Consistent neuroanatomical age-related volume differences across multiple samples

https://doi.org/10.1016/j.neurobiolaging.2009.05.013Get rights and content

Abstract

Magnetic resonance imaging (MRI) is the principal method for studying structural age-related brain changes in vivo. However, previous research has yielded inconsistent results, precluding understanding of structural changes of the aging brain. This inconsistency is due to methodological differences and/or different aging patterns across samples. To overcome these problems, we tested age effects on 17 different neuroanatomical structures and total brain volume across five samples, of which one was split to further investigate consistency (883 participants). Widespread age-related volume differences were seen consistently across samples. In four of the five samples, all structures, except the brainstem, showed age-related volume differences. The strongest and most consistent effects were found for cerebral cortex, pallidum, putamen and accumbens volume. Total brain volume, cerebral white matter, caudate, hippocampus and the ventricles consistently showed non-linear age functions. Healthy aging appears associated with more widespread and consistent age-related neuroanatomical volume differences than previously believed.

Introduction

Brain changes are inevitable in aging. Still, core questions remain a matter of debate: What structures change, when do they start aging, at what rates, and are some structures spared? Many cross-sectional studies have demonstrated neuroanatomical age-related volume differences in vivo by the use of magnetic resonance imaging (MRI) (Allen et al., 2005, Blatter et al., 1995, Courchesne et al., 2000, Fotenos et al., 2005, Good et al., 2001, Head et al., 2004, Head et al., 2005, Jernigan et al., 1991, Jernigan et al., 2001, Luft et al., 1999, Mu et al., 1999, Raz et al., 2000, Raz et al., 2004a, Raz et al., 2004b, Raz et al., 2005, Raz et al., 2007, Raz and Rodrigue, 2006, Salat et al., 2004, Sullivan et al., 1995, Sullivan et al., 2004, Taki et al., 2004, Tisserand et al., 2002, Walhovd et al., 2005a). Some structures are found to decline substantially, while others appear better preserved (Raz and Rodrigue, 2006). Different age trajectories have been observed, with some brain areas declining linearly from early in life, whereas others continue to increase in volume before eventually beginning to deteriorate (Allen et al., 2005, Good et al., 2001, Luft et al., 1999, Raz et al., 2004b, Walhovd et al., 2005a). Unfortunately, the results diverge much across studies, and differences in segmentation procedures and demarcation criteria complicate comparisons. Discrepant findings have been reported for most structures. Adding to this problem, in most studies only a few structures are segmented, making it difficult to assess the relative vulnerability of different structures to age.

The aim of the present paper was to overcome these problems. Data from five samples (one split-half making a total of six groups for analysis) were processed with the same segmentation tools, and the stability of age effects across samples was assessed for 16 subcortical structures as well as cortical volume and total brain volume. Three questions were asked: (1) Which structures show significant age-related volume differences across samples? (2) Which structures undergo the most prominent age-related changes, and which are relatively preserved? (3) Which structures are volumetrically changed in a linear fashion, and which show curvilinear (quadratic) age relationships?

Main findings from previous MRI studies on age-related differences in neuroanatomial volumes are summarized in the following. Further reviews can be found elsewhere (Raz and Rodrigue, 2006). It should be noted that the vast majority of studies reviewed below are of a cross-sectional nature, and unless longitudinal designs are explicitly noted, what is observed are age differences, rather than age changes. There is consensus that gray matter (GM) volume/thickness is smaller with higher age (Blatter et al., 1995, Courchesne et al., 2000, Fotenos et al., 2008, Good et al., 2001, Jernigan et al., 1991, Jernigan et al., 2001, Murphy et al., 1996, Pfefferbaum et al., 1994, Raz et al., 1997, Resnick et al., 2000, Salat et al., 2004, Sullivan et al., 1995, Sullivan et al., 2004, Walhovd et al., 2005a), and that this effect is seen early in life (Courchesne et al., 2000, Giedd, 2004, Giedd et al., 1999, Giedd et al., 1996, Lebel et al., 2008). Based on cross-sectional investigations, there generally appears to be somewhat greater GM loss in the cortex than in subcortical structures (Jernigan et al., 2001, Walhovd et al., 2005a). However, a longitudinal study has indicated at least as much shrinkage of the caudate and cerebellum as in the lateral frontal and orbitofrontal cortex (Raz et al., 2005). Aging of different parts of the cortex is highly heterogeneous, and cortical volume is included in the present study mainly to allow comparisons with subcortical structures. Detailed analyses of cortical thickness are reported elsewhere (Fjell et al., in press).

