Structure--Function Correlates of
Cognitive Decline in Aging
Jonas Persson1, Lars Nyberg1, Johanna Lind2, Anne Larsson3,
Lars-Go ¨ ran Nilsson4, Martin Ingvar2and Randy L. Buckner5
1Department of Psychology, Umea ˚ University, S-901 87 Umea ˚ ,
Sweden,2MR Research Center, Karolinska Hospital, S-171 76
Stockholm, Sweden,3Department of Radiation Sciences —
Radiation Physics, Umea ˚ University, S-901 87 Umea ˚ , Sweden,
4Department of Psychology, Stockholm University, S-106 91
Stockholm, Sweden and5Departments of Psychology,
Radiology, and Anatomy & Neurobiology, Howard Hughes
Medical Institute at Washington University, St Louis,
MO 63130, USA
To explore neural correlates of cognitive decline in aging, we used
longitudinal behavioral data to identify two groups of older adults
(n 5 40) that differed with regard to whether their performance
on tests of episodic memory remained stable or declined over
a decade. Analysis of structural and diffusion tensor imaging (DTI)
revealed a heterogeneous set of differences associated with
cognitive decline. Manual tracing of hippocampal volume showed
significant reduction in those older adults with a declining memory
performance as did DTI-measured fractional anisotropy in the
anterior corpus callosum. Functional magnetic resonance imaging
during incidental episodic encoding revealed increased activation in
left prefrontal cortex for both groups and additional right prefrontal
activation for the elderly subjects with the greatest decline in
memory performance. Moreover, mean DTI measures in the
anterior corpus callosum correlated negatively with activation in
right prefrontal cortex. These results demonstrate that cognitive
decline is associated with differences in the structure as well as
function of the aging brain, and suggest that increased activation
is either caused by structural disruption or is a compensatory
response to such disruption.
Keywords: aging, compensation, corpus callosum prefrontal diffusion-
tensor imaging, fMRI, hippocampus, longitudinal, memory
In vivo structural neuroimaging data and post-mortem exam-
ination of brain tissue have revealed a diverse array of age-
related changes in the brain. Changes in brain morphology
include a decline in total brain volume, cortical thinning and
gyral atrophy (Uylings and de Brabander, 2002; Raz et al., 2004).
Several neuroimaging studies have confirmed that there are age-
related changes in morphological characteristics of the brain
(e.g.Pfefferbaum et al., 1994; Blatter et al., 1995; Raz et al., 1997;
Good et al., 2001; Jernigan et al., 2001; Sowell et al., 2003), and
that these changes are prominent in the prefrontal cortex (PFC)
(Raz et al., 1997). White-matter degradation, in the form of
hyperintensities, reduced white-matter integrity and volume
loss, is also commonly observed (e.g. Ylikoski et al., 1995;
Gunning-Dixon and Raz, 2000; O’Sullivan et al., 2001; DeCarli
and Scheltens, 2002; Bartzokis et al., 2004; Head et al., 2004,
2005; DeCarli et al., 2005). Furthermore, the hippocampal
formation, a structure important to declarative memory, expe-
riences volume loss in advanced aging that is significantly
accelerated in early stages of Alzheimer’s disease (for reviews,
see Jack and Petersen, 2000; Raz et al., 2005).
In terms of cognitive performance, advanced aging is often
associated with decline in declarative memory, prominently
including episodic memory, probably as a result of both normal
aging processes and those associated with preclinical stages
of Alzheimer’s disease (Albert, 1997; Buckner, 2004; Hedden
and Gabrieli, 2004). Both age-associated influences on frontal-
striatal networks and the medial temporal lobe (MTL) have been
proposed as important factors in memory decline. Cross-
sectional studies of older adults that do not reach clinical
criteria for mild dementia have found negative correlations
between cognitive performance and the volume of hippocam-
pus proper (Golomb et al., 1994) and associated MTL structures
(Rodrigue and Raz, 2004). For example, Rodrigue and Raz
(2004) found that longitudinal changes in entorhinal cortex
predicted memory performance. Taken together, even though
several studies have failed to find significant relationships
between hippocampus volume and behavioral performance
(for a recent review, see Van Petten, 2004), these prior findings
suggest that decline in episodic memory relates to structural
changes in the hippocampus and related MTL structures.
While not consistent across all studies, several studies have ob-
served associations between measures of white-matter integrity
and others aspects of frontal-striatal anatomy and memory
and executive performance (Gunning-Dixon and Raz, 2000;
O’Sullivan et al., 2001; Madden et al., 2004).
More recently, a number of studies have examined age-
related differences in functional brain activity during cognitive
task performance. A noteworthy finding is that under certain
conditions, some frontal regions are relatively more active in
older than younger adults (e.g. Cabeza et al., 1997; Madden
et al., 1999; Reuter-Lorenz et al., 2000; Logan et al., 2002; Rosen
et al., 2002). The functional significance of such alterations in
brain activity in older age remains poorly understood. One
possibility is that increased frontal recruitment is beneficial
to performance in older age (Cabeza et al., 2002). Another
possibility is that increases in frontal activation reflect detri-
mental age-related changes (Kinsbourne, 1980; Buckner and
Logan, 2002) or a dedifferentiation of cognitive functions in
older age (Baltes and Lindenberger, 1997).
In the current study, longitudinal behavioral data were
obtained from an ongoing prospective study (Nilsson et al.,
1997) to identify two groups of older adults that differed with
regard to how their level of episodic memory performance
changed over time (Fig. 1A). One group involved participants
with a stable memory performance over time, and the other
group involved participants with a declining memory perfor-
mance over time. This selection was based on composite scores
from three episodic memory tests at three time points over
a decade-long period. Within the declining group the partic-
ipants could be subdivided into those declining from an initial
high level to a final intermediate level (decline high), and those
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Cerebral Cortex July 2006;16:907--915
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who declined from an initial intermediate level to a final low
level (decline low) (Fig. 1B).
