ADNI: FRIDAY HARBOR 2011 WORKSHOP SPECIAL ISSUE
CSF biomarker associations with change
in hippocampal volume and precuneus thickness:
implications for the Alzheimer’s pathological cascade
Nikki H. Stricker & Hiroko H. Dodge &
N. Maritza Dowling & S. Duke Han & Elena A. Erosheva &
William J. Jagust &
for the Alzheimer’s Disease Neuroimaging Initiative
#Springer Science+Business Media, LLC (outside the USA) 2012
Abstract Neurofibrillary tangles (NFT) and amyloid pla-
ques are hallmark neuropathological features of Alzheimer’s
disease (AD). There is some debate as to which neuropath-
ological feature comes first in the disease process, with early
autopsy studies suggesting that NFT develop first, and more
recent neuroimaging studies supporting the early role of
amyloid beta (Aβ) deposition. Cerebrospinal fluid (CSF)
biomarkers of Aβ42and hyperphosphorylated tau (p-tau)
have been shown to serve as in vivo proxy measures of
amyloid plaques and NFT, respectively. The aim of this
study was to examine the association between CSF bio-
markers and rate of atrophy in the precuneus and hippocam-
pus. These regions were selected because the precuneus
appears to be affected early and severely by Aβ deposition,
and the hippocampus similarly by NFT pathology. We pre-
dicted (1) baseline Aβ42would be related to accelerated rate
For the Alzheimer’s Disease Neuroimaging Initiative—Data used in
preparation of this article were obtained from the Alzheimer’s Disease
Neuroimaging Initiative (ADNI) database (adni.loni.ucla.edu). 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. A complete listing of ADNI
investigators can be found at: http://adni.loni.ucla.edu/wp-content/
N. H. Stricker
Psychology Service, VA Boston Healthcare System,
Boston, MA, USA
N. H. Stricker
Department of Psychiatry, Boston University School of Medicine,
Boston, MA, USA
H. H. Dodge
Department of Neurology, Oregon Health & Sciences University,
Portland, OR, USA
N. M. Dowling
Department of Biostatistics & Medical Informatics,
University of Wisconsin,
Madison, WI, USA
S. D. Han
Department of Behavioral Sciences,
Rush University Medical Center,
Chicago, IL, USA
E. A. Erosheva
Department of Statistics and School of Social Work,
University of Washington,
Seattle, WA, USA
W. J. Jagust
Helen Wills Neuroscience Institute,
University of California Berkeley,
Berkeley, CA, USA
W. J. Jagust
Life Sciences Division, Lawrence Berkeley National Laboratory,
Berkeley, CA, USA
N. H. Stricker (*)
VA Boston Medical Center (116B),
150 S. Huntington Ave,
Boston, MA 02130, USA
Brain Imaging and Behavior
of cortical thinning in the precuneus and volume loss in the
hippocampus, with the latter relationship expected to be
weaker, (2) baseline p-tau181pwould be related to accelerat-
ed rate of hippocampal atrophy and cortical thinning in the
precuneus, with the latter relationship expected to be
weaker. Using all ADNI cohorts, we fitted separate linear
mixed-effects models for changes in hippocampus and pre-
cuneus longitudinal outcome measures with baseline CSF
biomarkers modeled as predictors. Results partially sup-
ported our hypotheses: Both baseline p-tau181pand Aβ42
were associated with hippocampal atrophy over time. Nei-
ther p-tau181pnor Aβ42were significantly related to cortical
thinning in the precuneus over time. However, follow-up
analyses demonstrated that having abnormal levels of both
Aβ42and p-tau181pwas associated with an accelerated rate
of atrophy in both the hippocampus and precuneus. Results
support early effects of Aβ in the Alzheimer’s disease
process, which are less apparent than and perhaps dependent
on p-tau effects as the disease progresses. However, amyloid
deposition alone may be insufficient for emergence of sig-
nificant morphometric changes and clinical symptoms.