Less consistent results have been reported for the relationship between age and white matter (WM) volume. Some studies have found no age differences (Abe et al., 2008, Blatter et al., 1995, Good et al., 2001, Jernigan et al., 1991, Pfefferbaum et al., 1994, Sullivan et al., 2004), while others have found that total WM volume is negatively related to age (Allen et al., 2005, Guttmann et al., 1998, Jernigan et al., 2001, Walhovd et al., 2005a). Samples of varying ages may be a reason for the discrepant findings, and studies including the oldest participants tend to report age effects. One study (Courchesne et al., 2000) reported white matter to be negatively related to age only from 70 years of age onwards, and this age range has not been consistently included in aging studies. Jernigan and colleagues (Jernigan et al., 2001, Jernigan and Gamst, 2005) found that despite its later onset, white matter loss was more rapid than gray matter loss, and ultimately exceeded it. In recent years, there has been increased focus on the possibly curvilinear nature of age change in WM volume (Allen et al., 2005, Jernigan and Gamst, 2005, Walhovd et al., 2005a), with gains until middle age followed by later decrease. Non-linear fits tend to significantly increase the proportion of variance in WM volume explained by age. As for gray matter, results indicate somewhat less age-related loss in deep subcortical regions than in the cerebral lobes (Jernigan et al., 2001). For instance, although some decline has also been observed in brainstem volume (Walhovd et al., 2005a), several studies have reported no effect of age on volume of the pons (Luft et al., 1999, Raz et al., 1998, Raz et al., 2001, Raz et al., 1992, Van Der Werf et al., 2001).

In the following, age effects on different subcortical brain structures from 31 cross-sectional studies are reviewed (details are presented in Table 1). All studies tested effects of age on the volume of at least one of the subcortical structures/compartments included in the present study, and a short presentation of the main results from this literature is given below:

  • Hippocampus: The variability among studies is high. Nine of 15 studies reviewed here found that hippocampus shrank with age (Allen et al., 2005, Greenberg et al., 2008, Jernigan et al., 2001, Lupien et al., 2007, Mu et al., 1999, Raz et al., 2004a, Scahill et al., 2003, Schuff et al., 1999, Walhovd et al., 2005a), while five found no change (Du et al., 2006, Liu et al., 2003, Sullivan et al., 1995, Sullivan et al., 2005, Van Petten, 2004). In one study, age effects on hippocampal volume were found for men but not women (Pruessner et al., 2001). In addition, age effects on hippocampal volume normalized to global GM loss were not observed in a very large study (Good et al., 2001). Notably, three of the studies found non-linear effects of age (Allen et al., 2005, Lupien et al., 2007, Walhovd et al., 2005a), and one longitudinal study reported accelerated age-related hippocampal shrinkage (Raz et al., 2005). Part of the discrepant findings may thus stem from failure to account for non-linearity.

  • Amygdala: There have been fewer studies of age effects on the amygdala, but in sum, the reports indicate smaller age effects on the amygdala than on the hippocampus. Three studies found smaller volume of amygdala with higher age (Allen et al., 2005, Mu et al., 1999, Walhovd et al., 2005a), while two did not (Jernigan et al., 2001, Pruessner et al., 2001), and in one age effects relative to global GM loss were not observed (Good et al., 2001).

  • Thalamus/diencephalic structures: Four studies found smaller volume with higher age (Sullivan et al., 2004, Van Der Werf et al., 2001, Walhovd et al., 2005a, Xu et al., 2000), while two did not (Jernigan et al., 1991, Jernigan et al., 2001). In addition, one study found lack of age effects on the lateral thalamus relatively to global GM loss (Good et al., 2001).

  • Caudate: Caudate is the only structure where all the relevant studies are in coherence, with eight studies finding linear negative relationships with age (Greenberg et al., 2008, Gunning-Dixon et al., 1998, Hasan et al., 2008, Jernigan et al., 1991, Jernigan et al., 2001, Krishnan et al., 1990, Raz et al., 2003, Raz et al., 2005, Walhovd et al., 2005a).

  • Putamen: Four studies found age effects (Greenberg et al., 2008, Gunning-Dixon et al., 1998, Raz et al., 2003, Walhovd et al., 2005a). Additionally, in one study, age effects were found for men, but not for women (Nunnemann et al., 2007). Age effects were not found on the lenticular nuclei in one study (Jernigan et al., 2001), but these include the globus pallidus in addition to the putamen, and the latter may explain why effects were not found.

