Graph analysis of functional brain networks for cognitive control of action in traumatic brain injury

The evolution of cost-efficiency in neural networks during recovery from traumatic brain injury

A somewhat perplexing finding in the systems neuroscience has been the observation that physical injury to neural systems may result in enhanced functional connectivity (i.e., hyperconnectivity) relative to the typical network response. The consequences of local or global enhancement of functional connectivity remain uncertain and this is particularly true for the overall metabolic cost of the network. We examine the hyperconnectivity hypothesis in a sample of 14 individuals with TBI with data collected at approximately 3, 6, and 12 months following moderate and severe TBI. As anticipated, individuals with TBI showed increased network strength and cost early after injury, but by one-year post injury hyperconnectivity was more circumscribed to frontal DMN and temporal-parietal attentional control regions. Cost in these subregions was a significant predictor of cognitive performance. Cost-efficiency analysis in the Power 264 data parcellation suggested that at 6 months post injury the network requires higher cost connections to achieve high efficiency as compared to the network 12 months post injury. These results demonstrate that networks self-organize to re-establish connectivity while balancing cost-efficiency trade-offs.

Mapping the functional connectome in traumatic brain injury: What can graph metrics tell us?

OBJECTIVE

Traumatic brain injury (TBI) is associated with cognitive and motor deficits, and poses a significant personal, societal, and economic burden. One mechanism by which TBI is thought to affect cognition and behavior is through changes in functional connectivity. Graph theory is a powerful framework for quantifying topological features of neuroimaging-derived functional networks. The objective of this paper is to review studies examining functional connectivity in TBI with an emphasis on graph theoretical analysis that is proving to be valuable in uncovering network abnormalities in this condition.

METHODS

We review studies that have examined TBI-related alterations in different properties of the functional brain network, including global integration, segregation, centrality and resilience. We focus on functional data using task-related fMRI or resting-state fMRI in patients with TBI of different severity and recovery phase, and consider how graph metrics may inform rehabilitation and enhance efficacy. Moreover, we outline some methodological challenges associated with the examination of functional connectivity in patients with brain injury, including the sample size, parcellation scheme used, node definition and subgroup analyses.

RESULTS

The findings suggest that TBI is associated with hyperconnectivity and a suboptimal global integration, characterized by increased connectivity degree and strength and reduced efficiency of functional networks. This altered functional connectivity, also evident in other clinical populations, is attributable to diffuse white matter pathology and reductions in gray and white matter volume. These functional alterations are implicated in post-concussional symptoms, posttraumatic stress and neurocognitive dysfunction after TBI. Finally, the effects of focal lesions have been found to depend critically on topological position and their role in the network.

CONCLUSION

Graph theory is a unique and powerful tool for exploring functional connectivity in brain-injured patients. One limitation is that its results do not provide specific measures about the biophysical mechanism underlying TBI. Continued work in this field will hopefully see graph metrics used as biomarkers to provide more accurate diagnosis and help guide treatment at the individual patient level.

Mapping the Connectome Following Traumatic Brain Injury

There is a paucity of accurate and reliable biomarkers to detect traumatic brain injury, grade its severity, and model post-traumatic brain injury (TBI) recovery. This gap could be addressed via advances in brain mapping which define injury signatures and enable tracking of post-injury trajectories at the individual level. Mapping of molecular and anatomical changes and of modifications in functional activation supports the conceptual paradigm of TBI as a disorder of large-scale neural connectivity. Imaging approaches with particular relevance are magnetic resonance techniques (diffusion weighted imaging, diffusion tensor imaging, susceptibility weighted imaging, magnetic resonance spectroscopy, functional magnetic resonance imaging, and positron emission tomographic methods including molecular neuroimaging). Inferences from mapping represent unique endophenotypes which have the potential to transform classification and treatment of patients with TBI. Limitations of these methods, as well as future research directions, are highlighted.

Brain networks under attack: robustness properties and the impact of lesions

A growing number of studies approach the brain as a complex network, the so-called 'connectome'. Adopting this framework, we examine what types or extent of damage the brain can withstand-referred to as network 'robustness'-and conversely, which kind of distortions can be expected after brain lesions. To this end, we review computational lesion studies and empirical studies investigating network alterations in brain tumour, stroke and traumatic brain injury patients. Common to these three types of focal injury is that there is no unequivocal relationship between the anatomical lesion site and its topological characteristics within the brain network. Furthermore, large-scale network effects of these focal lesions are compared to those of a widely studied multifocal neurodegenerative disorder, Alzheimer's disease, in which central parts of the connectome are preferentially affected. Results indicate that human brain networks are remarkably resilient to different types of lesions, compared to other types of complex networks such as random or scale-free networks. However, lesion effects have been found to depend critically on the topological position of the lesion. In particular, damage to network hub regions-and especially those connecting different subnetworks-was found to cause the largest disturbances in network organization. Regardless of lesion location, evidence from empirical and computational lesion studies shows that lesions cause significant alterations in global network topology. The direction of these changes though remains to be elucidated. Encouragingly, both empirical and modelling studies have indicated that after focal damage, the connectome carries the potential to recover at least to some extent, with normalization of graph metrics being related to improved behavioural and cognitive functioning. To conclude, we highlight possible clinical implications of these findings, point out several methodological limitations that pertain to the study of brain diseases adopting a network approach, and provide suggestions for future research.

Assessing Quantitative Changes in Intrinsic Thalamic Networks in Blast and Nonblast Mild Traumatic Brain Injury: Implications for Mechanisms of Injury

In the global war on terror, the increased use of improvised explosive devices has resulted in increased incidence of blast-related mild traumatic brain injury (mTBI). Diagnosing mTBI is both challenging and controversial due to heterogeneity of injury location, trauma intensity, transient symptoms, and absence of focal biomarkers on standard clinical imaging modalities. The goal of this study is to identify a brain biomarker that is sensitive to mTBI injury. Research suggests the thalamus may be sensitive to changes induced by mTBI. A significant number of connections to and from various brain regions converge at the thalamus. In addition, the thalamus is involved in information processing, integration, and regulation of specific behaviors and mood. In this study, changes in task-free thalamic networks as quantified by graph theory measures in mTBI blast (N = 186), mTBI nonblast (N = 80), and controls (N = 21) were compared. Results show that the blast mTBI group had significant hyper-connectivity compared with the controls and nonblast mTBI group. However, after controlling for post-traumatic stress symptoms (PTSS), the blast mTBI group was not different from the controls, but the nonblast mTBI group showed significant hypo-connectivity. The results suggest that there are differences in the mechanisms of injury related to mTBI as reflected in the architecture of the thalamic networks. However, the effect of PTSS and its relationship to mTBI is difficult to distinguish and warrants more research.

