Elsevier

Neurobiology of Aging

Volume 34, Issue 10, October 2013, Pages 2239-2247
Neurobiology of Aging

Regular article
Critical ages in the life course of the adult brain: nonlinear subcortical aging

https://doi.org/10.1016/j.neurobiolaging.2013.04.006Get rights and content

Abstract

Age-related changes in brain structure result from a complex interplay among various neurobiological processes, which may contribute to more complex trajectories than what can be described by simple linear or quadratic models. We used a nonparametric smoothing spline approach to delineate cross-sectionally estimated age trajectories of the volume of 17 neuroanatomic structures in 1100 healthy adults (18–94 years). Accelerated estimated decline in advanced age characterized some structures, for example hippocampus, but was not the norm. For most areas, 1 or 2 critical ages were identified, characterized by changes in the estimated rate of change. One-year follow-up data from 142 healthy older adults (60–91 years) confirmed the existence of estimated change from the cross-sectional analyses for all areas except 1 (caudate). The cross-sectional and the longitudinal analyses agreed well on the rank order of age effects on specific brain structures (Spearman ρ = 0.91). The main conclusions are that most brain structures do not follow a simple path throughout adult life and that accelerated decline in high age is not the norm of healthy brain aging.

Introduction

The volume of most brain structures shrinks with age, but the degree of change is highly heterogeneous across different structures (Allen et al., 2005; Raz and Rodrigue, 2006). Also, age-related changes result from a complex interplay among various neurobiological processes, which is likely to have different impact in different phases of life. This is likely to produce more complex trajectories than what can be described by linear or the usually employed higher order polynomial (quadratic or even cubic) models (Fjell et al., 2010b). The present study was undertaken with the purpose of estimating trajectories across the age of 17 brain structures in a large cross-sectional sample (n = 1100). Parts of these data have been previously published (e.g., Fjell et al., 2009c), and we now reanalyze them by applying a statistical approach (the smoothing spline) sensitive to local changes in estimated rate of change (Fjell et al., 2010b). This makes it possible to identify critical ages where life phases characterized by relative stability are followed by periods where estimated atrophy accelerates or critical ages where periods of estimated reduction eventually level off. The cross-sectional results were compared with longitudinal atrophy rates from a sample of 142 healthy elderly drawn from the Alzheimer's Disease Neuroimaging Initiative (ADNI) (previously presented in Fjell et al., 2009b).

Previous literature, including the reports based on samples overlapping the present, indicates inverse U-shaped trajectories for hippocampus, cerebral white matter (WM), cerebellum WM, and the brain stem (Allen et al., 2005; Lupien et al., 2007; Walhovd et al., 2011), whereas U and J forms have been reported for the caudate and the ventricular system (Good et al., 2001; Sullivan et al., 1995; Walhovd et al., 2011). In contrast, mainly linear trajectories have been reported for amygdala, thalamus, accumbens, and putamen (Abe et al., 2008; Alexander et al., 2006; Allen et al., 2005; Curiati et al., 2009; Greenberg et al., 2008; Gunning-Dixon et al., 1998; Jernigan et al., 2001; Nunnemann et al., 2009; Raz et al., 2003; Sullivan et al., 2004; Walhovd et al., 2011). Both linear and quadratic reductions have been found for pallidum (Abe et al., 2008; Walhovd et al., 2011). The rational for the present study was to go beyond these general trends, by more accurately delineating the trajectories for the different structures across adult life and to identify critical ages characterized by changes in estimated rate of atrophy. We included volume for 17 major regions and structures estimated from the whole-brain segmentation approach in FreeSurfer (Fischl et al., 2002). Surface-based cortical thickness results were presented in a previous publication (Fjell et al., in press).

Section snippets

Cross-sectional sample

A total of 1100 healthy adults (424 men and 676 women), with an age range of 76 years (18–94 years, mean = 48, standard deviation = 20) were included, pooled from 5 independent studies. Distribution of participants across decades is shown in Table 1. All the healthy samples were screened for diseases and history of neurologic conditions and dementia, and none of the participants showed signs of cognitive dysfunction. The details of each of the subsamples are described in Supplementary Table 1,

Cross-sectional data

To compare the linear and the smoothing spline models, we calculated BIC for the relationship between each brain volume and age (Supplementary Table 2, also including the quadratic model for comparison purposes). Scatterplots illustrating the estimated trajectories are presented in Fig. 1 (structures) and Fig. 2 (ventricular system). Of the 17 tested regions, a nonlinear model represented the data best for 13 (TBV, cerebral cortex and WM, hippocampus, caudate, cerebellar WM, brain stem,

