The structural connectome of children with traumatic brain injury

Königs, Marsh; Heurn, L. W. Ernest van; Bakx, Roel; Vermeulen, R. Jeroen; Goslings, J. Carel; Poll-Thé, Bwee Tien; Wees, Marleen van der; Catsman‐Berrevoets, Coriene E.; Oosterlaan, Jaap; Pouwels, Petra J. W.

doi:10.1002/hbm.23614

Cited by 33 publications

(44 citation statements)

References 36 publications

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“…More recently, increased modularity and path length was also evident using fiber count-based tractography networks for adolescents with acute mild TBI (mTBI) compared to children with orthopedic injury ( Yuan et al, 2015 ). In contrast to Yuan and colleagues, Konigs et al, 2017 did not find differences in any network-based metrics between children with mTBI and TI. In agreement with Caeyenberghs et al, 2012 who also acquired DTI 3 years post-injury, characteristic path length was increased in the moderate-to-severe TBI group ( Konigs et al, 2017 ).…”

Section: Introductioncontrasting

confidence: 95%

“…In contrast to Yuan and colleagues, Konigs et al, 2017 did not find differences in any network-based metrics between children with mTBI and TI. In agreement with Caeyenberghs et al, 2012 who also acquired DTI 3 years post-injury, characteristic path length was increased in the moderate-to-severe TBI group ( Konigs et al, 2017 ). More recently, Dennis et al studied children and adolescents with post-acute moderate-to-severe TBI, and found network differences between children who experienced post-traumatic seizures and those who did not ( Dennis et al, 2017 ).…”

Section: Introductioncontrasting

confidence: 95%

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Graph theory analysis of DTI tractography in children with traumatic injury

Watson

DeMaster

Ewing‐Cobbs

2019

NeuroImage: Clinical

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ObjectiveTo evaluate brai structural connectivity in children with traumatic injury (TI) following a motor vehicle accident using graph theory analysis of DTI tractography data.MethodsDTI scans were acquired on a 3 T Philips scanner from children aged 8–15 years approximately 2 months post-injury. The TI group consisted of children with traumatic brain injury (TBI; n = 44) or extracranial injury (EI; n = 23). Healthy control children (n = 36) were included as an age-matched comparison group. A graph theory approach was applied to DTI tractography data to investigate injury-related differences in connectivity network characteristics. Group differences in structural connectivity evidenced by graph metrics including efficiency, strength, and modularity were assessed using the multi-threshold permutation correction (MTPC) and network-based statistic (NBS) methods.ResultsAt the global network level, global efficiency and mean network strength were lower, and modularity was higher, in the TBI than in the control group. Similarly, strength was lower and modularity higher when comparing the EI to the control group. At the vertex level, nodal efficiency, vertex strength, and average shortest path length were different between all pairwise comparisons of the three groups. Both nodal efficiency and vertex strength were higher in the control than in the EI group, which in turn were higher than in the TBI group. The opposite between-group relationships were seen with path length. These between-group differences were distributed throughout the brain, in both hemispheres. NBS analysis resulted in a cluster of 22 regions and 21 edges with significantly lower connectivity in the TBI group compared to controls. This cluster predominantly involves the frontal lobe and subcortical gray matter structures in both hemispheres.ConclusionsGraph theory analysis of DTI tractography showed diffuse differences in structural brain network connectivity in children 2 months post-TI. Network differences were consistent with lower network integration and higher segregation in the injured groups compared to healthy controls. Findings suggest that inclusion of trauma-exposed comparison groups in studies of TBI outcome is warranted to better characterize the indirect effect of stress on brain networks.

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Section: Introductioncontrasting

confidence: 95%

Section: Introductioncontrasting

confidence: 95%

Graph theory analysis of DTI tractography in children with traumatic injury

Watson

DeMaster

Ewing‐Cobbs

2019

NeuroImage: Clinical

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“…Again, this result is in agreement with previously reported increases in network segregation after acute childhood TBI (Yuan et al, ). In contrast, Königs et al () failed to find a significant alteration in segregation in a pediatric sample of chronic moderate to severe TBI that was comparable to ours. A possible explanation for these conflicting results could be the difference in connectome construction.…”

Section: Discussionsupporting

confidence: 61%

Impaired rich club and increased local connectivity in children with traumatic brain injury: Local support for the rich?

