Article

Brain Atrophy in Healthy Aging Is Related to CSF Levels of A 1-42

Center for the Study of Human Cognition, Department of Psychology, University of Oslo, Oslo, Norway.
Cerebral Cortex (Impact Factor: 8.67). 09/2010; 20(9):2069-79. DOI: 10.1093/cercor/bhp279
Source: PubMed

ABSTRACT

Reduced levels of β-amyloid1-42 (Aβ1-42) and increased levels of tau proteins in the cerebrospinal fluid (CSF) are found in Alzheimer’s disease (AD), likely
reflecting Aβ deposition in plaques and neuronal and axonal damage. It is not known whether these biomarkers are associated
with brain atrophy also in healthy aging. We tested the relationship between CSF levels of Aβ1-42 and tau (total tau and tau
phosphorylated at threonine 181) proteins and 1-year brain atrophy in 71 cognitively normal elderly individuals. Results showed
that under a certain threshold value, levels of Aβ1-42 correlated highly with 1-year change in a wide range of brain areas.
The strongest relationships were not found in the regions most vulnerable early in AD. Above the threshold level, Aβ1-42 was
not related to brain changes, but significant volume reductions as well as ventricular expansion were still seen. It is concluded
that Aβ1-42 correlates with brain atrophy and ventricular expansion in a subgroup of cognitively normal elderly individuals
but that reductions independent of CSF levels of Aβ1-42 is common. Further research and follow-up examinations over several
years are needed to test whether degenerative pathology will eventually develop in the group of cognitively normal elderly
individuals with low levels of Aβ1-42.

Cerebral Cortex September 2010;20:2069--2079
doi:10.1093/cercor/bhp279
Advance Access publication January 4, 2010
Brain Atrophy in Healthy Aging Is Related to CSF Levels of Ab1-42
Anders M. Fjell
1
, Kristine B. Walhovd
1
, Christine Fennema-Notestine
2,3
, Linda K. McEvoy
2
, Donald J. Hagler
2
, Dominic Holland
4
,
Kaj Blennow
5
, James B. Brewer
2,4
, Anders M. Dale
2,4
and the Alzheimer’s Disease Neuroimaging Initiative*
1
Center for the Study of Human Cognition, Department of Psychology, University of Oslo, 0317 Oslo, Norway,
2
Department of
Radiology,
3
Department of Psychiatry and
4
Department of Neuroscience, University of California, San Diego, La Jolla, CA 92093, USA
and
5
Clinical Neurochemistry Laboratory, Department of Neuroscience and Physiology, Sahlgrenska Academy, Go¨ teborg University,
SE-431 80 Mo¨ lndal, Sweden
*Data used in the preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database
(www.loni.ucla.edu\ADNI). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/
or provided data but did not participate in analysis or writing of this report. ADNI investigators include (complete listing available at
www.loni.ucla.edu\ADNI\Collaboration\ADNI_Manuscript_Citations.pdf).
Address correspondence to Dr Anders M. Fjell. Email: andersmf@psykologi.uio.no.
Reduced levels of b-amyloid
1-42
(Ab1-42) and increased levels of
tau proteins in the cerebrospinal fluid (CSF) are found in
Alzheimer’s disease (AD), likely reflecting Ab deposition in plaques
and neuronal and axonal damage. It is not known whether these
biomarkers are associated with brain atrophy also in healthy aging.
We tested the relationship between CSF levels of Ab1-42 and tau
(total tau and tau phosphorylated at threonine 181) proteins and 1-
year brain atrophy in 71 cognitively normal elderly individuals.
Results showed that under a certain threshold value, levels of Ab1-
42 correlated highly with 1-year change in a wide range of brain
areas. The strongest relationships were not found in the regions
most vulnerable early in AD. Above the threshold level, Ab1-42 was
not related to brain changes, but significant volume reductions as
well as ventricular expansion were still seen. It is concluded that
Ab1-42 correlates with brain atrophy and ventricular expansion in
a subgroup of cognitively normal elderly individuals but that
reductions independent of CSF levels of Ab1-42 is common. Further
research and follow-up examinations over several years are needed
to test whether degenerative pathology will eventually develop in
the group of cognitively normal elderly individuals with low levels
of Ab1-42.
Keywords: aging, amyloid, cerebral cortex, CSF biomarkers, MRI
Even healthy elderly adults have, on average, thinner cerebral
cortex, smaller volume of most subcortical structures, and
expanded cerebral ventricles compared with the younger
(Resnick et al. 2003; Raz et al. 2004; Allen et al. 2005; Fjell et al.
2009; Walhovd et al. forthcoming). A crucial question regards
whether these atrophic processes in the brains of healthy
elderly individuals are related to the cerebrospinal fluid (CSF)
biomarkers b-amyloid
1-42
(Ab1-42) and tau proteins, potentially
reflecting age-related neurodegenerative mechanisms. Deposi-
tions of extracellular plaques (Ab1-42) and intracellular
neurofibrillary tangles (tau) are believed to play causative roles
in neurodegeneration in Alzheimer’s disease (AD; Goedert and
Spillantini 2006; Spires-Jones et al. 2009a), and CSF levels of
these biomarkers correlate with rates of atrophy and ventric-
ular expansion (Hampel et al. 2005; de Leon et al. 2006; Chou
et al. 2009; Schuff et al. 2009). However, it is unclear whether
and to what extent these CSF biomarkers are related to
neurodegenerative effects also in healthy elderly individuals.
Improved knowledge of the role played by these CSF
biomarkers will greatly enhance our understanding of the
neurobiological correlates of brain atrophy in healthy aging and
of the specificity of the role of such biomarkers in age-related
degenerative diseases. Thus, the aim of the present study was
to examine the relationship between longitudinal brain
changes in healthy aging and CSF levels of Ab1-42 and tau
proteins.
The CSF level of the different biomarkers likely reflects
specific pathogenic processes in the brain. Total tau (T-tau) is
probably related to the intensity of the neuronal damage and
degeneration because a marked transient increase is found in
acute conditions such as stroke, and the magnitude of the
increase correlates with infarct size (Hesse et al. 2000). The
degree of increase in CSF T-tau in chronic disorders is highest
in conditions with the most intense neuronal degeneration,
such as Creutzfeldt--Jakob disease (Otto et al. 1997). A
moderate increase is found in AD, where degeneration is less
intense, and normal levels are found in patients with
Parkinson’s disease, where degeneration is limited to a small
brain region (Sjogren et al. 2001b). In addition, CSF T-tau levels
increase strongly with age even in healthy controls (Sjogren
et al. 2001b). Tau phosphorylated at threonine 181 (P-tau), in
contrast, does not reflect general neurodegeneration because
increased CSF levels have so far only been found in AD, with
normal levels both in acute conditions (Hesse et al. 2001) and
in intense chronic neurodegenerative disorders (Riemensch-
neider et al. 2003). Instead, CSF P-tau correlates with tangle
load in neocortex (Buerger et al. 2006), suggesting that it is
a marker for tau phosphorylation and tangle formation.
CSF Ab1-42 is reduced in several neurological conditions
(Winblad et al. 2008), including AD (Shaw et al. 2009). The
reason for the decrease in CSF Ab1-42 in AD is not clear, but
the most probable explanation is that Ab1-42 is deposited in
plaques, with lower amounts of Ab being free to diffuse into
CSF. This explanation is supported by the finding of a strong
correlation between low Ab1-42 in CSF and high numbers of
plaques in the neocortex and hippocampus (Strozyk et al.
2003), and between low Ab1-42 in CSF and high retention of
Pittsburgh compound-B (PIB) on positron emission tomogra-
phy (PET) scanning (Fagan et al. 2006).