Discussion

There were 3 main findings: first, a heterogeneous pattern of discontinuous age correlations in different age spans characterized the majority of brain regions, and critical ages for changes in estimated rates of atrophy could be identified. Second, accelerated estimated reduction with advanced age is not the norm of brain aging. Rather, different structures showed a mix of trajectories. When more negative (positive for cerebrospinal fluid) age-volume correlations were seen in the last part of

Disclosure statement

Dr 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.

None of the authors have any actual or potential conflicts of interest.

Acknowledgements

The present article was funded by the following grants: The Norwegian Research Council (177404 and 186092 to K.B.W., 175066 and 189507 to A.M.F., 204966 to L.T.W., 154313/V50 to I.R., 177458/V50 to T.E.), University of Oslo (to K.B.W. and A.M.F.), National Institutes of Health, USA (R37 AG011230 to N.R.), and by the European Research Council Starting Grant scheme to A.M.F and K.B.W.

The OASIS database is made available by the Washington University Alzheimer's Disease Research Center, Dr Randy

References (57)

  • D.L. Greenberg et al.

    Aging, gender, and the elderly adult brain: an examination of analytical strategies

    Neurobiol. Aging

    (2008)
  • D. Holland et al.

    Nonlinear registration of longitudinal images and measurement of change in regions of interest

    Med. Image Anal.

    (2011)
  • T.L. Jernigan et al.

    Effects of age on tissues and regions of the cerebrum and cerebellum

    Neurobiol. Aging

    (2001)
  • T.L. Jernigan et al.

    Changes in volume with age—consistency and interpretation of observed effects

    Neurobiol. Aging

    (2005)
  • K.M. Kennedy et al.

    Age-related differences in regional brain volumes: a comparison of optimized voxel-based morphometry to manual volumetry

    Neurobiol. Aging

    (2009)
  • S.J. Lupien et al.

    Hippocampal volume is as variable in young as in older adults: implications for the notion of hippocampal atrophy in humans

    Neuroimage

    (2007)
  • S. Nunnemann et al.

    Accelerated aging of the putamen in men but not in women

    Neurobiol. Aging

    (2009)
  • N. Raz et al.

    Trajectories of brain aging in middle-aged and older adults: regional and individual differences

    Neuroimage

    (2010)
  • N. Raz et al.

    Aging, sexual dimorphism, and hemispheric asymmetry of the cerebral cortex: replicability of regional differences in volume

    Neurobiol. Aging

    (2004)
  • N. Raz et al.

    Differential aging of the brain: patterns, cognitive correlates and modifiers

    Neurosci. Biobehav. Rev.

    (2006)
  • N. Raz et al.

    Differential age-related changes in the regional metencephalic volumes in humans: a 5-year follow-up

    Neurosci. Lett.

    (2003)
  • N. Raz et al.

    Volume of white matter hyperintensities in healthy adults: contribution of age, vascular risk factors, and inflammation-related genetic variants

    Biochim. Biophys. Acta

    (2012)
  • N. Schuff et al.

    Nonlinear time course of brain volume loss in cognitively normal and impaired elders

    Neurobiol. Aging

    (2012)
  • E.V. Sullivan et al.

    Age-related decline in MRI volumes of temporal lobe gray matter but not hippocampus

    Neurobiol. Aging

    (1995)
  • E.V. Sullivan et al.

    Effects of age and sex on volumes of the thalamus, pons, and cortex

    Neurobiol. Aging

    (2004)
  • K.B. Walhovd et al.

    Effects of age on volumes of cortex, white matter and subcortical structures

    Neurobiol. Aging

    (2005)
  • K.B. Walhovd et al.

    Consistent neuroanatomical age-related volume differences across multiple samples

    Neurobiol. Aging

    (2011)
  • L.T. Westlye et al.

    Differentiating maturational and aging-related changes of the cerebral cortex by use of thickness and signal intensity

    Neuroimage

    (2010)
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    Data used in the preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (http://www.loni.ucla.edu/ADNI). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this article. Complete listing of ADNI investigators is available at http://www.loni.ucla.edu/ADNI/Collaboration/ADNI_Manuscript_Citations.pdf.

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