REVIEWS Network dysfunction after traumatic brain injury David J. Sharp, Gregory Scott and Robert Leech Abstract | Diffuse axonal injury after traumatic brain injury (TBI) produces neurological impairment by disconnecting brain networks. This structural damage can be mapped using diffusion MRI, and its functional effects can be investigated in large-scale intrinsic connectivity networks (ICNs). Here, we review evidence that TBI substantially disrupts ICN function, and that this disruption predicts cognitive impairment. We focus on two ICNs—the salience network and the default mode network. The activity of these ICNs is normally tightly coupled, which is important for attentional control. Damage to the structural connectivity of these networks produces predictable abnormalities of network function and cognitive control. For example, the brain normally shows a ‘small-world architecture’ that is optimized for information processing, but TBI shifts network function away from this organization. The effects of TBI on network function are likely to be complex, and we discuss how advanced approaches to modelling brain dynamics can provide insights into the network dysfunction. We highlight how structural network damage caused by axonal injury might interact with neuroinflammation and neurodegeneration in the pathogenesis of Alzheimer disease and chronic traumatic encephalopathy, which are late complications of TBI. Finally, we discuss how network-level diagnostics could inform diagnosis, prognosis and treatment development following TBI. Sharp, D. J. et al. Nat. Rev. Neurol. advance online publication 11 February 2014; doi:10.1038/nrneurol.2014.15

Introduction

Computational, Cognitive and Clinical Neuroimaging Laboratory, Centre for Neuroscience, Division of Experimental Medicine, Imperial College London, Hammersmith Hospital Campus, Du Cane Road, London W12 0NN, UK (D.J.S., G.S., R.L.). Correspondence to: D.J.S. david.sharp@ imperial.ac.uk

Many questions about brain function cannot be answered by studying single neurons in isolation. Information processed at the neuronal level often needs to be integ­rated across brain regions to be functionally useful. 1 Researchers have shown growing interest in understanding how brain networks are organized to support this type of operation,2,3 and the dysfunction of large-scale brain networks is increasingly recognized to play an important part in many brain disorders.4 Traumatic brain injury (TBI) can produce both focal brain damage and diffuse axonal injury (DAI).5,6 Most previous work has investigated the clinical effects of focal injuries, which are often of limited value when attempting to predict clinical outcomes. Patients often have disabling problems in the domains of attention, memory and executive function. These ‘high-level’ cognitive functions require the integration of information processing across spatially distinct brain regions. Impairments in these domains can result from the effects of DAI on long-distance white matter tracts that connect nodes in distributed brain networks.7,8 Brain networks can be studied at many spatial scales (Box 1). In this Review, we focus primarily on the disruption of large-scale intrinsic connectivity Competing interests D.J.S. has received a research grant from Pfizer. G.S. receives research funding from GlaxoSmithKline via a Wellcome Trust grant. R.L. declares no competing interests.

networks (ICNs), as interactions between large-scale brain networks are particularly important for highlevel cognitive functions such as memory and attention.1 Moreover, neuroimaging advances have enabled investigation of these networks in clinical populations. ICNs are composed of brain regions that show temporally correlated neural activity.9,10 The functional architecture of these networks in part reflects underlying structural connectivity—that is, regions that are strongly connected by white matter tracts are likely to show similar functional properties. This phenomenon makes the function of ICNs vulnerable to the effects of TBI, because DAI commonly damages long-distance white matter tracts that connect nodes in these networks. As a result, TBI can be viewed as a classic example of a disorder in which network disruption produces clinically important impairments. Here, we review how TBI damages the brain’s structure and function, and how this damage is reflected in ICNs. We highlight the principles of network disruption after TBI, illustrated by its effects on two wellcharacterized ICNs: the default mode network (DMN) and the salience network (SN). Cognitive impairments of memory, attention and executive function after TBI are associated with abnormalities of DMN and SN function. For example, structural disconnection within the DMN is linked to impairments of sustained attention,8 and damage within the SN is linked to failure of DMN control and impairments of attention and executive

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REVIEWS Pathophysiology of TBI

Key points ■■ Diffuse axonal injury after traumatic brain injury (TBI) disconnects large-scale brain networks, leading to network dysfunction and cognitive impairment ■■ Interactions between the salience network and the default mode network are disrupted by TBI, producing impairments of cognitive control ■■ TBI shifts the brain away from the small-world architecture that is optimal for information processing, and particularly affects highly connected network hubs ■■ TBI can trigger neurodegenerative processes that can lead to conditions such as Alzheimer disease and chronic traumatic encephalopathy, which might result from the diffusion of misfolded proteins along damaged white matter tracts ■■ Network diagnostics can provide individual measures of the structural and functional integrity of intrinsic connectivity networks, and are likely to have clinical utility for predicting outcomes and guiding treatment development