Neurofibrillary tangles (NFT) and amyloid plaques are the
hallmark neuropathological features of Alzheimer’s disease
(AD). There is some debate about which neuropathological
feature comes first in the disease process. Early autopsy
studies suggested that NFT develop first (Braak et al.
1996), whereas some recent neuroimaging studies, particu-
larly those employing11C-PiB methods, support the early
role of amyloid deposition (see Jack et al. 2010 for review),
in line with the amyloid cascade hypothesis (J. A. Hardy and
Higgins 1992; see Hardy 2009 for a critical reappraisal of
this hypothesis). CSF biomarkers of phosphorylated tau and
Aβ42have been shown to serve as in vivo proxy measures
of NFT and amyloid plaques, respectively (Buerger et al.
2006; Clark et al. 2003; Shaw et al. 2009), and improve
diagnostic accuracy for AD (Hampel et al. 2003; Strozyk et
al. 2003). There is evidence that as amyloid plaques devel-
op, CSFAβ42decreases (Shaw et al. 2007), thus lower CSF
Aβ42suggests increased brain amyloid deposition. Unlike
total tau, which may be a general marker of neuronal dam-
age, p-tau is likely to reflect the formation of tangles in AD
(Blennow and Hampel 2003), with increased levels of CSF
p-tau thought to reflect increased NFT pathology.
Investigation of the relationship between CSF biomarkers
and regional changes on structural and functional MRI may
contribute to understanding the pathological mechanisms of
AD. Aβ-associated neurodegeneration manifests as cortical
thinning in regions vulnerable to early Aβ deposition and
this may begin prior to clinically evident cognitive impair-
ment (Becker et al. 2011). The precuneus is a site of prefer-
ential amyloid uptake in PiB studies (see Rabinovici and
Jagust 2009 for review), consistently shows hypometabo-
lism in FDG-PET studies of AD and atrophy/cortical thin-
ning in morphometric studies (Buckner et al. 2005), and is a
key part of the default network (Buckner et al. 2008), which
is important for memory function (Sperling et al. 2009).
Relationships between CSF Aβ42or amyloid load as mea-
sured by PiB and the precuneus have previously been dem-
onstrated in nondemented older adults and in MCI and AD
subjects (Becker et al. 2011; Chetelat et al. 2010; Fjell et al.
2008; Tosun et al. 2010). In contrast to the precuneus, the
hippocampus remains relatively free of amyloid deposition
during normal aging and early to mid-stage AD (Braak et al.
1996), and there is variability in the literature as to the
presence of an association between amyloid load and hip-
pocampal atrophy, with some studies supporting at least a
weak relationship (Apostolova et al. 2010; Beckett et al.
2010; Henneman et al. 2009; Mormino et al. 2009; Schuff et
al. 2009), and other studies not finding a significant rela-
tionship (Becker et al. 2011; Fagan et al. 2009).
A relationship between p-tau and both baseline hippo-
campal volume and rate of hippocampal atrophy has been
demonstrated across several studies (Apostolova et al. 2010;
Beckett et al. 2010; de Leon et al. 2006; Hampel et al. 2005;
Henneman et al. 2009; Tosun et al. 2010; but also see Schuff
et al. 2009), consistent with the well-established finding that
the hippocampus is an early site of NFT pathology in the
course of AD. While a number of studies have investigated
the association of CSF biomarkers and change in selected
brain regions, to our knowledge only one study to date has
investigated the potential interaction of multiple CSF bio-
markers on atrophy. Desikan et al. (2011) found an interac-
tion between Aβ42and p-tau181pstatus on entorhinal cortex
atrophy over time, with elevated atrophy in individuals with
abnormal levels of both Aβ42and p-tau181p. Follow-up
analyses further revealed that Aβ42status was associated
with accelerated atrophy in entorhinal cortex only among p-
tau181ppositive individuals in a nondemented sample. The
authors demonstrated this same effect within an “AD-vul-
nerable” region of interest (ROI) that averaged longitudinal
changes in multiple temporal and parietal regions affected
subsequently to the entorhinal cortex. They did not examine
this effect in the hippocampus and precuneus, and included
only nondemented subjects. There has been an increasing
emphasis on considering Alzheimer’s disease as a continu-
um, with the process beginning in otherwise “normal” indi-
viduals, and progressing slowly over time, with eventual
clinical expression resulting in the diagnostic classifications
of MCI and eventually AD dementia. Although the use of
Brain Imaging and Behavior
diagnostic classification is clinically useful, when studying
the effects of CSF biomarkers it is important to examine
effects across the entire disease spectrum. This focus sepa-
rates the current study from recent work that has examined
similar questions within diagnostic subgroups (Desikan et
al. 2011; Tosun et al. 2010).