An Evolutionary Computation Approach to Examine Functional Brain Plasticity

One common research goal in systems neurosciences is to understand how the functional relationship between a pair of regions of interest (ROIs) evolves over time. Examining neural connectivity in this way is well-suited for the study of developmental processes, learning, and even in recovery or treatment designs in response to injury. For most fMRI based studies, the strength of the functional relationship between two ROIs is defined as the correlation between the average signal representing each region. The drawback to this approach is that much information is lost due to averaging heterogeneous voxels, and therefore, the functional relationship between a ROI-pair that evolve at a spatial scale much finer than the ROIs remain undetected. To address this shortcoming, we introduce a novel evolutionary computation (EC) based voxel-level procedure to examine functional plasticity between an investigator defined ROI-pair by simultaneously using subject-specific BOLD-fMRI data collected from two sessions seperated by finite duration of time. This data-driven procedure detects a sub-region composed of spatially connected voxels from each ROI (a so-called sub-regional-pair) such that the pair shows a significant gain/loss of functional relationship strength across the two time points. The procedure is recursive and iteratively finds all statistically significant sub-regional-pairs within the ROIs. Using this approach, we examine functional plasticity between the default mode network (DMN) and the executive control network (ECN) during recovery from traumatic brain injury (TBI); the study includes 14 TBI and 12 healthy control subjects. We demonstrate that the EC based procedure is able to detect functional plasticity where a traditional averaging based approach fails. The subject-specific plasticity estimates obtained using the EC-procedure are highly consistent across multiple runs. Group-level analyses using these plasticity estimates showed an increase in the strength of functional relationship between DMN and ECN for TBI subjects, which is consistent with prior findings in the TBI-literature. The EC-approach also allowed us to separate sub-regional-pairs contributing to positive and negative plasticity; the detected sub-regional-pairs significantly overlap across runs thus highlighting the reliability of the EC-approach. These sub-regional-pairs may be useful in performing nuanced analyses of brain-behavior relationships during recovery from TBI.

Assessing the Relationship between Neurocognitive Performance and Brain Volume in Chronic Moderate-Severe Traumatic Brain Injury

Objectives

Characterize the scale and pattern of long-term atrophy in gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) in chronic moderate–severe traumatic brain injury (TBI) and its relationship to neurocognitive outcomes.

Participants

The TBI group consisted of 17 males with primary diagnosis of moderate–severe closed head injury. Participants had not received any systematic, post-acute rehabilitation and were recruited on average 8.36 years post-injury. The control group consisted of 15 males matched on age and education.

Main measures

Neurocognitive battery included widely used tests of verbal memory, visual memory, executive functioning, and attention/organization. GM, WM, and CSF volumes were calculated from segmented T1-weighted anatomical MR images. Voxel-based morphometry was employed to identify brain regions with differences in GM and WM between TBI and control groups.

Results

Chronic TBI results in significant neurocognitive impairments, and significant loss of GM and WM volume, and significant increase in CSF volume. Brain atrophy is not widespread, but it is rather distributed in a fronto-thalamic network. The extent of volume loss is predictive of performance on the neurocognitive tests.

Conclusion

Significant brain atrophy and associated neurocognitive impairments during the chronic stages of TBI support the notion that TBI results in a chronic condition with lifelong implications.

Traumatic Brain Injury as a Disorder of Brain Connectivity

OBJECTIVES

Recent advances in neuroimaging methodologies sensitive to axonal injury have made it possible to assess in vivo the extent of traumatic brain injury (TBI) -related disruption in neural structures and their connections. The objective of this paper is to review studies examining connectivity in TBI with an emphasis on structural and functional MRI methods that have proven to be valuable in uncovering neural abnormalities associated with this condition.

METHODS

We review studies that have examined white matter integrity in TBI of varying etiology and levels of severity, and consider how findings at different times post-injury may inform underlying mechanisms of post-injury progression and recovery. Moreover, in light of recent advances in neuroimaging methods to study the functional connectivity among brain regions that form integrated networks, we review TBI studies that use resting-state functional connectivity MRI methodology to examine neural networks disrupted by putative axonal injury.

RESULTS

The findings suggest that TBI is associated with altered structural and functional connectivity, characterized by decreased integrity of white matter pathways and imbalance and inefficiency of functional networks. These structural and functional alterations are often associated with neurocognitive dysfunction and poor functional outcomes.

CONCLUSIONS

TBI has a negative impact on distributed brain networks that lead to behavioral disturbance.

Disrupted Intrinsic Connectivity among Default, Dorsal Attention, and Frontoparietal Control Networks in Individuals with Chronic Traumatic Brain Injury

OBJECTIVES

Individuals with chronic traumatic brain injury (TBI) often show detrimental deficits in higher order cognitive functions requiring coordination of multiple brain networks. Although assessing TBI-related deficits in higher order cognition in the context of network dysfunction is promising, few studies have systematically investigated altered interactions among multiple networks in chronic TBI.

METHOD

We characterized disrupted resting-state functional connectivity of the default mode network (DMN), dorsal attention network (DAN), and frontoparietal control network (FPCN) whose interactions are required for internally and externally focused goal-directed cognition in chronic TBI. Specifically, we compared the network interactions of 40 chronic TBI individuals (8 years post-injury on average) with those of 17 healthy individuals matched for gender, age, and years of education.

