High Consistency of Regional Cortical
Thinning in Aging across Multiple Samples
Anders M. Fjell1,2, Lars T. Westlye1, Inge Amlien1,
Thomas Espeseth1, Ivar Reinvang1, Naftali Raz3,
Ingrid Agartz4,5,6, David H. Salat7, Doug N. Greve7,
Bruce Fischl7,8, Anders M Dale9,10,11and Kristine B. Walhovd1,2
1Center for the Study of Human Cognition, Department of
Psychology, University of Oslo, Norway,2Department of
Neuropsychology, Ullevaal University Hospital, Norway,
3Department of Psychology and Institute of Gerontology,
Wayne State University, Detroit, MI, USA,4Department of
Psychiatric Research, Diakonhjemmet Hospital, Norway,
5Department of Psychiatry, University of Oslo, Norway,6Human
Brain Informatics (HUBIN), Department of Clinical
Neuroscience, Psychiatry Section, Karolinska Institutet and
Hospital, Sweden,7Athinoula A. Martinos Center, MGH
8MIT Computer Science and Artificial Intelligence Laboratory,
MA, USA,9Multimodal Imaging Laboratory,10Department of
Radiology and11Department of Neurosciences, University of
California, SD, CA, USA
Cross-sectional magnetic resonance imaging (MRI) studies of
cortical thickness and volume have shown age effects on large
areas, but there are substantial discrepancies across studies
regarding the localization and magnitude of effects. These
25 discrepancies hinder understanding of effects of aging on brain
morphometry, and limit the potential usefulness of MR in research
on healthy and pathological age-related brain changes. The present
study was undertaken to overcome this problem by assessing the
consistency of age effects on cortical thickness across 6 different
30 samples with a total of 883 participants. A surface-based
segmentation procedure (FreeSurfer) was used to calculate cortical
thickness continuously across the brain surface. The results
showed consistent age effects across samples in the superior,
middle, and inferior frontal gyri, superior and middle temporal gyri,
35 precuneus, inferior and superior parietal cortices, fusiform and
lingual gyri, and the temporo-parietal junction. The strongest effects
were seen in the superior and inferior frontal gyri, as well as
superior parts of the temporal lobe. The inferior temporal lobe and
anterior cingulate cortices were relatively less affected by age. The
40 results are discussed in relation to leading theories of cognitive
Keywords: aging, cortex, frontal lobes, morphometry, MRI
In magnetic resonance imaging (MRI) studies, there is
45 consensus that higher age is associated with reduction of brain
volumes, including the cerebral cortex, as well as expansion
of the ventricular system (Jernigan et al. 1991; Pfefferbaum
et al. 1994; Blatter et al. 1995; Sullivan et al. 1995, 2004; Murphy
et al. 1996; Raz et al. 1997; Courchesne et al. 2000; Resnick et al.
50 2000; Good et al. 2001; Jernigan et al. 2001; Raz, Gunning-Dixon,
et al. 2004; Salat et al. 2004; Taki et al. 2004; Allen et al. 2005;
cumulative evidence also indicates that brain aging is not
uniform, and significant heterogeneity of age effects is
55 observed across brain regions (Raz et al. 1997, 2005; Good
et al. 2001; Jernigan et al. 2001; Raz, Gunning-Dixon, et al.
2004; Salat et al. 2004). Unfortunately, interpretation of this
heterogeneity is complicated by inconsistency among results
(Raz and Rodrigue 2006). The aim of the present study was to
address this problem by testing the consistency of age effects on
cortical thickness across 6 samples from 4 different research
Studies using manual drawing of regions of interest (ROIs)
on MRI scans have especially shown age effects on frontal
cortices, with significant but more moderate age effects in the
temporal, parietal, and occipital association areas. In contrast,
the primary sensory (especially visual) cortices seem largely
preserved (Raz, Gunning-Dixon, et al. 2004; Allen et al. 2005;
Raz and Rodrigue 2006). New automated and semiautomated
segmentation techniques have enabled studies of age effects
continuously across the cortical mantle without manually
defining ROIs. This facilitates comparison of results across
studies. In such studies, there is consensus that age effects are
strong in frontal or prefrontal areas (Good et al. 2001; Sato et al.
2003; Salat et al. 2004; Taki et al. 2004; Brickman et al. 2007;
Raz, Rodrigue and Haacke 2007; Abe et al. 2008; Kalpouzos
et al. 2008), in line with findings from manual morphometry
studies (Raz and Rodrigue 2006). However, in contrast to most
manual studies, several investigations using automated/semi-
automated methods find that the occipital lobes are negatively
affected by age (Sato et al. 2003; Salat et al. 2004; Taki et al.
2004; Abe et al. 2008; Kalpouzos et al. 2008). Moreover, most
have demonstrated age effects on parietal cortex (Good et al.
2001; Salat et al. 2004; Brickman et al. 2007; Abe et al. 2008),
and around the central sulcus (Good et al. 2001; Salat et al.
2004), although the exact localization of the effects vary.
Age effects on the anterior cingulate cortex (ACC) have
been found to be inconsistent between studies using auto-
mated/semiautomated techniques. In some studies (Salat et al.
2004; Abe et al. 2008), ACC thickening has been found,
whereas ACC preservation or reduction with age has been
found in others (Good et al. 2001; Tisserand et al. 2002;
Brickman et al. 2007; Vaidya et al. 2007; Kalpouzos et al. 2008).
In manual investigations, only mild or no age differences have
been found in the ACC (for a review, see Raz et al. 2000).
Automated/semiautomated studies have also reported discrep-
ant results within the prefrontal cortex. Some have found
sparing of the medial orbitofrontal cortex (Salat et al. 2004),
whereas others have reported age effects throughout most of
the anterior part of the brain (Taki et al. 2004; Abe et al. 2008).
? The Author 2008. Published by Oxford University Press. All rights reserved.
For permissions, please e-mail: firstname.lastname@example.org
Journal NameArt. No. CE Code
It is unclear whether such discrepancies are method or sample
dependent, although studies comparing different methods have
suggested that the former is likely (Tisserand et al. 2002;
Kennedy et al. 2008).
The temporal lobes and especially medial-temporal regions
are involved in memory processing, and have received much
attention in research on normal and pathological aging (Braak
and Braak 1985; Mesulam 1999). Temporal cortical thinning has
been observed in mild cognitive impairment and Alzheimer’s
110 disease (AD) (Dickerson et al. 2008; Fjell et al. 2008). Relative
sparing of temporal (Salat et al. 2004) and parahippocampal
(Raz et al. 1997; Raz, Gunning-Dixon, et al. 2004; Salat et al.
2004) cortices has been found in healthy aging, and the
entorhinal cortex has been found to be relatively less affected
115 by age than other cortical regions (Good et al. 2001; Raz et al.
2005). However, other studies have found temporal cortical
thinning in healthy aging also (Sato et al. 2003; Taki et al. 2004;
Brickman et al. 2007; Abe et al. 2008). Volume reductions
accompanied by volume increases in different parts of the
120 temporal cortex have also been observed (Kalpouzos et al.
2008). Longitudinal age-related shrinkage of the entorhinal
cortex was identified in one study (Du et al. 2006), whereas
others have found that such atrophy was limited to persons
with relatively poor, though normal cognitive performance
125 (Raz, Rodrigue, et al. 2004; Rodrigue and Raz 2004; Raz et al.
2008). Notably, no correlations of entorhinal volume with age
were found at baseline in these studies. Targeting morphomet-
ric effects in the temporal lobe with a newly developed
semiautomated technique, another study found that entorhinal
130 and perirhinal, but not posterior parahippocampal cortices,
were reduced in volume with age (Dickerson et al. 2008). This
could be attributed mainly to reduction of the surface area
rather than thickness of the cerebral cortex.