In sum, evidence indicates that T-tau and Ab1-42 are related
to axonal and neuronal damage in AD as well as in other
conditions (Winblad et al. 2008; Spires-Jones et al. 2009b) and
that T-tau increases with higher age even in healthy individuals
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(Sjogren et al. 2001b). Still, the relationship between the CSF
biomarkers and brain atrophy has received little attention in
the literature, and the results that have been reported are
mixed. One study found whole-brain volume to be positively
correlated with CSF levels of Ab1-42 but not of tau (Fagan et al.
2009), one did not find any relationship between the CSF
biomarkers and whole-brain atrophy (Sluimer et al. 2008), and
one found that low CSF Ab correlated with brain atrophy in
a cross-sectional sample, whereas high T-tau predicted more
marked ventricular widening during follow-up (Wahlund and
Blennow 2003). A final study did not find correlations between
hippocampal atrophy and CSF biomarkers (de Leon et al. 2006).
No studies have examined brain measures other than the
whole-brain, ventricular, or hippocampal volume. Of special
relevance is the study by Fagan and colleagues, where low
levels of Ab1-42 were found to be associated with smaller
whole-brain volume in a large sample (n
= 69) of healthy
elderly individuals (Clinical Dementia Rating [CDR]
= 0).
Further, all PIB-positive participants had low CSF levels of
Ab1-42, whereas almost all PIB-negative participants had high
CSF levels of Ab1-42. As mentioned previously, high retention
of PIB indicates high brain amyloid plaque load. The authors
interpreted their findings to indicate that there is toxicity
associated with Ab aggregation before the onset of clinically
detectable disease, whereas increases in tau may occur with
clinical onset and progression.
In the present study, we used data from the publicly available
Alzheimer’s Disease Neuroimaging Initiative (ADNI) database
to examine 71 healthy elderly individuals with CSF and mag-
netic resonance (MR) data at baseline and MR follow-up after 1
year. We used a newly developed nonlinear registration al-
gorithm that enables precise registration of serial scans, yield-
ing a one-to-one correspondence between each vertex in the
baseline and the follow-up scan. Change was measured con-
tinuously across the cortical surface, as well as in 15 subcortical
and 33 cortical regions of interest (ROIs) in each hemisphere,
and related to CSF levels of tau proteins and Ab1-42. We found
that Ab1-42 modulated volumetric brain reductions and
ventricular expansion in healthy elderly individuals when
a certain threshold value was reached and that the strongest
relationships existed in regions not especially vulnerable to AD.
Materials and Methods
The raw data used in the preparation of this article were obtained from
the ADNI database (www.loni.ucla.edu/ADNI). ADNI was launched in
2003 by the National Institute on Aging, the National Institute of
Biomedical Imaging and Bioengineering, the Food and Drug Adminis-
tration, private pharmaceutical companies, and nonprofit organizations.
The primary goal of ADNI has been to test whether serial MR imaging
(MRI), PET, other biological markers, and clinical and neuropsycho-
logical assessment can be combined to measure the progression of mild
cognitive impairment (MCI) and early AD. The principal investigator of
this initiative is Michael W. Weiner, VA Medical Center and University
of California—San Francisco. There are many coinvestigators, and
subjects have been recruited from more than 50 sites across the United
States and Canada. The initial goal of ADNI was to recruit 800 adults,
including healthy elderly individuals and MCI and AD patients to
participate and be followed for 2--3 years. For more information see
www.adni-info.org
Sample
Participants are 55--90 years of age, had an informant able to provide an
independent evaluation of functioning, and spoke either English or
Spanish. General inclusion/exclusion criteria are as follows for normal
subjects: Mini-Mental State Examination (MMSE; Folstein et al. 1975)
scores between 24 and 30 (inclusive), CDR (Morris 1993) of 0,
nondepressed, non-MCI, and nondemented. In addition, to minimize
the possibility of including individuals with early preclinical AD, only
participants who had the same or better score on CDR sum of boxes
(CDR-sb) at the time of follow-up were included. The subject pool was
further restricted to those subjects for whom adequate processed and
quality checked MR and CSF baseline data were available by February
2009. The total sample consisted of 71 healthy elderly individuals (31
women/40 men), with mean age 75.6 years (62.2--90.2) at baseline,
mean MMSE score of 29.9 (25--30) at baseline and 28.9 (25--30) at
follow-up, and CDR-sb 0.02 (0--0.5) at baseline and 0.0 (0--0) at follow-
up. For assessment of memory, Auditory Verbal Learning Test (AVLT;
Lezak 1995) was administered. The 5 learning trials were summed to 1
learning score, and we subtracted the number of intrusions. The same
was done for 30-min free recall. In addition, information about number
of apolipoprotein E (APOE) e4 alleles was available. Participants with at
least 1 e2 allele were excluded (resulting distribution: 37 e3/e3, 20 e3/
e4, 1 e 4/e4).
MR Acquisition and Analysis
All scans used for the present study were from 1.5 T scanners. Data
were collected acros s a variety of scanners with protocols in-
dividualized for each scanner, as defin ed at ht tp://www.loni.u-
cla.edu/ADNI/Research/Cores/index.shtml. Raw DICOM MRI scans
(including 2 T1-weighted vol umes per case) wer e downloa ded
from the public ADNI site (http://www.l oni.ucla.edu/ADNI/
Data/index.shtml) and processed as described elsewhere (Fennema-
Notestine et al. 2009). Briefly, these data were reviewed for quality,
automatica lly corrected for spatial distortion due to gradient non-
linearity (Jovicich et al. 20 06) and B
1
field inhomoge neity (Sled et al .
1998), registered, and averaged to improve signal to noise ratio.
Scans were segmented (Fischl et al. 2002), yielding volumetric data for
15 different subcortical structures. The procedure (Fischl et al. 2002,
2004) uses a probabilistic atlas and applies a Bayesian classification
rule to assign a neuroanatomical label to each voxel. The cortical
surface was reconstructed to measure thickne ss at each surface
point using a semiautomated approach (Dale and Sereno 1993; Dale
et al. 19 99; Fischl et al. 1999a, 1999b; Fischl and Dale 2000) . The
measurement technique u sed here has been validat ed via histological
(Ro sas et al. 2002) as well as manual measurements (Kuperberg et al.
2003). The cort ical surface was parcellated in 33 cortical sulci and
gyri (Fischl et al. 200 4; Desikan et al. 2006).
For the analyses of the longitudinal volume changes, each
participant’s dual 3D follow-up structural scans were rigid-body
aligned, averaged, and affine aligned to the baseline scan. Nonlinear
registration of the images was then carried out, where voxel centers
are moved about until a goo d match between the images is made.
Several methods exist for effecting this, including fluid deformati on
(Ch ristensen et al. 1996; Freeborough and Fox 1998; Miller et al.
1993) and tensor-based mo rphometry (TBM; Ashbur ner et al . 1999).
For the results presented here, however, we a pplied a method
(Holland et al. 2009) based on linear elasticity and closer in spirit to
TBM. It proceed s essentially as follows. The images are heavily blurred
(smoothed), making them almost identical, and a merit or potential
fun ction is calculated. This merit function expresses the intensity
difference betwe en the images at each voxel and depends on the
displacement field for the voxel centers of the image being
transformed; it is also regularized to keep the displacement field
spatially smooth. The merit function by design will hav e a minimum
when the displac ement field induces a good match between the
images. The displac ement field in general will turn cubic voxels into
displaced irregular hexahedrons whose volumes (Grandy 1997) give
the volume change field. The meri t function is minimized efficiently
usi ng standard numerical methods. Having found a displacement field
for the heavily blurred pai r of im ages, the blurring is reduced and the
procedure repeated, thus iteratively building up a bette r displacement
field. Two important additions to this are as follows: applying the final
displacement field to the imag e being transfor med, then nonlinearly
reg istering the resultant image to the same target, and nally tracing
2070 Brain Atrophy and CSF Levels of Ab1-42
d
Fjell et al.