RESULTS

The network-based statistic (NBS) on DMN-DAN-FPCN connectivity of these groups revealed statistically significant (p NBS2.58) reductions in within-DMN, within-FPCN, DMN-DAN, and DMN-FPCN connectivity of the TBI group over healthy controls. Importantly, such disruptions occurred prominently in between-network connectivity. Subsequent analyses further exhibited the disrupted connectivity patterns of the chronic TBI group occurring preferentially in long-range and inter-hemispheric connectivity of DMN-DAN-FPCN. Most importantly, graph-theoretic analysis demonstrated relative reductions in global, local and cost efficiency (p<.05) as a consequence of the network disruption patterns in the TBI group.

CONCLUSION

Our findings suggest that assessing multiple networks-of-interest simultaneously will allow us to better understand deficits in goal-directed cognition and other higher order cognitive phenomena in chronic TBI. Future research will be needed to better understand the behavioral consequences related to these network disruptions.

Brain Connectomics of Visual-Motor Deficits in Children with Developmental Coordination Disorder

OBJECTIVE

To extend preliminary findings on associated white matter deficits and structural connectivity in children with developmental coordination disorder (DCD).

STUDY DESIGN

Diffusion magnetic resonance imaging-based tractography was used to identify abnormal microstructural properties of specific sensorimotor white matter tracts in 21 children with DCD between 8 and 10 years of age and 20 age- and sex-matched typically developing controls. Graph theoretical analyses were applied to evaluate whole brain connectomics. Associations were also calculated between the tractography/connectome results and visual-motor performance, as measured with the Beery-Buktenica Developmental Test of Visual Motor Integration.

RESULTS

Significant positive correlations were obtained between visual-motor trace scores and fractional anisotropy (FA) in the retrolenticular limb of the internal capsule within the group with DCD. Moreover, lower FA in sensorimotor tracts and altered structural connectivity were observed for children with DCD. Compared with controls, subjects with DCD showed decreases in clustering coefficient, and global and local efficiency, suggesting weaker structural network segregation and integration. The degree of decreased global efficiency was significantly associated with poor visual-motor tracing outcomes, above and beyond FA reductions. Specifically, nodal efficiency at the cerebellar lobule VI and right parietal superior gyrus were found significant predictors to discriminate between children with DCD and those with typical development.

CONCLUSIONS

Specific white matter alterations and network topology features associate with visual-motor deficits and DCD diagnosis indicating the clinical potential of diffusion magnetic resonance imaging-based metrics for diagnosing DCD.

Theory of mind mediates the prospective relationship between abnormal social brain network morphology and chronic behavior problems after pediatric traumatic brain injury

Childhood and adolescence coincide with rapid maturation and synaptic reorganization of distributed neural networks that underlie complex cognitive-affective behaviors. These regions, referred to collectively as the 'social brain network' (SBN) are commonly vulnerable to disruption from pediatric traumatic brain injury (TBI); however, the mechanisms that link morphological changes in the SBN to behavior problems in this population remain unclear. In 98 children and adolescents with mild to severe TBI, we acquired 3D T1-weighted MRIs at 2-8 weeks post-injury. For comparison, 33 typically developing controls of similar age, sex and education were scanned. All participants were assessed on measures of Theory of Mind (ToM) at 6 months post-injury and parents provided ratings of behavior problems at 24-months post-injury. Severe TBI was associated with volumetric reductions in the overall SBN package, as well as regional gray matter structural change in multiple component regions of the SBN. When compared with TD controls and children with milder injuries, the severe TBI group had significantly poorer ToM, which was associated with more frequent behavior problems and abnormal SBN morphology. Mediation analysis indicated that impaired theory of mind mediated the prospective relationship between abnormal SBN morphology and more frequent chronic behavior problems. Our findings suggest that sub-acute alterations in SBN morphology indirectly contribute to long-term behavior problems via their influence on ToM. Volumetric change in the SBN and its putative hub regions may represent useful imaging biomarkers for prediction of post-acute social cognitive impairment, which may in turn elevate risk for chronic behavior problems.

Intrinsic Functional Connectivity Patterns Predict Consciousness Level and Recovery Outcome in Acquired Brain Injury

For accurate diagnosis and prognostic prediction of acquired brain injury (ABI), it is crucial to understand the neurobiological mechanisms underlying loss of consciousness. However, there is no consensus on which regions and networks act as biomarkers for consciousness level and recovery outcome in ABI. Using resting-state fMRI, we assessed intrinsic functional connectivity strength (FCS) of whole-brain networks in a large sample of 99 ABI patients with varying degrees of consciousness loss (including fully preserved consciousness state, minimally conscious state, unresponsive wakefulness syndrome/vegetative state, and coma) and 34 healthy control subjects. Consciousness level was evaluated using the Glasgow Coma Scale and Coma Recovery Scale-Revised on the day of fMRI scanning; recovery outcome was assessed using the Glasgow Outcome Scale 3 months after the fMRI scanning. One-way ANOVA of FCS, Spearman correlation analyses between FCS and the consciousness level and recovery outcome, and FCS-based multivariate pattern analysis were performed. We found decreased FCS with loss of consciousness primarily distributed in the posterior cingulate cortex/precuneus (PCC/PCU), medial prefrontal cortex, and lateral parietal cortex. The FCS values of these regions were significantly correlated with consciousness level and recovery outcome. Multivariate support vector machine discrimination analysis revealed that the FCS patterns predicted whether patients with unresponsive wakefulness syndrome/vegetative state and coma would regain consciousness with an accuracy of 81.25%, and the most discriminative region was the PCC/PCU. These findings suggest that intrinsic functional connectivity patterns of the human posteromedial cortex could serve as a potential indicator for consciousness level and recovery outcome in individuals with ABI.

SIGNIFICANCE STATEMENT

Varying degrees of consciousness loss and recovery are commonly observed in acquired brain injury patients, yet the underlying neurobiological mechanisms remain elusive. Using a large sample of patients with varying degrees of consciousness loss, we demonstrate that intrinsic functional connectivity strength in many brain regions, especially in the posterior cingulate cortex and precuneus, significantly correlated with consciousness level and recovery outcome. We further demonstrate that the functional connectivity pattern of these regions can predict patients with unresponsive wakefulness syndrome/vegetative state and coma would regain consciousness with an accuracy of 81.25%. Our study thus provides potentially important biomarkers of acquired brain injury in clinical diagnosis, prediction of recovery outcome, and decision making for treatment strategies for patients with severe loss of consciousness.