Box 1 | Brain networks The brain can be divided into distinct functional networks. These networks exist at a range of spatial scales, from microscopic neuronal networks of individual neurons and local synaptic interactions, through to large-scale networks of regions connected by large white matter tracts composed of millions of axons. In this article, we focus on large-scale, whole-brain networks. At this level, the brain’s activity can be fractionated into discrete intrinsic connectivity networks (ICNs) composed of nodes distributed across multiple cortical lobes. Nodes can be defined in a number of ways. Commonly, anatomical regions are used to define nodes in a network, but changes in brain activity are increasingly being used to functionally define nodes.101 Functionally defined networks are not restricted to humans: homologous ICNs have been defined in other species.102 In humans, ICNs show functional specialization, with different responses seen during different cognitive tasks, such as reasoning or language-related tasks. ICNs are often defined by the presence of coordinated brain activity at rest, and are sometimes referred to as ‘resting-state networks’. Similar patterns of activity can, however, be defined using task-evoked changes in brain activity. 10 ICNs should, therefore, be thought of as being constantly present, with activity and network affiliations being modulated by changes in behavioural state. The functional roles of networks cannot be fully understood by studying them in isolation. Instead, the dynamic interactions between ICNs need to be considered, and a major direction of future work will be in understanding the way in which information processing within specialized ICNs is integrated in hub regions that link networks. This is clinically important, because damage to the hubs that connect different networks will disrupt the cognitive functions that require the integration of information processing, including attention, memory and awareness, which are commonly impaired after traumatic brain injury.

function.11 The brain normally exhibits a ‘small-world architecture’, a combination of densely connected local hubs linked by more-sparse long-range connections. We describe how TBI seems to shift the DMN and SN away from this optimal structure—a change that particularly affects the connections of highly connected hub regions such as the posterior cingulate cortex (PCC). We then discuss the complexity inherent in brain network function after TBI, and describe how advanced approaches to modelling of brain dynamics could provide insights into the indirect effects of brain injury on network function. We discuss how axonal injury, neuro­ inflammation and neuro­degeneration might interact to influence the development of Alzheimer disease (AD) and chronic traumatic encephalopathy (CTE), which are late complications of TBI. Finally, we consider the potential prognostic and therapeutic implications of deriving network-­level descriptions of ICN integrity from i­ ndividual patients with TBI.

The pathophysiology of TBI is complex. The primary injury can produce both focal and diffuse effects, with two main mechanisms thought to be particularly important: direct contact and acceleration–deceleration. Focal injur­ies result from an object striking the head or the brain striking the inside of the skull, producing skull fractures or haematomas.12 Extradural and subdural haematomas are commonly seen after TBI, as are haematomas within the brain parenchyma. Focal injuries of this type usually occur in the orbitofrontal, temporal polar and occipital regions.13 Alternatively, diffuse multifocal injuries often result after rapid acceleration and deceleration, which imparts shear, tensile and compressive strains that mainly damage longdistance white matter connections via DAI, and can also damage blood vessels (diffuse vascular injury).5,14,15 In extreme cases, mechanical stress at the time of impact results in axonal shearing. Although axons usually remain intact, damage commonly occurs to the axolemma around the nodes of Ranvier, as well as to neurofilament sub­units, which disrupts axonal transport. Secondary damage can also be produced by processes triggered by the initial injury, such as ischaemia, raised intracranial pressure, infection, inflammation and neurodegeneration. The pattern of focal brain injuries, such as haema­tomas, often correlates poorly with clinical impairments after TBI.16 The presence of diffuse white matter injury seems to explain this discrepancy. DAI is a common pathology documented in all severities of TBI.14,17 It is almost universally present in cases of fatal brain injury,6 and shows a characteristic distribution across brain regions, with particularly severe cases resulting in widespread damage that includes the corpus callosum and brainstem.14 In patients with very poor outcomes, such as those in a vegetative state, damage to the subcortical white matter and/ or the thalamic relay nuclei is a common feature, whereas cortical damage can be minimal.18 In patients with better outcomes, recent neuroimaging studies show that DAI is an important determinant of persistent cognitive impairment.7,19 As a result, TBI is a prime candidate for a dis­ order in which large-scale network disconnection is a core mechanism underlying cognitive impairment.