The primary aim of this study was to examine the
association of CSF biomarkers and rate of atrophy in
the precuneus and hippocampus. These regions were
selected because the precuneus appears to be affected
early and severely by Aβ deposition, and the hippo-
campus similarly by NFT pathology. A secondary aim
was to extend the findings of Desikan and colleagues
by assessing the effect of a three-way interaction of
Aβ42, p-tau181p and time on rates of atrophy in the
precuneus and hippocampus. We predicted (1) baseline
Aβ42 would be related to accelerated rate of cortical
thinning in the precuneus and volume loss in the hip-
pocampus, with the latter relationship expected to be
weaker, (2) baseline p-tau181p would be related to ac-
celerated rate of hippocampal atrophy and cortical thin-
ning in the precuneus, with the latter relationship
expected to be weaker and (3) an interaction between
low Aβ42and high p-tau181pwould be associated with
an accelerated rate of atrophy in both the hippocampus
and precuneus. The precentral gyrus was selected as a
control region because we did not predict a relation-
ship with either CSF biomarker in this ROI as primary
motor regions remain relatively free of Alzheimer’s
pathology until late in the disease process.
Data used were obtained from the Alzheimer’s Disease
Neuroimaging Initiative (ADNI) database (www.loni.
ucla.edu/ADNI). ADNI was launched in 2003 by the Na-
tional Institute on Aging (NIA), the National Institute of
Biomedical Imaging and Bioengineering (NIBIB), the Food
and Drug Administration (FDA), and private pharmaceuti-
cal companies and non-profit organizations, as a 5-year
public-private partnership. The primary goal of ADNI has
been to test whether serial magnetic resonance imaging
(MRI), positron emission tomography (PET), other biolog-
ical markers, and clinical and neuropsychological assess-
ment can be combined to measure the progression of mild
cognitive impairment (MCI) and early Alzheimer’s disease
(AD). Determination of sensitive and specific markers of
very early AD progression is intended to aid researchers and
clinicians to develop new treatments and monitor their ef-
fectiveness, as well as lessen the time and cost of clinical
trials. The Principal Investigator of this initiative is Michael
W. Weiner, MD, VA Medical Center and University of
California – San Francisco. ADNI is the result of efforts of
many coinvestigators from a broad range of academic insti-
tutions and private corporations, and subjects have been
recruited from over 50 sites across the U.S. and Canada.
The initial goal of ADNI was to recruit 800 adults, ages 55
to 90, to participate in the research, approximately 200
cognitively normal older individuals to be followed for
3 years, 400 people with MCI to be followed for 3 years
and 200 people with early AD to be followed for 2 years
(see www.adni-info.org). This study was approved by each
ADNI-affiliated institution. Written informed consent was
obtained from all patients or authorized representatives par-
ticipating in the study.
Participants ADNI general eligibility criteria are described
Summary.aspx. Briefly, healthy controls (HC) had a Mini-
Mental State Exam (MMSE; Folstein et al. 1983) score
between 24 and 30 (inclusive), a global Clinical Dementia
Rating (CDR; Morris 1993) score of 0, and did not meet
criteria for MCI or dementia (Petersen et al. 2001). MCI
participants had MMSE scores between 24 and 30 (inclu-
sive), a memory complaint, evidence of objective memory
loss as measured by education adjusted scores on the
Wechsler Memory Scale Logical Memory II, a CDR of
0.5, absence of significant levels of impairment in other
cognitive domains, essentially preserved activities of daily
living, and an absence of dementia. Mildly demented AD
participants had MMSE scores between 20 and 26, global
CDR scores of 0.5 or 1.0, and met NINCDS/ADRDA cri-
teria for probable AD (McKhann et al. 1984). The ADNI
study collected CSF from approximately 50 % of partici-
pants at baseline, and from smaller subgroups subsequently.