The Vulnerability to Suicidal Behavior is Associated with Reduced Connectivity Strength

Suicidal behavior constitutes a major public health problem. Based on the stress–diathesis model, biological correlates of a diathesis might help to predict risk after stressor-exposure. Structural changes in cortical and subcortical areas and their connections have increasingly been linked with the diathesis. The current study identified structural network changes associated with a diathesis using a whole-brain approach by examining the structural connectivity between regions in euthymic suicide attempters (SA). In addition, the association between connectivity measures, clinical and genetic characteristics was investigated. We hypothesized that SA showed lower connectivity strength, associated with an increased severity of general clinical characteristics and an elevated expression of short alleles in serotonin polymorphisms. Thirteen euthymic SA were compared with fifteen euthymic non-attempters and seventeen healthy controls (HC). Clinical characteristics and three serotonin-related genetic polymorphisms were assessed. Diffusion MRI together with anatomical scans were administered. Preprocessing was performed using Explore DTI. Whole brain tractography of the diffusion-weighted images was followed by a number of streamlines-weighted network analysis using NBS. The network analysis revealed decreased connectivity strength in SA in the connections between the left olfactory cortex and left anterior cingulate gyrus. Furthermore, SA had increased suicidal ideation, hopelessness and self-reported depression, but did not show any differences for the genetic polymorphisms. Finally, lower connectivity strength between the right calcarine fissure and the left middle occipital gyrus was associated with increased trait anxiety severity (r_s = −0.78, p < 0.01) and hopelessness (r_s = −0.76, p < 0.01). SA showed differences in white matter network connectivity strength associated with clinical characteristics. Together, these variables could play an important role in predicting suicidal behavior.

Graph theory analysis of complex brain networks: new concepts in brain mapping applied to neurosurgery

Neuroanatomy has entered a new era, culminating in the search for the connectome, otherwise known as the brain's wiring diagram. While this approach has led to landmark discoveries in neuroscience, potential neurosurgical applications and collaborations have been lagging. In this article, the authors describe the ideas and concepts behind the connectome and its analysis with graph theory. Following this they then describe how to form a connectome using resting state functional MRI data as an example. Next they highlight selected insights into healthy brain function that have been derived from connectome analysis and illustrate how studies into normal development, cognitive function, and the effects of synthetic lesioning can be relevant to neurosurgery. Finally, they provide a précis of early applications of the connectome and related techniques to traumatic brain injury, functional neurosurgery, and neurooncology.

Dynamics of the connectome in Huntington's disease: A longitudinal diffusion MRI study

Objectives

To longitudinally investigate the connectome in different stages of Huntington's disease (HD) by applying graph theoretical analysis to diffusion MRI data.

Experimental design

We constructed weighted structural networks and calculated their topological properties. Twenty-two premanifest (preHD), 10 early manifest HD and 24 healthy controls completed baseline and 2 year follow-up scans. We stratified the preHD group based on their predicted years to disease onset into a far (preHD-A) and near (preHD-B) to disease onset group. We collected clinical and behavioural measures per assessment time point.

Principle observations

We found a significant reduction over time in nodal betweenness centrality both in the early manifest HD and preHD-B groups as compared to the preHD-A and control groups, suggesting a decrease of importance of specific nodes to overall network organization in these groups (FDR adjusted ps < 0.05). Additionally, we found a significant longitudinal decrease of the clustering coefficient in preHD when compared to healthy controls (FDR adjusted p < 0.05), which can be interpreted as a reduced capacity for internodal information processing at the local level. Furthermore, we demonstrated dynamic changes to hub-status loss and gain both in preHD and early manifest HD. Finally, we found significant cross-sectional as well as longitudinal relationships between graph metrics and clinical and neurocognitive measures.

Conclusions

This study demonstrates divergent longitudinal changes to the connectome in (pre) HD compared to healthy controls. This provides novel insights into structural correlates associated with clinical and cognitive functions in HD and possible compensatory mechanisms at play in preHD.

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Investigates characteristics of the connectome in Huntington's disease (HD).

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HD patients showed longitudinal changes in their structural connectome.

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Connectome dynamics correlated with changes in clinical and cognitive measures.

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Connectomics provides novel insights into compensatory strategies of the diseased brain.

A voxelwise approach to determine consensus regions-of-interest for the study of brain network plasticity

Despite exciting advances in the functional imaging of the brain, it remains a challenge to define regions of interest (ROIs) that do not require investigator supervision and permit examination of change in networks over time (or plasticity). Plasticity is most readily examined by maintaining ROIs constant via seed-based and anatomical-atlas based techniques, but these approaches are not data-driven, requiring definition based on prior experience (e.g., choice of seed-region, anatomical landmarks). These approaches are limiting especially when functional connectivity may evolve over time in areas that are finer than known anatomical landmarks or in areas outside predetermined seeded regions. An ideal method would permit investigators to study network plasticity due to learning, maturation effects, or clinical recovery via multiple time point data that can be compared to one another in the same ROI while also preserving the voxel-level data in those ROIs at each time point. Data-driven approaches (e.g., whole-brain voxelwise approaches) ameliorate concerns regarding investigator bias, but the fundamental problem of comparing the results between distinct data sets remains. In this paper we propose an approach, aggregate-initialized label propagation (AILP), which allows for data at separate time points to be compared for examining developmental processes resulting in network change (plasticity). To do so, we use a whole-brain modularity approach to parcellate the brain into anatomically constrained functional modules at separate time points and then apply the AILP algorithm to form a consensus set of ROIs for examining change over time. To demonstrate its utility, we make use of a known dataset of individuals with traumatic brain injury sampled at two time points during the first year of recovery and show how the AILP procedure can be applied to select regions of interest to be used in a graph theoretical analysis of plasticity.

