Article

Multispectral Quantitative MR Imaging of the Human Brain: Lifetime Age-related Effects

Department of Radiology, Boston Medical Center, Boston University School of Medicine, 820 Harrison Ave, FGH Building 3rd Floor, Boston, MA 02118.
Radiographics (Impact Factor: 2.73). 09/2013; 33(5):1305-19. DOI: 10.1148/rg.335125212
Source: PubMed

ABSTRACT Quantitative magnetic resonance (MR) imaging allows visualization of age-related changes in the normal human brain from functional, biochemical, and morphologic perspectives. Findings at quantitative MR imaging support age-related microstructural changes in the brain: (a) volume expansion, increased myelination, and axonal growth, which establish neural connectivity in neurodevelopment, followed by (b) volume loss, myelin breakdown, and axonal degradation, leading to the disruption of neural integrity later in life. A rapid growth change followed by a continuous slower change in quantitative MR parameters can be modeled with a logarithmic or exponential decay function. The age dependencies during adulthood often fit a quadratic model for transitional changes with accelerated aging effects or a linear model for steady changes.Understanding these general trends over the human life span can improve assessment for a specific disease by helping determine appropriate study settings. Once a consensus on acquisition techniques and image processing algorithms has been reached, quantitative MR imaging can play an important role in the assessment of disease states affecting the brain.

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    • "Fitting the data with our model further suggested that different white matter bundles follow a similar maturational trajectory but with different developmental onsets. This finding, being in agreement with (Prastawa et al. 2010), allowed us deriving a ''general'' maturational equation: similarly to univariate studies during childhood over a larger age range (Watanabe et al. 2013; Engelbrecht et al. 1998; van Buchem et al. 2001; Lebel et al. 2012), changes in the Mahalanobis distance with age in infants could be described by an exponential decay. This modeling allowed us to compute the relative maturational delays between the bundles, confirming that the most dramatic changes in the white matter occur during the first post-natal year, with a total relative maturational delay of 49 weeks between the most and the least mature bundles. "
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    • "Fitting the data with our model further suggested that different white matter bundles follow a similar maturational trajectory but with different developmental onsets. This finding, being in agreement with (Prastawa et al. 2010), allowed us deriving a ''general'' maturational equation: similarly to univariate studies during childhood over a larger age range (Watanabe et al. 2013; Engelbrecht et al. 1998; van Buchem et al. 2001; Lebel et al. 2012), changes in the Mahalanobis distance with age in infants could be described by an exponential decay. This modeling allowed us to compute the relative maturational delays between the bundles, confirming that the most dramatic changes in the white matter occur during the first post-natal year, with a total relative maturational delay of 49 weeks between the most and the least mature bundles. "
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