Page 2
back through the disp lacement elds thus calculated to find t he net
displacement field; and restricting to ROIs and zooming when tissue
structures are separated by only a voxel or two. These additional
features ena ble very precise registra tion involving large or subtle
deformations, even at small spatial scales with lo w boundary contrast.
Although large deformations are allowed by multiple nonlinear
registration (or relaxation) steps, nonphysical deformations are
precluded because at each level of blur ring the image undergoing
deformation is c onstrained to conform to the target. Note that
calculating the deformation fiel d does not depend on initially
segmenting tissue. Thi s deformation field was used to align s cans at
the subvoxel level.
The follow-up aligned image underwent skull stripping and
volumetric segmentations (subcortical structures, as well as hippocam-
pal and cerebellar gray matter), with labels applied from the baseline
scan. For the cortical reconstructions, surface coordinates for the white
matter and pial boundaries were derived from the baseline images
and mapped onto the follow-up images using the deformation field.
Parcellations from the baseline image were then applied to the follow-
up image. This resulted in a one-to-one correspondence between each
vertex in the base image and the follow-up image. The procedure
produces an estimate of the percent cortical volume loss at each vertex
and within each ROI. To the extent that regional cortical areas are
relatively stable across time points, the volume change is likely driven
almost exclusively by changes in thickness.
As the procedure is fully automated, the test--retest reliability is 1.
The method has also been validated in model studies of complex
spherical shell geometries with low contrast and noise, where
a prescribed volume change is numerically estimated to accuracies of
within 0.5% (Holland et al., in preparation).
CSF Acquisition and Analysis
CSF samples were obtained by lumbar puncture using a standardized
protocol, as described in the ADNI procedures manual ( http://
www.adni-info.org/index) at the participating clinical sit es. The C SF
samples were transferre d into polypropylene transfer tubes,
freezed on dry ice within 1 h of collection, and shipped overnight
to the ADNI Biomarker Core laboratory at the University of
Pennsylvania Medical Center. The CSF aliquots were stored in bar
code--labeled polypropylene vials at
80 °C. All CSF samples were
analyzed over a 14-day period. Test--retest analyses of a subset of the
samples showed excelle nt analytical performance, with r
2
values for
test--retest results of 0.85--0.98 (Shaw et al. 2009). T-tau, P-tau, and
Ab1-42 levels in CSF were determined by the Luminex xMAP
technology using the INNO-BIA AlzBio3 kit (Innogenetics, Ghent,
Belgium), as previously described in detail (Olsson et al. 2005). In
brief, the method is based on flow cytometric separation of antibody-
coated mic rospheres tha t are labeled with a specific mixture of 2
fluorescent dyes. After binding of a bi otinylated reporter antibody,
quantification is made by binding of a third fluorochrome coupled to
stre ptavidin. The method has been shown to h ave high analytical
precision (Olsson et al. 2005).
Statistical Analysis
To reduce the number of comparisons, right and left hemisphere values
were averaged for each ROI. Partial correlations controlling for the
effect of age were carried out to test the relationship between
percentage change in each of the 48 ROIs and CSF levels of Ab1-42,
T-tau, and P-tau, as well as the ratio between each of the tau measures
and Ab1-42. Because it is likely that the CSF biomarker levels will be
related to brain changes only when certain concentrations are reached,
we tested for nonlinear relationships between the CSF biomarkers and
percentage change in brain volume by introducing a quadratic term as
an additional predictor in a multiple regression analyses (ROI
= b
0
+
b
1
age
+
b
2
CSF biomarker
+
b
3
[CSF biomarker 3 CSF biomarker]
+
e).
More correlations were found between Ab1-42 and brain change than
between the tau biomarkers and brain change, so this analysis was
restricted to Ab1-42. If b
3
was significant, a nonparametric local
smoothing technique (the smoothing spline) was used. Given
a sequence of data (X
i
, Y
i
); i = 1, ..., n, with E (Y
i
|X
i
) = g(x
i
), the
smoothing spline estimate of g minimizes
+
n
i
=1
Y
i
g ðX
i
Þ
2
+
k
Z
g ##ðxÞ
2
dx
where the smoothing parameter k controls the trade-off between
fidelity (closeness of g(X
i
)toY
i
) and smoothness (the size of the
average second derivative of g). With no smoothing (k
= 0), g simply
interpolates the data, whereas with infinite smoothing (k / N),
g corresponds to the line fit by ordinary least squares. Smoothing level
was set to 5e
5
. The smoothing spline was used because the quadratic
model is unlikely to represent a reasonable relationship between the
CSF biomarkers and brain atrophy.
Samples were divided into the high-Ab1-42 or the low-Ab1-42 group
based on whether their levels were above or below the break point of the
smoothing spline function. As brain change is expressed as percentage
change relative to baseline, 1-sample t-tests were carried out to
determine whether 1-year change in each of the ROIs for each of the 2
groups was significantly different from zero. Intracranial volume was not
used as covariate in this analysis because change expressed as percentage
inherently is corrected for initial brain size. Independent samples t-tests
were used to determine whether percentage change in each ROI differed
between the high- and the low-Ab1-42 group, and whether baseline
differences in volume or thickness of each ROI existed between the
groups. Percentage change in each ROI for each group was also
calculated point by point across the brain surface and shown as an
overlay on a template brain. Correlations between Ab1-42 and
percentage change were run separately in each of the groups, and
Fisher’s z-transformed correlation coefficients were calculated to test for
differences between the groups. Surface maps of the relationships
between Ab1-42 and percentage cortical change within each group were
also calculated by general linear models and displayed on the template
brain. The statistical results were corrected for multiple comparisons
(false discovery rate
<
0.05). Mean memory performance at baseline, as
well as change in memory score over 1 year, for AVLT learning and 30-
min delayed recall was compared between the Ab1-42 groups by
independent samples t-tests. Finally, number of APOE e4 alleles was
related to CSF biomarker levels by partial correlations controlling for age,
and a possible difference in the mean number of e4 alleles in the Ab1-42
groups was explored by independent samples t-tests. APOE was also
correlated with percentage change in each ROI (partial correlations
controlling for age), and multiple regressions with APOE, Ab1-42, and
APOE
3 Ab1-42 group as simultaneous predictors were carried out to test
for possible interaction effects on percentage change in selected ROIs.
Results
Partial correlations between percentage change over 1 year and
CSF biomarkers, controlling for the effect of age, are shown in
Tables 1 and 2. Ab1-42 was the CSF biomarker showing the
highest correlations with brain change, with significant corre-
lations in 27 ROIs. Correlations greater than 0.30 (P
= 0.01) were
found for putamen, thalamus, banks of the superior temporal
sulcus, isthmus cingulate, caudal middle frontal, pallidum,
superior frontal, caudate, accumbens, pars opercularis, and
superior temporal cortex, as well as correlations less than
0.30 for the lateral and inferior lateral ventricles. Hippocampal
reduction was not significantly related to Ab1-42 levels (r
= 0.19,
NS). T-tau was not significantly related to change in any ROI,
whereas P-tau was related to change in 9 ROIs (negative
correlations with amygdala, paracentral cortex, posterior cingu-
late, cerebral white matter, and pallidum; positive correlations
with the 4 ventricular ROIs). The tau/Ab1-42 ratio measures
were generally sensitive to change in several ROIs. For instance,
Ab1-42--P-tau correlated
0.43 with amygdala,
0.40 with para-
central cortex, and 0.44 or higher with the 4 ventricular ROIs.