The central question of this study was whether stable or
declining longitudinal performance was associated with atypical
functional and structural measures of the brain. More specifi-
cally, we were interested in regions of the brain that have been
associated with episodic memory functions, such as the hippo-
campus and the prefrontal cortex. Differences in white matter
may also be important as multiple forms of white matter de-
gradation have been associated with cognitive decline in aging
presumably through their connections with cortical and sub-
cortical structures. To explore structural correlates of longitu-
dinal performance, manual measures of hippocampal volume
and diffusion tensor imaging (DTI) measures of white-matter
integrity were obtained. To explore functional correlates,
functional magnetic resonance imaging (fMRI) was used to
assess brain activity while participants performed a semantic
categorization task that promoted incidental encoding of
a word list. This type of task has consistently been associated
with left-lateralized prefrontal activation in younger adults
(e.g. Demb et al., 1995; Wagner et al., 1998) with older adults
often showing increased and more extensive activation, includ-
ing increased right PFC activation (Logan et al., 2002; Lustig and
Buckner, 2004; for a review, see Park and Gutchess, 2005). The
imaging data were acquired in a separate scanning experiment
~2 years after the last longitudinal behavioral session.
Materials and Methods
Informed consent was obtained from 40 participants who were paid for
participation in accordance with the guidelines of the Swedish Council
for Researchinthe Humanitiesand SocialSciences. Allparticipantswere
recruited from ‘The Betula Prospective Cohort Study: Memory, Health,
and Aging’ (Nilsson et al., 1997). They were divided in two groups based
on longitudinal memory performance. The following three tasks were
used. (1) Yes/no recognition of faces. Each of the 16 pictures was
presented for 8 s. During retrieval, participants were presented with
a sequence offaces, 12of whichwere new,and 12 previously presented.
Targetand distracterpictureswereshown oneby oneina randomorder
in imperative form were presented and participants were asked to try to
remember them. Each sentence, consisting of a verb and a noun, was
presented visually for 8 s. The participants were instructed to perform
the action presented on the card. If the action included an external
object, the object was provided by the experimenter. Task (3) was
similar to task (2) but the participants were instructed to remember the
sentence without enactment. Following each of the encoding con-
ditions (2 and 3) the participants were given a free recall test. List order
and materials were counterbalanced across participants.
Mean memory score (SEM) for the stable group (n = 20) was 22.5
(1.10) at T1, 22.3 (1.09) at T2 and 22.2 (1.09) at T3; and for the declining
group (n = 20) 26.3 (0.91) at T1, 20.7 (0.89) at T2 and 18.0 (1.00) at T3.
In order to ensure that longitudinal change and between-group differ-
ences were not attributable to regression-to-the mean artefacts, we
made sure that (i) the declining group did not start out with
considerably higher memory performance than the total group from
which the participants were initially selected (declining = 26.3; total
sample = 24.1; effect size of the difference = 0.47, which is <1 SD from
the distribution of the total sample, and suggests that the groups did not
differ on overall performance); (ii) that the decline in performance was
significant from T1 to T2 [paired-samples t-test t(19) = 6.27, P <0.001],
as well as T2 to T3 [t(19) = 2.64, P <0.05]; and (iii) that performance for
the declining group was below the average for the total sample at T3
(declining = 18.0; total sample = 20.2). All participants scored 25 or
above on the mini-mental state examination (MMSE) (Folstein et al.,
Demographic characteristics and behavioral performance
Longitudinal memory performanceP
Recognition — accuracy
Recognition — RT
66.05 (5.66)65.34 (7.14)NS
Mean scores are shown (SD). Age range for both groups was 49--74 years. MMSE 5
Mini-Mental State Examination (maximum 5 30). SRB 5 Word Comprehension (maximum 5
30). Edu 5 Education (years). Recognition -- accuracy 5 delayed yes/no recognition at
fMRI session (hits -- false alarms). Recognition -- RT 5 delayed yes/no recognition at
fMRI session (reaction time in ms).
Figure 1. Memory performance over time (assessed by the sum of three episodic
memory tests). Groups were divided into individuals showing either stable memory
performance over time or declining memory performance over time (A). The declining
group was further subdivided into individuals declining from a high to a moderate level
of performance, and individuals declining from a moderate to low level of performance
(B). Error bars show SEM.
Cognitive Decline in Aging
Persson et al.
Data on MMSE and word comprehension (SRB) were acquired at T3, and
recognition data were acquired after the scanning session. Given the
possible effects of vascular conditions, such as hypertension, on brain
function and anatomy, we compared the two groups on markers for
vascular problems. These measurements included blood pressure, self-
reported use of blood-pressure-lowering medication and self-reported
history of vascular conditions. There were no differences between the
stableor decliningindividuals oneithersystolic[decline =139.0, stable =
139.5; t(38) <1] or diastolic [decline = 87.8, stable = 84.8; t(38) = 1.02,
P = 0.32] blood pressure. Also, there were no differences between the
groups on self-reported use of blood-pressure-lowering medication or
history of vascular conditions. For the declining low versus declining
high comparison, 7 participants were categorized as declining from
a moderate to a low level of memory performance, and 13 as declining
from a high to a moderate level of memory performance. Importantly,
therewasminimal agedifference between thegroups(age -- lowdecline
= 67.4; age -- high decline = 64.4). All subjects were native Swedish
speakers, and had no reported neurological problems that might cause
dementia. Vision was normal or corrected to near normal using scanner-
compatible glasses or contact lenses.
