The structural connectome in traumatic brain injury: A meta-analysis of graph metrics

Imms, Phoebe; Clemente, Adam; Cook, Mark; D’Souza, Wendyl; Wilson, Peter H.; Jones, Derek K.; Caeyenberghs, Karen

doi:10.1016/j.neubiorev.2019.01.002

Cited by 65 publications

(46 citation statements)

References 65 publications

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“…Graph theory can help identify alterations in the global brain connectivity organization [64]. Previous studies have shown that TBI disrupts the optimal 'small-world' architecture of the network (as often observed in the healthy population), affecting the optimal balance between local segregation within and global integration between specialized independent subnetworks [64]; [65]; [66]. We seek to evaluate how TBI impairs this optimal integration/segregation balance at baseline and during balance perturbation.…”

Section: Effect Of Tbi On Eeg Brain Connectivity and Graph Measuresmentioning

confidence: 99%

Graph-theoretical Analysis of EEG Functional Connectivity during Balance Perturbation in Traumatic Brain Injury: A Pilot Study

Handiru

Alivar

Hoxha

et al. 2020

Preprint

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Traumatic Brain Injury (TBI) often results in balance impairment, increasing the risk for falls and further injuries. In this study, we investigated the postural instability in a group of 12 TBI patients vs 9 healthy controls (HC) by studying their brain’s response to an external perturbation of the computerized dynamic posturography platform. A 64-channel EEG was used to noninvasively record the brain’s anticipatory response while the participant was performing the dynamic posturography task. We used the global graph-theoretic network measures (global efficiency and modularity) derived from source-space EEG connectivity in different frequency bands as quantitative measures of the functional integration and segregation of the brain network during the task. The center of pressure (COP) displacement and the Berg Balance Scale (BBS) were used as balance outcomes. Furthermore, we used the Diffusion Tensor Imaging (DTI) data from all the participants to explore the association between the measures of the structural integrity of the white matter (Fractional Anisotropy, Mean Diffusivity, and Mode of Anisotropy) and the balance outcomes.Our findings revealed significant changes in functional segregation between the TBI group and controls during the task and also group-level differences in DTI measures. Also, we observed significant differences between theta-band modularity and global efficiency during rest vs. task within both the groups (TBI and HC). In terms of the neural correlates, we observed a distinct role played by different frequency bands; increase in theta-band modularity during the task was shown to be highly correlated with the poorer COP displacement and BBS in the TBI group; alpha-band and beta-band graph-theoretic measures were correlated with the measures of structural integrity. In terms of the regions of interest pertaining to the postural control, we noticed a significant cortical activity during the task in the paracentral lobule, post-central gyrus, cingulate gyrus, superior parietal gyrus, and other regions often reported to be involved in postural stability.Our study, although limited by small sample size, provides insights into the neural correlates of the balance deficits due to brain injury. Our future studies will focus on the larger sample size and also the effect of stabilometry platform training on the functional brain network modulation in TBI.

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Section: Effect Of Tbi On Eeg Brain Connectivity and Graph Measuresmentioning

confidence: 99%

Graph-theoretical Analysis of EEG Functional Connectivity during Balance Perturbation in Traumatic Brain Injury: A Pilot Study

Handiru

Alivar

Hoxha

et al. 2020

Preprint

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“…5 This view is supported by recent studies using graph theoretical analyses demonstrating alterations in network measures, such as global efficiency, clustering coefficient, and betweenness-centrality in TBI patients compared to healthy controls. 6,7 However, these changes in network metrics are unable to determine the patterns of degeneration within the brain networks. 8 It is essential to understand how the initial brain trauma relates to future patterns of degeneration in TBI patients.…”