CSF biomarker acquisition procedures for ADNI are de-
scribed in detail elsewhere (Jagust et al. 2010; Petersen et
al. 2010; Trojanowski et al. 2010). A measure derived from
the components of the CDR known as “sum of boxes”
(CDR-SB) was calculated to further estimate level of clini-
cal impairment. The data used in the current analysis was
downloaded on 6/1/2011. Participants with CSF data and
baseline and follow-up MRI scans (interval info) that met
globalquality control criteriawere usedinthe current analysis
MR scanning and brain morphometry Protocols are de-
scribed in detail at http://adni.loni.ucla.edu/research/protocols/
mri-protocols/. Two T1-weighted volumes were acquired for
each participant. Volumetric (Fischl et al. 2002; Fischl,
Salat et al. 2004) and cortical surface reconstruction (Dale
et al. 1999; Fischl et al. 1999; Fischl, van der Kouwe et al.
2004) methods based on FreeSurfer software, optimized for
use on large, multi-site datasets, were used. To measure
Brain Imaging and Behavior
thickness, the cortical surface was reconstructed (Dale et al.
1999) and parcellated into distinct ROIs (Desikan et al.
2006; Fischl, van der Kouwe et al. 2004). Details of the
application of these methods to the ADNI data have been
described in full elsewhere (Fennema-Notestine et al. 2009).
Three a priori selected ROIs were included in the present
analyses: precuneus cortical thickness, precentral gyrus cor-
tical thickness and hippocampal volume.
Statistical analyses We used linear mixed effects (LME)
multiple regression (Diggle et al. 2002) to model hip-
pocampus, precuneus and precentral gyrus as three sep-
arate longitudinal outcomes. Each model included time
in months (0, 6th, 12th, 18thand 24thfrom baseline),
baseline CSF biomarkers (Aβ42and p-tau181p) and their
interactions with time as predictors, and baseline age,
sex, and ApoE4 status as control variables. CSF bio-
markers were normalized using the Blom’s rank normal-
ization algorithm (Conover and Inman 1981) so that
estimated effects of these two biomarkers on the out-
come could be meaningfully compared. Intercept and
time were treated as random effects in all models. All
models assumed an unstructured within-subject error
covariance structure. Restricted maximum likelihood
was used for estimation. Pairwise interactions between
CSF biomarkers and time were of substantive interest.
That is, we were interested in examining the interplay
between CSF biomarkers at baseline and trajectories
over time in outcome measures (hippocampal volume,
precuneus and precentral gyrus thickness). We first car-
ried out the LME analyses described above on the
complete data set (n0342). Next, we applied the same
LME models to each diagnostic group separately (nor-
mal controls, MCI, and AD) to examine the effect of
baseline CSF biomarkers on longitudinal outcomes of
interest within each group. Quadratic and higher order
time variables did not improve the model fitness indi-
cated by Bayesian information and log-likelihood crite-
ria for any models and is thus not included. The overall
fit of the models was examined using a combination of
formal fit criteria and visual inspection of residual plots.
Results were considered significant when at p<.05; we
also provide Bonferroni-adjusted p-values in Table 2.
CSF measures were obtained in a subset of ADNI subjects.
Out of 819 subjects enrolled in the ADNI I study, 342
subjects had information on Aβ42, p-tau181p, and valid as-
sessment of hippocampal brain volume, precuneus and pre-
central gyrus measures at baseline. These subjects were
included in the current analyses using their follow-up
assessments up to 24 months. Demographic characteristics
are presented in Table 1.
Hippocampal volume Across all subjects, there was a
main effect of time, and this effect persisted within each
diagnostic subgroup. There was no main effect of Aβ42,
meaning that Aβ42was not associated with hippocampal
volume at baseline, either across all groups or within
diagnostic groups. There was a main effect of p-tau181p;
higher p-tau181pwas associated with lower hippocampal
volume, and this effect persisted within the AD group.
