Diffusion Tensor Tractography Reveals Disrupted Topological Efficiency in White Matter Structural Networks in Multiple Sclerosis

Shu, Ni; Duan, Yunyun; Wang, Jun; Chen, Yu; Dong, Hui; Ye, Jing; He, Yong

doi:10.1093/cercor/bhr039

Cited by 301 publications

(325 citation statements)

References 64 publications

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“…This method has been used to show differences in graph metrics (Shu et al. 2011) in patients with multiple sclerosis. However, there may exist even more relevant differences in brain network topology than were detected in our study.…”

Section: Discussionmentioning

confidence: 99%

Organizational topology of brain and its relationship to ADHD in adolescents with d‐transposition of the great arteries

et al. 2016

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ObjectiveLittle is currently known about the impact of congenital heart disease (CHD) on the organization of large‐scale brain networks in relation to neurobehavioral outcome. We investigated whether CHD might impact ADHD symptoms via changes in brain structural network topology in a cohort of adolescents with d‐transposition of the great arteries (d‐TGA) repaired with the arterial switch operation in early infancy and referent subjects. We also explored whether these effects might be modified by apolipoprotein E (APOE) genotype, as the APOE ε2 allele has been associated with worse neurodevelopmental outcomes after repair of d‐TGA in infancy.MethodsWe applied graph analysis techniques to diffusion tensor imaging (DTI) data obtained from 47 d‐TGA adolescents and 29 healthy referents to construct measures of structural topology at the global and regional levels. We developed statistical mediation models revealing the respective contributions of d‐TGA, APOE genotype, and structural network topology on ADHD outcome as measured by the Connors ADHD/DSM‐IV Scales (CADS).ResultsChanges in overall network connectivity, integration, and segregation mediated worse ADHD outcomes in d‐TGA patients compared to healthy referents; these changes were predominantly in the left and right intrahemispheric regional subnetworks. Exploratory analysis revealed that network topology also mediated detrimental effects of the APOE ε4 allele but improved neurobehavioral outcomes for the APOE ε2 allele.ConclusionOur results suggest that disruption of organization of large‐scale networks may contribute to neurobehavioral dysfunction in adolescents with CHD and that this effect may interact with APOE genotype.

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Section: Discussionmentioning

confidence: 99%

Organizational topology of brain and its relationship to ADHD in adolescents with d‐transposition of the great arteries

et al. 2016

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“…To define the network edges, i.e., the brain region pair-wise connections, a threshold value of 3 was selected for the fiber bundles, which means that two regions were considered structurally connected only if at least the two end points of 3 fibers were located in each of the two regions [23]. A threshold selection can reduce the risk of false-positive connections due to noise or the limitations in the deterministic tractography and can simultaneously ensure the size of the largest connected component (i.e., 90) in the networks [24].…”

Section: Network Edge Definitionmentioning

confidence: 99%

Disrupted White Matter Network and Cognitive Decline in Type 2 Diabetes Patients

Zhang

Liu

et al. 2016

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Abstract. Type 2 diabetes mellitus is accompanied by cognitive impairment and is associated with an increased risk of dementia. Damage to brain structures such as white matter network disruption may underlie this cognitive disturbance. In the present study, 886 non-diabetic and 163 type 2 diabetic participants completed a battery of neuropsychological tests. Among them, 38 diabetic patients and 34 non-diabetic participants that matched the patients for age/sex/education received a magnetic resonance imaging-based diffusion tensor imaging. Then we calculated the topological properties of the white matter network using a graph theoretical method to investigate network efficiency differences between groups. We found that type 2 diabetic patients had inferior performances compared to the non-diabetic controls, in several cognitive domains involving executive function, spatial processing, memory, and attention. We also found that diabetic patients exhibited a disrupted topological organization of the white matter network (including the global network properties, i.e., network strength, global efficiency, local efficiency and shortest path length, and the nodal efficiency of the right rolandic operculum) in the brain. Moreover, those global network properties and the nodal efficiency of the right rolandic operculum both had positive correlations with executive function in the patient group. The results suggest that type 2 diabetes mellitus leads to an alteration in the topological organization of the cortical white matter network and this alteration may account for the observed cognitive decline.

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“…The structural connectomes defined in the previous section are networks and it has become popular to examine these networks using various network measures [2,32,40,44,47,58]. The collection of network measures used here are given in Table 2.…”

Section: Network Analysismentioning

confidence: 99%

“…Such network measures allow high-level summaries of brain network topology which have been shown to be useful, reliable bio-markers in discriminating normal and abnormal brain networks [32,40,47]. Rubinov and Sporns recently presented a comprehensive summary of such measures in relation to their use on structural and functional brain networks [44].…”

Section: Introductionmentioning

confidence: 99%

Structural network analysis of brain development in young preterm neonates

et al. 2014

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Preterm infants develop differently than those born at term and are at higher risk of brain pathology. Thus, an understanding of their development is of particular importance. Diffusion tensor imaging (DTI) of preterm infants offers a window into brain development at a very early age, an age at which that development is not yet fully understood. Recent works have used DTI to analyze structural connectome of the brain scans using network analysis. These studies have shown that, even from infancy, the brain exhibits small-world properties. Here we examine a cohort of 47 normal preterm neonates (i.e., without brain injury and with normal neurodevelopment at 18 months of age) scanned between 27 and 45 weeks post-menstrual age to further the understanding of how the structural connectome develops. We use fullbrain tractography to find white matter tracts between the 90 cortical and sub-cortical regions defined in the University of North Carolina Chapel Hill neonatal atlas. We then analyze the resulting connectomes and explore the differences between weighting edges by tract count versus fractional anisotropy. We observe that the brain networks in preterm infants, much like infants born at term, show high efficiency and clustering measures across a range of network scales. Further, the development of many individual region-pair connections, particularly in the frontal and occipital lobes, is significantly correlated with age. Finally, we observe that the preterm infant connectome remains highly efficient yet becomes more clustered across this age range, leading to a significant increase in its small-world structure.

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Diffusion Tensor Tractography Reveals Disrupted Topological Efficiency in White Matter Structural Networks in Multiple Sclerosis

Cited by 301 publications

References 64 publications

Organizational topology of brain and its relationship to ADHD in adolescents with d‐transposition of the great arteries

Organizational topology of brain and its relationship to ADHD in adolescents with d‐transposition of the great arteries

Disrupted White Matter Network and Cognitive Decline in Type 2 Diabetes Patients

Structural network analysis of brain development in young preterm neonates

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