Structural network maturation of the preterm human brain

Zhao, Tengda; Mishra, Virendra; Jeon, Tina; Ouyang, Minhui; Peng, Qinmu; Chalak, Lina F.; Wisnowski, Jessica L.; Heyne, Roy J.; Rollins, Nancy; Shu, Ni; Huang, Hao

doi:10.1016/j.neuroimage.2018.06.047

Cited by 57 publications

(37 citation statements)

References 95 publications

(123 reference statements)

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“…It develops throughout childhood, adolescence, and adulthood [68] , [69] , [70] , [71] ; and is sustained throughout the lifespan [72] . Specifically, the connection density between rich-club regions and the rest of the cortex increases during the third trimester [24] in neonates [73] , and even in late adolescence [74] . The intelligence quotient of typically developing children exhibits a stronger positive association with rich-club connectivity than with feeder and local connectivity [75] .…”

Section: Rich-club Of Humansmentioning

confidence: 99%

“… Cao et al [72] 126 36.8 ± 21.2, 7–85 rs-fMRI 1024 cortical and subcortical regions CC Functional RC connectivity has an inverted U-shaped lifespan trajectory. Zhao et al [73] 77 25.0–41.4 weeks DTI 58 cortical regions N f × FA Efficiency of RC networks is increased more rapidly than that of non-RC networks in term-born brain networks. Baker et al [74] 31 16.58 ± 0.54 DTI 80 cortical and subcortical regions N f and FA RC connectivity between subcortical regions decreased over time.…”

Section: Rich-club Of Humansmentioning

confidence: 99%

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Rich-club in the brain’s macrostructure: Insights from graph theoretical analysis

Kim

Min

2020

Computational and Structural Biotechnology Journal

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The brain is a complex network. Growing evidence supports the critical roles of a set of brain regions within the brain network, known as the brain’s cores or hubs. These regions require high energy cost but possess highly efficient neural information transfer in the brain’s network and are termed the rich-club. The rich-club of the brain network is essential as it directly regulates functional integration across multiple segregated regions and helps to optimize cognitive processes. Here, we review the recent advances in rich-club organization to address the fundamental roles of the rich-club in the brain and discuss how these core brain regions affect brain development and disorders. We describe the concepts of the rich-club behind network construction in the brain using graph theoretical analysis. We also highlight novel insights based on animal studies related to the rich-club and illustrate how human studies using neuroimaging techniques for brain development and psychiatric/neurological disorders may be relevant to the rich-club phenomenon in the brain network.

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Section: Rich-club Of Humansmentioning

confidence: 99%

Section: Rich-club Of Humansmentioning

confidence: 99%

Rich-club in the brain’s macrostructure: Insights from graph theoretical analysis

Kim

Min

2020

Computational and Structural Biotechnology Journal

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“…Early exposure to extrauterine life due to preterm birth affects around 11% of births, and is closely associated with neurodevelopmental, cognitive and psychiatric impairment [2,3,4], and alterations to development [5] that are apparent using in vivo imaging techniques. At the macro scale, these alterations can be characterised by charting white matter connections between brain regions using diffusion MRI (dMRI) [6,7,8,9,10,11,12].…”

Section: Introductionmentioning

confidence: 99%

Hierarchical complexity of the macro-scale neonatal brain

Blesa

Galdi

Cox³

et al. 2020

Preprint

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The human adult structural connectome has a rich topology composed of nodal hierarchies containing highly diverse connectivity patterns, aligned to the diverse range of functional specialisations in the brain. The emergence of this hierarchical complexity in human development is unknown. Here, we substantiate the hierarchical tiers and complexity of brain networks in the newborn period, assess correspondences with hierarchical complexity in adulthood, and investigate the effect of preterm birth, a leading cause of neurocognitive impairment and atypical brain development, on hierarchical complexity. We report that the neonatal and adult structural connectomes are both composed of distinct hierarchical tiers. Consistency of ROIs is found at both ends of this hierarchy during early life and in adulthood, but significant differences are evident in intermediate tiers. The neonatal connectome is hierarchically complex in term born neonates, but hierarchically complexity is altered in association with preterm birth. This is mainly due to diversity of connectivity patterns in Tier 3, which is comprised of regions that underlie sensorimotor processing and its integration with cognitive information. For neonates and adults, the highest tier (comprising hub regions) is ordered, rather than complex, with more homogeneous connectivity patterns in structural hubs. This suggests that the brain develops first a more rigid hierarchical structure in hub regions allowing for the development of greater and more diverse functional specialisation in lower level regions, while connectivity underpinning this diversity is dysmature in infants born preterm.

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“…WM structural connectivity differences can be studied using a network neuroscience approach. This approach conceptualizes the structure and function of the brain as a large‐scale neural network, and has contributed to our understanding of brain organization from birth (Huang et al, ; Song et al, ; Zhao et al, ) to healthy aging (Sala‐Llonch, Bartrés‐Faz, & Junqué, ), as well as in various neurological disorders such as Alzheimer's Disease (Bachmann et al, ; Jalili, ), TBI (van der Horn et al, ), multiple sclerosis (Kocevar et al, ; Rocca et al, ), epilepsy (Garcia‐Ramos et al, ; Jiang, Li, Chen, Ye, & Zheng, ; Sone et al, ), and schizophrenia (Lo et al, ; Olejarczyk & Jernajczyk, ; van den Heuvel, Mandl, Stam, Kahn, & Hulshoff Pol, ). Various network measures can be derived using graph‐theoretical approaches such as modularity, clustering coefficient, path length, and small‐worldness that can inform both network segregation and integration (Rubinov & Sporns, ).…”

Section: Introductionmentioning

confidence: 99%

Understanding white matter structural connectivity differences between cognitively impaired and nonimpaired active professional fighters

Mishra

Sreenivasan

Zhuang

et al. 2019

Human Brain Mapping

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Long‐term traumatic brain injury due to repeated head impacts (RHI) has been shown to be a risk factor for neurodegenerative disorders, characterized by a loss in cognitive performance. Establishing the correlation between changes in the white matter (WM) structural connectivity measures and neuropsychological test scores might help to identify the neural correlates of the scores that are used in daily clinical setting to investigate deficits due to repeated head blows. Hence, in this study, we utilized high angular diffusion MRI (dMRI) of 69 cognitively impaired and 70 nonimpaired active professional fighters from the Professional Fighters Brain Health Study, and constructed structural connectomes to understand: (a) whether there is a difference in the topological WM organization between cognitively impaired and nonimpaired active professional fighters, and (b) whether graph‐theoretical measures exhibit correlations with neuropsychological scores in these groups. A dMRI derived structural connectome was constructed for every participant using brain regions defined in AAL atlas as nodes, and the product of fiber number and average fractional anisotropy of the tracts connecting the nodes as edges. Our study identified a topological WM reorganization due to RHI in fighters prone to cognitive decline that was correlated with neuropsychological scores. Furthermore, graph‐theoretical measures were correlated differentially with neuropsychological scores between groups. We also found differentiated WM connectivity involving regions of hippocampus, precuneus, and insula within our cohort of cognitively impaired fighters suggesting that there is a discernible WM topological reorganization in fighters prone to cognitive decline.

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Structural network maturation of the preterm human brain

Cited by 57 publications

References 95 publications

Rich-club in the brain’s macrostructure: Insights from graph theoretical analysis

Rich-club in the brain’s macrostructure: Insights from graph theoretical analysis

Hierarchical complexity of the macro-scale neonatal brain

Understanding white matter structural connectivity differences between cognitively impaired and nonimpaired active professional fighters

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