Although, as reviewed above, well-established patterns of age
135 effects exist, significant discrepancies are observed across
samples (for a review, see Raz and Rodrigue 2006). Such
between-study variability complicates interpretation of the
findings. Multiple reasons have been invoked to explain the
discrepant results, and at least 3 seem to be of great
140 importance. First, the differences in methodology of image
processing may contribute to variance in the results (Tisserand
et al. 2002; Kennedy et al. 2008). Second, characteristics of
through uncontrolled differences in health and genetic
145 endowment. Finally, although the sample sizes employed
appear quite large, they still may be insufficient to detect
subtle differences in age effects on specific cortical regions.
The present study was designed to address these problems.
We applied a uniform semiautomated method of cortical
thickness measurement yielding thickness estimates continu-
ously across the entire cortical mantle to a large sample of
healthy adults recruited from multiple populations. The data
include 6 samples (3 from the United States, 2 from Norway,
and one from Sweden) drawn from 4 research centers, with
a total of 883 participants. The data were analyzed both sample-
wise and pooled. The principal aim was to examine the
consistency of age effects on regional cortical thickness across
samples. This was done to test which cortical areas consistently
underwent age-related thinning or thickening, and which areas
were differentially affected by age in different samples.
For measurement of cortical thickness, FreeSurfer software ½AQ2?
freely downloadable from http://surfer.nmr.mgh.harvard.edu/,
was used. This approach provides reliable measures of cortical
thickness continuously across the whole cortical mantle
without manually defining ROIs. Although automated methods
may have some undesirable features, such as resolution loss in
registration of morphologically different brains to a common
stereotactic space, and the need for smoothing, they have
several advantages over manual methods. First, they require
minimal intervention by highly trained personnel and allow
processing of many brains in a reasonable time frame. Second,
they are characterized by very high reliability and repeatability
of measures. Third, they allow a hypothesis-free search for
patterns of differences without the need to define anatomically
plausible ROIs, enabling detection of differences in regions
where precise anatomical definitions and placement of
anatomical borders would not be feasible.
Materials and Methods
The details of each of the 6 samples are described in Table 1, where key
publications and sample selection criteria are indicated, and in
Supplementary Table 1. The total number of participants was 883,
with an age range of 75 years (18--93 years). All samples were screened
for history of neurological conditions. Even though subclinical effects
of pathological processes cannot be ruled out without follow-up data, it
is likely that effects can largely be attributed to the influence of
nondemented aging. Twenty-two participants were excluded due to
bad scan quality, including overfolding, MR artifacts, errors during data
transfer or saving, converting errors, or deviant signal intensity in the
Sample CountryN (% f) Age mean
Key publicationsMain screening instruments/inclusion criteria
(Walhovd et al. 2005)
(Espeseth et al. 2008)
(Jonsson et al. 2006; Nesvag et al. 2008)
(Marcus et al. 2007)
Similar to sample 4
(Raz, Gunning-Dixon, et al. 2004)
Health interview, MMSE [ 26, BDI \ 16, IQ [ 85, RH only
Health interview, IQ [ 85
Health interview, DSM-III-R, WASI vocabulary [ 16a
Health interview, CDR 5 0c, MMSE [ 25c, RH only
Similar to sample 4
Health interview, BIMCT [ 30, GDQ \ 15, RH only, neuroradiology
Note: Nor, Norway; Swe, Sweden; % f, percentage of female participants; MMSE, Mini Mental Status Exam (Folstein et al. 1975); BDI, Beck Depression Inventory (Beck 1987); BIMCT, Blessed
Information--Memory--Concentration Test (Blessed et al. 1968); CDR, clinical dementia rating (Berg 1984, 1988; Morris 1993); GDQ, Geriatric Depression Questionnaire (Auer and Reisberg 1997); RH, right
handed; WASI, Wechsler Abbreviated Scale of Intelligence (Wechsler 1999).
aAvailable for 70 participants.
bAvailable for all participants $60 years, and sporadically for the rest. 1: less than high school grad., 2: high school grad., 3: some college, 4: college grad., 5: beyond college.
cAvailable for participants $60 years only.
Page 2 of 12
Cortical Thinning in Aging
Fjell et al.
190 MRI images. Most of these scans were possible to process, but were
deemed not to be of sufficient quality to yield valid results. Included in
these 22 were a small number of participants that were excluded due
to WM abnormalities. We find it unlikely that excluding this small
number of problematic scans bias the data.
195 MR Acquisition
All participants were scanned on 1.5T magnets, but from 2 different
manufacturers (Siemens, Erlangen, Germany; General Electric CO [GE],
Milwaukee, WI), and 4 different models (Siemens Symphony Quantum,
Siemens Sonata, Siemens Vision, GE Signa). With the exceptions of the
200 data from sample 4 and 5, all the samples were from different scanners.
All participants within each sample were scanned on the same scanner.
T1-weighted sequences were acquired (3D magnetization prepared
gradient-echo (MP--RAGE) for the Siemens scanners, and 3D spoiled
gradient recalled (SPGR) pulse sequences for GE. Slice thickness were
205 between 1.25 mm (samples 4 and 5) and 1.5 mm (sample 1), with
acquisition matrices of 256 3 192 (samples 1, 3, and 6) or 256 3 256
(samples 2, 4, and 5). For 4 of the samples (samples 1, 2, 4, and 5),
multiple scans were acquired within the same scanning session, and
averaged to increase the signal-to-noise ratio (SNR). The detailed
210 sequences used are presented in Table 2. Examples of scan quality from
each sample are presented in Figure 1.
Cortical Thickness Analyses
Cortical thickness measurements were obtained by reconstructing
representations of the gray/white matter boundary (Dale and Sereno
215 1993; Dale et al. 1999) and the cortical surface, and then calculating the
distance between these surfaces at each point across the cortical
mantle. This method uses both intensity and continuity information
from the entire 3D MR volume in segmentation and deformation
procedures to construct representations of cortical thickness. The
maps are created using spatial intensity gradients across tissue classes
and are therefore not simply reliant on absolute signal intensity. The
maps produced are not restricted to the voxel resolution of the original
data and thus are capable of detecting submillimeter differences
between groups (Fischl and Dale 2000). This has been validated using
histology and MR (Rosas et al. 2002; Kuperberg et al. 2003
colleagues (Rosas et al. 2002) have shown that processing of MR images
of autopsy brains gave cortical thickness estimates within ±0.25 mm of
those obtained using neuropathologic methods and were statistically
indistinguishable. Thickness measures may be mapped on the ‘‘inflated’’
or ‘‘semi-inflated’’ surface of each participant’s reconstructed brain
(Dale et al. 1999; Fischl et al. 1999), allowing visualization of data across
the entire cortical surface without interference from cortical folding.
Maps were smoothed using a circularly symmetric Gaussian kernel
across the surface with a FWHM
participants using a nonrigid high-dimensional spherical averaging
method to align cortical folding patterns (Fischl et al. 1999). This
procedure provides accurate matching of morphologically homologous
). Rosas and
of 15 mm and averaged across
Sample MRI scannerMRI protocol
Sample 11.5T Siemens Symphony
Two 3D MP--RAGE T1-weighted sequences
TR/TE/TI/FA 5 2730 ms/4 ms/1000 ms/7?
Matrix 5 192 3 256
Scan time: 8.5 min per volume.
Each volume consisted of 128 sagittal slices
(1.33 3 1 3 1 mm).
Two 3D MP--RAGE T1-weighted sequences
Sample 2 1.5T Siemens
TR/TE/TI/FA 5 2730 ms/3.43 ms/1000 ms/7?