Disrupted brain network topology in pediatric posttraumatic stress disorder: A resting-state fMRI study

Children exposed to natural disasters are vulnerable to the development of posttraumatic stress disorder (PTSD). Recent studies of other neuropsychiatric disorders have used graph-based theoretical analysis to investigate the topological properties of the functional brain connectome. However, little is known about this connectome in pediatric PTSD. Twenty-eight pediatric PTSD patients and 26 trauma-exposed non-PTSD patients were recruited from 4,200 screened subjects after the 2008 Sichuan earthquake to undergo a resting-state functional magnetic resonance imaging scan. Functional connectivity between 90 brain regions from the automated anatomical labeling atlas was established using partial correlation coefficients, and the whole-brain functional connectome was constructed by applying a threshold to the resultant 90 * 90 partial correlation matrix. Graph theory analysis was then used to examine the group-specific topological properties of the two functional connectomes. Both the PTSD and non-PTSD control groups exhibited "small-world" brain network topology. However, the functional connectome of the PTSD group showed a significant increase in the clustering coefficient and a normalized characteristic path length and local efficiency, suggesting a shift toward regular networks. Furthermore, the PTSD connectomes showed both enhanced nodal centralities, mainly in the default mode- and salience-related regions, and reduced nodal centralities, mainly in the central-executive network regions. The clustering coefficient and nodal efficiency of the left superior frontal gyrus were positively correlated with the Clinician-Administered PTSD Scale. These disrupted topological properties of the functional connectome help to clarify the pathogenesis of pediatric PTSD and could be potential biomarkers of brain abnormalities.

Altered functional connectivity in the brain default-mode network of earthquake survivors persists after 2 years despite recovery from anxiety symptoms

Although acute impact of traumatic experiences on brain function in disaster survivors is similar to that observed in post-traumatic stress disorders (PTSD), little is known about the long-term impact of this experience. We have used structural and functional magnetic resonance imaging to investigate resting-state functional connectivity and gray and white matter (WM) changes occurring in the brains of healthy Wenchuan earthquake survivors both 3 weeks and 2 years after the disaster. Results show that while functional connectivity changes 3 weeks after the disaster involved both frontal-limbic-striatal and default-mode networks (DMN), at the 2-year follow-up only changes in the latter persisted, despite complete recovery from high initial levels of anxiety. No gray or WM volume changes were found at either time point. Taken together, our findings provide important new evidence that while altered functional connectivity in the frontal-limbic-striatal network may underlie the post-trauma anxiety experienced by survivors, parallel changes in the DMN persist despite the apparent absence of anxiety symptoms. This suggests that long-term changes occur in neural networks involved in core aspects of self-processing, cognitive and emotional functioning in disaster survivors which are independent of anxiety symptoms and which may also confer increased risk of subsequent development of PTSD.

Disconnection of network hubs and cognitive impairment after traumatic brain injury

Traumatic brain injury (TBI) affects brain connectivity, which disrupts the large-scale networks that support cognitive function. Fagerholm et al. use graph analysis, with brain regions as nodes and white matter tracts as connections, to show that TBI particularly disrupts the connectivity of hubs, and that this disconnection predicts cognitive impairment.

Traumatic brain injury affects brain connectivity by producing traumatic axonal injury. This disrupts the function of large-scale networks that support cognition. The best way to describe this relationship is unclear, but one elegant approach is to view networks as graphs. Brain regions become nodes in the graph, and white matter tracts the connections. The overall effect of an injury can then be estimated by calculating graph metrics of network structure and function. Here we test which graph metrics best predict the presence of traumatic axonal injury, as well as which are most highly associated with cognitive impairment. A comprehensive range of graph metrics was calculated from structural connectivity measures for 52 patients with traumatic brain injury, 21 of whom had microbleed evidence of traumatic axonal injury, and 25 age-matched controls. White matter connections between 165 grey matter brain regions were defined using tractography, and structural connectivity matrices calculated from skeletonized diffusion tensor imaging data. This technique estimates injury at the centre of tract, but is insensitive to damage at tract edges. Graph metrics were calculated from the resulting connectivity matrices and machine-learning techniques used to select the metrics that best predicted the presence of traumatic brain injury. In addition, we used regularization and variable selection via the elastic net to predict patient behaviour on tests of information processing speed, executive function and associative memory. Support vector machines trained with graph metrics of white matter connectivity matrices from the microbleed group were able to identify patients with a history of traumatic brain injury with 93.4% accuracy, a result robust to different ways of sampling the data. Graph metrics were significantly associated with cognitive performance: information processing speed (R² = 0.64), executive function (R² = 0.56) and associative memory (R² = 0.25). These results were then replicated in a separate group of patients without microbleeds. The most influential graph metrics were betweenness centrality and eigenvector centrality, which provide measures of the extent to which a given brain region connects other regions in the network. Reductions in betweenness centrality and eigenvector centrality were particularly evident within hub regions including the cingulate cortex and caudate. Our results demonstrate that betweenness centrality and eigenvector centrality are reduced within network hubs, due to the impact of traumatic axonal injury on network connections. The dominance of betweenness centrality and eigenvector centrality suggests that cognitive impairment after traumatic brain injury results from the disconnection of network hubs by traumatic axonal injury.

Cognitive network neuroscience

Network science provides theoretical, computational, and empirical tools that can be used to understand the structure and function of the human brain in novel ways using simple concepts and mathematical representations. Network neuroscience is a rapidly growing field that is providing considerable insight into human structural connectivity, functional connectivity while at rest, changes in functional networks over time (dynamics), and how these properties differ in clinical populations. In addition, a number of studies have begun to quantify network characteristics in a variety of cognitive processes and provide a context for understanding cognition from a network perspective. In this review, we outline the contributions of network science to cognitive neuroscience. We describe the methodology of network science as applied to the particular case of neuroimaging data and review its uses in investigating a range of cognitive functions including sensory processing, language, emotion, attention, cognitive control, learning, and memory. In conclusion, we discuss current frontiers and the specific challenges that must be overcome to integrate these complementary disciplines of network science and cognitive neuroscience. Increased communication between cognitive neuroscientists and network scientists could lead to significant discoveries under an emerging scientific intersection known as cognitive network neuroscience.

