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:671-684. DOI: 10.1016/j.neuroimage.2012.09.004
Source: PubMed

ABSTRACT 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, Aug 30, 2015
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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.
    NeuroImage 02/2015; 114. DOI:10.1016/j.neuroimage.2015.04.009 · 6.36 Impact Factor
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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.
    NeuroImage 01/2015; 109. DOI:10.1016/j.neuroimage.2015.01.011 · 6.36 Impact Factor
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    • "Similarly, TIV can be a confound in the analysis of group differences or covariate correlates if there is an imbalance in head size between groups, an association of TIV with the covariate of interest, or an interaction involving TIV (Ueda et al., 2010). Beyond volumetric analysis, TIV may need to be accounted for in structural connectivity measures (Dennis et al., 2013). In neurodegenerative conditions such as Alzheimer's disease (AD) TIV may be used as a proxy for maximum pre-morbid brain volume, which in turn might relate to cognitive reserve (Perneczky et al., 2010). "
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    ABSTRACT: Total intracranial volume (TIV/ICV) is an important covariate for volumetric analyses of the brain and brain regions, especially in studies of neurodegenerative diseases, where it can provide a proxy of maximum pre-morbid brain volume. The gold-standard method is manual delineation of brain scans, but this requires careful work by trained operators. We evaluated Statistical Parametric Mapping 12 (SPM12) automated segmentation for TIV measurement in place of manual segmentation and also compared it with SPM8 and FreeSurfer 5.3.0. For T1-weighted MRI acquired from 288 participants in a multi-centre clinical trial in Alzheimer's disease we find a high correlation between SPM12 TIV and manual TIV (R(2)=0.940, 95% Confidence Interval (0.924, 0.953)), with a small mean difference (SPM12 40.4±35.4ml lower than manual, amounting to 2.8% of the overall mean TIV in the study). The correlation with manual measurements (the key aspect when using TIV as a covariate) for SPM12 was significantly higher (p<0.001) than for either SPM8 (R(2)=0.577 CI (0.500, 0.644)) or FreeSurfer (R(2)=0.801 CI (0.744, 0.843)). These results suggest that SPM12 TIV estimates are an acceptable substitute for labour-intensive manual estimates even in the challenging context of multiple centres and the presence of neurodegenerative pathology. We also briefly discuss some aspects of the statistical modelling approaches to adjust for TIV.
    NeuroImage 09/2014; 104. DOI:10.1016/j.neuroimage.2014.09.034 · 6.36 Impact Factor
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