There was an interaction of time and CSF biomarkers:
higher Aβ42was associated with a smaller decline in
hippocampal volume over time, and higher p-tau was
associated with a larger decline in hippocampal volume
over time. When running the model within subgroups,
the interaction of Aβ42 and time persisted only within
the NC group, suggesting that Aβ42at baseline does not
significantly affect change over time within MCI and
AD groups. P-tau181pshowed the opposite pattern: base-
line p-tau181pdoes not significantly affect change in hip-
pocampal volume over time in the NC group, whereas
within the MCI and AD groups, higher p-tau181pat base-
line was associated with greater decline in hippocampal
volume over time (Table 2). Figure 1 shows examples
of the trajectories of hippocampal volume. We illustrate
the effect of high/low CSF biomarkers on hippocampal
volume over time for the diagnostic group where sig-
nificant interactions of biomarkers and time were found
using the coefficients obtained in the mixed effects
models. For models where hippocampal volume is the
outcome, models were run both with and without con-
trolling for baseline estimated total intracranial vault
(eTIV) volume (Buckner et al. 2004) and the same
pattern of results was obtained. We present results of
models without controlling for eTIV.
Precuneus thickness Across all subjects there was a main
effect of time, and this effect persisted within each diagnos-
tic group. There was no main effect of Aβ42, either across
all groups or within diagnostic groups. There was a main
effect of p-tau181p; higher p-tau181pwas associated with low-
er precuneus thickness at baseline. Within groups, this main
effect was significant only within the MCI group. There
were no interactions with time, either across all groups or
Precentral gyrus thickness Across all subjects there was a
main effect of time, and this effect persisted within each
individual group. There was no main effect of Aβ42, either
across all groups or within individual groups. There was a
main effect of p-tau181pfor all groups combined; higher p-
tau181pwas associated with lower precentral gyrus thickness
Brain Imaging and Behavior
at baseline. Within diagnostic groups, this main effect was
significant only within the NC group. There were no inter-
actions with time, either across all groups or within groups.
In a set of secondary analyses, we tested for potential
interaction effects of p-tau181pand Aβ42to determine if
having both abnormal p-tau181pand Aβ42values has an
added effect on atrophy over time in our selected ROIs.
We dichotomized high and low values of each CSF bio-
marker for the 3-way interaction to facilitate interpretation
of results. Previously defined cut-offs were applied (Shaw et
al. 2009): abnormal p-tau181pwas defined as p-tau181p>
23 pg/ml (−0.34818 using blom normalized scores), and
abnormal Aβ42was defined as Aβ42<192 pg/ml (0.4395
using blom normalized scores). The proportion of those with
both abnormal p-tau181pand Aβ42was 81.7 %, 64.0 % and
21.3 % among AD, MCI, and normal controls, respectively
(Pearson chi-square test, p<0.001). In the entire sample
(NC, MCI and AD), there was a 3-way interaction of ab-
normal p-tau181pand abnormal Aβ42with time for each ROI
(HCV p00.040, precuneus p00.0007, precentral gyrus p0
0.029), after controlling for gender, ApoE4, and baseline
age, p-tau and Aβ42(continuous variables) and 2-way inter-
actions of p-tau and time, and Aβ42and time. That is,
having the combination of both abnormal p-tau181pand
Aβ42resulted in an additional increased rate of atrophy over
time beyond the additive effect of each biomarker. There
was also a significant interaction of p-tau and time (p0
0.012) when outcome was hippocampal volume (all diag-
nostic groups combined). Within subgroups, the 3-way in-
teraction remained significant only for the AD group for
precuneus (p00.005) and precentral gyrus (p00.019)
These results demonstrate that across the Alzheimer’s dis-
ease spectrum from normal aging to early dementia, CSF
biomarkers exert an influence on rate of atrophy, although