Expert consensus document: Mind the gaps—advancing research into short-term and long-term neuropsychological outcomes of youth sports-related concussions

Sports-related concussions and repetitive subconcussive exposure are increasingly recognized as potential dangers to paediatric populations, but much remains unknown about the short-term and long-term consequences of these events, including potential cognitive impairment and risk of later-life dementia. This Expert Consensus Document is the result of a 1-day meeting convened by Safe Kids Worldwide, the Alzheimer's Drug Discovery Foundation, and the Andrews Institute for Orthopaedics and Sports Medicine. The goal is to highlight knowledge gaps and areas of critically needed research in the areas of concussion science, dementia, genetics, diagnostic and prognostic biomarkers, neuroimaging, sports injury surveillance, and information sharing. For each of these areas, we propose clear and achievable paths to improve the understanding, treatment and prevention of youth sports-related concussions.

MRI for coma emergence and recovery

PURPOSE OF REVIEW

Data from MRI can be used to generate detailed maps of central nervous system anatomy and functional activation. Here, we review new research that integrates advanced MRI acquisition and analysis to predict and track recovery following severe traumatic brain injury (TBI) or anoxic ischemic encephalopathy (AIE) following cardiac arrest.

RECENT FINDINGS

Diffusion tensor MRI studies of comatose TBI patients demonstrate specific distributions of white matter damage that are robustly associated with long-term functional outcomes. In unconscious patients with AIE, whole brain diffusion restriction has prognostic significance, as do regional changes in diffusion restriction or anisotropy. Results using functional MRI suggest that coma following TBI and cardiac arrest is associated with disconnections within cerebral architectures associated with arousal and conscious perception. The relation between these disconnections and postinjury recovery is being explored in ongoing cohorts.

SUMMARY

MRI of the brain is feasible in critically ill patients following TBI or cardiac arrest, revealing patterns of structural damage and functional disconnection that can help predict outcome in the long term. Prospective studies are needed to validate these findings and to identify relationships between MRI-defined alterations and specific postinjury cognitive and behavioural phenotypes.

Structural connectivity abnormality in children with acute mild traumatic brain injury using graph theoretical analysis

The traumatic biomechanical forces associated with mild traumatic brain injury (mTBI) typically impart diffuse, as opposed to focal, brain injury potentially disrupting the structural connectivity between neural networks. Graph theoretical analysis using diffusion tensor imaging was used to assess injury-related differences in structural connectivity between 23 children (age 11-16 years) with mTBI and 20 age-matched children with isolated orthopedic injuries (OI) scanned within 96 h postinjury. The distribution of hub regions and the associations between alterations in regional network measures and symptom burden, as assessed by the postconcussion symptom scale score (PCSS), were also examined. In comparison to the OI group, the mTBI group was found to have significantly higher small-worldness (P < 0.0001), higher normalized clustering coefficients (P < 0.0001), higher normalized characteristic path length (P = 0.007), higher modularity (P = 0.0005), and lower global efficiency (P < 0.0001). A series of hub regions in the mTBI group were found to have significant alterations in regional network measures including nodal degree, nodal clustering coefficient, and nodal between-ness centrality. Correlation analysis showed that PCSS total score acquired at the time of imaging was significantly associated with the nodal degree of two hubs, the superior frontal gyrus at orbital section and the middle frontal gyrus. These findings provide new evidence of acute white matter alteration at both global and regional network level following mTBI in children furthering our understanding of underlying mechanisms of acute neurological insult associated with mTBI.

Modern network science of neurological disorders

Modern network science has revealed fundamental aspects of normal brain-network organization, such as small-world and scale-free patterns, hierarchical modularity, hubs and rich clubs. The next challenge is to use this knowledge to gain a better understanding of brain disease. Recent developments in the application of network science to conditions such as Alzheimer's disease, multiple sclerosis, traumatic brain injury and epilepsy have challenged the classical concept of neurological disorders being either 'local' or 'global', and have pointed to the overload and failure of hubs as a possible final common pathway in neurological disorders.

The rich get richer: brain injury elicits hyperconnectivity in core subnetworks

There remains much unknown about how large-scale neural networks accommodate neurological disruption, such as moderate and severe traumatic brain injury (TBI). A primary goal in this study was to examine the alterations in network topology occurring during the first year of recovery following TBI. To do so we examined 21 individuals with moderate and severe TBI at 3 and 6 months after resolution of posttraumatic amnesia and 15 age- and education-matched healthy adults using functional MRI and graph theoretical analyses. There were two central hypotheses in this study: 1) physical disruption results in increased functional connectivity, or hyperconnectivity, and 2) hyperconnectivity occurs in regions typically observed to be the most highly connected cortical hubs, or the "rich club". The current findings generally support the hyperconnectivity hypothesis showing that during the first year of recovery after TBI, neural networks show increased connectivity, and this change is disproportionately represented in brain regions belonging to the brain's core subnetworks. The selective increases in connectivity observed here are consistent with the preferential attachment model underlying scale-free network development. This study is the largest of its kind and provides the unique opportunity to examine how neural systems adapt to significant neurological disruption during the first year after injury.

Diffusion tensor MRI of chemotherapy-induced cognitive impairment in non-CNS cancer patients: a review

Patients with non-central nervous system cancers often experience subtle cognitive deficits after treatment with cytotoxic agents. Therapy-induced structural changes to the brain could be one of the possible causes underlying these reported cognitive deficits. In this review, we evaluate the use of diffusion tensor imaging (DTI) for assessing possible therapy-induced changes in the microstructure of the cerebral white matter (WM) and provide a critical overview of the published DTI research on therapy-induced cognitive impairment. Both cross-sectional and longitudinal DTI studies have demonstrated abnormal microstructural properties in WM regions involved in cognition. These findings correlated with cognitive performance, suggesting that there is a link between reduced "WM integrity" and chemotherapy-induced impaired cognition. In this paper, we will also introduce the basics of diffusion tensor imaging and how it can be applied to evaluate effects of therapy on structural changes in cerebral WM. The review concludes with considerations and discussion regarding DTI data interpretation and possible future directions for investigating therapy-induced WM changes in cancer patients. This review article is part of a Special Issue entitled: Neuroimaging Studies of Cancer and Cancer Treatment.