  • Pallidum: None of the four studies reporting on pallidum volume in relation to age found linear negative relationships (Gunning-Dixon et al., 1998, Jernigan et al., 2001, Luft et al., 1999, Raz et al., 2003), while a quadratic relationship was found in a fifth study (Walhovd et al., 2005a).

  • Accumbens area: Only two studies have been reported, and both found linear negative relationships with age (Jernigan et al., 2001, Walhovd et al., 2005a).

  • Brainstem: Smaller volume of the brainstem with higher age was found in one study (Walhovd et al., 2005a), while the ventral pons has been found to be well preserved in another (Raz et al., 2001), and no significant age change was observed in pontine structures in a third study (Sullivan et al., 2005).

  • Cerebellum: Five studies have found negative age relationships for total cerebellar volume, cerebellar GM, cerebellar WM, or other cerebellar compartments (Jernigan et al., 2001, Liu et al., 2003, Luft et al., 1999, Raz et al., 2001, Sullivan et al., 2000, Walhovd et al., 2005a). In one study, no effects on cerebellar WM (Sullivan et al., 2000) were found, in contrast to a more recent study (Walhovd et al., 2005b). One study observed that the age changes were best described by an exponential fit (Luft et al., 1999). Longitudinal findings of age-related decline in cerebellar volume have been more dramatic than cross-sectional, and comparable to the declines in the association cortices and the caudate nucleus (Raz et al., 2005)

  • CSF: There is agreement across studies that CSF compartments increase in volume with age (Coffey et al., 1998, Cohen et al., 1992, Good et al., 2001, Greenberg et al., 2008, Gur et al., 1991, Jernigan et al., 2001, Scahill et al., 2003, Sullivan et al., 1995, Walhovd et al., 2005a). Some studies have also found non-linear age changes (Good et al., 2001, Sullivan et al., 1995, Walhovd et al., 2005a).

The differences observed across studies may be related to sample characteristics, segmentation procedures, demarcation criteria, and procedures for intracranial volume (ICV) corrections. Based on the above findings, the following set of hypotheses could be made:

H1

Caudate nucleus, nucleus accumbens, and cerebellar volume will be negatively related to age in all samples, while CSF/ventricular volume will be positively related.

H2

Hippocampus, amygdala, putamen, thalamus volume will generally decline with age, but not consistently across all six samples.

H3

Pallidum and brainstem volume will not be consistently related to age, and age effects will be found only in a minority of the samples.

These hypotheses are strictly based on the previous findings, assuming the results of the present multi-sample study would most likely to be representative of previous findings. However, to the extent that standardizing segmentation and analysis techniques has effect, such empirical hypotheses may not be confirmed, and greater consistency may be found.

Section snippets

Samples

The details of each of the samples are described in Table 2 and Supplementary Tables 1 and 2, where key publications with in-depth inclusion criteria are provided, including description of approvals by the relevant ethical committees. The total n of the samples was 883, with an age range of 75 years (18–93 years). All samples were screened for neurological conditions. It is likely that effects on the volume of the different brain structures largely can be attributed to the influence of normal

Relationships with age

The mean volumes of the different anatomical structures are shown per decade for the total sample in Table 3. Table 4 shows the estimated percentage volumetric change in each structure per decade based on the raw volumes of the total sample. Average percent linear change per decade based on the ICV-corrected volumes for each sample is shown in Supplementary Table 3. An illustration of the age effects on the morphometry of the three-dimensional segmentations of each structure is given in

Discussion

For most neuroanatomical volumes, age effects were observed across samples. Of the 18 neuroanatomical volumes tested, including total brain volume, 12 showed age effects in all six samples, while four showed effects in five of the samples. Only the 4th ventricle (related in three samples) and the brainstem (related in four of the samples) were not related to age in a consistent fashion. The first hypothesis based on previous reports was that caudate nucleus, nucleus accumbens, and cerebellum

Acknowledgements

Funding: The Norwegian Research Council (177404/W50 to K.B.W., 175066/D15 to A.M.F., 154313/V50 to I.R., 177458/V50 to T.E.), University of Oslo (to K.B.W. and A.M.F.); the National Institutes of Health (R01-NS39581, R01-RR16594, P41-RR14075, R01-AG11230, and R01-RR13609); the Mental Illness and Neuroscience Discovery (MIND) Institute; The Biomedical Informatics Research Network Project (BIRN, http://www.nbirn.net, funded by the National Center for Research Resources at the National Institutes

References (88)

  • M.F. Folstein et al.