Structural disconnection after TBI Until recently, studying the distribution and extent of DAI, or its functional consequences, in vivo has been difficult. CT and standard MRI underestimate the extent of white matter damage after TBI. T2-weighted gradient-echo MRI can detect microbleeds, which are surrogate markers of DAI, and are associated with cognitive impairment. 20 Microbleed imaging, however, only shows the ‘tip of the iceberg’ of underlying DAI. Another advanced MRI technique, diffusion MRI (Box 2), has provided a step change in our ability to identify and quantify DAI.21–24 Diffusion MRI allows estimation of the amount of water diffusion in a number of directions at each point in an image. From this information, metrics such as fractional anisotropy can be calculated to quantify the degree of white matter disruption.22 Although DAI is often widespread after TBI, the

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REVIEWS Box 2 | Techniques for investigating brain networks Recent advances in neuroimaging allow investigation of changes in structural and functional connectivity in the human brain that are relevant to traumatic brain injury (TBI).

Structural connectivity Developments in diffusion imaging have allowed in vivo assessment of white matter tracts in humans. The technique has predominantly been used in two ways: first, tractography allows the trajectory of white matter tracts to be defined; and second, standardized measures of diffusion properties (such as fractional anisotropy or mean diffusivity) can be used to assess white matter microstructure at specific locations. In the context of TBI, estimates of the extent of white matter damage produced by diffuse axonal injury can be derived. However, tractography algorithms fail in patients with considerable white matter damage. One way to deal with this problem is to measure the damage produced by TBI using standardized tracts derived from control populations, an approach that yields more-reliable estimates of white matter integrity.103 Functional connectivity Functional MRI (fMRI) is used widely, and is based on detection of changes in blood oxygenation as a marker of underlying neural activity. Functional connectivity is inferred from correlated patterns of blood-flow changes across brain regions. Correlational analysis techniques range from simple regression using datadriven approaches such as independent component analysis,9 to dynamic causal modelling that estimates causal interactions between brain regions. 104 fMRI is an indirect measure of neural activity and has relatively poor temporal resolution. Therefore, other techniques such as magnetoencephalography and electroencephalography can provide complementary information.

regional distribution of injury is important in explaining the profile of cognitive impairment that is produced. Regarding high-level cognitive functions such as attention, memory and executive function, damage to specific network connections could be critical in determining the pattern of impairments (Table 1). For example, the extent of damage to the fornix—the main outflow tract of the ­hippocampus—is related to impairments of associative memory that are common after TBI.7

Disruption of functional networks In addition to mapping structural damage, MRI and other neuroimaging techniques can be used to study network function (Box 2). Given the presence of focal injury and the effects of DAI on structural connections within and between networks, it is unsurprising that TBI affects network function. Functional MRI (fMRI) studies have demonstrated complex, sometimes contradictory, patterns of functional network abnormalities (Table 1). These abnormalities have manifested as either changes in interactions between nodes (that is, changes in functional connectivity), or as changes in the activity of nodes in the network studied in isolation.8,25,26 The patterns of network dysfunction produced by TBI are complex, but some unifying principles are emerging. These principles can be illustrated by the effect of TBI on two well-characterized ICNs: the DMN and the SN (see Supplementary Video 1 online).27,28

Disconnection within the DMN The DMN consists of brain regions that have high metabolic activity and highly coordinated activity.29 Its core nodes are the PCC and ventro­medial prefrontal cortex (VMPFC). Damage to the connectivity and function

of these nodes is observed in many types of neurological and psychiatric disease.30 In TBI, varying amounts of damage are seen within the cingulum bundle 8—a tract that includes connections between the PCC and VMPFC.31 This damage correlates with impairments of sustained attention, whereby patients are unable to maintain attentional focus over time. Increased damage to the cingulum bundle is associated with worsening attention, a deficit that is also predicted by reductions in functional connectivity within the DMN.8 This finding illustrates how damage to the connections of an ICN can alter the functional connectivity of that ICN and affect cognitive ­functions supported by the network.