Next, nonlinear relationships between change over 1 year
and CSF biomarkers were tested. Regressions were run with
Cerebral Cortex September 2010, V 20 N 9 2071
Page 3
the ROIs as dependent variable and age, Ab1-42, and Ab1-42 3
Ab1-42 simultaneously as predictors (see Materials and Meth-
ods). The results are shown in Supplemental Table 1. Significant
nonlinear relationships were found for 20 ROIs (amygdala,
accumbens, caudate, cerebral white matter, thalamus, the lateral
ventricles, inferior lateral ventricles, third ventricle, caudal
middle frontal, fusiform, isthmus cingulate, pars opercularis,
posterior cingulate, precentral, precuneus, superior frontal,
superior temporal, supramarginal, pars triangularis, and rostral
middle frontal). To depict these relationships, a nonparametric
local smoothing technique was used (the smoothing spline). The
results for 12 selected cortical ROIs are shown in Figure 1 and for
6 subcortical ROIs in Figure 2.
Ab1-42 was related to percentage change over 1 year when
levels were below an apparent break point. By visual inspection
of the fit lines, the break point was found to be at Ab1-42
175 pg/mL. Thus, the sample was split into a high (Ab1-
42
>
175, n = 45) and a low (Ab1-42 <175, n = 26) group. Mean
age was not significantly different between groups (75.0 vs.
76.5 years in the high and the low group, respectively, t(69)
=
1.02, P = 0.31). Thinner cortex at baseline in the low group was
found in isthmus of the cingulate, precuneus, superior frontal,
and superior parietal cortex. Percentage volume change across
the cortical mantle for each group is shown in Figure 3. Annual
percentage reduction varied across the cortex but was typically
around 0.5% in affected areas, including most of the lateral and
inferior parts of the temporal lobes, inferior parietal gyrus,
precuneus, inferior and middle frontal gyri, medial and lateral
orbitofrontal, and the medial parts of the superior frontal gyrus.
Brain changes in the low-Ab1-42 group were generally larger
than in the high group. Both groups showed significant 1-year
change in numerous ROIs (33 ROIs for the low-Ab1-42 group
and 32 ROIs for the high-Ab1-42 group; Table 3). The average
annual changes seemed larger for almost all ROIs for the low-
compared with the high-Ab1-42 group, but due to the smaller
number of participants in the low-Ab1-42 group, the differ-
ences were statistically significant only for 5 ROIs (pallidum,
thalamus, lateral and inferior lateral ventricles, banks of the
superior temporal sulcus).
Interestingly, percentage change did not significantly corre-
late with Ab1-42 level in the high-Ab1-42 group (all r ’s < 0.28),
but changes in 43 ROIs were significantly correlated with Ab1-
42 level in the low-Ab1-42 group (Tables 4 and 5). In 31 of
these ROIs, correlations in the low-Ab1-42 group were
significantly stronger than in the high-Ab1-42 group. Figure 4
shows the relationship between Ab1-42 and cortical changes
point by point across the brain surface in each group
separately. For the high group, no significant effects were
seen. For the low group, large and scattered effects were seen,
especially strong in superior frontal gyrus, posterior and
isthmus cingulate, and occipital and inferior parietal cortex.
Finally, independent samples t-tests were used to compare
memory performance in the Ab1-42 groups. No significant
differences were found for total learning (42.0 vs. 40.9 in the
low and high groups, respectively, t(69)
= 0.53, P = 0.60) and
30-min delayed recall (7.3 vs. 7.2, t(69)
= 0.14, P = 0.89).
Furthermore, no significant group differences in change in
scores over 1 year (score year 3 -- score year 1) were found for
total learning (
2.12 vs.
0.76 in the low and high groups,
respectively, t(69)
=
0.68, P = 0.50) or 30-min delayed recall
(0.42 vs.
0.20, t(69) = 0.71, P = 0.48).
APOE Analyses
Presence of APOE e4 alleles (0, 1, or 2) correlated significantly
with the CSF biomarker levels of Ab1-42 (r
=
0.57, P
<
0.10
5
)
Table 1
Partial correlations between baseline CSF biomarkers and subcortical volumetric reductions and
ventricular expansion over 1 year, controlled for the effect of age
T-tau P-tau Ab1-42 T-tau--Ab1-42 P-tau--Ab1-42
Subcortical ROIs
Accumbens 0.15 0.16 0.33 0.37 0.30
Amygdala 0.09 0.29 0.27 0.32 0.43
Brainstem 0.15 0.14 0.13 0.30 0.20
Caudate 0.07 0.09 0.33 0.36 0.28
Cerebellar gray matter 0.01 0.06 0.11 0.16 0.13
Cerebellar white matter 0.02 0.19 0.17 0.13 0.24
Cerebral white matter 0.17 0.26 0.27 0.40 0.37
Hippocampus 0.14 0.07 0.19 0.07 0.17
Pallidum 0.16 0.25 0.34 0.36 0.35
Putamen 0.09 0.18 0.38 0.36 0.33
Thalamus 0.11 0.17 0.37 0.41 0.34
Ventricular ROIs
Lateral ventricles 0.13 0.33 0.33 0.38 0.46
Inferior lateral ventricles 0.16 0.34 0.30 0.38 0.44
Third ventricle 0.13 0.33 0.33 0.38 0.46
Fourth ventricle 0.16 0.34 0.30 0.38 0.44
Note: Values in bold, P \ 0.05 (r $ 0.24); values in bold and italics, P \ 0.001 (r $ 0.39). A
negative correlation means that higher CSF concentrations are related to more brain atrophy (i.e.
negative change in volume) and less ventricular expansion, and a positive correlation means that
lower CSF concentrations are related to more atrophy and less ventricular expansion.
Table 2
Partial correlations between baseline CSF biomarkers and cortical volume reductions over 1 year,
controlled for the effect of age
T-tau P-tau Ab1-42 T-tau--Ab1-42 P-tau--Ab1-42
Cingulate, caudal anterior 0.08 0.08 0.26 0.30 0.17
Cingulate, rostral anterior 0.05 0.03 0.24 0.27 0.10
Cingulate, posterior 0.11 0.27 0.26 0.32 0.37
Cingulate, isthmus 0.09 0.09 0.35 0.40 0.25
Frontal, superior 0.05 0.15 0.33 0.30 0.29
Frontal, caudal middle 0.05 0.11 0.35 0.33 0.28
Frontal, rostral middle 0.04 0.10 0.26 0.29 0.22
Frontal, pars opercularis 0.04 0.07 0.32 0.35 0.23
Frontal, pars triangularis 0.00 0.00 0.26 0.29 0.15
Frontal, pars orbitalis 0.04 0.05 0.21 0.22 0.17
Frontal, lateral orbital 0.05 0.01 0.25 0.20 0.17
Frontal, medial orbital 0.03 0.00 0.20 0.25 0.14
Frontal, pole 0.06 0.10 0.08 0.20 0.15
Parietal, precentral gyrus 0.04 0.23 0.27 0.28 0.37
Parietal, postcentral gyrus 0.09 0.14 0.12 0.07 0.23
Parietal, paracentral gyrus 0.02 0.29 0.23 0.20 0.40
Parietal, superior 0.09 0.22 0.22 0.16 0.39
Parietal, inferior 0.02 0.14 0.21 0.26 0.29
Parietal, supramarginal 0.15 0.18 0.29 0.37 0.31
Parietal, precuneus 0.16 0.16 0.28 0.38 0.29
Temporal, parahippocampal 0.03 0.07 0.20 0.19 0.16
Temporal, entorhinal 0.01 0.16 0.18 0.13 0.22
Temporal, pole 0.07 0.18 0.16 0.20 0.24
Temporal, superior 0.02 0.08 0.31 0.33 0.24
Temporal, middle 0.01 0.07 0.23 0.24 0.21
Temporal, inferior 0.00 0.09 0.17 0.21 0.22
Temporal, transverse 0.05 0.15 0.14 0.26 0.05
Temporal, BSTS 0.11 0.10 0.36 0.40 0.27
Temporal, fusiform 0.09 0.12 0.25 0.34 0.26
Occipital, lateral 0.08 0.11 0.14 0.26 0.22
Occipital, pericalcarine 0.10 0.02 0.07 0.14 0.03
Occipital, lingual 0.17 0.00 0.22 0.36 0.12
Occipital, cuneus 0.13 0.11 0.13 0.23 0.15
Note: Values in bold, P \ 0.05 (r $ 0.24); values in bold and italics, P \ 0.001 (r $ 0.3 9). BSTS,
banks of the superior temporal sulcus. A negative correlation means that higher CSF
concentrations are related to more brain atrophy (i.e. negative change in volume) and less
ventricular expansion, and a positive correlation means that lower CSF concentrations are related
to more atrophy and less ventricular expansion.