Section: Introductionmentioning

confidence: 99%

Network diffusion modeling predicts neurodegeneration in traumatic brain injury

Poudel

Verhelst

et al. 2020

Ann Clin Transl Neurol

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Objective Traumatic brain injury (TBI) is a heterogeneous disease with multiple neurological deficits that evolve over time. It is also associated with an increased incidence of neurodegenerative diseases. Accordingly, clinicians need better tools to predict a patient’s long‐term prognosis. Methods Diffusion‐weighted and anatomical MRI data were collected from 17 adolescents (mean age = 15y8mo) with moderate‐to‐severe TBI and 19 healthy controls. Using a network diffusion model (NDM), we examined the effect of progressive deafferentation and gray matter thinning in young TBI patients. Moreover, using a novel automated inference method, we identified several injury epicenters in order to determine the neural degenerative patterns in each TBI patient. Results We were able to identify the subject‐specific patterns of degeneration in each patient. In particular, the hippocampus, temporal cortices, and striatum were frequently found to be the epicenters of degeneration across the TBI patients. Orthogonal transformation of the predicted degeneration, using principal component analysis, identified distinct spatial components in the temporal–hippocampal network and the cortico‐striatal network, confirming the vulnerability of these networks to injury. The NDM model, best predictive of the degeneration, was significantly correlated with time since injury, indicating that NDM can potentially capture the pathological progression in the chronic phase of TBI. Interpretation These findings suggest that network spread may help explain patterns of distant gray matter thinning, which would be consistent with Wallerian degeneration of the white matter connections (i.e., “diaschisis”) from diffuse axonal injuries and multifocal contusive injuries, and the neurodegenerative patterns of abnormal protein aggregation and transmission, which are hallmarks of brain changes in TBI. NDM approaches could provide highly subject‐specific biomarkers relevant for disease monitoring and personalized therapies in TBI.

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“…The view that the human brain is a network of cortical and subcortical areas connected via white matter tracts emerged several decades ago and has been very useful in characterizing brain structure and function in both healthy participants and diseased populations (Hagmann et al (2008); Griffa et al (2013); Caeyenberghs and Leemans (2014) ;Fischer et al (2014); van den Heuvel and Fornito (2014); Yuan et al (2014); Baker et al (2015); Collin et al (2016); Drakesmith et al (2015); Aerts et al (2016); Nelson et al (2017); Vidaurre et al (2018); Imms et al (2019), to name just a few of the studies). Structural networks can be constructed using data collected non-invasively via magnetic resonance imaging (MRI) and diffusion MRI (dMRI).…”

Section: Introductionmentioning

confidence: 99%

Optimization of graph construction can significantly increase the power of structural brain network studies

Messaritaki

Dimitriadis

Jones

2019

Preprint

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Structural brain networks derived from diffusion magnetic resonance imaging data have been used extensively to describe the human brain, and graph theory has allowed quantification of their network properties. Schemes used to construct the graphs that represent the structural brain networks differ in the metrics they use as edge weights and the algorithms they use to define the network topologies. In this work, twenty graph construction schemes were considered. The schemes use the number of streamlines, the fractional anisotropy, the mean diffusivity or other attributes of the tracts to define the edge weights, and either an absolute threshold or a datadriven algorithm to define the graph topology. The test-retest data of the Human Connectome Project were used to compare the reproducibility of the graphs and their various attributes (edges, topologies, graph theoretical metrics) derived through those schemes, for diffusion images acquired with three different diffusion weightings. The impact of the scheme on the statistical power of the study and on the number of participants required to detect a difference between populations or an effect of an intervention was also calculated.The reproducibility of the graphs and their attributes depended heavily on the graph construction scheme. Graph reproducibility was higher for schemes that used thresholding to define the graph topology, while data-driven schemes performed better at topology reproducibility. Additionally, schemes that used thresholding resulted in better reproducibility for local graph theoretical metrics, while data-driven schemes performed better for global metrics. Crucially, the number of participants required to detect a difference between populations or an effect of an intervention could change by a large factor depending on the scheme used, affecting the power of studies to reveal the effects of interest.

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The structural connectome in traumatic brain injury: A meta-analysis of graph metrics

Cited by 65 publications

References 65 publications

Graph-theoretical Analysis of EEG Functional Connectivity during Balance Perturbation in Traumatic Brain Injury: A Pilot Study

Graph-theoretical Analysis of EEG Functional Connectivity during Balance Perturbation in Traumatic Brain Injury: A Pilot Study

Network diffusion modeling predicts neurodegeneration in traumatic brain injury

Optimization of graph construction can significantly increase the power of structural brain network studies

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