Abnormal activity of nodes within the DMN Nodes in the DMN show abnormalities of activation that are observed during performance of tasks that require focused attention.8,25 This response has been interpreted as a compensatory mechanism.25 However, the DMN usually shows load-dependent deactivation as task difficulty increases,32 and failure of this network to deactivate has been associated with poorer cognitive function in both healthy individuals and patients with TBI.11,33 DMN activity is normally high when attention is internally directed, and during actions that are reasonably automatic. When responding to an unexpected event in the environment, this internally focused mode of operation needs to be inhibited, allowing the brain to rapidly switch to a controlled mode wherein actions are tightly coupled to external events. Viewed in this context, increased activity in the DMN during this type of behaviour can be seen as a failure of control of the DMN, which might produce ‘interference’ to normal network operations.34 The above results should be placed in context of the high overall metabolic activity of nodes in the DMN.29 Widespread decreases in metabolism are observed in this network, as measured by fluorodeoxyglucose PET. This finding suggests that changes in the metabolic activity of the DMN after TBI could contribute to the dynamic abnormalities in activity that are seen during changing behaviour.35–37 Impaired coordination between networks Efficient cognitive function depends on coordinated activity between networks, and abnormal interactions between the SN and the DMN are seen after TBI.11 The SN responds to external events that are behaviourally salient,28 and seems to signal the need to reduce activity in the DMN (see Supplementary Video 1 online). We studied the switch from automatic to controlled behaviour using the stop-signal task (SST).11,35 In this test, individuals make reasonably automatic motor responses to a visual cue, but occasionally have to inhibit their responses after a ‘stop’ cue unexpectedly appears. Stopping is associ­ ated with activation of a right lateralized part of the SN and deactivation of the DMN. 38 Impaired response inhib­ition during attempted stopping is accompanied by failure to deactivate the DMN. The location of DAI is crucial to explaining the failure of DMN control.11 Structural damage to a particular SN tract that connects

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REVIEWS Table 1 | Network disruption and associated impairments after TBI Method

Network disruption

Associated clinical and cognitive impairment

Reduced white matter integrity

Correlation with information-processing speed and executive function105 Correlation with learning, memory and executive function7,47,106 Correlation with functional outcome scores23,107 Blast-related TBI24

Structural disconnection DTI

Functional network disruption H2150 PET

Increased activity overlapping SN or FPCN

Impaired memory retrieval112

fMRI

Increased activity overlapping SN or FPCN

Correlation with working memory52,55 Correlation with executive function53 Impaired working memory26,54,108,109 Impaired attention and executive function8,25,110,111 Impaired executive function113 Failure of DMN deactivation associated with impaired sustained attention8 Failure of DMN deactivation associated with impaired inhibitory control11 Impaired visual attention25

Decreased activity overlapping SN or FPCN

Increased activity in DMN

Arterial spin labelling fMRI

Decreased perfusion overlapping DMN

None114

Functional connectivity abnormalities in DMN fMRI

fMRI, DTI

Increased Reduced in posterior regions and increased frontally Decreased117

Correlation with post-concussive symptoms40,41 Correlation with cognitive impairment and symptoms116

Increased; negatively correlated with cingulate tract white matter integrity Increased; positively correlated with corpus callosum white matter integrity

None115

Functional connectivity associated with recovery from coma*61 Functional connectivity negatively correlated with consciousness impairment.‡ DMN FC in locked-in syndrome not significantly different from controls57 Persistent vegetative state§58

Functional connectivity negatively correlated with impairment to sustained attention44

Functional connectivity abnormalities of other ICNs fMRI

Decreased in FPCN Decreased in motor network Decreased inter-hemispheric connectivity Abnormal in thalamus Abnormal in multiple ICNs Abnormal in multiple ICNs (graph theoretical analyses)

Correlation None43,56 Correlation Correlation Correlation Correlation Correlation None121

with working memory52

Magnetoencephalography

Decreased in multiple networks Abnormal across multiple frequency bands

None51 Correlation with cognitive measures50

EEG

Decreased frontally Decreased small-worldness (graph theoretical analysis)

Blast-related TBI48 None49

Widespread hypometabolism including regions overlapping DMN

Correlation with clinical severity37 None35,36,122

with with with with with

memory impairment118,119 cognitive impairment and symptoms45 post-concussive symptoms119,120 post-concussion symptoms46 executive function47

Metabolic PET F-fluorodeoxyglucose PET

18

*Non-TBI cases. ‡Two trauma cases, 12 non-trauma cases. §Two TBI cases, one non-trauma case. Abbreviations: DMN, default mode network; DTI, diffusion tensor imaging; fMRI, functional MRI; FPCN, frontoparietal control network; H 2150, radiolabelled water; SN, salience network; TBI, traumatic brain injury.

the right anterior insula to the midline presupplementary motor area–dorsal anterior cingulate cortex is a strong and speci­fic predictor of failure to appropriately deactivate the DMN. This finding supports a model of cognitive control, in which responses of the SN to unexpected events trigger changes in other large-scale networks, including d ­ eactivation of the DMN.39

Altered resting-state connectivity ICN abnormalities after TBI have been widely observed in resting-state fMRI, which is acquired in the absence of a specific task. Both increases and decreases in connectivity have been observed in a number of networks, including the DMN and SN.40–44 Several studies have reported that these abnormalities correlate with cognitive impairment