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and marginally with P-tau (r = 0.26, P = 0.051) and T-tau (r =
0.24, P = 0.075), all analyses controlled for age. t-Tests showed
that the low-Ab1-42 group had significantly higher mean
number of e4 alleles than the high-Ab1-42 group (0.71 vs.
0.15, respectively, t(36.64)
= 4.38, P
<
0.0005).
APOE correlated significantly (P
<
0.05) with volume
reductions in thalamus (r
=
0.27), pallidum (
0.34), and
amygdala (
0.28). Multiple regressions with APOE, Ab1-42, and
APOE
3 Ab1-42 were run for the significant ROIs to test
whether APOE interacted with Ab1-42 in predicting volume
reductions. In no case did the interaction term approach
significance.
Discussion
CSF levels of Ab1-42 and tau proteins have been related to
atrophy in AD and other degenerative conditions (Braak and
Braak 1991; Winblad et al. 2008), but it has not been known
how these biomarkers relate to brain changes in healthy elderly
individuals. The present study showed that levels of CSF
biomarkers, especially Ab1-42, correlated with ventricular
expansion and volumetric reductions of large areas of the
brain, not restricted to the structures most vulnerable to the
effects of AD. To our knowledge, this is the first study to test
the relationship between the CSF biomarkers and MRI-derived
brain measures other than the whole-brain, ventricular, or
hippocampal volume in healthy elderly individuals. In addition,
the sample size allowed for testing of nonlinear relationships,
which revealed that only when the CSF level of Ab1-42 was
below a certain threshold did low Ab1-42 correlate with
volumetric reductions and ventricular expansion. This suggests
that levels above this break point may reflect naturally
occurring individual differences in the amount of CSF Ab1-42,
which are not indicative of neuronal damage. However, levels
of Ab1-42 below this threshold, likely reflect or are causally
related to brain atrophy.
We hypothesized that a relationship between the CSF
biomarkers and brain atrophy would exist in healthy aging, as
T-tau and Ab1-42 are also related to other conditions involving
neuronal or axonal damage than AD (Winblad et al. 2008;
Spires-Jones et al. 2009b), and T-tau correlates with age in
healthy samples (Sjogren et al. 2001b). The present results,
which show that low CSF levels of Ab1-42 correlated with
ventricular expansion and volumetric reductions in widespread
areas, indicate that Ab1-42 may play a role in the brain changes
observed in healthy aging. However, the nonlinear analyses
revealed that this was true for the participants with low levels
of Ab1-42 only. Thus, it seems that Ab1-42 is related to
accelerated brain changes only in a subgroup of healthy elderly
individuals.
The one large study of the relationship between the CSF
biomarkers and brain volume found a relationship between low
levels of Ab 1-42 and smaller whole-brain volumes (Fagan et al.
2009). The present longitudinal results are in general
Figure 1. Nonlinear relationships between Ab1-42 and volume reductions for cortical ROIs. A nonparametric local smoothing technique (the smoothing spline) was used to
depict nonlinear relationships (red lines) for the 71 participants. A relationship between Ab1-42 and percentage change was found only when the concentration of Ab1-42 in the
CSF was below 175 pg/mL.
Cerebral Cortex September 2010, V 20 N 9 2073
Page 5
accordance with this finding, showing relationships between
Ab1-42 and annual percentage change in several brain regions.
Furthermore, all PIB-positive participants in that study had Ab1-
42 values of less than 500 pg/mL, whereas most PIB-negative
participants had higher values. The cutoff point of 500 pg/mL
obtained from the enzyme-linked immunosorbent A b1-42 assay
used in that study is roughly equivalent to the empirically
established break point value of 175 pg/mL obtained with the
Luminex Ab1-42 assay used in the present study. Fagan and
colleagues, however, did not observe a correlation between
levels of tau and brain size. The present findings indicate that P-
tau, but not T-tau, is related to ventricular expansion and volume
reductions in healthy elderly individuals, although to a lesser
extent than Ab1-42. The use of longitudinal brain measures in the
current study, which may be more sensitive than cross-sectional
measures, and the analysis of specific brain structures rather than
whole-brain volume, may account for this discrepancy.
Because studies have shown that in healthy elderly
individuals, a reduction in CSF Ab1-42, but not T-tau or P-tau,
predicts cognitive decline and development of AD (Gustafson
et al. 2003; Skoog et al. 2003; Stomrud et al. 2007), it is possible
that the low-Ab1-42 group reflects preclinical AD. Interestingly,
the break point of 175 pg/mL is close to the recently reported
mean value of 164 for the MCI group in ADNI (Shaw et al.
2009), and well within 1 standard deviation (
±55). We tried to
minimize the possibility of including participants with pre-
clinical AD by excluding healthy controls showing any
functional decline as indicated by the CDR-sb score. Further-
more, the memory performance did not differ significantly
between the groups. However, the only way to exclude with
certainty the possibility that the correlations observed in the
present study are caused by preclinical AD is by autopsy. Still,
one possible interpretation of the present results is that
because Ab1-42 is related to volumetric reductions in healthy
elderly individuals in brain areas typically resistant to AD-
related atrophy in early stages of the disease, this biomarker
may reflect neocortical amyloid deposition and brain injury
associated with the process of Ab aggregation in healthy
persons as well as in AD patients. For instance, although
accumbens, caudate, pallidum, putamen, and thalamus all may
be affected in later stages of AD, these areas are affected to
Figure 2. Nonlinear relationships between Ab1-42 and volumetric reductions of
subcortical ROIs and ventricular expansion. A nonparametric local smoothing
technique (the smoothing spline) was used to depict nonlinear relationships (red
lines) for the 71 participants.
Figure 3. Cortical reductions in the high- and the low-Ab1-42 groups. Based on the break point of the smoothing spline graphs (see Figs 1 and 2), a high-Ab1-42 group and
a low-Ab1-42 group were defined. Atrophy was calculated as percentage change in cortex point by point across the brain surface. The reductions are generally larger in the low-
than in the high-Ab1-42 group.
2074 Brain Atrophy and CSF Levels of Ab1-42
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a much lesser extent than the hippocampus in initial phases
(Fennema-Notestine et al. 2009). In the present study,
hippocampal volume reductions did not correlate more
strongly with the CSF biomarker levels than did other
subcortical structures. Reductions of the entorhinal cortex,
which is also heavily affected in initial phases of AD (Du et al.
2007; Fennema-Notestine et al. 2009; McEvoy et al. 2009), were
not significantly related to any of the CSF biomarkers.