The MRI data were acquired in a separate experiment in 2002
and 2003. There was variation in the temporal lag between the last
behavioral session (T3) and the scanning experiment since data for
the behavioral sessions were collected over an extended time. This,
together with the temporal lag between the behavioral assessments and
the MRI session, could have implications for the interpretation of the
temporal relationship between the behavioral and MRI results.
fMRI Data Acquisition
A Philips Intera 1.5 T scanner (Philips Medical Systems, The Nether-
lands), equipped for echo-planar imaging (EPI), was used for magnetic
resonance imaging. To acquire blood-oxygen level-dependent contrast
images a T2*-weighted single-shot gradient echo EPI sequence was used
with the following parameters: TR3000 ms, TE50 ms, flip angle 90?, field
of view 22 3 22 cm, 64 3 64 matrix and 3.9 mm slice thickness. Thirty-
three contiguous transaxial slices positioned to include the whole brain
volume were acquired every 3.0 s. To avoid signals resulting from pro-
gressive saturation, five ‘dummy scans’ were acquired and discarded
prior to the image acquisition. In the scanner, cushions inside the head
coil were used to reduce head movement,and headphones were used to
dampen the scanner noise. Responses were collected with a fiber-optic
response box held in the right hand (Lumitouch Reply System, Light-
wave Medical Industries, Canada). Stimuli were projected on to a semi-
transparent screen at the head of the bore, viewed by the subject via
a mirror mounted on the head coil. Sixty-nine functional volumes
per run were collected across four separate runs for each partici-
pant. Structural high-resolution T1- and T2-weighted images were
fMRI Behavioral Tasks
During the functional runs, a blocked-task paradigm was used, alternat-
ing between categorization task blocks in which the participants were
asked to categorize words as either abstract (e.g. democracy) or con-
crete (e.g. hammer) that served as incidental word encoding (30 s) and
fixation blocks (21 s) (Demb et al., 1995). Each functional run started
and ended with brief fixation blocks (12 s). Four identical runs were
performed that each consisted of four categorization blocks con-
taining 10 words. Two of the blocks included words that had been
presented twice prior to scanning and the other two blocks included
novel words (i.e. words that had not been presented previously during
the study). For the purpose of the present analyses, data were collapsed
across novel and previously presented words. The words were abstract
and concrete nouns presented in lowercase Courier New font (font size
60 points). After the scanning session, memory performance was tested
using a self-paced old/new recognition test.
fMRI Data Analysis
All functional images were pre-processed and analyzed using SPM99
(Wellcome Department of Cognitive Neurology, London) implemented
in Matlab 6.1 (Mathworks Inc.). Prior to analysis, all image volumes were
realigned with respect to the first image volume using sinc interpola-
tion. The images were then normalized using affine and smooth
nonlinear transformations to an EPI template in the Montreal Neuro-
logical Institute (MNI) space. Finally, all normalized images were
spatially smoothed with a 6.0 mm full-width at half-maximum Gaussian
kernel. Within-participant statistical contrasts used the general linear
model. The encoding condition was modeled as a fixed response (box-
car) waveform convolved with the hemodynamic response function.
Statistical parametric maps (SPMs) were generated using t-statistics to
identify regions activated according to the model. Group data were
analyzed using a random-effects model. All reported activations passed
a whole-brain false discovery rate (FDR) (Genovese et al., 2002) of
P < 0.05 with an extent threshold of 20 contiguous voxels. For the
region-of-interest (ROI) analyses, we selected peak coordinates that
have both been associated with incidental encoding in previous neuro-
imaging studies and were activated during incidental encoding in the
ROIs were defined without bias to the subsequent tests that explored
differences between groups. The ROI in each case was defined as an 8.0
mm radius sphere surrounding the specific coordinates. Regions are
discussed in reference to their approximate Brodmann area (BA). These
ROIs were located in left dorsal frontal cortex (BA 6/44: –42, 4, 32), left
inferior frontal cortex (BA 45/47: –48, 20, –6), right dorsal frontal cortex
(BA 6/44: 48, 4, 32), and right ventral frontal cortex (BA 47: 50, 20, –12).
Figure 2. Transverse sections show significant activation during semantic catego-
rization (incidental encoding) as compared to fixation baseline (FDR corrected
threshold at P <0.05). The anatomical template is used as the backdrop.
Cerebral Cortex July 2006, V 16 N 7 909
To create the ROIs and extract percent signal change we used the SPM
ROI toolbox (http://sourceforge.net/projects/spm-toolbox).
Volumetric Image Analysis
For the volumetric measurements, a T1-weighted 3D gradiant echo
sequence was used with the following parameters: TR24ms, TE6ms, flip
angle 35? and field of view 18 3 18 cm. One hundred and twenty-four
coronal slices with a slice thickness of 1.8 mm were acquired in 160 3
160 matrices and reconstructed to 256 3 256 matrices. Two averages
were used. After acquisition, the T1-weighted images were aligned to
correct for undesirable effects of head tilt (to the left or right shoulder),
pitch (forward or backward) and rotation (to the right or to the left)
using BrainImage 5.2.5, public domain software (http://www.stanford.
edu/group/cap/research/neuroimaging/imageanalysis). The alignment
is facilitated if the image volume can be viewed with equal dimensions
in all directions, and bicubic interpolation between the slices was per-
formed to obtain volumes with slice thickness equal to the pixel size. To
correct for head pitch, the axial plane was tilted so it passed through the
long axis of the right hippocampus, visualized on a parasagittal section.