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REVIEWS or post-concussive symptoms.45–47 Studies using electroencephalography and magneto­encephalography, which provide higher temporal resolution than fMRI, have also demonstrated disrupted functional connectivity after a range of severities of TBI.48–51

Abnormalities in other networks Network abnormalities have been observed in other ICNs after TBI. For example, increased activation is frequently seen in nodes of ICNs involved in the control of cognitively demanding tasks.8,52,53 These changes are not always associated with impaired behavioural performance, suggesting that they might represent compensatory increases in cognitive control.25,54,55 Reduced connectivity has also been observed within the motor network during performance of a simple motor task,56 and disruptions to functional connectivity between the right and left inferior frontal gyri correlate with ­behavioural performance on a working memory task.52 Altered states of consciousness Abnormalities of functional connectivity have been demonstrated in patients with altered states of consciousness following TBI.57,58 Disruption to functional connectivity within the DMN has been reported across a range of states, including anaesthesia,59 minimal consciousness,57 vegetative state,58 brain death60 and coma.61 Metabolic activity and functional connectivity in the PCC seems to be particularly sensitive to states of awareness and arousal, and is reduced in patients in the vegetative state.62 DMN connectivity as a whole is decreased in proportion to the degree of impairment of consciousness.57 Notably, patients with locked-in syndrome, who might not have marked cognitive impairment but are unable to move, show relatively normal DMN connectivity. Moresubtle abnormalities of awareness after TBI are associated with abnormal SN function.63 After TBI, patients frequently show persistent problems with self-awareness. This manifests as problems monitoring their own behaviour, which is associated with reduced functional connectivity from the dorsal anterior cingulate cortex to other parts of the SN. In general, the prognostic potential of these observations have been demonstrated by studies showing that DMN connectivity predicts recovery from coma,61 and identification of these network abnormalities provides a target for novel treatments (see section on network-based diagnostics below).

Small-world disruption Functional connectivity analyses are based on the correlation in activity between sets of brain regions, or nodes. However, connectivity patterns can also be represented graphically, whereby sets of interacting nodes (brain regions or populations of neurons) are connected by edges (white matter tracts). This abstraction provides a detailed descriptive framework for understanding network structure that can readily be applied to the study of disease states.3 Analysis of graphs produced from structural and functional brain imaging data reveals the presence of densely

interconnected modules, as well as hub regions that have high connectivity across modules. These highly connected hubs correspond to brain regions where multiple ICNs overlap, thereby providing an anatomical substrate for interactions between networks.64,65 One important principle of the organization of many complex networks, including the brain, is that they exhibit a small-world architecture.66 In this type of organization, highly connected local modules that are involved in specialized processing are optimally combined with sparse, long-distance connections that allow integration across these modules. In TBI, DAI to long-distance white matter tracts is thought to disrupt the brain’s small-world architecture. Evidence for this hypothesis is provided from analyses of structural and fMRI data.47,67 We investigated functional interactions of the DMN and SN using a graph theoretical approach.67 Patients with TBI who had cognitive impairments and evidence of damage to white matter tracts were studied by means of resting-state fMRI. Compared with controls, overall functional connectivity was reduced after TBI, with longer average path lengths required to connect nodes in the network, and reduced network efficiency. These results suggest that the DMN and SN are shifted away from the healthy small-world organization following TBI—a result to be expected if DAI damages long-distance connections between processing modules. The functional connectivity of highly connected hub regions was particularly susceptible to alteration by TBI, with the PCC showing a marked reduction in connectivity. Similar reductions in the connectivity of hub regions have been reported in comatose patients following other types of brain injury. 68 These transmodal hub regions are thought to support integration of information processing across more-specialized domain-specific operations, raising the possibility that the disruption of awareness and attention after brain injury is produced by their disconnection.

Complexity in network dysfunction The effects of TBI on network function are likely to be complicated, in part because of the effects of both focal and diffuse injury on network activity, and also because the brain is a dynamic system in which global activity changes rapidly over time as a result of complex inter­ actions that are constrained by white matter connections.69 Even damage to a single white matter tract can have diverse functional effects. Moreover, although damage to a single tract can have predictable effects within a local ICN,8 the consequences of local damage can also be seen in remote but interacting regions.11 Equally, structural disconnection can result in seemingly paradoxical effects, such as increased functional connectivity between nodes that remain connected by intact tracts.44 Lesions to specific white matter tracts in animal models are beginning to be used to study the effects of network disconnection in a controlled way. In an important study, the corpus callosum of non­human primates was sectioned to study the relationship between structural and functional interactions across