this effect varies by region, CSF biomarker, and sample
Baseline association results showed a significant re-
lationship between p-tau181p and hippocampal volume,
whereas the relationship between Aβ42and hippocampal
volume was not significant, but a trend was demonstrat-
ed. These baseline results supported our prediction that
p-tau181pwould have a stronger relationship with hippo-
campal volume than Aβ42. The lack of baseline association of
demonstratedinprior PiBand CSF biomarker studies (Becker
et al. 2011; Fagan et al. 2009; Fjell et al. 2010), whereas other
studies have demonstrated at least a weak relationship
(Apostolova et al. 2010; Henneman et al. 2009; Mormino
et al. 2009). A somewhat different pattern of results was
revealed for rates of change. Within the entire sample, both p-
tau181pand Aβ42were significantly associated with acceler-
ated rates of hippocampal atrophy. Within-group results dem-
onstrated a relationship between Aβ42and accelerated rate of
hippocampal atrophy within the NC group, but not within the
Table 1 Baseline characteristics
All DX Combined
N at each follow up
Age mean (std)
Ethnicity (% Caucasian)
Years of education: mean (std)
Apoe4 (at least one e4 allele) %
Hippocampal volume (mm3)
Precuneus thickness (mm)
Precentral gyrus thickness (mm)
+: Kruskal-Wallis test for continuous variables and Pearson Chi-Square test for categorical variables; Fisher’s exact test used for ethnicity due to
small numbers of non-Caucasian participants
*Normalized values using Blom’s rank normalization algorithm
Brain Imaging and Behavior
Table 2 Results of mixed effects models
ALL DX together
Precentral Gyrus thickness
In all models, the following variables are controlled: age at baseline, gender, and ApoE (at least having one E4 vs. none)
Tau and abeta are normalized using Blom’s rank normalization algorithm
** significant at p<0.0042, multiple comparison adjusted p-value
Brain Imaging and Behavior
NC group may differ since some subjects are destined to
develop AD while others are not, the early detection of effects
of Aβ42only in the NC group is consistent with a potential
initiating role as suggested by Jack et al. (2010). In contrast,
within-group results demonstrated a relationship between p-
MCI and AD groups, consistent with studies showing that
rates of brain atrophy and clinical progression correlate well
with pathological indices of NFT (Josephs et al. 2008).
Henneman et al. (2009) similarly found that CSF p-tau181p
predicted an accelerated rate of hippocampal atrophy when
collapsing across normal and clinical groups, but in that study
results did not persist within diagnostic subgroups, potentially
due to much smaller sample sizes relative to the current study:
In their study, the total sample size combining normal, MCI
and AD was 75 subjects. Other studies have demonstrated a
significant relationship between p-tau231por p-tau181pand
accelerated hippocampal atrophy in MCI (de Leon et al.
2006; Fjell et al. 2010; Hampel etal. 2005; Tosunet al. 2010).
Considered together, our results demonstrate that lower
baseline Aβ42in the NC group and higher baseline p-tau181p
in the MCI and AD groups is associated with an accelerated
rate of hippocampal atrophy over time. Our results are
consistent with the biomarker model proposed by Hyman
(2011) and further supported by data from Lo et al. (2011) in
which Aβ may exert an effect early in the disease, but it has
relatively smaller effects later. That is, once there are clinical-
ly detectable symptoms warranting a diagnosis of MCI or AD
dementia, the downstream effects of Aβ become uncoupled
from Aβ itself. Hyman’s model emphasizes a two-stage pro-
cess in which intervention efforts may be beneficial very early
in the disease process, before there is any clinically detectable
cognitive symptoms or MRI atrophy, whereas once clinical
symptoms emerge, Aβ or another early initiating factor has
already instigated the pathological cascade and may be less
important for predicting disease progression and ineffective as
a treatment target in these later stages of the disease.