Matrix: 256 3 256
Scan time: 8 min and 46 s per volume
Each volume consisted of 128 sagittal slices
(1.33 3 1 3 1 mm)
One 3D SPGR pulse T1-weighted sequence
Sample 3 1.5T General
TR/TE/FA 5 24 ms/6.0 ms/35?, number
of excitations were 2
Matrix: 256 3 192
Each volume consisted of 1.5-mm coronal
slices, no gap, FOV 5 24 cm
Three to 4 individual T1-weighted MP--RAGE
TR/TE/TI/FA 5 9.7 ms/4.0 ms/20 ms/10?
Matrix 5 256 3 256.
Each volume consisted of 128 sagittal slices
(1.25 3 1 3 1 mm).
See sample 4
One 3D SPGR pulse T1-weighted sequence
Sample 4 1.5T Siemens
See sample 4
TR/TE/FA 5 24 ms/5.0 ms/30?
Matrix 5 256 3 192
Each volume consisted of 124 contiguous axial
slices (1.30 3 0.94 3 0.86 mm),
FOV 5 22 cm
Note: FOV, field of view; FA, flip angle; TR, repetition time; TE, echo time; TI, inversion time.
Figure 1. Example scans from each sample. Scans representative of image quality of
one young and one elderly participant from each of the samples are shown (because
sample 4 and 5 are from the same scanner, only examples from sample 4 are shown.
All scans are converted from their native format to Freesurfer format. Samples 1, 2,
and 4 are taken from Siemens scanners, and 2--4 acquisitions were averaged from
each participant to yield high contrast and signal to noise ratio. Sample 2 and 6 are
from GE scanners (Signa), with one acquisition. The cortex--CSF boundary (red) and
the gray--white boundary (yellow) are indicated by the thin line. Anatomical
differences between the scans from each sample are incidental.
Cerebral Cortex Page 3 of 12
cortical locations among participants on the basis of each individual’s
anatomy while minimizing metric distortion, resulting in a measure of
240 cortical thickness for each person at each point on the reconstructed
surface. Statistical comparisons of global data and surface maps were
generated by computing a GLM of the effects of each variable on
thickness at each vertex. In addition, by use of a validated automated
labeling system (Fischl et al. 2004; Desikan et al. 2006), the cortex was
245 divided into 33 different gyral-based areas in each hemisphere, and
mean thickness in each was calculated (see Supplementary Fig. 1).
The thickness estimation procedure is automated, but requires
manual checking of the accuracy of the spatial registration and the WM
250 intervention are insufficient removal of nonbrain tissue (typically dura
in superior brain areas) and inclusion of vessels adjacent to the cortex
(especially, in the temporal lobes). In addition, if large field in-
homogeneity exists, small parts of WM may mistakenly be misclassified
as GM, thus obscuring the GM/WM boundary. These types of errors are
255 limited in spatial extension, typically seen in a minor area of the brain
in a few slices, but are nevertheless routinely corrected by manual inter-
ventions. However, some researchers argue that manual interventions
are not always necessary, and have shown that cortical thickness can be
estimated reliably across field strength, scanner upgrade and manufac-
260 turer without any manual intervention (Han et al. 2006), showing
reliable correlations with cognitive function (Dickerson, Feczko, et al.
2008; Dickerson, Fenstermacher, et al. 2008
segmentations. The types of errors that most often require user
First, general linear models (GLMs) were used to test the relationship
265 between age and cortical thickness at each vertex across the entire
cortical mantle in each sample separately, with effects of sex regressed
out. To handle the problem of multiple comparisons in neuroimaging
data, false discovery rate (FDR) < 0.05, was applied to threshold the
initial data. However, because widespread and robust effects were
270 expected, results were also presented with higher P value thresholds
(P < 10–3to 10–9) to better visualize where the effects of age on cortical
thickness were strongest. In addition, the degree of overlap between
results from the different samples was calculated based on the number
of samples in which each of the P value thresholds was reached for each
275 surface vertex. This information was color coded and projected onto
a template brain. To present the reader with numerical information,
correlations with mean thickness in each of the 33 cortical regions
(mean of left and right hemisphere) as well as the results of one-way
ANOVAs (F-statistics) are included as supplementary information.
Next, all samples were included simultaneously in one GLM. The
effect of age, with the main effect of sample regressed out, and the age
3 sample interaction, were modeled. The results are presented both at
FDR < 0.05, and P < 10–15to 10–25(uncorrected). Post hoc regression
analyses from selected regions of interest (ROIs) were performed to
investigate the basis of between-samples variation in age effects. Finally,
to test whether nonlinear effects of age could be identified, the GLMs
were repeated with both age and age2included as regressors.
Widespread age differences in cortical thickness were ob-
served across samples. However, the magnitude of effects
varied among samples and brain regions. For illustration
purposes, mean thickness for each point on the cortical
surface for 3 age groups (young, middle-aged, and older adults)
is presented in Figure 2.
Results of the GLM analyses of age differences in cortical
thickness are shown in Figure 3. When FDR <0.05 was used as
threshold, it was clear that age was associated with thinning of
the cortex across almost the entire brain surface. Still, for 3 of
the samples (samples 4--6), bilateral thickening in the medial
frontal cortex, including anterior cingulate gyrus, was seen.
The age effects were generally smaller in sample 3 than in the
other samples, but even in that sample, thinning appeared most
prominent in the prefrontal regions, and the overlap among the
samples was generally substantial. Using a P value scale from
10–3to 10–9(uncorrected) allowed inspection of regionally
differential effects. The frontal cortices, that is, superior and
inferior frontal gyri, were among the areas most strongly
affected by age across all samples. Age did generally have more
moderate effects on the medial-temporal cortices (parahippo-
campal and entorhinal), although some thinning was seen in 4
of the samples. Lateral inferior parts of the temporal lobes were
Figure 2. Mean cortical thickness in 3 age groups. Mean thickness in each hemisphere for the age groups \40 years, 40--60 years, and [60 years are color coded and
projected onto an inflated template brain for better visualization of effects buried in sulci. Note that the participants from all the samples are pooled together in each of the age
groups, with no corrections for scanner or sample.
Page 4 of 12
Cortical Thinning in Aging
Fjell et al.
among the best preserved in 4 of the samples. Superior parts of
the lateral temporal lobes were more affected by age than the
inferior parts. When the higher P value threshold was used, the
315 thickening in the medial frontal cortex was only seen in
samples 4 and 5, and only for the left hemisphere. In addition to
these analyses, region-based analyses are presented in Supple-
mentary Table 1. Here, correlations between age in each
subsample and for the total sample are shown, along with
320 corresponding F values for age group based ANOVAs.
The degree of overlap between the studies is shown in
Figure 4. When effects were seen in 5 or all 6 samples for
a given area, it was regarded as an area with consistent age
effects. When effects were seen in none or one of the samples,
325 it was regarded as an area of preservation. When FDR < 0.05
was used, large areas showed consistent age effects across
studies, especially frontal cortices, where effects were seen in
superior, middle, and inferior frontal cortices in all 6 samples.
Also superior and middle temporal gyri were affected in all
330 samples, as were also fusiform and lingual gyri, and the
temporoparietal junction. The occipital lobe and entorhinal
cortex were affected by age in 4 of the samples, whereas the
inferior temporal lobe and anterior cingulate were generally
spared. Effects in the right precuneus were found in 4 of the
335 samples, and in all or 5 of the samples in the left. When the
threshold was increased to P < 10–6(uncorrected), the frontal
lobes (superior and inferior frontal gyrus) stood out as related
to age in 5 of the samples. Superior parts, especially posteriorly,
of the temporal lobes were also relatively prone to effects of
340 age, although there was some variability between samples and
between different parts of the gyri.