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Figure 1 | Levels of brain network investigation. Brain networks can be investigated on a | the microscopic scale by measuring synchronous intrinsic firing within cell assemblies; b | the macroscopic scale by measuring structural connections using diffusion MRI; and c | large-scale ICNs using functional MRI. d | Activity within large-scale ICNs can be mapped onto cognitive function, thereby informing the development of network models of cognitive control, for example. e | Dynamic computational models that take into account the electrophysiological activity and large-scale white matter structure are needed in order to better understand the role of ICNs following trauma. Abbreviation: ICN, intrinsic connectivity network. Permission for part a obtained from Nature Publishing Group © Harris, K. D. and Thiele, A. Nat. Rev. Neurosci. 10, 509–523 (2011).

the hemispheres.70 Locally, interhemispheric functional connectivity was reduced, whereas global functional connectivity was almost normal, as connectivity increased across interhemispheric connections that remained intact. This result emphasizes the importance of considering the interactions of distributed networks, as local damage can have higher-order effects, perhaps as a result of homeostatic changes within the global system in response to damage. A major issue in studying these complex effects is how best to quantify the dynamic aspects of network function. Until now, most work has simplified the problem by viewing network activity as constant over time, but the way that networks rapidly change over time needs to be incorporated to enable development of a detailed and clinically useful theoretical framework. Methods are rapidly evolving to describe the global dynamics of brain network function, and are likely to prove clinically useful (Figure 1). For example, dynamic descriptions of brain activity can be derived within the framework of complex dynamical systems theory.71,72 This theory provides a description of how complex phenom­ena within a system can emerge from interactions of its component parts. For example, the stability of brain network activity over time can be studied, providing a detailed description of network fluctuations.73 An investigational approach that combines detailed descriptions of structural connectivity with computational models of neural dynamics is providing a way to study brain interactions at the global level. 71,73,74 Computational models of network activity typically include simple models of neuronal activity within a population of neurons (a node), as well as interactions between nodes that are constrained by white matter anatomy.

Simulated neural dynamics can then be compared with empirically observed brain activity, allowing the biological plausibility of the model to be assessed while developing a mechanistic explanation of how neural dynamics are produced. We have shown that interactions between the DMN and SN can be investigated in this way.74 Data from computational modelling and empirical neuro­ imaging in these studies converge to provide a model of how attentional state can shift from a constrained and focused mode to an unfocused and exploratory mode. The work suggests that increased activity within the DMN increases the variability or metastability of brain dynamics, whereas the opposite effect is produced by the SN, which supports a focused mode of operation. The approach is potentially clinically useful, as the effects of virtual lesions in white matter connections can be studied and compared with empirical observations of patients with similar patterns of brain injury. Ultimately, this method may allow the effects of brain injury and network treatments to be predicted in individual patients.

Degeneration and inflammation TBI is often considered to be a static insult. Evolving complications from TBI, however, can be observed decades after the initial trauma,75 and patients might go on to develop neurodegenerative disorders such as AD.76 Our understanding of these late con­s equences is likely to be informed by studying patterns of network dysfunction.

Neurodegeneration after TBI Compelling evidence suggests that TBI can trigger neuro­degeneration, and that this response is a major determinant of long-term outcome.77 Dementias such

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Microglial activation

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Interrupted axonal transport and axonal bulb formation

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Normal

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Figure 2 | Effects of TBI across spatial scales. a | At the microscopic scale, diffuse axonal injury can interrupt axonal transport, produce axonal bulbs, and trigger neuroinflammation through microglial activation. These abnormalities can eventually lead to neurodegeneration and persistent neuroinflammation, which might be associated with abnormal diffusion of misfolded neurodegenerative proteins along axons. b | At the macroscopic scale, injuries can be readily apparent in large white matter tracts. Diffuse axonal injury preferentially damages certain white matter tracts, such as the corpus callosum— seen here in a postmortem specimen with black regions of haemorrhage, indicative of underlying damage. c | At the wholebrain scale, damage to tracts interrupts long-distance communication between brain regions. Damage to white matter microstructure is illustrated here as reduced fractional anisotropy (red regions within a white matter skeleton; green indicates intact structure). d | This white matter damage can result in disruption of interactions between nodes of a brain network, represented here as reduced interactions between the default mode network (red/yellow regions). Abbreviation: TBI, traumatic brain injury. Permission for part b obtained from D. P. Agamanolis, Northeast Ohio Medical University, OH, USA.

