Differentiating maturational and aging-related changes of the cerebral cortex by use of thickness and signal intensity
Introduction
Delineating normal life span development of the cerebral cortex provides indispensible knowledge informing the interpretation of the deviating courses related to developmental and age-related diseases (Tau and Peterson, 2010). Alterations in cortical volume and thickness have been mapped in vivo in development (Giedd et al., 1999, Gogtay et al., 2004, Shaw et al., 2008, Sowell et al., 2003, Sowell et al., 2004, Tamnes et al., 2010) and aging (Fjell et al., 2009, Good et al., 2001, Jernigan et al., 2001, Raz et al., 2004, Salat et al., 2004, Westlye et al., 2009b) using magnetic resonance imaging (MRI). Accumulating evidence is converging on decreases in cortical thickness and volume from late childhood. Although the neurobiological underpinnings of the morphometric changes are not established, it is likely that fundamentally different processes induce cortical thinning in development and the atrophy seen in adulthood (Sowell et al., 2003). The maturational cortical thinning may be caused by regressive use-dependent selective synapse elimination (Bourgeois and Rakic, 1993, Huttenlocher and Dabholkar, 1997) in combination with progressive proliferation of myelin into the neuropil (Shaw et al., 2008, Sowell et al., 2001, Yakovlev and Lecours, 1967). These processes refine local and distributed network connectivity and enhance the efficiency and synchronicity of signal transmission (Tau and Peterson, 2010). Cortical thinning is thus regarded an early maturational marker. In contrast, adult cortical thinning has been attributed to degenerative shrinkage of large neurons (Terry et al., 1987), loss of myelinated axonal fibers (Nairn et al., 1989), deafferentation (Bertoni-Freddari et al., 2002), and reduction in synaptic density (Morrison and Hof, 1997).
Cortical thickness fails to distinguish between maturational and aging-related neurobiological processes. However, by mapping intracortical T1-weighted signal intensity, we may differentiate the neurobiological processes associated with development from those associated with aging. T1-weighted signal intensity reflects the underlying tissue proton relaxation times, which is related to degree of myelination (Barbier et al., 2002, Clark et al., 1992, Eickhoff et al., 2005, Walters et al., 2003). Signal intensity alterations may provide an independent biomarker of structural alterations in development, aging, and disease (Salat et al., 2009, Westlye et al., 2009b).
The aim of this study was to track cortical GM and subcortical WM development and aging by surface-based mapping of cortical and subcortical T1-weighted signal intensity and R2* as well as cortical thickness in 429 participants aged 8–85 years. We anticipated signal intensity increases from childhood through adolescence and decline in the latter half of life. Since signal intensity is related to tissue myeloarchitecture, we expected cortical trajectories similar to those demonstrated in white matter microstructural maturation, with increases well into adulthood (Lebel et al., 2008, Westlye et al., in press). This stands in contrast to the monotone cortical thinning from childhood. In line with evidence of a maturational posterior–anterior gradient (Gogtay et al., 2004), we expected early maturation of sensorimotor areas including visual cortices, followed by association areas and last in higher-order frontal and posterior parietal cortices. Following an inverse ontogenetic course in aging (Courchesne et al., 2000, Raz, 2000), we expected earliest decline in frontoparietal cortices and latest in sensorimotor areas.
Several biophysical variables influence the T1-weighted signal. Changes in T2 and T2* relaxation secondary to iron depositions may modulate the T1-weighted signal intensity, even on strongly T1-weighted acquisitions like the MP-RAGE sequence used in the present study. Therefore, possible confounding effects of bioaccumulated iron, with consequent reduction in T2* relaxation times, were controlled by including T2* relaxation rate (R2*) computed from multiecho sequences as additional covariates in the statistical models. Since R2* also may index relevant age-related biophysical variability, we delineated the age trajectories for intracortical GM and subjacent WM R2* across the life span.
Section snippets
Subjects
The sample was drawn from the first wave of two ongoing longitudinal research projects at the Center for the Study of Human Cognition at the University of Oslo, namely, Neurocognitive Development and Cognition and Plasticity through the Lifespan and is overlapping with the sample in Westlye et al. (in press). The studies were approved by the Regional Ethical Committee of Southern Norway (REK-Sør). Participants were recruited through newspaper ads and among students and employees at the
Quadratic effects of age
Supplementary Fig. 2 shows statistical p maps (p < .01) from GLMs testing for quadratic effects of age on intracortical GM and subcortical WM T1-weighted signal intensity and intracortical GM R2* while regressing out linear effects of age and sex. Widespread negative quadratic effects (inverse U curves) of age were observed across measures.
Intracortical GM T1-weighted signal intensity
Fig. 1A shows color-coded surface mappings of the timing of maximum cortical GM T1-weighted signal intensity (after removal of ICV-related variance per
Discussion
Cortical thinning in development and aging are caused by different neurobiological processes, but this cannot be monitored in vivo by use of imaging-derived cortical thickness measures alone. Here, we show that signal intensity obtained from heavily T1-weighted MRI scans is a promising biomarker of cortical age-related changes. In contrast to cortical thickness, T1-weighted signal intensity showed nonmonotone relationships with age, with strong maturational increases, a relatively stable
Disclosure statement
Anders M. Dale is a founder and holds equity in CorTechs Labs, Inc, and also serves on the Scientific Advisory Board. The terms of this arrangement have been reviewed and approved by the University of California, San Diego, in accordance with its conflict of interest policies. All other authors state that there are no actual or potential conflicts of interest. Appropriate approval and procedures were used concerning human subjects participating in the study.
Acknowledgments
This study was supported by the Norwegian Research Council (grant 177404/W50 to K.B.W. and 175066/D15 to A.M.F.) and the University of Oslo to K.B.W. and A.M.F. The authors wish to express their gratitude to three anonymous reviewers for constructive comments on an earlier version of the article.
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