Next, the effect of age on cortical thickness was tested with
all samples pooled together, and the effect of sample
regressed out. A movie of the continuous reduction of
thickness is presented in Supplemental Material 1, and the
statistical results are presented in Figure 5. With FDR < 0.05 as
threshold, negative effects of age on cortical thickness were
seen across the entire cortical mantle. Thus, the scale was
increased to P < 10–15
to 10–25. The pattern of effects
mimicked the results from the analyses of the separate
samples. The strongest age effects were found in the superior,
middle, and inferior frontal gyri, and in the superior temporal
gyri as well as the superior parts of the middle temporal gyri.
Further, the temporoparietal junction was also very prone to
the effects of age, as were the pericalcarine cortex, fusiform
and lingual gyri. Medial parts of the temporal lobes, as well as
precuneus, were relatively more preserved, as were the post-
and precentral gyri, superior parietal gyri, and the occipital
lobe. The effects appeared symmetrically distributed across
The interaction effect of sample 3 age on cortical thickness
was modeled and the results are presented in Figures 6 and 7.
With a threshold of FDR <0.05, several areas showed sample 3
age interactions. These were mostly overlapping with areas of
large age effects across studies. To investigate the basis of the
interaction effects further, 5 ROIs were drawn on the inflated
template brain surface. These were guided by areas of large
sample age 3 interaction effects, and mean thickness in each
ROI was calculated. As can be seen from the scatter plots
(Fig. 7), the interaction effects are mainly due to different rates
of estimated age-related declines across samples. None of the
Figure 3. Age effects on cortical thickness in each sample. The figure shows the effect of age on cortical thickness across the entire brain surface when effects of sex were
regressed out. The results are color coded and projected onto a semi-inflated template brain for better visualization of effects buried in sulci. Each row represents the results from
one sample. On the left side of the figure, the effects are thresholded at FDR\0.05 (corrected for multiple comparisons). On the right side, the results are color coded by use of
a wider P value scale.
Cerebral Cortex Page 5 of 12
ROIs showed positive relationships with age. In 2 ROIs, the
inferior and superior frontal gyri, thickness correlated signif-
icantly with age in all samples, whereas in a third, the middle
375 temporal gyrus, this was found for 5 of the samples. For these
areas, there was variation in the strength of the relationships.
For instance, correlations ranged from –0.51 to –0.75 for the
superior frontal gyrus. Thus, sample 3 age interactions were to
a large extent caused by differences in quantity, not quality, of
380 the age-relationships. Two exceptions to this pattern were
found. First, the left medial frontal cortex showed thickening
with age in 3 of the samples, whereas relative preservation was
found in the other samples. Second, interaction effects could
be seen in inferior lateral temporal areas as well as the
entorhinal cortex, which were due to thinning in some of the
samples (i.e., samples 1, 2, and 5), with relative sparing or only
small effects in the other samples. The same was true for the
Finally, we tested for presence of nonlinear (quadratic)
relationships by repeating the GLM analyses with age and age2
as simultaneous predictors. This analysis was run in each of the
samples separately. In no case did age2yield significant effects
(FDR < 0.05) on cortical thickness.
Figure 5. Age effects in the total sample. The figure shows the effects of age on cortical thickness when all samples were included in the same analysis (n 5 883), with main
effect of sample regressed out. In the second row, a higher P value threshold was employed. Even with a P value threshold of 10?25, large effects were seen in several areas.
Figure 4. Consistency across samples. The number of samples in which a statistical effect was reached is color coded and projected onto a semi-inflated template brain. The
first row depicts the results when a threshold of FDR\0.05 was used. As can be seen, age-related thinning of the cerebral cortex is seen in all or 5 of the samples across most
of the brain surface. In the second and third row, higher P value thresholds were used.
Page 6 of 12
Cortical Thinning in Aging
Fjell et al.
395 The present study investigated age effects on the cerebral
cortex across multiple large samples, providing a unique
opportunity to assess degree of consistency. Because the same
preprocessing procedures were performed for all brains,
a significant component of interstudy variability was elimi-
400 nated. Several conclusions can be drawn from the findings.
First, advanced age was associated with widespread thinning of
the cerebral cortex. When a commonly employed statistical
threshold (FDR <0.05) was used, thinning was observed across
most of the cortical surface. Second, the magnitude of age
405 differences in thickness varied across cortical regions. Age
effects were strongest in the prefrontal cortex, especially in
superior, lateral, and medial regions. The superior temporal gyri
at the lower bank of the Sylvian fissure were also heavily
affected. In contrast, inferior temporal, parahippocampal, and
410 entorhinal gyri, anterior cingulate, paracentral gyrus and the
precuneus, were relatively more preserved. Third, although
there was generally good agreement among the different
samples, some discrepancies were also observed. For example,
in one of the samples (sample 3), the effects of age were
weaker than in the other 5. Still, also in that sample, clear age-
related thinning was found in the prefrontal cortex. A puzzling
finding was thickening of an area in medial prefrontal cortex
that was observed in the 3 American but not in the 3
Scandinavian samples. Unstable results were also found in
medial-temporal areas (parahippocampal and entorhinal gyri)
and occipital cortices. Still, large areas were consistently
affected across all or 5 of the 6 samples, that is, the superior,
middle, and inferior frontal cortices, superior and middle
temporal cortices, temporoparietal junction and pre- and
postcentral gyri. Finally, nonlinear age effects were not
observed within any of the samples.
Consistency of Effects of Age on Cortical Thickness
The degree of consistency of age-related differences in cortical
thickness across samples depended on spatial location. Some
regions, such as most of the lateral prefrontal as well as
Figure 6. Sample 3 age interaction effects. The figure shows which areas of the cerebral cortex that were affected differently by age across samples. The color-coded areas
represent significant age 3 sample interaction effects. Note that the areas which display age 3 sample interactions are the ones where the strongest age effects were found.
Figure 7. Scatter plots of mean thickness in selected cortical areas. Manual ROIs were drawn on the inflated template brain surface. The areas in which age and sample
interacted were used to guide the manual drawing of ROIs. Mean thickness in different cortical areas were calculated, and plotted against age. The ROIs are shown in the upper
row. The scatter plots are shown in the middle row. The participants from each sample are coded in different colors. The last row depicts the Pearson correlation coefficients
between age and mean thickness in each of the ROIs. The coefficients are given above each bar if P # 0.05 (uncorrected), and not given if not significant (P [ 0.05).
Cerebral Cortex Page 7 of 12
superior parts of the medial prefrontal cortices, showed age-
related thinning in virtually all samples, whereas others, such as
the occipital cortices, were affected by age in some samples
only. Further, inferior temporal cortices, including the ento-
435 rhinal and parahippocampal cortices, were not consistently
related to age across samples. However, the superior and
middle temporal gyri showed consistent age effects. Motor
cortex was related to age in 4--6 of the samples, depending on
region. Age effects in these regions were also reported by Salat
440 et al. (2004). The ACC was not consistently prone to thinning
with age, and showed increased thickness in some of the
samples. Finally, parts of the medial orbitofrontal cortex
showed preservation with age, both in American and Scandi-
navian samples. In addition to the inconsistencies evident from
445 the P-thresholded statistics, interaction effects of sample 3 age
on cortical thickness were also found in areas of consistent
thinning (e.g., superior frontal gyrus). These effects were due
to differences between samples in the magnitude of age effects,
as illustrated in Figure 7.