as AD and CTE can be a long-term consequence of TBI, with both single and repetitive injuries predisposing individuals to late cognitive decline. 78–81 These observations suggest a prolonged, dynamic element to the pathophysiology of TBI, and interactions between the initial injury, neurodegenerative proteins and chronic ­neuroinflammation seem to be important.82,83

Mechanisms of neurodegeneration Neurodegeneration after TBI is characterized by accumulation of varying amounts of amyloid-β plaques and neurofibrillary tangles composed of hyperphosphorylated tau, which are hallmarks of AD.77,84 Growing evidence indicates that these misfolded proteins can pass from neuron to neuron via prion-like trans-synaptic spread, a mechanism for the progression of neurodegeneration that might be common to various diseases.85 In the case of tau, studies in rodents have shown that aggregated tau can initiate the formation of neuro­ fibrillary tangles, and that this process can spread trans-­synaptically from an initially affected brain region to remote but connected areas.86 The spread of pathogenic proteins, therefore, is likely to be constrained by the organization of white matter tracts. As a result, the pattern of neuro­degeneration might reflect the structure of large-scale ICNs. Accumulating evidence supports

this notion. Following TBI, regionally selective atrophy is common in parts of the DMN.87 Neurodegeneration occurs preferentially within the DMN in AD,88 and within the SN in the behavioural variant of fronto­ temporal dementia. 89 Computational modelling to simulate the possible spread of abnormal proteins along white matter tracts shows that a simple diffusion mechanism could produce the observed complex patterns of brain atrophy, in the absence of selective ­vulnerability or regional specificity to neurodegeneration.90

Neuroinflammation after TBI Activation of microglia after TBI is central to the neuro­ inflammatory response to injury, and can persist for months to years after TBI.82,91,92 This persistent neuro­ inflammation might influence the spread of abnormal proteins, and could be a causative factor in neuro­ degeneration following TBI. 93 Neuroinflammation is often located at the sites of axonal pathology,91,92 but over time can be seen far removed from focal injuries as it tracks the Wallerian degeneration of damaged axons.82,94 Whether persistent inflammation occurs as a response to pathology such as amyloid plaques and neurofibrillary tangles, or whether an unregulated inflammatory response drives the d ­ evelopment of neurodegeneration, remains unclear.

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Figure 3 | Network diagnostics in the assessment and management of traumatic brain injury. a | The first step is to define both the structural and functional integrity of specific networks following trauma. Structural white matter connections connecting ICN nodes are shown (from van den Heuvel et al.123), together with representative slices from these networks. b | Summary measures of network dysfunction could then be defined. A matrix of functional connectivity is shown for illustration. The colour of each square represents the strength of either functional or structural connectivity. For functional connectivity, the strength of connections within an ICN could be represented on the diagonal of the matrix, with connectivity between different networks by the squares on the off-diagonal of the matrix. Diffusion MRI estimates of the structural integrity of tracts in each network are shown below. c | This information could be used to select cognitive treatments on the basis of the function of relevant ICNs—for example, drug treatments to improve attention and memory impairments, or transcranial magnetic stimulation to treat impairments of speed of processing. Permission for part a obtained from John Wiley & Sons, Inc. © van den Heuvel, M. P. et al. Hum. Brain Mapp., 30, 3127–3141 (2009).

Neurodegeneration and disrupted connectivity The above findings relating to neurodegeneration and inflammation raise important questions about how the disruption of network connectivity after TBI influences these processes. The results suggest, first, that the initial pattern of injury might provide the starting point for chronic neuroinflammation and neurodegeneration that develops over many years; second, that the evolution of these processes is constrained by the structure of the white matter connections; and third, that the development of late complications after TBI should be predictable through the network-based analysis of neuro­inflammation and neurodegeneration. Longitudinal application of molecular neuroimaging of PET radioligands to investigate in vivo amyloid, tau and activated microglia, together with application of DTI, holds great promise for teasing apart the relationship between white matter damage, inflammation and ­neurodegeneration after TBI.

Network-based diagnostics Advances in the understanding of brain network structure and function after TBI have important clinical implications. Long-term outcome after TBI is notoriously difficult to predict, and few effective treatments are available for the persistent clinical problems. Therefore,

improved ways of diagnosing injury, predicting clinical outcome, and guiding the development of novel ­treatments are needed.