    “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician

    J. Psychiatr. Res.

    (1975)
  • C.D. Good et al.

    A voxel-based morphometric study of ageing in 465 normal adult human brains

    Neuroimage

    (2001)
  • D.L. Greenberg et al.

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

    Neurobiol. Aging

    (2008)
  • R.C. Gur et al.

    Brain region and sex differences in age association with brain volume: a quantitative MRI study of healthy young adults

    Am. J. Geriatr. Psychiatry

    (2002)
  • T.L. Jernigan et al.

    Cerebral structure on MRI, Part I: localization of age-related changes

    Biol. Psychiatry

    (1991)
  • 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)
  • E.G. Jonsson et al.

    Brain-derived neurotrophic factor gene (BDNF) variants and schizophrenia: an association study

    Prog. Neuropsychopharmacol. Biol. Psychiatry

    (2006)
  • J. Jovicich et al.

    MRI-derived measurements of human subcortical, ventricular and intracranial brain volumes: reliability effects of scan sessions, acquisition sequences, data analyses, scanner upgrade, scanner vendors and field strengths

    Neuroimage

    (2009)
  • C. Lebel et al.

    Microstructural maturation of the human brain from childhood to adulthood

    Neuroimage

    (2008)
  • R.S. Liu et al.

    A longitudinal study of brain morphometrics using quantitative magnetic resonance imaging and difference image analysis

    Neuroimage

    (2003)
  • 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)
  • R. Nesvag et al.

    Regional thinning of the cerebral cortex in schizophrenia: effects of diagnosis, age and antipsychotic medication

    Schizophr. Res.

    (2008)
  • S. Nunnemann et al.

    Accelerated aging of the putamen in men but not in women

    Neurobiol. Aging

    (2009)
  • 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 age-related changes in the regional metencephalic volumes in humans: a 5-year follow-up

    Neurosci. Lett.

    (2003)
  • N. Schuff et al.

    Age-related metabolite changes and volume loss in the hippocampus by magnetic resonance spectroscopy and imaging

    Neurobiol. Aging

    (1999)
  • 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.

    Preservation of hippocampal volume throughout adulthood in healthy men and women

    Neurobiol. Aging

    (2005)
  • E.V. Sullivan et al.

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

    Neurobiol. Aging

    (2004)
  • Y. Taki et al.

    Voxel-based morphometry of human brain with age and cerebrovascular risk factors

    Neurobiol. Aging

    (2004)
  • D.J. Tisserand et al.

    Regional frontal cortical volumes decrease differentially in aging: an MRI study to compare volumetric approaches and voxel-based morphometry

    Neuroimage

    (2002)
  • Y.D. Van Der Werf et al.

    Thalamic volume predicts performance on tests of cognitive speed and decreases in healthy aging. A magnetic resonance imaging-based volumetric analysis

    Brain Res. Cogn. Brain Res.

    (2001)
  • C. Van Petten

    Relationship between hippocampal volume and memory ability in healthy individuals across the lifespan: review and meta-analysis

    Neuropsychologia

    (2004)
  • K.B. Walhovd et al.

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

    Neurobiol. Aging

    (2005)
  • S. Auer et al.

    The GDS/FAST staging system

    Int. Psychogeriatr.

    (1997)
  • G. Bartzokis et al.

    White matter structural integrity in healthy aging adults and patients with Alzheimer disease: a magnetic resonance imaging study

    Arch. Neurol.

    (2003)
  • A.T. Beck et al.

    Beck Depression Inventory Scoring Manual

    (1987)
  • L. Berg

    Clinical dementia rating

    Br. J. Psychiatry

    (1984)
  • L. Berg

    Clinical dementia rating (CDR)

    Psychopharmacol. Bull.

    (1988)
  • D.D. Blatter et al.

    Quantitative volumetric analysis of brain MR: normative database spanning 5 decades of life

    AJNR Am. J. Neuroradiol.

    (1995)
  • G. Blessed et al.

    The association between quantitative measures of dementia and of senile change in the cerebral grey matter of elderly subjects

    Br. J. Psychiatry

    (1968)
  • C.E. Coffey et al.

    Sex differences in brain aging: a quantitative magnetic resonance imaging study

    Arch. Neurol.

    (1998)
  • E. Courchesne et al.

    Normal brain development and aging: quantitative analysis at in vivo MR imaging in healthy volunteers

    Radiology

    (2000)
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