When all the samples were pooled together, age effects were
seen across almost the entire brain surface. This indicates that
if the number of participants gets high enough, global age
effects are detected. Further, it indicates that data from
different studies and scanners may increase the sensitivity to
455 age effects, even in areas where consistency across samples is
not impressive. For instance, age effects in inferior temporal
areas (parahippocampal, entorhinal, inferior temporal gyri)
were not seen consistently across samples, but thickness in
these areas showed up as related to age in the analysis involving
460 all the samples. Thus, it may be beneficial to pool data from
different studies, samples, and scanners to increase the
sensitivity of the tests employed.
As reviewed, previous studies have yielded partly inconsis-
tent results. We found that when using identical approaches to
465 data processing and analysis, there was generally good
consistency across samples. Even though inconsistent results
were found for some cortical regions, these inconsistencies
were much smaller than what would have been expected from
previous literature. A substantial part of the variability in
470 previous reports may be due to one or more of at least 5
factors: 1) Different statistical analyses performed: The exact
results obtained depend on the analytic strategy used, for
example, which variables are corrected for. Differences in
strategy may lead to inconsistent results. 2) Different de-
475 marcation criteria for ROI analyses: Automated segmentation
approaches allow statistics to be done without predefined
ROIs. This reduces the problem of different definitions of brain
areas between studies. 3) Image quality: The image quality
differs between studies, leading to different SNR and contrast-
480 to-noise ratio (CNR). In the present study, relatively consistent
results, at least in some brain areas, were found across samples
from different scanners and sequences. 4) Different segmen-
tation procedures: The tools used to segment the MR scans into
tissue classes, and the procedures used to spatially normalize
485 the data, may have a huge impact on the results (Tisserand et al.
2002). Of the studies using automated/semiautomated techni-
ques reviewed in the introduction, the majority were based on
voxel-based morphometry (VBM). In the present study,
a surface-based approach was used, similar to the basic
490 methods of Salat and colleagues (Salat et al. 2004). We believe
that this approach has advantages compared with the
traditional VBM method. FreeSurfer uses surface geometry in
intersubject registration, which in our experience results in
a better matching of homologous cortical regions. The target
used for registration is the white matter surface geometry,
which makes the registration invariant to morphometric
changes in GM. Hence, the registration is the same even in
case of GM atrophy, which is important when studying groups
where this is expected, such as in aging and dementia.
However, this approach will not be invariant to WM changes
that correlate with changes in GM. The approach used also
allows separation of the 2 components of volume (thickness
and surface area). These do not necessarily follow each other
(Dickerson et al. 2008), and to be able to study each in isolation
may increase both accuracy and sensitivity to brain changes. 5)
Sample characteristics: Differences in sample characteristics
may be an important source of discrepancies. For example,
between-sample inconsistency observed with regards to age
differences in occipital (pericalcarine) and medial-temporal
(entorhinal) cortices may stem from sample-specific contribu-
tion of unmeasured vascular risk factors. A longitudinal study
showed that unlike healthy adults, persons with known
vascular risk, for example, hypertension, show shrinkage of
the pericalcarine regions as well as proliferation of occipital
white matter abnormalities (Raz, Rodrigue, Kennedy, et al.
In the present study, factors 1, 2, and 4 were eliminated.
Thus, the inconsistencies that were observed may be attributed
to differences in scan quality between the samples or differ-
ences in sample characteristics. Even though all samples were
well screened, it is certainly possible that unmeasured differ-
ences in somatic or psychological factors have influence on
discrepancies between the samples. Also, the age range
sampled may have affected the results. For instance, the age
range in sample 3 is narrower than in the other samples, which
may have contributed to the somewhat lower age correlations
in this study. One could speculate that the 3 Nordic samples
were more alike regarding culturally dependent factors, such as
nutrition, education, and medical care, because health care and
education are free in Norway and Sweden. However, this does
not appear to be a major factor of influence here, because the
findings for samples 1 and 2 (Norway) was very similar to the
findings from samples 4 and 5 (United States). Further, level of
education was fairly similar across the samples. Regarding scan
quality, all samples but samples 3 and 6 were based on at least 2
T1runs that were averaged. Averaging of multiple runs may
enhance the SNR/CNR, contributing to more valid and
thickness estimates. This could contribute to the somewhat
higher age correlations in these samples. However, it needs to
be stressed that even though interaction effects were found,
the general pattern of effects was highly replicable across the
Comparing automated and manual methods was not a goal of
the study, but the regional distribution of the observed effects
of age on cortical thickness corresponded quite well to the age
differences reported in a previous study with manual volume
measures. One dataset used in this study (sample 6) almost
completely overlapped with the sample that has previously
been used in a manual morphometry study (Raz, Gunning-
Dixon, et al. 2004), where volume was calculated in 9 different
cortical ROIs. The main results from that investigation are in
good agreement with the findings from the present study
although the overlap is not complete. Corresponding age
effects were found for lateral prefrontal and orbitofrontal
Page 8 of 12
Cortical Thinning in Aging
Fjell et al.
555 cortices, precentral gyrus, postcentral gyrus and fusiform gyrus,
and no age differences in the anterior cingulate gyrus and the
visual cortex were observed with either method. However, the
previously reported age effect on parahippocampal gyrus and
inferior temporal cortex was not found in the present analysis,
560 and the present but not the previous study found an effect in the
inferior parietal lobule. Still, an agreement this close is remark-
able in the light of the manifest differences in segmentation
approach, the ROI versus vertex-based statistics, the volume vs.
thickness measures, and the fact that adjustments for body
565 height/intracranial volume differed between the studies.
Effects of Age versus Aging
A limitation of the present study is that the estimates of age-
related changes are based on cross-sectional data. Thus, the
observed effects reflect the influences associated with chro-
570 nological age, not necessarily the process of aging. To assess
individual variability in change requires a longitudinal design.
Unfortunately, only a handful of longitudinal studies of regional
brain changes have been conducted so far. Comparison of
cross-sectional and longitudinal effects indicates that the latter
575 sometimes reveal aging trends that would not be observed with
a cross-sectional design alone (Du et al. 2006). This means
that cross-sectional studies, possibly due to the large inter-
individual variability in initial brain volumes, may underestimate
real age changes. However, longitudinal data have also been
580 found to largely agree with cross-sectional estimates (Resnick
et al. 2003; Fotenos et al. 2005). Longitudinal studies have
methodological problems as well, e.g. selective drop out.
Participants who are less healthy, less socioeconomically
endowed, and prone to greater emotional distress are less
585 likely to volunteer for multiple measurements, thus biasing
estimates of change. Also, it is extremely difficult to collect
longitudinal data comparable to the 6 decade range covered in
most cross-sectional studies. Even relatively short follow-ups of
5--6 years are difficult to complete due to scanner changes and
590 upgrades, which may affect the volumetric estimates (Han et al.
2006). Thus, both cross-sectional and longitudinal designs
remain necessary for studying the aging brain.
Implication of the Findings for Cognitive Aging: The
Frontal Lobe Hypothesis
595 Although investigations of brain aging patterns are interesting
in their own right, their primary importance is for advancing
our understanding of the mechanisms underpinning adult
cognitive development and aging. Previous studies have
provided some evidence of associations between neuroana-
600 tomical age-related differences in specific regions and perfor-
mance on specific age-sensitive cognitive tasks. For instance,
smaller prefrontal cortices seem to predict poorer perfor-
mance on age-sensitive tests of executive functions (Raz et al.
1998; Gunning-Dixon and Raz 2003) and other cognitive
605 operations dependent on executive control (Head et al.
