Regular articleCritical ages in the life course of the adult brain: nonlinear subcortical 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)
- et al.
Aging in the CNS: comparison of gray/white matter volume and diffusion tensor data
Neurobiol. Aging
(2008) - et al.
Normal neuroanatomical variation due to age: the major lobes and a parcellation of the temporal region
Neurobiol. Aging
(2005) - et al.
A unified approach for morphometric and functional data analysis in young, old, and demented adults using automated atlas-based head size normalization: reliability and validation against manual measurement of total intracranial volume
Neuroimage
(2004) - et al.
Longitudinal CSF and MRI biomarkers improve the diagnosis of mild cognitive impairment
Neurobiol. Aging
(2006) - et al.
Accelerated age-related cortical thinning in healthy carriers of apolipoprotein E epsilon 4
Neurobiol. Aging
(2008) - et al.
Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain
Neuron
(2002) - et al.
Sequence-independent segmentation of magnetic resonance images
Neuroimage
(2004) - et al.
When does brain aging accelerate? Dangers of quadratic fits in cross-sectional studies
Neuroimage
(2010) - et al.
The relationship between diffusion tensor imaging and volumetry as measures of white matter properties
Neuroimage
(2008) - et al.
A voxel-based morphometric study of ageing in 465 normal adult human brains
Neuroimage
(2001)
Aging, gender, and the elderly adult brain: an examination of analytical strategies
Neurobiol. Aging
Nonlinear registration of longitudinal images and measurement of change in regions of interest
Med. Image Anal.
Effects of age on tissues and regions of the cerebrum and cerebellum
Neurobiol. Aging
Changes in volume with age—consistency and interpretation of observed effects
Neurobiol. Aging
Age-related differences in regional brain volumes: a comparison of optimized voxel-based morphometry to manual volumetry
Neurobiol. Aging
Hippocampal volume is as variable in young as in older adults: implications for the notion of hippocampal atrophy in humans
Neuroimage
Accelerated aging of the putamen in men but not in women
Neurobiol. Aging
Trajectories of brain aging in middle-aged and older adults: regional and individual differences
Neuroimage
Aging, sexual dimorphism, and hemispheric asymmetry of the cerebral cortex: replicability of regional differences in volume
Neurobiol. Aging
Differential aging of the brain: patterns, cognitive correlates and modifiers
Neurosci. Biobehav. Rev.
Differential age-related changes in the regional metencephalic volumes in humans: a 5-year follow-up
Neurosci. Lett.
Volume of white matter hyperintensities in healthy adults: contribution of age, vascular risk factors, and inflammation-related genetic variants
Biochim. Biophys. Acta
Nonlinear time course of brain volume loss in cognitively normal and impaired elders
Neurobiol. Aging
Age-related decline in MRI volumes of temporal lobe gray matter but not hippocampus
Neurobiol. Aging
Effects of age and sex on volumes of the thalamus, pons, and cortex
Neurobiol. Aging
Effects of age on volumes of cortex, white matter and subcortical structures
Neurobiol. Aging
Consistent neuroanatomical age-related volume differences across multiple samples
Neurobiol. Aging
Differentiating maturational and aging-related changes of the cerebral cortex by use of thickness and signal intensity
Neuroimage
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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.