Furthermore, it has been suggested that P-tau may be a more
specific marker for AD than T-tau or Ab1-42 (Wallin et al. 2006)
because elevated levels of P-tau are not found in the CSF of
patients with acute stroke (Hesse et al. 2001), Lewy body
(Parnetti et al. 2001), or vascular dementia (Sjogren et al.
2001a). Also, autopsy studies have shown more nonspecific
distributions of Ab1-42 than tau (Braak and Braak 1991). In the
Table 3
Baseline volume and thickness, and annual percentage volume change for the low-Ab1-42 (n 5
26) and the high-Ab1-42 (n 5 45) groups
Baseline 1-year change
Low
Ab1-42
High
Ab1-42
Difference
P
Low
Ab1-42
High
Ab1-42
Difference
P
Subcortical ROIs
Accumbens 884 902 NS 0.86 0.36 *
Amygdala 2919 2943 NS 1.02 0.61 NS
Brainstem 21 033 20 705 NS 0.38 0.32 NS
Caudate 6430 6562 NS 0.40 0.00 NS
Cerebellar gray matter 99 106 95 857 NS 0.38 0.33 NS
Cerebellar white matter 25 184 24 708 NS 0.78 0.52 NS
Cerebral white matter 431 469 431 316 NS 0.80 0.47 *
Hippocampus 7398 7346 NS 1.01 0.73 NS
Pallidum 3190 3296 NS 0.60 0.21 **
Putamen 8868 9256 NS 0.62 0.26 *
Thalamus 12 300 12 272 NS 0.99 0.51 **
Ventricular ROIs
Lateral ventricles 37 531 33 504 NS 5.92 3.26 **
Inferior lateral ventricles 2448 2279 NS 7.33 3.83 **
Third ventricle 1840 1780 NS 3.94 2.03 *
Fourth ventricle 2125 1969 NS 1.06 0.29 NS
Cortical ROIs
Cingulate, caudal anterior 2.59 2.55 NS 0.50 0.20 **
Cingulate, rostral anterior 2.70 2.76 NS 0.51 0.27 NS
Cingulate, posterior 2.33 2.37 NS 0.34 0.06 NS
Cingulate, isthmus 2.30 2.38 ** 0.37 0.03 NS
Frontal, superior 2.29 2.41 ** 0.42 0.09 NS
Frontal, caudal middle 2.14 2.20 NS 0.63 0.18 *
Frontal, rostral middle 1.96 2.02 NS 0.45 0.20 NS
Frontal, pars opercularis 2.21 2.24 NS 0.57 0.24 *
Frontal, pars triangularis 2.04 2.07 NS 0.59 0.29 NS
Frontal, pars orbitalis 2.43 2.48 NS 0.58 0.39 NS
Frontal, lateral orbital 2.43 2.49 NS 0.64 0.37 NS
Frontal, medial orbital 2.16 2.23 * 0.54 0.32 NS
Frontal, pole 2.54 2.59 NS 0.58 0.68 NS
Parietal, precentral gyrus 2.02 2.05 NS 0.15 0.12 NS
Parietal, postcentral gyrus 1.67 1.70 NS 0.22 0.22 NS
Parietal, paracentral gyrus 1.93 1.99 NS 0.17 0.07 NS
Parietal, superior 1.78 1.84 ** 0.23 0.01 NS
Parietal, inferior 2.02 2.08 * 0.48 0.32 NS
Parietal, supramarginal 2.16 2.18 NS 0.60 0.29 NS
Parietal, precuneus 1.92 1.99 ** 0.32 0.12 NS
Temporal, parahippocampal 2.41 2.44 NS 0.58 0.31 NS
Temporal, entorhinal 3.28 3.29 NS 0.73 0.48 NS
Temporal, pole 3.56 3.62 NS 0.61 0.46 NS
Temporal, superior 2.3 5 2.41 NS 0.61 0.27 NS
Temporal, middle 2.56 2.61 NS 0.62 0.39 NS
Temporal, inferior 2.59 2.64 NS 0.57 0.41 NS
Temporal, transverse 1.84 1.94 * 0.28 0.21 NS
Temporal, BSTS 2.11 2.14 NS 0.78 0.36 NS
Temporal, fusiform 2.35 2.41 NS 0.58 0.29 *
Occipital, lateral 1.83 1.86 NS 0.21 0.13 NS
Occipital, pericalcarine 1.33 1.33 NS 0.03 0.16 NS
Occipital, lingual 1.68 1.71 NS 0.13 0.10 NS
Occipital, cuneus 1.65 1.66 NS 0.00 0.04 NS
Note: Values in bold indicate that the change is different from zero at P \ 0.05. ‘*’ indicates
a trend toward significance (P \ 0.10) between low- and high-Ab1-42 groups; ‘‘**’ indicates
significance (P \ 0.05) between low- and high-Ab1-42 groups. NS, not significant (P [ 0.10);
BSTS, banks of the superior temporal sulcus. The differences in baseline volume/thickness and
volume change between the groups are tested by independent samples t-tests. Baseline volumes
are in mm
3
and baseline thickness is in mm, whereas atrophy is expressed as annual percentage
change.
Table 4
Correlations between subcortical volume change and ventricular expansion over 1 year and Ab1-
42 within the low-Ab1-42 (n 5 26) and the high-Ab1-42 (n 5 45) groups
Low Ab1-42 High Ab1-42 Difference z-score
Subcortical ROIs
Accumbens 0.64 0.17 2.27
Amygdala 0.70 0.10 2.97
Brainstem 0.41 0.14 1.14
Caudate 0.67 0.22 2.27
Cerebellar gray matter 0.48 0.05 1.83
Cerebellar white matter 0.09 0.06 0.12
Cerebral white matter 0.65 0.06 3.23
Hippocampus 0.42 0.19 0.99
Pallidum 0.36 0.15 0.87
Putamen 0.54 0.28 1.23
Thalamus 0.64 0.11 2.51
Ventricular ROIs
Lateral ventricles 0.63 0.02 2.79
Inferior lateral ventricles 0.63 0.07 3.14
Third ventricle 0.57 0.08 2.82
Fourth ventricle 0.55 0.05 2.20
Note: Values in bold, P \ 0.05; values in bold and italics, P \ 0.01. The differences between the
correlations were tested with Fisher’s z-transformed correlation coefficients. A positive correlation
indicates that lower CSF concentrations are associated with more atrophy and less ventricular
expansion.
Table 5
Correlations between cortical volume reductions over 1 year and Ab1-42 within the low-Ab1-42
(n 5 26) and the high-Ab1-42 (n 5 45) groups
Low Ab1-42 High Ab1-42 Difference z-score
Cingulate, caudal anterior 0.46 0.24 0.97
Cingulate, rostral anterior 0.44 0.22 0.96
Cingulate, posterior 0.69 0.07 3.00
Cingulate, isthmus 0.65 0.12 2.54
Frontal, superior 0.63 0.23 1.96
Frontal, caudal middle 0.57 0.06 2.26
Frontal, rostral middle 0.61 0.09 2.38
Frontal, pars opercularis 0.59 0.15 2.03
Frontal, pars triangularis 0.54 0.04 2.17
Frontal, pars orbitalis 0.58 0.13 2.05
Frontal, lateral orbital 0.60 0.13 2.17
Frontal, medial orbital 0.47 0.17 1.30
Frontal, pole 0.60 0.04 2.52
Parietal, precentral gyrus 0.59 0.06 2.38
Parietal, postcentral gyrus 0.59 0.17 1.95
Parietal, paracentral gyrus 0.58 0.06 2.32
Parietal, superior 0.58 0.02 2.63
Parietal, inferior 0.72 0.01 3.46
Parietal, supramarginal 0.64 0.07 2.65
Parietal, precuneus 0.58 0.19 1.81
Temporal, parahippocampal 0.51 0.04 2.32
Temporal, entorhinal 0.40 0.18 0.93
Temporal, pole 0.65 0.06 2.75
Temporal, superior 0.67 0.10 2.74
Temporal, middle 0.67 0.02 3.04
Temporal, inferior 0.64 0.08 3.23
Temporal, transverse 0.30 0.28 0.08
Temporal, BSTS 0.58 0.24 1.61
Temporal, fusiform 0.60 0.10 3.06
Occipital, lateral 0.62 0.04 2.95
Occipital, pericalcarine 0.12 0.11 0.89
Occipital, lingual 0.51 0.09 2.51
Occipital, cuneus 0.36 0.16 0.83
Note: Values in bold, P \ 0.05; values in bold and italics, P \ 0.01. The differences between the
correlations were tested with Fisher’s z-transformed correlation coefficients. BSTS, banks of the
superior temporal sulcus. A positive correlation indicates that lower CSF concentrations are
associated with more atrophy and less ventricular expansion.