The functional oculomotor network and saccadic cognitive control in healthy elders

Decline in executive function is the most common age-associated cognitive deficit and may be a risk factor for neurodegenerative disease. The antisaccade (AS) task involves inhibition of a prepotent visuomotor response and is a well-validated executive function test in aging and neurodegeneration. We investigated the functional connectivity of the cortical oculomotor network during successful AS performance in healthy elders. Elevated BOLD activity in the right lateral frontal eye field (rlatFEF), a region linked to volume loss in individuals with impaired AS performance, was associated with worse AS performance and weaker network efficiency. In contrast, hub integrity of the right dorsolateral prefrontal cortex (rDLPFC) and anterior cingulate cortex (rACC) was associated with better AS performance. These data suggest that while several right lateral frontal regions are central nodes in the oculomotor network, the rlatFEF demonstrates early neural aberrations and the rDLPFC and rACC continue to support inhibitory cognitive control in healthy elders. We conclude that alterations in AS task functional connectivity, quantified as hub and network efficiency, may be clinically-relevant biomarkers of cognitive decline in executive functioning.

Topological correlations of structural and functional networks in patients with traumatic brain injury

Despite an increasing amount of specific correlation studies between structural and functional connectivity, there is still a need for combined studies, especially in pathological conditions. Impairments of brain white matter (WM) and diffuse axonal injuries are commonly suspected to be responsible for the disconnection hypothesis in traumatic brain injury (TBI) patients. Moreover, our previous research on TBI patients shows a strong relationship between abnormalities in topological organization of brain networks and behavioral deficits. In this study, we combined task-related functional connectivity (using event-related fMRI) with structural connectivity (derived from fiber tractography using diffusion MRI data) estimates in the same participants (17 adults with TBI and 16 controls), allowing for direct comparison between graph metrics of the different imaging modalities. Connectivity matrices were computed covering the switching motor network, which includes the basal ganglia, anterior cingulate cortex/supplementary motor area, and anterior insula/inferior frontal gyrus. The edges constituting this network consisted of the partial correlations between the fMRI time series from each node of the switching motor network. The interregional anatomical connections between the switching-related areas were determined using the fiber tractography results. We found that graph metrics and hubs obtained showed no agreement in both groups. The topological properties of brain functional networks could not be solely accounted for by the properties of the underlying structural networks. However, combining complementary information from both imaging modalities could improve accuracy in prediction of switching performance. Direct comparison between functional task-related and anatomical structural connectivity, presented here for the first time in TBI patients, links two powerful approaches to map the patterns of brain connectivity that may underlie behavioral deficits in brain-injured patients.

Disrupted modular organization of resting-state cortical functional connectivity in U.S. military personnel following concussive 'mild' blast-related traumatic brain injury

Blast-related traumatic brain injury (TBI) has been one of the "signature injuries" of the wars in Iraq and Afghanistan. However, neuroimaging studies in concussive 'mild' blast-related TBI have been challenging due to the absence of abnormalities in computed tomography or conventional magnetic resonance imaging (MRI) and the heterogeneity of the blast-related injury mechanisms. The goal of this study was to address these challenges utilizing single-subject, module-based graph theoretic analysis of resting-state functional MRI (fMRI) data. We acquired 20min of resting-state fMRI in 63 U.S. military personnel clinically diagnosed with concussive blast-related TBI and 21 U.S. military controls who had blast exposures but no diagnosis of TBI. All subjects underwent an initial scan within 90days post-injury and 65 subjects underwent a follow-up scan 6 to 12months later. A second independent cohort of 40 U.S. military personnel with concussive blast-related TBI served as a validation dataset. The second independent cohort underwent an initial scan within 30days post-injury. 75% of the scans were of good quality, with exclusions primarily due to excessive subject motion. Network analysis of the subset of these subjects in the first cohort with good quality scans revealed spatially localized reductions in the participation coefficient, a measure of between-module connectivity, in the TBI patients relative to the controls at the time of the initial scan. These group differences were less prominent on the follow-up scans. The 15 brain areas with the most prominent reductions in the participation coefficient were next used as regions of interest (ROIs) for single-subject analyses. In the first TBI cohort, more subjects than would be expected by chance (27/47 versus 2/47 expected, p<0.0001) had 3 or more brain regions with abnormally low between-module connectivity relative to the controls on the initial scans. On the follow-up scans, more subjects than expected by chance (5/37, p=0.044) but fewer subjects than on the initial scans had 3 or more brain regions with abnormally low between-module connectivity. Analysis of the second TBI cohort validation dataset with no free parameters provided a partial replication; again more subjects than expected by chance (8/31, p=0.006) had 3 or more brain regions with abnormally low between-module connectivity on the initial scans, but the numbers were not significant (2/27, p=0.276) on the follow-up scans. A single-subject, multivariate analysis by probabilistic principal component analysis of the between-module connectivity in the 15 identified ROIs, showed that 31/47 subjects in the first TBI cohort were found to be abnormal relative to the controls on the initial scans. In the second TBI cohort, 9/31 patients were found to be abnormal in identical multivariate analysis with no free parameters. Again, there were not substantial differences on the follow-up scans. Taken together, these results indicate that single-subject, module-based graph theoretic analysis of resting-state fMRI provides potentially useful information for concussive blast-related TBI if high quality scans can be obtained. The underlying biological mechanisms and consequences of disrupted between-module connectivity are unknown, thus further studies are required.

Traumatic brain injury, neuroimaging, and neurodegeneration

Depending on severity, traumatic brain injury (TBI) induces immediate neuropathological effects that in the mildest form may be transient but as severity increases results in neural damage and degeneration. The first phase of neural degeneration is explainable by the primary acute and secondary neuropathological effects initiated by the injury; however, neuroimaging studies demonstrate a prolonged period of pathological changes that progressively occur even during the chronic phase. This review examines how neuroimaging may be used in TBI to understand (1) the dynamic changes that occur in brain development relevant to understanding the effects of TBI and how these relate to developmental stage when the brain is injured, (2) how TBI interferes with age-typical brain development and the effects of aging thereafter, and (3) how TBI results in greater frontotemporolimbic damage, results in cerebral atrophy, and is more disruptive to white matter neural connectivity. Neuroimaging quantification in TBI demonstrates degenerative effects from brain injury over time. An adverse synergistic influence of TBI with aging may predispose the brain injured individual for the development of neuropsychiatric and neurodegenerative disorders long after surviving the brain injury.