Predicting outcomes As discussed above, diffusion MRI already provides a sensitive method for investigating white matter injury after TBI. Diffusion imaging measurements can detect evolving pathological processes after TBI,22 and provide predictive information about long-term outcome that complements clinical measures. 23 To make diffusion MRI a useful diagnostic tool, this approach needs to be applicable to individuals. Recently developed tools enable detailed visualization of the distribution of white matter damage in individual patients, which might prove clinically useful.95,96 In addition, we have recently used machine-learning techniques to detect the complex patterns of white matter injury that are suggestive of DAI, and to predict the associated cognitive impairments in individual patients.97 These analytical methods are likely to become increasingly valuable for making robust predictions of clinical outcome on the basis of complex ­neuroimaging data. Our work shows that it is informative to consider the pattern of white matter damage after TBI in the

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REVIEWS context of damage to the connections of individual ICNs (Figure 2). Although the effects of injury are complex, damage to specific connections within ICNs have predictable effects on network function and cognitive impairment.8,11 We suggest that a network-level description of structural and functional damage will be a useful clinical tool (Figure 3a). Such a tool could combine diffusion MRI with measures of ICN functional connectivity derived from resting-state fMRI to provide a network diagnostic that incorporates structural and functional information. Interactions between networks could also be incorporated by measuring functional ­connectivity between networks (Figure 3a).

Treatment development Network level diagnostic information could be used to develop treatments aimed at improving specific network dysfunction (Figure 3b). For example, reproducible DMN dysfunction is observed across a wide-range of conditions30 and could, therefore, represent a biomarker to guide development of network-based treatments. One promising approach is the use of electrophysiological techniques to stimulate or inhibit specific ICNs. Invasive deep brain stimulation of the thalamus has already been shown to benefit patients in minimally conscious states after TBI,98 possibly by enhancing cortico-cortical interactions. The invasive nature of this approach, however, limits its widespread applicability. Noninvasive brain stimulation such as transcranial magnetic stimulation and transcranial direct current stimulation provides an attractive alternative to invasive deep brain stimulation. These methods use scalp stimulation to induce electrical changes in the underlying cortex, so need to be applied at a particular location. A detailed understanding of network structure and function could provide a clear rationale for intervention and guide the location and parameters of stimulation. For example, if network interactions between the SN and DMN are abnormal, stimulation over nodes within the SN might enhance SN function and thereby normalize interactions with the DMN. Alternatively, drugs might be selected on the basis of their actions on network function. For example, dopaminergic agents have been proposed as cognitive enhancers that might ameliorate many of the impairments commonly observed after TBI. 99 Only modest efficacy has been observed for agents such as methylphenidate in treating impairments 1. 2.

3.

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Mesulam, M. M. From sensation to cognition. Brain 121, 1013–1052 (1998). Hagmann, P. et al. Mapping the structural core of human cerebral cortex. PLoS Biol. 6, e159 (2008). Bullmore, E. & Sporns, O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci. 10, 186–198 (2009). Zhang, D. & Raichle, M. E. Disease and the brain’s dark energy. Nat. Rev. Neurol. 6, 15–28 (2010). Smith, D. H., Meaney, D. F. & Shull, W. H. Diffuse axonal injury in head trauma. J. Head Trauma Rehabil. 18, 307–316 (2003).

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of processing speed and attention after TBI,100 but a personalized approach might be required to select patients who have relevant network dysfunction and will be likely to respond well. Information about network structure and function in individual patients will provide important information about activity in specific ICNs that could be used to guide a network-based approach to drug selection.

Conclusions and future directions Important advances in network science are beginning to inform how TBI affects brain function, how network abnormalities influence behaviour, and how pathological processes that affect long-term outcome are triggered by brain injury. TBI partially disconnects large-scale brain networks through DAI to white matter tracts. Advanced MRI can be used to map the structural and functional consequences of this damage, and the effects on ICNs provide an informative level of description. Abnormalities within these networks can then be used to predict cognitive impairments after TBI. Network diagnostic tests that are based on the definition of network integrity in individuals have great potential to improve prognostication and guide the development of new treatments. To make use of advances in neuroimaging in a clinical setting, standardized approaches to image acquisition and analysis—applicable to individual patients—need to be adopted. Quantitative MRI techniques such as diffusion tensor imaging are ready to be applied in the clinic, but variability in their application make their interpreta­tion challenging. Advanced neuroimaging provides a way of understanding the heterogeneity of TBI, which is a key challenge to researchers and clinicians. Future work should move away from treating TBI as a homogenous entity, and instead use neuroimaging to personalize the evaluation and selection of new treatments. Review criteria Literature on which this Review is based was accessed via searches of PubMed and MEDLINE (all years). Search criteria included “traumatic brain injury”, “head injury”, “diffuse axonal injury”, “traumatic axonal injury”, “network”, “connectivity”, “disconnection”, “dementia”, and combinations of these terms. We also identified articles through searches of our own files and the reference lists of previous review articles. Full-text articles in English were included.

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