Cerebral Cortex September 2010, V 20 N 9 2075
Page 7
present study, P-tau showed weaker correlations with volume
reductions than Ab1-42.
The question of whether the correlations between the CSF
levels of Ab1-42 and brain atrophy in seemingly cognitively
healthy participants are due to preclinical AD is difficult to
settle. One reason for this is that although it is established that
low-Ab1-42 CSF levels are associated with deposition and
aggregation of Ab1-42 in the brain, the main constituent of
neuritic plaques, the exact causal mechanisms for the role of
Ab in AD are still not known. For instance, in a recent study it
was found that high anti-A b titers were related to clearance of
amyloid from the brain, but progressive neurodegeneration was
not prevented, cognition was not improved, and survival did
not increase (Holmes et al. 2008). This indicates that more
studies are needed to allow better understanding of the re-
lationship between Ab oligomers and neurodegeneration in AD
(Zetterberg et al. 2009). According to one interesting hypo-
thesis, a major contribution of Ab to the pathophysiology of AD
is its synaptotoxic effects (Shankar et al. 2008), related to
a causal chain of events including inhibition of long-term
potentiation (LTP), removal of glutamate receptors, and
elimination of glutamate synapses (Zetterberg et al. 2009).
However, aggregated forms of Ab in fibrils and plaques seem
not to impact synaptic function (Shankar et al. 2008), which
may explain why some persons have high amounts of fibrillar
Ab in the brain but are cognitively normal (Reiman et al. 2009).
In a recent review paper, Zetterberg and colleagues suggest
that extended clinical follow-up is needed before it can be
concluded whether such persons are protected from Ab
toxicity by effective sequestration of Ab in inert aggregates
or by other factors, or whether they will eventually show
cognitive reductions (Zetterberg et al. 2009). Thus, it is very
difficult to determine whether the role played by Ab1-42 in
brain atrophy in nondemented elderly is due to preclinical AD,
or whether Ab1-42 can cause brain changes in healthy elderly
individuals who will not develop AD. Senile plaques have been
found in elderly individuals without dementia (Bennet et al.
2006; Green et al. 2000), but as preclinical manifestations of AD
likely occur years before clinical symptoms are detectable, it is
impossible to exclude the possibility that AD-related processes
caused the relationship between CSF Ab1-42 and atrophy
observed in the present study. In the largest longitudinal study
of healthy aging published to date (Resnick et al. 2003), the
authors argued that the observed longitudinal decline was not
caused by preclinical dementia, based on the uniformity of the
longitudinal changes seen across all individuals in the sample.
In the current study, significant atrophy was also seen in the
group of participants with high levels of Ab1-42. In this group,
no relationships between Ab1-42 and annual brain changes
were found. Thus, atrophy in healthy aging seems to be caused
by a mixture of processes, where some are related to Ab1-42,
whereas others are not. Importantly, all humans produce Ab,
but only some experience synaptic impairment and AD. The
destructive effects of Ab and amyloid precursor protein (APP)
on the brain may be harmful in AD and possibly healthy aging,
although they may be important during brain development. Ab
and APP appear to be involved in eliminating synapses (Priller
et al. 2006), neuronal cell body death and axonal degeneration
(Nikolaev et al. 2009), and restricting mature forms of LTP
(Townsend et al. 2006), and it has been suggested that these
developmental mechanisms are ‘‘hijacked in Alzheimer’s disease’’
(Nikolaev et al. 2009, p. 982). Furthermore, it is possible that only
specific forms of Ab, not expressed in the general population,
cause AD, for example, soluble Ab oligomers (Zetterberg et al.
2009). However, as noted in a recent review, reliable methods for
measuring Ab oligomers in biological fluids are needed to
determine the validity of this hypothesis (Zetterberg et al. 2009).
The present data indicate that brain atrophy in healthy elderly
individuals can be, but is not necessarily, related to CSF levels of
Ab1-42. It is currently unclear whether the atrophy related to
Ab1-42 is caused by preclinical AD.
Presence of APOE e4 alleles is known to increase the risk for
AD and also to be related to lower CSF levels of Ab1-42
(Andersson et al. 2007; Glodzik-Sobanska et al. 2007; Sunderland
et al. 2004) and higher PIB distribution volumes (Reiman
et al. 2009). A correlation between number of APOE e4
alleles and Ab1-42 was also found in the present study. Still,
APOE was weakly related to percentage brain change over
1 year and did not interact with Ab1-42 in prediction of
atrophy. Thus, the present data do not indicate that levels of
Ab1-42 are related to higher rates of volumetric reductions
in e4 carriers than in e3 homozygotes, contrary to what
might have been predicted from a vulnerability model of the
role of APOE in degenerative brain conditions.
Figure 4. Correlations between Ab1-42 and annual cortical change. Correlations between cortical change point by point across the brain surface and Ab1-42 were calculated,
color coded, and displayed as an overlay on the template brain. This was done for the high- and the low-Ab1-42 groups separately. The lower P value threshold is equal to a false
discovery rate of \0.05 (corrected for multiple comparisons).
2076 Brain Atrophy and CSF Levels of Ab1-42
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Fjell et al.
Page 8
A limitation of the present study is that the number of
participants in the low-Ab1-42 group is relatively low (n
= 26).
Thus, it is possible that a few individuals with very low Ab1-42
and accelerated brain changes disproportionally impact the
results. Longitudinal CSF data in addition to follow-up MR scans
could be used to more accurately map the relationship
between atrophy and the changes on CSF Ab1-42 levels.
In conclusion, ventricular expansion and volumetric brain
reductions over 1 year in healthy elderly individuals were
related to levels of CSF Ab1-42 below a certain threshold value,
whereas significant atrophy independent of the level of Ab1-42
was found for participants above this threshold. The strongest
relationships between Ab1-42 and atrophy were found for
brain areas not especially vulnerable to AD pathology, but
further research and follow-up examinations over several years
are needed to test whether degenerative pathology will
eventually develop in this group of cognitively normal elderly
individuals.
Funding
This work was supported by a grant (#U24 RR021382) to the
Morphometry Biomedical Informatics Research Network
(http://www.nbirn.net), which is funded by the National
Center for Research Resources at the National Institutes of
Health (NIH), United States, and by the National Institute on
Aging at the NIH (U01 AG024904 and R01 AG031224). The
work of K.B.W. and A.M.F. was supported by the Norwegian
Research Council. Data collection and sharing for this project
was funded by the ADNI (principal investigator: Michael Weiner;
NIH grant U01 AG024904). ADNI is funded by the National
Institute on Aging, the National Institute of Biomedical Imaging
and Bioengineering, and through generous contributions from
the following: Pfizer Inc., Wyeth Research, Bristol-Myers Squibb,
Eli Lilly & Co., GlaxoSmithKline, Merck & Co. Inc., AstraZeneca
AB, Novartis Pharmaceuticals Corp., Alzheimer’s Association,
Eisai Global Clinical Development, Elan Corporation plc, Forest
Laboratories, and the Institute for the Study of Aging, with
participation from the US Food and Drug Administration.
