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      Mapping an index of the myelin g-ratio in infants using magnetic resonance imaging

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          Abstract

          Optimal myelination of neuronal axons is essential for effective brain and cognitive function. The ratio of the axon diameter to the outer fiber diameter, known as the g-ratio, is a reliable measure to assess axonal myelination and is an important index reflecting the efficiency and maximal conduction velocity of white matter pathways. Although advanced neuroimaging techniques including multicomponent relaxometry (MCR) and diffusion tensor imaging afford insight into the microstructural characteristics of brain tissue, by themselves they do not allow direct analysis of the myelin g-ratio. Here, we show that by combining myelin content information (obtained with mcDESPOT MCR) with neurite density information (obtained through NODDI diffusion imaging) an index of the myelin g-ratio may be estimated. Using this framework, we present the first quantitative study of myelin g-ratio index changes across childhood, examining 18 typically developing children 3 months to 7.5 years of age. We report a spatiotemporal pattern of maturation that is consistent with histological and developmental MRI studies, as well as theoretical studies of the myelin g-ratio. This work represents the first ever in vivo visualization of the evolution of white matter g-ratio indices throughout early childhood.

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          White matter in learning, cognition and psychiatric disorders.

          White matter is the brain region underlying the gray matter cortex, composed of neuronal fibers coated with electrical insulation called myelin. Previously of interest in demyelinating diseases such as multiple sclerosis, myelin is attracting new interest as an unexpected contributor to a wide range of psychiatric disorders, including depression and schizophrenia. This is stimulating research into myelin involvement in normal cognitive function, learning and IQ. Myelination continues for decades in the human brain; it is modifiable by experience, and it affects information processing by regulating the velocity and synchrony of impulse conduction between distant cortical regions. Cell-culture studies have identified molecular mechanisms regulating myelination by electrical activity, and myelin also limits the critical period for learning through inhibitory proteins that suppress axon sprouting and synaptogenesis.
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            Longitudinal development of human brain wiring continues from childhood into adulthood.

            Healthy human brain development is a complex process that continues during childhood and adolescence, as demonstrated by many cross-sectional and several longitudinal studies. However, whether these changes end in adolescence is not clear. We examined longitudinal white matter maturation using diffusion tensor tractography in 103 healthy subjects aged 5-32 years; each volunteer was scanned at least twice, with 221 total scans. Fractional anisotropy (FA) and mean diffusivity (MD), parameters indicative of factors including myelination and axon density, were assessed in 10 major white matter tracts. All tracts showed significant nonlinear development trajectories for FA and MD. Significant within-subject changes occurred in the vast majority of children and early adolescents, and these changes were mostly complete by late adolescence for projection and commissural tracts. However, association tracts demonstrated postadolescent within-subject maturation of both FA and MD. Diffusion parameter changes were due primarily to decreasing perpendicular diffusivity, although increasing parallel diffusivity contributed to the prolonged increases of FA in association tracts. Volume increased significantly with age for most tracts, and longitudinal measures also demonstrated postadolescent volume increases in several association tracts. As volume increases were not directly associated with either elevated FA or reduced MD between scans, the observed diffusion parameter changes likely reflect microstructural maturation of brain white matter tracts rather than just gross anatomy.
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              Life-span changes of the human brain white matter: diffusion tensor imaging (DTI) and volumetry.

              Magnetic resonance imaging volumetry studies report inverted U-patterns with increasing white-matter (WM) volume into middle age suggesting protracted WM maturation compared with the cortical gray matter. Diffusion tensor imaging (DTI) is sensitive to degree and direction of water permeability in biological tissues, providing in vivo indices of WM microstructure. The aim of this cross-sectional study was to delineate age trajectories of WM volume and DTI indices in 430 healthy subjects ranging 8-85 years of age. We used automated regional brain volume segmentation and tract-based statistics of fractional anisotropy, mean, and radial diffusivity as markers of WM integrity. Nonparametric regressions were used to fit the age trajectories and to estimate the timing of maximum development and deterioration in aging. Although the volumetric data supported protracted growth into the sixth decade, DTI indices plateaued early in the fourth decade across all tested regions and then declined slowly into late adulthood followed by an accelerating decrease in senescence. Tractwise and voxel-based analyses yielded regional differences in development and aging but did not provide ample evidence in support of a simple last-in-first-out hypothesis of life-span changes.
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                Author and article information

                Journal
                9215515
                20498
                Neuroimage
                Neuroimage
                NeuroImage
                1053-8119
                1095-9572
                5 April 2016
                22 February 2016
                15 May 2016
                15 May 2016
                : 132
                : 225-237
                Affiliations
                [a ]Waisman Center, University of Wisconsin-Madison, Madison, WI 53705, USA
                [b ]Department of Neuroimaging, King’s College London, Institute of Psychiatry, London SE5 8AF, UK
                [c ]Department of Kinesiology, University of Wisconsin-Madison, Madison, WI 53705, USA
                [d ]Advanced Baby Imaging Lab, Brown University School of Engineering, Providence, RI 02912, USA
                [e ]Department of Psychiatry, University of Wisconsin-Madison, Madison, WI 53719, USA
                [f ]Department of Medical Physics, University of Wisconsin-Madison, Madison, WI 53705, USA
                [g ]Department of Pediatric Radiology, Children’s Hospital Colorado, Aurora, CO, USA
                [h ]Department of Radiology, University of Colorado Denver, Denver, CO, USA
                Author notes
                [* ]Corresponding author. deaniii@ 123456wisc.edu (D.C. Dean)
                Article
                NIHMS773879
                10.1016/j.neuroimage.2016.02.040
                4851913
                26908314
                afdaf8c6-1521-4e33-b147-d17d5a38d92b

                This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/).

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                Neurosciences
                Neurosciences

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