From that operation, the alignment proceeded as previously described
by Raz et al. (2004).
The hippocampal formation (hippocampus) was manually traced on
every other interpolated coronal slice, using a computer mouse, and
measured with NIH Image public domain software (version 1.60; http://
rsb.info.nih.gov/nih-image/). The left and right hippocampus were mea-
sured separately, and volumes were computed by multiplying the total
number of voxels for each hippocampus by the voxel size of 0.695 mm3.
The total number of coronal slices used to outline the hippocampus
varied between 17--23 per participant.
Beginning rostrally, the first slice used was the one where the
mammilary bodies were clearly visible, whereas the caudal boundary
was marked by the slice showing the fornices rising from the fimbria. As
previously noted by Raz et al. (2004) this definition of the hippocampus
is a conservative one. To outline the rostral part of the hippocampus and
separate this part from the adjacent amygdala, the temporal horn of the
lateral ventricle was used as a landmark. Laterally, the white matter of
the temporal lobe was used as a border, and medially, the subiculum was
demarcated from the cortex of the parahippocampal gyrus by tracing
the subiculum to its most medial position and draw a horizontal line at
its medial curve. Any part of the subiculum above this line was included
as a part of the hippocampus. In the caudal slices, the white matter of
the temporallobewasused as theventral border.Inapplyingthe rulesof
demarcation, questionable cases were resolved by consulting correla-
tive and general brain atlases.
All volumetric measurements were performed by the same operator
(J.L.), who was blind to the demographic characteristics of the
participants. To ensure reliability of the tracing, the operator underwent
an extensive training program. In this process, the novice operator
traced a set of previously measured brains, together with an experi-
enced operator, side-by-side. The training proceeded until the measures
of the hippocampus areas did not differ by more than 10% from the
previously trained operator ensuring that they were following the same
tracing conventions. The final reliability estimate (ICC; measured on
a new set of five brains) for this operator was 0.97. Since head and body
size is highly correlated with total intracranial volume, correction for
overall differences in body size was performed (similar to Rodrigue and
Figure 3. Transverse sections show the location of the four frontal ROIs used for
analysis. Bars show average percent signal change for participants with a declining
and stable memory performance over time. Error bars show standard error of the
mean. The regions include right ventral frontal cortex (BA 47, A), left inferior frontal
cortex (BA 45/47, B), right dorsal frontal cortex (BA 6/44, C), and left dorsal frontal
cortex (BA 6/44, D). Brodmann area labels should be considered approximate. (E)
Average percent signal change in the right ventral frontal ROI for participants in relation
to memory performance over time (high and low decline and stable). (F) Brain
responses (FDR corrected threshold at P < 0.05) during semantic categorization
compared to fixation baseline for participants with stable memory performance
(bottom) and declining memory performance (top). Activations are displayed on
transverse sections of an anatomical template brain. A blue arrow points to the region
Cognitive Decline in Aging
Persson et al.
Raz, 2004). We used height to adjust for differences in body size via the
analysis of covariance according to the formula: adjusted volume = raw
volume – b 3 (height -- mean height), where b is the slope of regression
of the appropriate ROI volume on height. This procedure removes
variance associated with body (and head) size, and redefines data points
as the difference between an individual’s measures and others of similar
size in the sample.
Diffusion Tensor Imaging
Participants were imaged using a single-shot spin echo EPI sequence,
and cardiac gating was used to reduce motion artefacts due to pulsation
of blood and cerebro-spinal fluid. The following imaging parameters
were used: TRshortest, TE77 ms, field-of-view 23 3 23 cm, acquisition
matrix 96 3 96 reconstructed to 128 3 128 and flip angle 90?. Fifty-four
3.0 mm thick contiguous axial slices were acquired. The DTI sequence
was repeated four times, and the images were averaged using a script
implemented in Matlab 6.1. The DTI sequence included six sets of dif-
fusion gradients placed along non-colinear directions [b = 1000 s/mm2;
gradient directions (x, y, z) = (1, 0, 0), (0, 1, 0), (0, 0, 1), (1/O2, 1/O2, 0),
(1/O2, 0, 1/O2), (0, 1/O2, 1/O2)] and one set without diffusion
weighting (b = 0 s/mm2).
The averaged images were processed using a custom toolbox in
SPM99 that calculated the diffusion tensor eigenvalues in each voxel.
Fractional anisotropy (FA) maps were then calculated. The non-
diffusion-weighted image was normalized to a common template in
MNI space, and the resulting affine and non-linear transformation
parameters were applied to the anisotropy images. Finally, the FA
maps were smoothed with a Gaussian kernel of 8 mm full width at half
maximum. The ROIs were outlined on the non-diffusion weighted
image and each ROI was manually outlined using MRICro (http://
www.psychology.nottingham.ac.uk/staff/cr1/mricro.html) by the oper-
ator (J.P.) who was blind to the participants’ demographic character-
istics. The ROIs included the genu, splenium and body of the corpus
callosum. The ROIs were clearly visible on the non-diffusion-weighted
images, and standard anatomical landmarks were used to define these
regions (e.g. O’Sullivan et al., 2001). The ROIs were then superimposed
on the FA maps and mean values for each region and for each participant
A random-effects analysis including all participants revealed
task-sensitive brain regions. Consistent with previous findings
(e.g. Demb et al., 1995; Wagner et al., 1998), increased
activation was observed in multiple frontal regions, including
bilateral dorsal (BA 6/44) and ventral (BA 45/47) frontal cortex
(Fig. 2). Activated frontal regions in the overall analysis served as
a basis for defining specific ROIs for group comparisons. Four
spherical ROIs were used, and the mean signal change was com-
puted for the declining and stable groups separately (Fig. 3).