2008). This is in line with neuropsychological studies showing
that executive functions, which heavily depend on frontal
neural circuits (e.g., fronto-striatal circuits), are among the
cognitive functions to decline most with advancing age
610 (Connelly et al. 1991; Schretlen et al. 2000). Previous research,
as well as the present findings, support the selective
vulnerability of the prefrontal cortex in aging. This fits the so-
called ‘‘last in, first out’’ hypothesis, according to which the
brain areas that are latest to develop phylogenetically and
ontogenetically are the first to be affected by aging. An
important aspect in the frontal lobe theory of aging is the
assumed reduced efficiency of the executive function in-
hibition (Connelly et al. 1991), for example, as assessed by the
Stroop task (but see Rabbitt et al. 2001). In functional MRI
studies, the center of activation in such tasks is typically within
the anterior cingulate gyrus (Bush et al. 2000). However, the
present results indicate that this area of the prefrontal cortex is
relatively spared. Thus, there is no simple correspondence
between morphometric age-related changes in prefrontal
cortex and reduced inhibition in aging.
Relatively weaker effects were seen in parahippocampal and
entorhinal gyri, which are affected early in the AD disease
process (Braak and Braak 1991). Taken together with the
findings of frontal morphometric reductions, this fits with
a proposal of a double dissociation between AD and healthy
aging. According to this theory, early AD selectively attacks the
medial-temporal lobes and adjacent cortical areas, leading to
memory problems, whereas an anterior to posterior gradient
exists in healthy aging, causing executive problems (Head et al.
2005). Still, reduced memory function is seen also in healthy
aging (Craik and Jennings 1992), and memory complaints are
common in elderly, reported by up to 50% of people aged 64
and over (Reid and Maclullich 2006). Thus, because recol-
lective memory also depends on frontal cortical structures
(Craik and Grady 2002), it is possible that reduced memory
function in healthy aging is related to the morphometric
changes observed in these brain areas (West 1996).
An important limitation of MRI studies of aging is the maximum
CNR that can be achieved. Lower CNR will decrease the
accuracy of the thickness estimation, and will probably vary
across the cortical surface (Han et al. 2006), for example, due
to some areas with higher degree of myelination than other, as
in visual areas (Braitenberg and Schuz 1991). Also, the narrow
separation between putamen or hippocampus and the adjacent
cortical GM may cause a problem in finding the GM/WM
surface around the insular and entorhinal cortical regions,
which may again increase the variability of the thickness
estimates (Han et al. 2006). If this is not systematically related
to age, it will probably reduce the age-relationships observed. If
it is systematically related to age, it may falsely enhance or
create age-relationships. Future research efforts should try to
estimate to what extent regional differences in age correlations
can be attributed to regional differences in CNR. The problem
of varying CNR across the cortex affects all segmentation
techniques. Another question regards the need for smoothing
of data, which can reduce noise and thus improve reliability of
the thickness estimates (Han et al. 2006), at the cost of lower
spatial resolution. Han and colleagues have shown that
thickness measurement variability becomes smaller as smooth-
ing level increases (Han et al. 2006). The current smoothing
level of 15-mm FWHM will be sufficient to reduce thickness
measurement variability by at least 50%. By use of manual
methods, smoothing would not be necessary. However,
thickness cannot be reliably measured by manual methods,
because both the localization and the orientation of the white
and pial surfaces must be known for proper measurements to
be obtained (Fischl and Dale 2000).
Cerebral Cortex Page 9 of 12
675 The present study shows that age-related differences in cortical
thickness are widespread and robust, and are especially strong
in the superior and inferior frontal gyri and in the superior
temporal cortices. These findings were obtained from data
from 6 different samples, from 3 countries, and with use of
680 scanners from 2 manufacturers. The automated approach
employed in this study appears a reliable method to boost
the statistical power of the studies of brain aging and to
improve generalizability of the findings across various popula-
tions. It is concluded that consistent effects of age are likely to
685 be found when the segmentation techniques employed are
standardized across samples and studies.
materialcanbe foundat: http://www.cercor.
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, R37-AG11230, and R01-
695 RR13609); the Mental Illness and Neuroscience Discovery
Institute; The Wallenberg Foundation and the Swedish Medical
Research Council (K2004-21X-15078-01A 45, K2007-62X-
15077-04-1, and K2007-62X-15078-04-3). The National Center
for Research Resources (P41-RR14075, R01 RR16594-01A1 and
700 the NCRRBIRN Morphometric
RR021382); the National Institute for Biomedical Imaging and
Bioengineering (R01 EB001550, R01EB006758); the National
NS052585-01); as well as the Mental Illness and Neuroscience
705 Discovery Institute; and is part of the National Alliance for
Medical Image Computing (NAMIC), funded by the National
Institutes of Health through the NIH Roadmap for Medical
Research (grant U54 EB005149); additional support was
provided by The Autism & Dyslexia Project funded by the
710 Ellison Medical Foundation.
Disorders and Stroke(R01
We thank Vivi Agnete Larsen for assistance in processing of the data.
We thank the developers of the OASIS (Open Access Series of Imaging
Studies) database for access to MRI data constituting samples 4 and 5 of
715 the present work. Conflict of Interest: None declared.
Address correspondenceto Anders M.
Psychology, Pb. 1094 Blindern, 0317 Oslo, Norway. Email: andersmf@
720 Abe O, Yamasue H, Aoki S, Suga M, Yamada H, Kasai K, Masutani Y,
Kato N, Kato N, Ohtomo K. 2008. Aging in the CNS: comparison of
gray/white matter volume and diffusion tensor data. Neurobiol
Allen JS, Bruss J, Brown CK, Damasio H. 2005. Normal neuroanatomical
variation due to age: the major lobes and a parcellation of the
American Psychological Association. 1987. Diagnostic and statistical
manual of mental disorders. 3rd ed., revised. Washington (DC):
American Psychiatric Association
American Psychological Association. 1994. Diagnostic and statistical
manual of mental disorders. 4th ed. Washington (DC): American
Auer S, Reisberg B. 1997. The GDS/FAST staging system. Int
Psychogeriatr. 9(Suppl. 1):167--171.
Beck AaSR. 1987. Beck Depression Inventory scoring manual. New
York: The Psychological Corporation.
Berg L. 1984. Clinical Dementia Rating. Br J Psychiatry. 145:339.
Berg L. 1988. Clinical Dementia Rating (CDR). Psychopharmacol Bull.
Blatter DD, Bigler ED, Gale SD, Johnson SC, Anderson CV, Burnett BM,
Parker N, Kurth S, Horn SD. 1995. Quantitative volumetric analysis
of brain MR: normative database spanning 5 decades of life. AJNR
Am J Neuroradiol. 16:241--251.
Blessed G, Tomlinson BE, Roth M. 1968. The association between
quantitative measures of dementia and of senile change in the
Braak H, Braak E. 1985. On areas of transition between entorhinal
allocortex and temporal isocortex in the human brain. Normal
morphology and lamina-specific pathology in Alzheimer’s disease.
Acta Neuropathol Berl. 68:325--332.
Braak H, Braak E. 1991. Neuropathological staging of Alzheimer-related
changes. Acta Neuropathol. 82:239--259.
Braitenberg V, Schuz A. 1991. Anatomy of the cortex. Berlin: Springer-
Brickman AM, Habeck C, Zarahn E, Flynn J, Stern Y. 2007. Structural
MRI covariance patterns associated with normal aging and neuro-
psychological functioning. Neurobiol Aging. 28:284--295.
Bush G, Luu P, Posner MI. 2000. Cognitive and emotional influences in
anterior cingulate cortex. Trends Cogn Sci. 4:215--222.
Connelly SL, Hasher L, Zacks RT. 1991. Age and reading: the impact of
distraction. Psychol Aging. 6:533--541.
Courchesne E, Chisum HJ, Townsend J, Cowles A, Covington J, Egaas B,
Harwood M, Hinds S, Press GA. 2000. Normal brain development and
aging: quantitative analysis at in vivo MR imaging in healthy
volunteers. Radiology. 216:672--682.
