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

Caeyenberghs, Karen; Verhelst, Helena; Clemente, Adam; Wilson, Peter H.

doi:10.1016/j.neuroimage.2016.12.003

Cited by 105 publications

(117 citation statements)

References 104 publications

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“…The findings presented above are consistent with the broader literature, where hyperconnectivity is a response to neural disruption (36, 37), and specifically with the TBI literature, where hyperconnectivity has been observed in humans (15–19, 23) as well as animal models (47) in TBI. However, unlike the aging literature, which shows a pattern of more distributed network representation through dedifferentiation of networks (26–31), our findings show a strengthening of connections within networks.…”

Section: Discussionsupporting

confidence: 90%

“…While the use of graph theory to explore functional connectivity has been shown to be a sensitive marker postinjury, there is concern that graph theory metrics of functional connectivity are somewhat limited as a specific marker, given the multifactorial mechanisms that result in hyperconnectivity (36). Though this study was limited in terms of sample size, perhaps with greater power, distinct cognitive profiles may be linked to specific functional connectivity responses post injury.…”

Section: Discussionmentioning

confidence: 99%

“…The goal was to first replicate findings in moderate and severe chronic TBI, where investigators have observed hyperconnectivity in core networks [e.g., DMN; for review see Ref. (36, 37)]. Second, we aimed to determine if hyperconnectivity could be explained by dedifferentiation of networks by comparing network response during task and rest, and if so, which nodes were driving these altered relationships (i.e., most costly nodes, or “hubs”).…”

Section: Introductionmentioning

confidence: 99%

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Dedifferentiation Does Not Account for Hyperconnectivity after Traumatic Brain Injury

et al. 2017

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ObjectiveChanges in functional network connectivity following traumatic brain injury (TBI) have received increasing attention in recent neuroimaging literature. This study sought to understand how disrupted systems adapt to injury during resting and goal-directed brain states. Hyperconnectivity has been a common finding, and dedifferentiation (or loss of segregation of networks) is one possible explanation for this finding. We hypothesized that individuals with TBI would show dedifferentiation of networks (as noted in other clinical populations) and these effects would be associated with cognitive dysfunction.MethodsGraph theory was implemented to examine functional connectivity during periods of task and rest in 19 individuals with moderate/severe TBI and 14 healthy controls (HCs). Using a functional brain atlas derived from 83 functional imaging studies, graph theory was used to examine network dynamics and determine whether dedifferentiation accounts for changes in connectivity. Regions of interest were assigned to one of three groups: task-positive, default mode, or other networks. Relationships between these metrics were then compared with performance on neuropsychological tests.ResultsHyperconnectivity in TBI was most commonly observed as increased within-network connectivity. Network strengths within networks that showed differences between TBI and HCs were correlated with performance on five neuropsychological tests typically sensitive to deficits commonly reported in TBI. Hyperconnectivity within the default mode network (DMN) during task was associated with better performance on Digit Span Backward, a measure of working memory [R2(18) = 0.28, p = 0.02]. In other words, increased differentiation of networks during task was associated with better working memory. Hyperconnectivity within the task-positive network during rest was not associated with behavior. Negative correlation weights were not associated with behavior.ConclusionThe primary hypothesis that hyperconnectivity occurs through dedifferentiation was not supported. Instead, enhanced connectivity post injury was observed within network. Results suggest that the relationship between increased connectivity and cognitive functioning may be both state (rest or task) and network dependent. High-cost network hubs were identical for both rest and task, and cost was negatively associated with performance on measures of psychomotor speed and set-shifting.

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Section: Discussionsupporting

confidence: 90%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Dedifferentiation Does Not Account for Hyperconnectivity after Traumatic Brain Injury

et al. 2017

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“…This sensitivity suggests that dynamical networks such as the brain can produce fairly drastic changes in dynamical behavior given minute changes in physiological topology, consistent with observations of critical dynamics in human and animal neurophysiology [47, 48]. Moreover, these results also suggest that minor, targeted structural changes through concussive injury can lead to drastic changes in overall brain function [49, 50], via altering the controllability landscape of the brain [24]. We further observed that these topological modifications were task-agnostic edge deletions, signifying that even in a linear regime, the presence of an unfavorable edge can have a profoundly negative impact on the controllability of a network.…”

Section: Contribution and Future Directionssupporting

confidence: 76%

Role of graph architecture in controlling dynamical networks with applications to neural systems

Kim¹,

Soffer²,

Kahn³

et al. 2017

Nature Phys

123

166

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Networked systems display complex patterns of interactions between components. In physical networks, these interactions often occur along structural connections that link components in a hard-wired connection topology, supporting a variety of system-wide dynamical behaviors such as synchronization. While descriptions of these behaviors are important, they are only a first step towards understanding and harnessing the relationship between network topology and system behavior. Here, we use linear network control theory to derive accurate closed-form expressions that relate the connectivity of a subset of structural connections (those linking driver nodes to non-driver nodes) to the minimum energy required to control networked systems. To illustrate the utility of the mathematics, we apply this approach to high-resolution connectomes recently reconstructed from Drosophila, mouse, and human brains. We use these principles to suggest an advantage of the human brain in supporting diverse network dynamics with small energetic costs while remaining robust to perturbations, and to perform clinically accessible targeted manipulation of the brain’s control performance by removing single edges in the network. Generally, our results ground the expectation of a control system’s behavior in its network architecture, and directly inspire new directions in network analysis and design via distributed control.

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“…This work suggests that hyperconnectivity in moderate and severe TBI patients may be present regardless of recovery phase (acute, subacute, or chronic phase) and does not represent a transient process as found in other mTBI studies. Thus hyperconnectivity might become a useful prognostic tool to predict outcomes in moderate and severe TBI 33 . Differences in the results between studies may be attributed to differences in severity of TBI of the studied cohorts, region selection methods, time from injury, graph metrics utilized, the nature of connectivity studied and extent of gray and white matter damage.…”

Section: Rsfmri Findingsmentioning

confidence: 99%

Applications of Resting State Functional MR Imaging to Traumatic Brain Injury

O’Neill

Davenport

Murugesan

et al. 2017

Neuroimaging Clinics of North America

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SYNOPSIS TBI includes a broad spectrum of injury severities and pathologies. Functional magnetic resonance imaging (fMRI), both resting state (rs)and task, has been used often in research to study the effects of TBI. While rs-fMRI is not currently applicable in clinical diagnosis of TBI, computer aided tools are making this a possibility for the future. Specifically, graph theory is being used to study the change in networks after TBI. Machine learning methods allows researchers to build models capable of predicting injury severity and recovery trajectories. Magnetoencephalography (MEG) has a much higher temporal resolution and can be used to supplement the rs-fMRI. There are multiple limitations to overcome and factors to consider before rs-fMRI can be used as a clinical tool. However, both rs-fMRI and rs-MEG show promise as tools to predict recovery trajectories and evaluate therapies.

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

Cited by 105 publications

References 104 publications

Dedifferentiation Does Not Account for Hyperconnectivity after Traumatic Brain Injury

Dedifferentiation Does Not Account for Hyperconnectivity after Traumatic Brain Injury

Role of graph architecture in controlling dynamical networks with applications to neural systems

Applications of Resting State Functional MR Imaging to Traumatic Brain Injury

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