Task switching in traumatic brain injury relates to cortico-subcortical integrity

Suppressing and flexibly adapting actions are a critical part of our daily behavioral repertoire. Traumatic brain injury (TBI) patients show clear impairments in this type of action control; however, the underlying mechanisms are poorly understood. Here, we tested whether white matter integrity of cortico-subcortical pathways could account for impairments in task switching, an important component of executive functioning. Twenty young adults with TBI and eighteen controls performed a switching task requiring attention to global versus local stimulus features. Diffusion weighted images were acquired and whole brain tract-based spatial statistics (TBSS) were used to explore where white matter damage was associated with switching impairment. A crossing fiber model and probabilistic tractography further identified the specific fiber populations. Relative to controls, patients with a history of TBI had a higher switch cost and were less accurate. The TBI group showed a widespread decline in fractional anisotropy (FA) throughout the TBSS skeleton. FA in the superior corona radiata showed a negative relationship with switch cost. More specifically, this involved cortico-subcortical loops with the (pre-)supplementary motor area and superior frontal gyrus. These findings provide evidence for damage to frontal-subcortical projections in TBI, which is associated with task switching impairments.

Neural recruitment after mild traumatic brain injury is task dependent: a meta-analysis

Individuals with mild traumatic brain injury (TBI) often have deficits in processing speed and working memory (WM) and there is a growing literature using functional imaging studies to document these deficits. However, divergent results from these studies revealed both hypoactivation and hyperactivation of neural resources after injury. We hypothesized that at least part of this variance can be explained by distinct demands between WM tasks. Notably, in this literature some WM tasks use discrete periods of encoding, maintenance, and retrieval, whereas others place continuous demands on WM. The purpose of this meta-analysis is to examine the differences in neural recruitment after mTBI to determine if divergent findings can be explained as a function of task demand and cognitive load. A comprehensive literature review revealed 14 studies using functional magnetic resonance imaging to examine brain activity of individuals with mTBI during working memory tasks. Three of the fourteen studies included reported hypoactivity, five reported hyperactivity, and the remaining six reported both hypoactivity and hyperactivity. Studies were grouped according to task type and submitted to GingerALE maximum likelihood meta-analyses to determine the most consistent brain activation patterns. The primary findings from this meta-analysis suggest that the discrepancy in activation patterns is at least partially attributable to the classification of WM task, with hyperactivation being observed in continuous tasks and hypoactivation being observed during discrete tasks. We anticipate that differential task load expressed in continuous and discrete WM tasks contributes to these differences. Implications for the interpretation of fMRI signals in clinical samples are discussed.

Maturation of cognitive control: delineating response inhibition and interference suppression

Cognitive control is integral to the ability to attend to a relevant task whilst suppressing distracting information or inhibiting prepotent responses. The current study examined the development of these two subprocesses by examining electrophysiological indices elicited during each process. Thirteen 18 year-old adults and thirteen children aged 8-11 years (mean=9.77 years) completed a hybrid Go/Nogo flanker task while continuous EEG data were recorded. The N2 topography for both response inhibition and interference suppression changed with increasing age. The neural activation associated with response inhibition became increasingly frontally distributed with age, and showed decreases of both amplitude and peak latency from childhood to adulthood, possibly due to reduced cognitive demands and myelination respectively occurring during this period. Interestingly, a significant N2 effect was apparent in adults, but not observed in children during trials requiring interference suppression. This could be due to more diffuse activation in children, which would require smaller levels of activation over a larger region of the brain than is reported in adults. Overall, these results provide evidence of distinct maturational processes occurring throughout late childhood and adolescence, highlighting the separability of response inhibition and interference suppression.

Altered structural networks and executive deficits in traumatic brain injury patients

Recent research on traumatic brain injury (TBI) has shown that impairments in cognitive and executive control functions are accompanied by a disrupted neural connectivity characterized by white matter damage. We constructed binary and weighted brain structural networks in 21 patients with chronic TBI and 17 healthy young adults utilizing diffusion tensor tractography and calculated topological properties of the networks using a graph theoretical method. Executive function was assessed with the local global task and the trail making task, requiring inhibition, updating, and switching. The results revealed that TBI patients were less successful than controls on the executive tasks, as shown by the higher reaction times, higher switch costs, and lower accuracy rates. Moreover, both TBI patients and controls exhibited a small world topology in their white matter networks. More importantly, the TBI patients demonstrated increased shortest path length and decreased global efficiency of the structural network. These findings suggest that TBI patients have a weaker globally integrated structural brain network, resulting in a limited capacity to integrate information across brain regions. Furthermore, we showed that the white matter networks of both groups contained highly connected hub regions that were predominately located in the parietal cortex, frontal cortex, and basal ganglia. Finally, we showed significant correlations between switching performance and network property metrics within the TBI group. Specifically, lower scores on the switching tasks corresponded to a lower global efficiency. We conclude that analyzing the structural brain network connectivity provides new insights into understanding cognitive control changes following brain injury.

Brain connectivity and postural control in young traumatic brain injury patients: A diffusion MRI based network analysis

Our previous research on traumatic brain injury (TBI) patients has shown a strong relationship between specific white matter (WM) diffusion properties and motor deficits. The potential impact of TBI-related changes in network organization of the associated WM structural network on motor performance, however, remains largely unknown. Here, we used diffusion tensor imaging (DTI) based fiber tractography to reconstruct the human brain WM networks of 12 TBI and 17 control participants, followed by a graph theoretical analysis. A force platform was used to measure changes in body posture under conditions of compromised proprioceptive and/or visual feedback. Findings revealed that compared with controls, TBI patients showed higher betweenness centrality and normalized path length, and lower values of local efficiency, implying altered network organization. These results were not merely a consequence of differences in number of connections. In particular, TBI patients displayed reduced structural connectivity in frontal, parieto-premotor, visual, subcortical, and temporal areas. In addition, the decreased connectivity degree was significantly associated with poorer balance performance. We conclude that analyzing the structural brain networks with a graph theoretical approach provides new insights into motor control deficits following brain injury.