Craik FIM, Grady CL. 2002. Aging, memory, and frontal lobe functioning.
In: Stuss DT, Knight RT, editors. Principles of frontal lobe function.
Oxford: Oxford University Press. p. 528--540.
Craik FIM, Jennings JJ. 1992. Human memory. In: Craik FIM, Salthouse
TA, editors. The handbook of aging and cognition. Hillsdale (NJ):
Lawrence Erlbaum Associates. p. 51--110.
Dale AM, Fischl B, Sereno MI. 1999. Cortical surface-based analysis. I.
Segmentation and surface reconstruction. Neuroimage. 9:179--194.
Dale AM, Sereno MI. 1993. Improved localization of cortical activity by
combining EEG and MEG with MRI cortical surface reconstruction:
a linear approach. J Cogn Neurosci. 5:162--176.
Delis DC, Kramer JH, Kaplan E, Ober BA. 1987. California Verbal
Learning Test. San Antonio (TX): The Psychological Corporation
Desikan RS, Segonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D,
Buckner RL, Dale AM, Maguire RP, Hyman BT, et al. 2006. An
automated labeling system for subdividing the human cerebral
cortex on MRI scans into gyral based regions of interest. Neuro-
Dickerson BC, Feczko E, Augustinack JC, Pacheco J, Morris JC, Fischl B,
Buckner RL. Forthcoming 2008 Differential effects of aging and
Alzheimer’s disease on medial temporal lobe cortical thickness and
surface area. Neurobiol Aging.
Dickerson BC, Fenstermacher E, Salat DH, Wolk DA, Maguire RP,
Desikan R, Pacheo J, Quinn BT, Van der Kouwe A, Greve DN, et al.
2008. Detection of cortical thickness correlates of cognitive
performance: Reliability across MRI scan sessions, scanners and
field strength. Neuroimage. 39:10--18.
Du AT, Schuff N, Chao LL, Kornak J, Jagust WJ, Kramer JH, Reed BR,
Miller BL, Norman D, Chui HC, et al. 2006. Age effects on atrophy
rates of entorhinal cortex and hippocampus. Neurobiol Aging.
Espeseth T, Westlye LT, Fjell AM, Walhovd KB, Rootwelt H, Reinvang I.
2008. Accelerated age-related cortical thinning in healthy carriers of
apolipoprotein E varepsilon4. Neurobiol Aging. 29:329--340.
Page 10 of 12
Cortical Thinning in Aging
Fjell et al.
Fischl B, Dale AM. 2000. Measuring the thickness of the human cerebral
cortex from magnetic resonance images. Proc Natl Acad Sci USA.
805 Fischl B, van der Kouwe A, Destrieux C, Halgren E, Segonne F, Salat DH,
Busa E, Seidman LJ, Goldstein J, Kennedy D, et al. 2004. Automatically
parcellating the human cerebral cortex. Cereb Cortex. 14:11--22.
Fjell AM, Walhovd KB, Amlien I, Bjørnerud A, Reinvang I, Gjerstad L,
Cappelen T, Willoch F, Due-Tønnessen P, Grambaite R, et al. 2008.
Morphometric changes in the episodic memory network and tau
pathologic features correlate with memory performance in patients
with mild cognitive impairment. Am J Neuroradiol. 29:1--7.
Folstein MF, Folstein SE, McHugh PR. 1975. ‘‘Mini-mental state’’. A
practical method for grading the cognitive state of patients for the
clinician. J Psychiatr Res. 12:189--198.
Fotenos AF, Snyder AZ, Girton LE, Morris JC, Buckner RL. 2005.
Normative estimates of cross-sectional and longitudinal brain
volume decline in aging and AD. Neurology. 64:1032--1039.
Good CD, Johnsrude IS, Ashburner J, Henson RN, Friston KJ,
Frackowiak RS. 2001. A voxel-based morphometric study of ageing
in 465 normal adult human brains. Neuroimage. 14:21--36.
Gunning-Dixon FM, Raz N. 2003. Neuroanatomical correlates of
selected executive functions in middle-aged and older adults:
a prospective MRI study. Neuropsychologia. 41:1929--1941.
825 Han X, Jovicich J, Salat D, van der Kouwe A, Quinn B, Czanner S, Busa E,
Pacheco J, Albert M, Killiany R, et al. 2006. Reliability of MRI-derived
measurements of human cerebral cortical thickness: the effects of
field strength, scanner upgrade and manufacturer. Neuroimage.
830 Head D, Rodrigue KM, Kennedy KM, Raz N. 2008. Neuroanatomical and
cognitive mediators of age-related differences in episodic memory.
Head D, Snyder AZ, Girton LE, Morris JC, Buckner RL. 2005. Frontal-
hippocampal double dissociation between normal aging and
Alzheimer’s disease. Cereb Cortex. 15:732--739.
Jernigan TL, Archibald SL, Berhow MT, Sowell ER, Foster DS,
Hesselink JR. 1991. Cerebral structure on MRI, Part I: Localization
of age-related changes. Biol Psychiatry. 29:55--67.
Jernigan TL, Archibald SL, Fennema-Notestine C, Gamst AC, Stout JC,
Bonner J, Hesselink JR. 2001. Effects of age on tissues and regions of
the cerebrum and cerebellum. Neurobiol Aging. 22:581--594.
Jonsson EG, Edman-Ahlbom B, Sillen A, Gunnar A, Kulle B, Frigessi A,
Vares M, Ekholm B, Wode-Helgodt B, Schumacher J, et al. 2006.
Brain-derived neurotrophic factor gene. BDNF. Variants and
schizophrenia: an association study. Prog Neuropsychopharmacol
Biol Psychiatry. 30:924--933.
Kalpouzos G, Chetelat G, Baron JC, Landeau B, Mevel K, Godeau C,
Barre L, Constans JM, Viader F, Eustache F, et al. Forthcoming 2008.
Voxel-based mapping of brain gray matter volume and glucose
metabolism profiles in normal aging. Neurobiol Aging.
Kennedy KM, Erickson KI, Rodrigue KM, Voss MW, Colcombe SJ,
Kramer AF, Acker JD, Raz N. 2008. Age-related differences in
regional brain volumes: a comparison of optimized voxel-based
morphometry to manual volumetry. Neurobiol Aging.
855 Kruggel F. 2006. MRI-based volumetry of head compartments:
normative values of healthy adults. Neuroimage. 30:1--11.
Marcus DS, Wang TH, Parker J, Csernansky JG, Morris JC, Buckner RL.
2007. OpenAccess Series of Imaging Studies (OASIS):cross-sectional
MRI data in young, middle aged, nondemented, and demented older
adults. J Cogn Neurosci. 19:1498--1507.
Mesulam MM. 1999. Neuroplasticity failure in Alzheimer’s disease:
bridging the gap between plaques and tangles. Neuron. 24:521--529.
Morris JC. 1993. The Clinical Dementia Rating (CDR): current version
and scoring rules. Neurology. 43:2412--2414.
865 Murphy DG, DeCarli C, McIntosh AR, Daly E, Mentis MJ, Pietrini P,
Szczepanik J, Schapiro MB, Grady CL, Horwitz B, et al. 1996. Sex
differences in human brain morphometry and metabolism: an in vivo
quantitative magnetic resonance imaging and positron emission
tomography study on the effect of aging. Arch Gen Psychiatry.
Nesvag R, Lawyer G, Varnas K, Fjell AM, Walhovd KB, Frigessi A,
Jonsson EG, Agartz I. 2008. Regional thinning of the cerebral cortex
in schizophrenia: effects of diagnosis, age and antipsychotic
medication. Schizophr Res. 98:16--28.