Industry partnerships are coordinated through the Foundation
for the National Institutes of Health. The grantee organization is
the Northern California Institute for Research and Education,
and the study is coordinated by the Alzheimer’s Disease
Cooperative Study at the University of California, San Diego.
ADNI data are disseminated by the Laboratory of Neuro Imaging
at the University of California, Los Angeles.
Supplementary Material
Supplementary material can be found at: http://www.cercor
.oxfordjournals.org/.
Notes
We thank Robin Jennings, Michele Perry, Chris Pung, and Elaine Wu for
downloading and preprocessing the MRI data. Conflict of Interest :
None declared.
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  • Source
    • "Structural changes include gray-matter atrophy in hippocampal volume (Bourgeat et al., 2010; Chetelat et al., 2010; Mormino et al., 2009; Oh et al., 2014; Storandt et al., 2009 ), temporal pole, superior frontal cortex (Dickerson et al., 2009), and frontal and posterior association cortices (Becker et al., 2011; Oh et al., 2011). Cerebrospinal fluid Ab was related to longitudinal changes in cortical thinning in lateral and medial frontal and temporal cortices and the posterior cingulate in cognitively intact older adults (Fjell et al., 2010) and also in reduced structural integrity of the default mode network (Spreng and Turner, 2013). Studies examining functional changes in relation to Ab pathology in cognitively normal elderly have found alterations in resting-state functional connectivity and aberrant task-related hyperactivation in brain regions collectively known as default mode network that is largely overlapping with amyloid deposition (Buckner et al., 2005; Elman et al., 2014; Hedden et al., 2009; Mormino et al., 2011; Sperling et al., 2009). "
    [Show abstract] [Hide abstract] ABSTRACT: The accumulation of beta-amyloid (A beta) peptides, a pathological hallmark of Alzheimer's disease (AD), has been associated with functional alterations, often in an episodic memory system with a particular emphasis on medial temporal lobe function. The topography of A beta deposition, however, largely overlaps with frontoparietal control (FPC) regions implicated in cognitive control that has been shown to be impaired in early mild AD. To understand the neural mechanism underlying early changes in cognitive control with AD, we examined the impact of A beta deposition on task-evoked FPC activation using functional magnetic resonance imaging (fMRI) in humans. Forty-three young and 62 cognitively normal older adults underwent an fMRI session during an executive contextual task in which task difficulty varied: single (either letter case or vowel/consonant judgment task) vs dual (switching between letter case and vowel/consonant decisions) task. Older subjects additionally completed F-18-florbetaben positron emission tomography scans and were classified as either amyloid positive (A beta+) or negative (A beta-). Consistent with previous reports, age-related increases in brain activity were found in FPC regions commonly identified across groups. For both task conditions, A beta-related increases in brain activity were found compared with baseline activity. For higher cognitive control load, however, A beta+ elderly showed reduced task-switching activation in the right inferior frontal cortex. Our findings suggest that with A beta deposition, brain activation in the cognitive control region reaches a maximum with lower control demand and decreases with higher control demand, which may underlie early impairment in cognitive control with AD progression.
    Full-text · Article · Feb 2016 · The Journal of Neuroscience : The Official Journal of the Society for Neuroscience
  • Source
    • "Structural changes include gray-matter atrophy in hippocampal volume (Bourgeat et al., 2010; Chetelat et al., 2010; Mormino et al., 2009; Oh et al., 2014; Storandt et al., 2009 ), temporal pole, superior frontal cortex (Dickerson et al., 2009), and frontal and posterior association cortices (Becker et al., 2011; Oh et al., 2011). Cerebrospinal fluid Ab was related to longitudinal changes in cortical thinning in lateral and medial frontal and temporal cortices and the posterior cingulate in cognitively intact older adults (Fjell et al., 2010) and also in reduced structural integrity of the default mode network (Spreng and Turner, 2013). Studies examining functional changes in relation to Ab pathology in cognitively normal elderly have found alterations in resting-state functional connectivity and aberrant task-related hyperactivation in brain regions collectively known as default mode network that is largely overlapping with amyloid deposition (Buckner et al., 2005; Elman et al., 2014; Hedden et al., 2009; Mormino et al., 2011; Sperling et al., 2009). "
    [Show abstract] [Hide abstract] ABSTRACT: The accumulation of amyloid-beta (Aβ) peptides, a pathologic hallmark of Alzheimer's disease, has been associated with functional alterations in cognitively normal elderly, most often in the context of episodic memory with a particular emphasis on the medial temporal lobes. The topography of Aβ deposition, however, highly overlaps with frontoparietal control (FPC) regions implicated in cognitive control/working memory. To examine Aβ-related functional alternations in the FPC regions during a working memory task, we imaged 42 young and 57 cognitively normal elderly using functional magnetic resonance imaging during a letter Sternberg task with varying load. Based on 18F-florbetaben-positron emission tomography scan, we determined older subjects' amyloid positivity (Aβ+) status. Within brain regions commonly recruited by all subject groups during the delay period, age and Aβ deposition were independently associated with load-dependent frontoparietal hyperactivation, whereas additional compensatory Aβ-related hyperactivity was found beyond the FPC regions. The present results suggest that Aβ-related hyperactivation is not specific to the episodic memory system but occurs in the PFC regions as well.
    Full-text · Article · Aug 2015 · Neurobiology of aging
  • Source
    • "Several nonlinear methods have been used to model the relationship between biomarkers and neuroimaging-derived measurements. For example, polynomial regression has been used to model agerelated atrophy in relation to CSF Ab levels using a quadratic term (Fjell et al., 2010). Other methods do not need to explicitly model the parametric form of the association: generalized additive models have been used to model the effect of aging (Schuff et al., 2012), splines to model the relationship between brain atrophy and CSF Ab levels (Insel et al., 2014), and local regression methods to track the evolution of several biomarkers in dominantly inherited AD as a function of the estimated years from expected symptom onset (Bateman et al., 2012). "
    [Show abstract] [Hide abstract] ABSTRACT: The progression of Alzheimer's disease (AD) is characterized by complex trajectories of cerebral atrophy that are affected by interactions with age and apolipoprotein E allele ε4 (APOE4) status. In this article, we report the nonlinear volumetric changes in gray matter across the full biological spectrum of the disease, represented by the AD-cerebrospinal fluid (CSF) index. This index reflects the subject's level of pathology and position along the AD continuum. We also evaluated the associated impact of the APOE4 genotype. The atrophy pattern associated with the AD-CSF index was highly symmetrical and corresponded with the typical AD signature. Medial temporal structures showed different atrophy dynamics along the progression of the disease. The bilateral parahippocampal cortices and a parietotemporal region extending from the middle temporal to the supramarginal gyrus presented an initial increase in volume which later reverted. Similarly, a portion of the precuneus presented a rather linear inverse association with the AD-CSF index whereas some other clusters did not show significant atrophy until index values corresponded to positive CSF tau values. APOE4 carriers showed steeper hippocampal volume reductions with AD progression. Overall, the reported atrophy patterns are in close agreement with those mentioned in previous findings. However, the detected nonlinearities suggest that there may be different pathological processes taking place at specific moments during AD progression and reveal the impact of the APOE4 allele. Copyright © 2015 Elsevier Inc. All rights reserved.
    Full-text · Article · Jul 2015 · Neurobiology of aging
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