity (middle; orange lines represent edges linking a cluster of nodes), modular organization (right; modules outlined by grey shading) and rich-club organization (right; rich-club edges in orange). Image in part b is adapted, with permission, from REF. 149, PLoS http://creativecommons.org/licenses/by/3.0/. a Functional connectivity b Structural connectivity c Undirected matrix d Directed matrix 1 1 2 2 3 3 . i i . N N 1 2 3 . N 1 2 3 . N j j e Undirected graph f Directed graph Diffusion tractography An MRI technique for reconstructing large-scale Topological analysis white-matter fibres based on g 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. Nature Reviews | Neuroscience 160 | MARCH 2015 | VOLUME 16 www.nature.com/reviews/neuro © 2015 Macmillan Publishers Limited. All rights reserved REVIEWS a b c d e 4 2 1 3 f Effective connectivity g Quantitative difference Qualitative difference Group A Group B Group A Group B Normal Abnormal At risk Figure 1 | Connectomics can track and predict patterns of disease spread. a | Typically,Nature a brain-imaging Reviews | Neuroscience experiment may compare
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