A significant between-group difference was observed in the
right ventral prefrontal ROI [BA 47, t(38) = 2.20, P <0.05] (Fig.
3A,F) with greater activity in the declining group. Response
magnitudes did not differ between the groups for either of the
two left frontal ROIs [BA 6/44, t(38) = 0.37, P = 0.72 (Fig. 3D);
BA 45/47, t(38) = 0.47, P = 0.64 (Fig. 3B)] or the right posterior-
dorsal ROI [BA 6/44, t(38) = 0.49, P = 0.63 (Fig. 3C)]. The level of
right frontal recruitment was also analyzed as the residual
magnitude after variance in left frontal activation was removed,
and the declining group still showed greater residual activation
in the right ventral prefrontal ROI [F(1,39) = 4.71, P < 0.05].
When the declining group was divided into subgroups, it was
found that the magnitude of right ventral prefrontal activity was
maximal for the declining-low group (Fig. 3E).
Hippocampus volumes differed as a function of group. Both
left and right hippocampus volumes were significantly smaller
for individuals with declining compared to stable memory
performance over time [left: t(36) = 2.33, P < 0.05; right t(36)
= 2.01, P < 0.05] (Fig. 4A,C). When the declining group was
Figure 4. Mean height adjusted volume of the left (A, B) and right (C, D) hippocampus (in mm3). (A, C) Mean hippocampus volume for individuals with stable (light gray) and
declining (dark gray) longitudinal memory performance. (B, D) Mean hippocampus volume for individuals with stable (light gray), declining high (gray), and declining low (dark gray)
longitudinal memory performance. Error bars show SEM.
Cerebral Cortex July 2006, V 16 N 7 911
divided into subgroups, it was found that the reduction in left
hippocampus volume was most apparent for the declining-low
group (Fig 4B), whereas the right hippocampus reduction was
of a similar magnitude for both declining groups (Fig. 4D).
There was no significant difference between the group that
declined from a high to moderate performance level, and the
group declining from a moderate level to a low level of
performance in either left [t(36) = 1.98, P = 0.17] or right
[t(36) = –0.49, P = 0.62]. The difference in left hippocampus
volume between the stable and the group declining from
a moderate to a low level was significant [t(36) = 2.98, P <
0.01], as was the difference in right hippocampus volume
between the stable group and the group declining from a high
to a moderate performance level [t(36) = 1.78, P < 0.05, one-
tailed]. As a post-hoc analysis we computed the correlation
between volume in right and left hippocampus and fMRI
signal change in the right ventral frontal ROI. No significant
correlations were found.
Figure 5. (A) ROIs (top, genu; middle, body; bottom, splenium) outlined on transverse slices of fractional anisotropy (FA) images. High signal intensity (brightness) reflects higher
FA. (B) Mean FA as a function of longitudinal memory performance and ROI (light gray, stable; dark gray, declining). (C) Mean FA as a function of longitudinal memory performance
and ROI (light gray, stable; gray, declining high; dark gray, declining low). (D) Scatterplots show a post-hoc correlation between mean FA in the ROIs and percent signal change in
the right ventral frontal ROI (filled circle, declining group; filled square, stable group). Error bars show SEM.
Cognitive Decline in Aging
Persson et al.
Based on O’Sullivan et al. (2001) and Head et al. (2004), FA
values for three regions of the corpus callosum were computed
(anterior, middle, posterior; Fig. 5A). A group difference was
apparent in the anterior region with a higher FA value in the
stable group (Fig. 5B), and when the declining group was
divided into subgroups, a significant difference between the
stable and decline-low groups was found [(t(1,39) = 2.37, P <
0.05] (Fig. 5B). As a post-hoc analysis we also computed the
correlation between mean FA in anterior corpus callosum and
fMRI signal change in the right ventral frontal ROI. A significant
negative correlation between FA in anterior corpus callosum
and brain activation was observed (r = –.385, P <0.05) (Fig. 5D).
No other correlations were significant. Thus, while provisional,
the association between anterior white matter differences and
functional activation is intriguing.
The present study provides evidence for neuroanatomical and
functional differences associated with longitudinal decline in
episodic memory performance. The observed differences on
multiple structural and functional measures is direct evidence
for brain differences between cognitively declining and stable
individuals, and is unlikely to be attributable to regression
artefacts. Older adults with declining memory performance
showed differences in DTI measures of anterior white matter as
well as reduced hippocampus volume compared to older adults
with preserved memory performance. The combination of
hippocampal and anterior white-matter differences suggests
that multiple factors are contributing to cognitive decline
(Albert, 1997; Buckner, 2004; Hedden and Gabrieli, 2004).
Moreover, differences in functional activation were noted in
the form of increased recruitment associated with memory
decline. This combination of associations is indicative of
a structural and functional pattern of change in aging that may
reflect detrimental processes, the emergence of compensation,
Our results support the hypothesis that the hippocampus plays
a role in episodic memory in old age. These results are in line
with evidence from cross-sectional studies (e.g. Golomb et al.,
1994; Rodrigue and Raz, 2004) that show a negative correlation
between volume of hippocampus and structures of the MTL and
cognitive performance, as well as extant data that suggest
hippocampus volume decline is an early predictor of memory
impairment associated with Alzheimer’s disease (for a review,
see Jack and Petersen, 2000). The observed link between
cognitive performance and hippocampus volume is further
supported by the finding of a correlation between longitudinal
deterioration of cognitive performance and hippocampus
volume (Golomb et al., 1996).