Verhelst

Linden

Pauw

et al. 2018

Human Brain Mapping

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Recent evidence has shown the presence of a "rich club" in the brain, which constitutes a core network of highly interconnected and spatially distributed brain regions, important for high-order cognitive processes. This study aimed to map the rich club organization in 17 young patients with moderate to severe TBI (15.71 ± 1.75 years) in the chronic stage of recovery and 17 age- and gender-matched controls. Probabilistic tractography was performed on diffusion weighted imaging data to construct the edges of the structural connectomes using number of streamlines as edge weight. In addition, the whole-brain network was divided into a rich club network, a local network and a feeder network connecting the latter two. Functional outcome was measured with a parent questionnaire for executive functioning. Our results revealed a significantly decreased rich club organization (p values < .05) and impaired executive functioning (p < .001) in young patients with TBI compared with controls. Specifically, we observed reduced density values in all three subnetworks (p values < .005) and a reduced mean strength in the rich club network (p = .013) together with an increased mean strength in the local network (p = .002) in patients with TBI. This study provides new insights into the nature of TBI-induced brain network alterations and supports the hypothesis that the local subnetwork tries to compensate for the biologically costly subnetwork of rich club nodes after TBI.

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“…These mechanisms commonly give rise to acceleration-deceleration injuries that result in diffuse axonal injury (DAI), which refers to the extensive structural damage that occurs to otherwise highly organized neural tissue due to the abrupt stretching, twisting, and shearing of axons in the event of a mechanical blow. DAI is critically related to functional outcomes following early brain injury, as it leads to reductions in white matter integrity, disrupting the connectivity of the neural networks that give rise to behavioral and cognitive function (6). Plasticity moves anteriorly during typical neural development, where the frontal and temporal regions of the brain are among the last to develop (7,8).…”

Section: Introductionmentioning

confidence: 99%

Longitudinal Neuroimaging in Pediatric Traumatic Brain Injury: Current State and Consideration of Factors That Influence Recovery

et al. 2019

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Traumatic brain injury (TBI) is a leading cause of death and disability for children and adolescents in the U.S. and other developed and developing countries. Injury to the immature brain varies greatly from that of the mature, adult brain due to numerous developmental, pre-injury, and injury-related factors that work together to influence the trajectory of recovery during the course of typical brain development. Substantial damage to brain structure often underlies subsequent functional limitations that persist for years following pediatric TBI. Advances in neuroimaging have established an important role in the acute management of pediatric TBI, and magnetic resonance imaging (MRI) techniques have a particular relevance for the sequential assessment of long-term consequences from injuries sustained to the developing brain. The present paper will discuss the various factors that influence recovery and review the findings from the present neuroimaging literature to assess altered development and long-term outcome following pediatric TBI. Four MR-based neuroimaging modalities have been used to examine recovery from pediatric TBI longitudinally: (1) T 1 -weighted structural MRI is sensitive to morphological changes in gray matter volume and cortical thickness, (2) diffusion-weighted MRI is sensitive to changes in the microstructural integrity of white matter, (3) MR spectroscopy provides a sensitive assessment of metabolic and neurochemical alterations in the brain, and (4) functional MRI provides insight into the functional changes that occur as a result of structural damage and typical developmental processes. As reviewed in this paper, 13 cohorts have contributed to only 20 studies published to date using neuroimaging to examine longitudinal changes after TBI in pediatric patients. The results of these studies demonstrate considerable heterogeneity in post-injury outcome; however, the existing literature consistently shows that alterations in brain structure, function, and metabolism can persist for an extended period of time post-injury. With larger sample sizes and multi-site cooperation, future studies will be able to further examine potential moderators of outcome, such as the developmental, pre-injury, and injury-related factors discussed in the present review.

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The structural connectome of children with traumatic brain injury

Cited by 33 publications

References 36 publications

Graph theory analysis of DTI tractography in children with traumatic injury

Graph theory analysis of DTI tractography in children with traumatic injury

Impaired rich club and increased local connectivity in children with traumatic brain injury: Local support for the rich?

Longitudinal Neuroimaging in Pediatric Traumatic Brain Injury: Current State and Consideration of Factors That Influence Recovery

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