Development of brain structural connectivity between ages 12 and 30: A 4-Tesla diffusion imaging study in 439 adolescents and adults

Imaging Genetics Center, Laboratory of Neuro Imaging, UCLA School of Medicine, Los Angeles, CA, USA.
NeuroImage (Impact Factor: 6.36). 09/2012; 64C(1):671-684. DOI: 10.1016/j.neuroimage.2012.09.004
Source: PubMed


Understanding how the brain matures in healthy individuals is critical for evaluating deviations from normal development in psychiatric and neurodevelopmental disorders. The brain's anatomical networks are profoundly re-modeled between childhood and adulthood, and diffusion tractography offers unprecedented power to reconstruct these networks and neural pathways in vivo. Here we tracked changes in structural connectivity and network efficiency in 439 right-handed individuals aged 12 to 30 (211 female/126 male adults, mean age=23.6, SD=2.19; 31 female/24 male 12year olds, mean age=12.3, SD=0.18; and 25 female/22 male 16year olds, mean age=16.2, SD=0.37). All participants were scanned with high angular resolution diffusion imaging (HARDI) at 4T. After we performed whole brain tractography, 70 cortical gyral-based regions of interest were extracted from each participant's co-registered anatomical scans. The proportion of fiber connections between all pairs of cortical regions, or nodes, was found to create symmetric fiber density matrices, reflecting the structural brain network. From those 70×70 matrices we computed graph theory metrics characterizing structural connectivity. Several key global and nodal metrics changed across development, showing increased network integration, with some connections pruned and others strengthened. The increases and decreases in fiber density, however, were not distributed proportionally across the brain. The frontal cortex had a disproportionate number of decreases in fiber density while the temporal cortex had a disproportionate number of increases in fiber density. This large-scale analysis of the developing structural connectome offers a foundation to develop statistical criteria for aberrant brain connectivity as the human brain matures.

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Available from: Emily L. Dennis
    • "This is consistent with a recent longitudinal study that found increases and decreases in streamline density in late adolescence [Baker et al., 2015]. Our findings extend reports that both increases and decreases in local efficiency occur between 12 and 30 years [Dennis et al., 2013], and in adulthood [Gong et al., 2009]. Other studies found an increase in global efficiency over the ages 2–18 [Hagmann et al., 2010] and a decrease in local and global efficiency between the ages 4 and 40 [Lim et al., 2015]. "
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    ABSTRACT: The brain is a network and our intelligence depends in part on the efficiency of this network. The network of adolescents differs from that of adults suggesting developmental changes. However, whether the network changes over time at the individual level and, if so, how this relates to intelligence, is unresolved in adolescence. In addition, the influence of genetic factors in the developing network is not known. Therefore, in a longitudinal study of 162 healthy adolescent twins and their siblings (mean age at baseline 9.9 [range 9.0-15.0] years), we mapped local and global structural network efficiency of cerebral fiber pathways (weighted with mean FA and streamline count) and assessed intelligence over a three-year interval. We find that the efficiency of the brain's structural network is highly heritable (locally up to 74%). FA-based local and global efficiency increases during early adolescence. Streamline count based local efficiency both increases and decreases, and global efficiency reorganizes to a net decrease. Local FA-based efficiency was correlated to IQ. Moreover, increases in FA-based network efficiency (global and local) and decreases in streamline count based local efficiency are related to increases in intellectual functioning. Individual changes in intelligence and local FA-based efficiency appear to go hand in hand in frontal and temporal areas. More widespread local decreases in streamline count based efficiency (frontal cingulate and occipital) are correlated with increases in intelligence. We conclude that the teenage brain is a network in progress in which individual differences in maturation relate to level of intellectual functioning. Hum Brain Mapp, 2015. © 2015 Wiley Periodicals, Inc.
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    • "Left-hemisphere networks are well known to be dominant in language tasks, whilst the right-hemisphere is associated with visuospatial abilities (Geschwind and Galaburda, 1985; Herve et al., 2013; Toga and Thompson, 2003). Although connectomic investigations (Caeyenberghs and Leemans, 2014; Nielsen et al., 2013; Tomasi and Volkow, 2012) have examinated lateralized organisation at the nodal-level, no study has specifically investigated lateralization of the elderly connectome Sexual dimorphism has been an active area of research for the last few decades, with increasing interest from connectomic investigations (Dennis et al., 2013; Duarte-Carvajalino et al., 2012; Gong et al., 2009; Ryman et al., 2014). Across the lifespan, males have been shown to demonstrate greater performance in visuospatial tasks, whilst females excel on verbal tasks (Gur et al., 2012; Hoogendam et al., 2014; Kimura, 2004). "
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    ABSTRACT: Investigations of the human connectome have elucidated core features of adult structural networks, particularly the crucial role of hub-regions. However, little is known regarding network organisation of the healthy elderly connectome, a crucial prelude to the systematic study of neurodegenerative disorders. Here, whole-brain probabilistic tractography was performed on high-angular diffusion-weighted images acquired from 115 healthy elderly subjects (age 76-94 years; 65 females). Structural networks were reconstructed between 512 cortical and subcortical brain regions. We sought to investigate the architectural features of hub-regions, as well as left-right asymmetries, and sexual dimorphisms. We observed that the topology of hub-regions is consistent with a young adult population, and previously published adult connectomic data. More importantly, the architectural features of hub connections reflect their ongoing vital role in network communication. We also found substantial sexual dimorphisms, with females exhibiting stronger inter-hemispheric connections between cingulate and prefrontal cortices. Lastly, we demonstrate intriguing left-lateralized subnetworks consistent with the neural circuitry specialised for language and executive functions, while rightward subnetworks were dominant in visual and visuospatial streams. These findings provide insights into healthy brain ageing and provide a benchmark for the study of neurodegenerative disorders such as Alzheimer's disease (AD) and Frontotemporal Dementia (FTD). Copyright © 2015. Published by Elsevier Inc.
    Full-text · Article · Feb 2015 · NeuroImage
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    • "A direct comparison with other previous tractography-based network studies is difficult, however, as existing studies focussed on specific age groups: childhood (Hagmann et al., 2010), childhood or early adulthood (Dennis et al., 2013; Tymofiyeva et al., 2013) or only individuals older than sixty years (Fischer et al., 2014). "
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    ABSTRACT: The organizational network changes in the human brain across lifespan have been mapped using functional and structural connectivity data. Brain network changes provide valuable insights into the processes underlying senescence. Nonetheless, the altered network density in elderly severely compromises the usefulness of network analysis in to study the aging brain. We successfully circumvented this problem by focussing on the critical structural network backbone, using a robust tree representation. Whole-brain networks’ minimum spanning trees were determined in a dataset of diffusion-weighted images from 382 healthy subjects, ranging in age from 20.2 to 86.2 years. Tree-based metrics were compared with classical network metrics. In contrast to the tree-based metrics, classical metrics were highly influenced by age-related changes in network density. Tree-based metrics showed linear and non-linear correlation across adulthood and are in close accordance with results from previous histopathological characterizations of the changes in white matter integrity in the aging brain.
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