In the presence of an association with change in memory
performance, the present study did not find significant differ-
ences between the two declining groups. This suggests that
absolute performance per se may not be the most critical factor,
but rather the decline of memory performance over time. A
further important consideration regarding our observed associ-
ations with hippocampus volume is that participants with early
stages of Alzheimer’s disease are probably included in our
sample of older adults. While clinical screening was performed
to rule out mild to moderate Alzheimer’s disease, the low scores
for global cognition in some individuals as well as the associa-
tion between hippocampal volume and memory performance
are suggestive of the earliest stages of preclinical Alzheimer’s
disease. R.L. Buckner et al. (in preparation) have shown that
association between hippocampus volume and neuropsycho-
logical memory scores can largely be accounted for by the
earliest stages of dementia, including individuals whose global
cognition scores (e.g. MMSE) remain in the normal range at the
beginning stages of the disease. It is presently unknown how
many of the participants who displayed reduced hippocampus
volume and memory performance are at the earliest stages of
dementia, but early-stage Alzheimer’s disease may be the cause
of the observed association.
Older adults with a declining memory performance also
showed reduced FA in the anterior part of the corpus callosum
compared to their stable counterparts. The finding of group
differences in the anterior part of the corpus callosum is
supported by previous DTI studies that indicate that this region
is specifically susceptible to age-related atrophy, while posterior
regions are relatively spared (Pfefferbaum et al., 2000, 2005;
O’Sullivan et al., 2001; Head et al., 2004; Madden et al., 2004).
The difference in FA was most evident between the stable group
and the group declining from a moderate to a low level of
performance, suggesting that white-matter integrity may con-
tribute to memory dysfunction in old age. These results concur
with findings of negative correlations between white-matter
integrity and behavioral performance in anterior parts, but not
for posterior parts of the corpus callosum in older adults
(O’Sullivan et al., 2001; Madden et al., 2004).
In the context of other studies that have shown dissociation
between hippocampus volume loss and age-associated differ-
ences in anterior white matter (e.g. Head et al., 2005), the
present data are most consistent with a heterogeneous sample
of older individuals for whom damage to multiple brain systems
is contributing to cognitive decline.
Older individuals with cognitive decline showed increased
recruitment of specific frontal regions. By combining longitu-
dinal behavioral data with functional neuroimaging, this study
provides evidence that the increases in frontal activation
observed in aging are related to age-related decline in cognitive
function. Specifically, increased right ventral frontal activation
during semantic categorization was associated with a history of
declining episodic memory performance, with the greatest
activation level for the group of elderly individuals declining
from a moderate to a low level of memory performance. Strong
right frontal activation is atypical for this type of task in studies
of younger adults (Cabeza and Nyberg, 2000) but previous
studies have observed such right-lateralized activity in groups of
older adults (Logan et al., 2002). In some of these past studies
increases in dorsal frontal regions has been emphasized, which
were not robust here. Ventral regions have been less well
By associating increased recruitment of frontal cortex with
declining memory performance our results are consistent with
the possibility that such activity differences relate to age-
associated disturbance in brain function (Kinsbourne, 1980;
Buckner and Logan, 2002; Li and Sikstro ¨ m, 2002; Logan et al.,
2002). These results suggest that additional frontal activation
Cerebral Cortex July 2006, V 16 N 7 913
may be elicited by disruption of brain networks, and may be
related to a noisier processing system associated with increas-
ing age (Li and Sikstro ¨ m, 2002). Indeed, in the present sample of
individuals, the presence of additional frontal activation may be
a marker for cognitive decline. Further support for this possi-
bility comes from findings of greater frontal activity in patients
suffering from dementia, or at risk for dementia, compared to
non-demented elderly (Becker et al., 1996; Woodard et al.,
1998; Ba ¨ ckman et al., 1999; Bookheimer et al., 2000; Grady
et al., 2003). In the present study, the post-hoc negative corre-
lation between fractional anisotropy and right PFC activation
tentatively suggests that differences in frontal white matter may
associate with cognitive decline linked to activation increases.
At first glance, these results seem to contradict earlier
findings that show positive correlations between additional
frontal recruitment and behavioral performance (i.e. compen-
sation). It is important to note that when the whole sample is
considered, frontal recruitment reflects detrimental perfor-
mance, but within the group of individuals with declining
performance, additional frontal recruitment may still show
positive correlations with performance (discussed by Cabeza
et al., 2002). Such complex interactions between activation and
performance were found in a recent study by Grady et al.
(2003). They noted that individuals in early stages of
Alzheimer’s disease showed higher frontal recruitment com-
pared to healthy older adults. When the dementia group was
considered in isolation, however, they found that performance
was positively correlated with frontal activation. Within the
present sample, this possibility was partly supported by the
finding of a positive, although non-significant, correlation be-
tween memory performance during the imaging study and right
ventral frontal activation in the group of individual declining
from a moderate to a low level of performance (data not
shown). The most likely explanation for the combination of
results is that detrimental brain decline leads to the need for
compensatory processes (Buckner, 2004).
It seems probable that there are limitations to functional
compensation. Studies of individuals with severe cognitive
impairment have found frontal under-recruitment rather than
over-recruitment (Kato et al., 2001; Elgh et al., 2003). This
indicates that patterns of frontal activity are not directly
reflecting the relative need for compensation, as this should
be greatest for the most cognitively impaired. Limitations to
functional compensation may also explain previous findings of
no additional frontal activity in low-performing older adults. The
boundaries for functional compensation remain to be deter-
mined, but there is suggestive evidence that the nature of the
cognitive task is one important factor (Burggren et al., 2002).
By integrating structural, neuroimaging and behavioral meas-
ures we characterized cognitive aging at multiple levels.