Oldfield RC. 1971. The assessment and analysis of handedness: the
Edinburgh inventory. Neuropsychologia. 9:97--113
Osterrieth PA. 1944. Le test de copie dune figure complexe. Arch
Psychol. 30:206--356;(Translated by J. Corwin and F.W. Bylsma.
1993). Clin Neuropsychol. 7:9--15
Pfefferbaum A, Mathalon DH, Sullivan EV, Rawles JM, Zipursky RB,
Lim KO. 1994. A quantitative magnetic resonance imaging study of
changes in brain morphology from infancy to late adulthood. Arch
Rabbitt P, Lowe C, Shilling V. 2001. Frontal tests and models for
cognitive ageing. Eur J Cogn Psychol. 13:5--28.
Radloff SL. 1977. The CES-D scale: a self-report depression scale for
research in the general population. Appl Psychol Measure.
Raz N, Gunning FM, Head D, Dupuis JH, McQuain J, Briggs SD,
Loken WJ, Thornton AE, Acker JD. 1997. Selective aging of the
human cerebral cortex observed in vivo: differential vulnerability of
the prefrontal gray matter. Cereb Cortex. 7:268--282.
Raz N, Gunning-Dixon FM, Head D, Dupuis JH, Acker JD. 1998.
Neuroanatomical correlates of cognitive aging: evidence from
structural magnetic resonanceimaging.Neuropsychology. 12:95--114.
Raz N, Gunning-Dixon F, Head D, Rodrigue KM, Williamson A, Acker JD.
2004. Aging, sexual dimorphism, and hemispheric asymmetry of the
cerebral cortex: replicability of regional differences in volume.
Neurobiol Aging. 25:377--396.
Raz N, Lindenberger U, Ghisletta P, Rodrigue KM, Kennedy KM,
Acker JD. 2008. Neuroanatomical correlates of fluid intelligence in
healthy adults and persons with vascular risk factors. Cereb Cortex.
Raz N, Lindenberger U, Rodrigue KM, Kennedy KM, Head D,
Williamson A, Dahle C, Gerstorf D, Acker JD. 2005. Regional brain
changes in aging healthy adults: general trends, individual differ-
ences and modifiers. Cereb Cortex. 15:1676--1689.
Raz N, Rodrigue KM. 2006. Differential aging of the brain: Patterns,
cognitive correlates and modifiers. Neurosci Biobehav Rev. 30:730.
Raz N, Rodrigue KM, Haacke EM. 2007. Brain aging and its modifiers:
insights fromin vivoneuromorphometry and
weighted imaging. Ann N Y Acad Sci. 1097:84--93.
Raz N, Rodrigue KM, Head D, Kennedy KM, Acker JD. 2004. Differential
aging of the medial temporal lobe: a study of a five-year change.
Raz N, Rodrigue KM, Kennedy KM, Acker JD. 2007. Vascular health and
longitudinal changes in brain and cognition in middle-aged and
older adults. Neuropsychology. 21:149--157.
Raz N, Williamson A, Gunning-Dixon F, Head D, Acker JD. 2000.
Neuroanatomical and cognitive correlates of adult age differences in
acquisition of a perceptual-motor skill. Microsc Res Tech. 51:85--93.
Reid LM, Maclullich AM. 2006. Subjective memory complaints and
cognitive impairment in older people. Dement Geriatr Cogn Disord.
Resnick SM, Goldszal AF, Davatzikos C, Golski S, Kraut MA, Metter EJ,
Bryan RN, Zonderman AB. 2000. One-year age changes in MRI brain
volumes in older adults. Cereb Cortex. 10:464--472.
Resnick SM, Lamar M, Driscoll I. 2007. Vulnerability of the orbitofrontal
cortex to age-associated structural and functional brain changes.
Ann N Y Acad Sci. 1121:562--575
Resnick SM, Pham DL, Kraut MA, Zonderman AB, Davatzikos C. 2003.
Longitudinal magnetic resonance imaging studies of older adults:
a shrinking brain. J Neurosci. 23:3295--3301.
Rey A. 1941. Psychological examination of traumatic encephalopathy.
Arch Psychol. 28:286--340; sections translated by J. Corwin and F.W.
Bylsma, 1993. The Clinical Neuropsychologist:4--9.
Rodrigue KM, Raz N. 2004. Shrinkage of the entorhinal cortex over five
years predicts memory performance in healthy adults. J Neurosci.
Rosas HD, Liu AK, Hersch S, Glessner M, Ferrante RJ, Salat DH, van der
Kouwe A, Jenkins BG, Dale AM, Fischl B. 2002. Regional and
progressive thinning of the cortical ribbon in Huntington’s disease.
Cerebral Cortex Page 11 of 12
Salat DH, Buckner RL, Snyder AZ, Greve DN, Desikan RS, Busa E, Download full-text
Morris JC, Dale AM, Fischl B. 2004. Thinning of the cerebral cortex
in aging. Cereb Cortex. 14:721--730.
Sato K, Taki Y, Fukuda H, Kawashima R. 2003. Neuroanatomical
database of normal Japanese brains. Neural Netw. 16:1301--1310.
Schretlen D, Pearlson GD, Anthony JC, Aylward EH, Augustine AM,
Davis A, Barta P. 2000. Elucidating the contributions of processing
differences in fluid intelligence. J Int Neuropsychol Soc. 6:52--61.
Spitzer RL, Williams JBW, Gibbon M. 1986. Structural Clinical Interview
for DSM-III-R—Non-patient Version. (SCID-NP). New York: Bio-
metrics Research Department, New York State Psychiatric Institute
Spitzer RL, Williams JBW, Gibbon M, First MB. 1988. Structured Clinical
Interview for DSM-III-R—Patient Version. (SCID-P). New York:
Biometrics Research Department, New York State Psychiatric
960 Sullivan EV, Marsh L, Mathalon DH, Lim KO, Pfefferbaum A. 1995. Age-
related decline in MRI volumes of temporal lobe gray matter but not
hippocampus. Neurobiol Aging. 16:591--606.
Sullivan EV, Rosenbloom M, Serventi KL, Pfefferbaum A. 2004. Effects of
age and sex on volumes of the thalamus, pons, and cortex.
Neurobiol Aging. 25:185--192.
Taki Y, Goto R, Evans A, Zijdenbos A, Neelin P, Lerch J, Sato K, Ono S,
Kinomura S, Nakagawa M, et al. 2004. Voxel-based morphometry of
human brain with age and cerebrovascular risk factors. Neurobiol
Tisserand DJ, Pruessner JC, Sanz Arigita EJ, van Boxtel MP, Evans AC,
Jolles J, Uylings HB. 2002. Regional frontal cortical volumes decrease
differentially in aging: an MRI study to compare volumetric
Trahan DE, Larrabee GJ. 1988. Continuous Visual Memory Test. Odessa
(FL): Psychological Assessment Resources
Vaidya JG, Paradiso S, Boles Ponto LL, McCormick LM, Robinson RG.
2007. Aging, grey matter, and blood flow in the anterior cingulate
cortex. Neuroimage. 37:1346--1353.
Walhovd KB, Fjell AM, Reinvang I, Lundervold A, Dale AM, Eilertsen DE,
Quinn BT, Salat D, Makris N, Fischl B. 2005. Effects of age on
volumes of cortex, white matter and subcortical structures. Neuro-
biol Aging. 26:1261--1270; discussion 1275--1268.
Wechsler D. 1999. Wechsler Abbreviated Scale of Intelligence. San
Antonio (TX): The Psychological Corporation.
West RL. 1996. An application of prefrontal cortex function theory to
cognitive aging. Psychol Bull. 120:272--292.
Page 12 of 12
Cortical Thinning in Aging
Fjell et al.