Between-group differences in hippocampus volume and ante-
rior white-matter integrity suggest a relationship between
structural disruption and cognitive decline in old age. Augmen-
tation of frontal activation may be a productive response to such
changes. Given the temporal lag between longitudinal behav-
ioral and MRI assessments, the exact sequence of relationships
between age-related changes in the structure and function of
the brain, and behavioral performance has yet to be determined.
In future longitudinal studies it will be interesting to further
explore the underlying structural and physiological changes
that elicit age-related alterations in brain activation, and to
explore the extent to which such alterations are able to
We thank K. Rodrigue for assistance with the hippocampal measure-
ments. This study was supported by The Bank of Sweden Tercentenary
Foundation. R.L.B. was supported by the Howard Hughes Medical
Addresscorrespondence to Jonas Persson, Departmentof Psychology,
Umea ˚ University, S-901 87 Umea ˚ , Sweden. Email: firstname.lastname@example.org.
Trans R Soc Lond B Biol Sci 352:1703--1709.
Ba ¨ ckman L, Andersson JLR,Nyberg L, Winblad B, Nordberg A, Almkvist O
(1999) Brain regions associated with episodic retrieval in normal
aging and Alzheimer’s disease. Neurology 52:1861--1870.
Baltes PB, Lindenberger U (1997) Emergence of a powerful connection
between sensory and cognitive functions across the adult life span:
a new window to the study of cognitive aging? Psychol Aging
Bartzokis G, Sultzer D, Lu PH, Nuechterlein KH, Mintz J, Cummings JL
(2004) Heterogeneous age-related breakdown of white matter
structural integrity: implications for cortical ‘disconnection’ in aging
and Alzheimer’s disease. Neurobiol Aging 25:843--851.
Becker JT, Mintun MA, Aleva K, Wiseman MB, Nichols T, DeKosky ST
(1996) Compensatory reallocation of brain resources supporting
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. Am J
Bookheimer SY, Strojwas MH, Cohen MS, Saunders AM, Pericak-Vance
MA, Mazziotta JC, Small GW (2000) Patterns of brain activation in
people at risk for Alzheimer’s disease. N Eng J Med 343:450--456.
Buckner RL (2004) Memory and executive function in aging and AD:
multiple factors that cause decline and reserve factors that com-
pensate. Neuron 44:195--208.
Buckner RL, Logan J (2002) Frontal contributions to episodic memory
encoding in the young and elderly. In: The cognitive neuroscience of
memory encoding and retrieval (Parker EA, Wilding EL, Bussey T,
eds). Philadelphia: Psychology Press.
Burggren AC, Small GW, Sabb FW, Bookheimer SY (2002) Specificity of
brain activation patterns in people at genetic risk for Alzheimer
disease. Am J Geriatr Psychiatry 10:44--51.
Cabeza R, Nyberg L (2000) Imaging cognition II: Empirical review of
275 PET and fMRI studies. J Cogn Neurosci 12:1--47.
Cabeza R, Grady CL, Nyberg L, McIntosh AR, Tulving E, Kapur S, Jennings
JM, Houle S, Craik FIM (1997) Age-related differences in neural
activity during memory encoding and retrieval: A positron emission
tomography study. J Neurosci 17:391--400.
Cabeza R, Anderson ND, Locantore JK, McIntosh AR (2002) Aging
gracefully: compensatory brain activity in high-performing older
adults. Neuroimage 17:1394--1402.
DeCarli C, Massaro J, Harvey D, Hald J, Tullberg M, Au R, Beiser A,
D’Agostino R, Wolf PA (2005) Measures of brain morphology and
infarction in the framingham heart study: establishing what is
normal. Neurobiol Aging 26:491--510.
DeCarli C, Scheltens P (2002) Structural brain imaging. In: Vasuclar
cognitive impairment (Erkinjuntti T, Gauthier S, eds), pp. 433--457.
London: Martin Dunitz.
Demb JB, Desmond JE, Wagner AD, Vaidya CJ, Glover GH, Gabrieli JDE
(1995) Semantic encoding and retrieval in the left inferior prefrontal
cortex: a functional MRI study of task difficulty and process
specificity. J Neurosci 15:5870--5878.
Cognitive Decline in Aging
Persson et al.
Elgh E, Larsson A, Eriksson S, Nyberg L (2003) Altered prefrontal brain Download full-text
activity in persons at risk for Alzheimer’s disease: an fMRI study.
Int Psychogeriatr 15:121--133.
Folstein MF, Folstein SE, and 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.
Genovese CR, Lazar NA, Nichols T (2002) Thresholding of statistical
maps in functional neuroimaging using false discovery rate. Neuro-
Golomb J, Kluger A, de Leon M, Ferris SH, Convit A, Mittleman M, Cohen
J, Rusinek H, De Santi S, George AE (1994) Hippocampal formation
size in normal human aging: a correlate of delayed secondary
memory performance. Learn Mem 1:45--54.
Golomb J, Kluger A, deLeon MJ, Ferris SH, Mittelman M, Cohen J, George
AE (1996) Hippocampal formation size predicts declining memory
performance in normal aging. Neurology 47:810--813.
Good CD, Johnsrude IS, Ashburner J, Henson RNA, Friston KJ,
Frackowiak RSJ (2001) A voxel-based morphometric study of aging
in 465 normal adult human brains. Neuroimage 14:21--36.
Grady CL, McIntosh AR, Beig S, Keightley ML, Burian H, Black SE (2003)
Evidence from functional neuroimaging of a compensatory pre-
frontal network in Alzheimer’s disease. J Neurosci 23:986--993.
Gunning-Dixon F, Raz N (2000) The cognitive correlates of white
matter abnormalities in normal aging: a quantitative review.
Head D, Buckner RL, Shimony JS, Williams LE, Akbudak E, Conturo TE,
McAvoy M, Morris JC, Snyder AZ (2004) Differential vulnerability of
anterior white matter in nondemented aging with minimal acceler-
ation in dementia of the Alzheimer type: evidence from diffusion
tensor imaging. Cereb Cortex 14:410--423.
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.
Hedden T, Gabrieli JDE (2004) Insights into the ageing mind: a view
from cognitive neuroscience. Nat Rev Neurosci 5:87--96.
Jack CR, Petersen RC (2000) Structural imaging approaches to
Alzheimer’s disease. In: Early diagnosis and treatment of Alzheimer’s
disease (Scinto M, Daffner, KR eds), pp. 127--148. Totowa, NJ:
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.
Kato T, Knopman D, Liu HY (2001) Dissociation of regional activation in
mild AD during visual encoding — a functional MRI study. Neurology
Kinsbourne M (1980) Attentional dysfunction and the elderly: theoret-
ical models and research perspectives. In: New directions in memory
and aging: Proceedings of the George A Talland Memorial Confer-
ence (Poon LW, Fozard JL, Cermak LS, Arenberg D, Thompson LW,
eds), pp. 113--129. Hillsdale, NJ: Lawrence Erlbaum.
Li SC, Sikstro ¨ m S (2002) Integrative neurocomputational perspectives
on cogntive aging, neuromodulation, and representation. Neurosci
Biobehav Rev 26:795--808.
Logan JM, Sanders AL, Snyder AZ, Morris JC, Buckner RL (2002) Under-
recruitment and nonselective recruitment: dissociable neural mech-
anisms associated with aging. Neuron 33:827--840.
Lustig C, Buckner RL (2004) Preserved neural correlates of priming in
old age and dementia. Neuron 42:865--875.
Madden DJ, Turkington TG, Provenzale JM, Denny LL, Hawk TC, Gottlob
LR, Coleman RE (1999) Adult age differences in the functional
neuroanatomy of verbal recognition memory. Hum Brain Mapp
Madden DJ, Whiting WL, Huettel SA, White LE, MacFall JR, Provenzale
JM (2004) Diffusion tensor imaging of adult age differences in
cerebral white matter: relation to response time. Neuroimage 21:
Nilsson L-G., Ba ¨ ckman L, Erngrund K, Nyberg L, Adolfsson R, Bucht G,
Karlsson S, Widing M, Winblad G (1997) The Betula prospective
cohort study: memory, health and aging. Aging Neuropsych Cogn
O’Sullivan M, Jones DK, Summers PE, Morris RG, Williams SCR, Markus
HS (2001) Evidence for cortical ‘disconnection’ as a mechanism of
age-related cognitive decline. Neurology 57:632--638.
Park DC, Gutchess AH (2005) Long-term memory and aging: a cogni-
tive neuroscience approach. In: Cognitive neuroscience of aging
(Cabeza R, Nyberg L, Park DC, eds), pp. 218--245. New York: Oxford
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 Neurol 51:874--887.
Pfefferbaum A, Sullivan EV, Hedehus M, Lim KO, Adalsteinsson E,
Moseley M (2000) Age-related decline in brain white matter
anisotropy measured with spatially corrected echo-planar diffusion
tensor imaging. Magn Res Med 44:259--268.
Pfefferbaum A, Adalsteinsson E, Sullivan EV (2005) Frontal circuitry
degradation marks healthy adult aging: evidence from diffusion
tensor imaging. Neuroimage 26:891--899.
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 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, 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 differences and
modifiers. Cereb Cortex, in press.
Reuter-Lorenz PA, Jonides J, Smith EE, Hartley A, Miller A, Marschuetz
C, Koeppe RA (2000) Age differences in the frontal lateralization
of verbal and spatial working memory revealed by PET. J Cogn
Rodrigue KM, Raz N (2004) Shrinkage of the entorhinal cortex over five
years predicts memory performace in healthy adults. J Neurosci
Rosen AC, Prull MW, O’Hara R, Race EA, Desmond JE, Glover GH,
Yesavage JA, Gabrieli JD (2002) Variable effects of aging on frontal
lobe contributions to memory. Neuroreport 13:2425--2428.
Sowell ER, Peterson BS, Thompson PM, Welcome SE, Henkenius AL,
Toga AW (2003) Mapping cortical change across the human life
span. Nat Neurosci 6:309--315.
Uylings HBM, de Brabander JM (2002) Neuronal changes in normal
human aging and Alzheimer’s disease. Brain Cogn 49:268--276.
Van Petten C (2004) Relationship between hippocampal volume and
memory ability in healthy individuals across the lifespan: review and
meta-analysis. Neuropsychologia 42:1394--1413.
Wagner AD, Schacter D, Rotte M, Koustaal W, Maril A, Dale AM,
Rosen BR, Buckner RL (1998) Building memories: remembering
and forgetting of verbal experiences as predicted by brain activity.
Woodard JL, Grafton ST, Votaw JR, Green RC, Dobraski ME, Hoffman JM
(1998) Compensatory recruitment of neural resources during overt
rehearsal of word lists in Alzheimer’s disease. Neuropsychology
Ylikoski A, Erkinjuntti T, Raininko R, Sarna S, Sulkava R, Tilvis R (1995)
White-matter hyperintensities on MRI in the neurologically non-
diseased elderly: analysis of cohorts of consecutive subjects aged 55
to 85 years living at home. Stroke 26:1171--1177.
Cerebral Cortex July 2006, V 16 N 7 915