Because accumulation of amyloid beta is theorized to
occur prior to NFT (Jack et al. 2010), we hypothesized that
baseline Aβ42would be related to accelerated rates of cor-
tical thinning in the precuneus given evidence of early
amyloid deposition in this region. However, a pattern oppo-
site to that we predicted was demonstrated: baseline Aβ42
did not predict accelerated rates of cortical thinning in the
Fig. 1 Synergistic effect of
CSF (P-tau181pand Aβ42) vs
time on the rate of hippocampal
atrophy. The plotted lines
represent fitted values
conditioned upon mean
baseline CSF and 1SD above
and below mean baseline CSF.
Only interactions with an
adjusted p-value below 0.10 are
Brain Imaging and Behavior
precuneus, whereas it did predict accelerated rate of hippo-
campal atrophy as discussed above. In fact, neither CSF
biomarker predicted accelerated rates of cortical thinning
in the precuneus. A baseline association of p-tau181pand
precuneus thickness was demonstrated, whereas a baseline
association of Aβ42and precuneus was not. The precuneus
is assumed to be an early site of amyloid deposition based
largely on the findings of multiple PiB studies that have
demonstrated preferential amyloid uptake in this region
(Aizenstein et al. 2008; Mintun et al. 2006; Rowe et al.
2007) and its involvement in the default network (Buckner
et al. 2008). A few PiB studies have shown a significant
baseline association between amyloid uptake and hippocam-
pal volume (Becker et al. 2011; Chetelat et al. 2010; Fjell et
al. 2008), and one study has shown PiB uptake is associated
with accelerated hippocampal atrophy in MCI (Tosun et al.
2010). Because PiB and CSF measures of amyloid may not
be equivalent, this alone could explain our discrepant find-
ings. Our use of an ROI analysis approach, as opposed to
voxelwise analysis that may be more sensitive to localized
changes within subregions of the precuneus, may also ex-
plain our findings. However, autopsy studies have not dem-
onstrated a predilection for early amyloid plaque
accumulation in the precuneus relative to other areas of the
neocortex (Nelson et al. 2009), so further investigation of
the relationship between amyloid and the precuneus is
We also predicted a significant relationship between p-
tau181pand accelerated rates of precuneus thinning based on
a hypothesized indirect relationship. An indirect relationship
was expected due to the extensive anatomical and functional
connections between posterior cortical regions including the
precuneus and medial temporal lobe regions affected early
in AD, including the hippocampus (Dorfel et al. 2009;
Kobayashi and Amaral 2003; Teipel et al. 2010). Decreased
resting state functional connectivity between the precuneus
and the hippocampus (as well as other regions of the default
network) has been demonstrated in patients with early AD,
PiB+normal healthy elderly and PiB- normal healthy elder-
ly ApoE4 allele carriers (Sheline, Morris et al. 2010;
Sheline, Raichle et al. 2010). Results did not support this
hypothesis; we found no baseline or longitudinal associa-
tions beween p-tau181pand precuneus thickness. To our
knowledge no other studies have directly assessed this
An interaction between low Aβ42and high p-tau181pwas
associated with greater cortical thinning in the precuneus
and precentral gyrus and hippocampal atrophy over time,
extending the findings of Desikan et al. (2011) to these
additional regions in a combined NC, MCI and AD sample.
This suggests that having both abnormal Aβ42and p-tau181p
leads to accelerated atrophy. The significance of this effect
in our “control” region (precentral gyrus) was surprising and
may point toward more diffuse effects of these combined
biomarkers. Inconsistent with the results of Desikan et al.
(2011), this 3-way interaction persisted only within the AD
group for cortical thickness measures. Although the analy-
ses performed on the entire sample demonstrated this effect
in all regions studied, the 3-way interaction was not signif-
icant within NC or MCI groups when examined
There are several limitations that must be considered
when interpreting these results. First, we selected a small
number of potential biomarkers to focus on in this study to
maintain a narrow focus. We did not include t-tau or ratio
values of Aβ42and tau. We also included only a small
number of a priori ROIs, instead of performing exploratory
voxelwise analyses. Because ROIs average across an entire
region, subtle group difference that may be detected within
such a region by voxelwise analysis may be missed; in other
words, ROI analyses may be less sensitive. However, ex-
amining these effects using ROIs is important, as this is
likely to be a widely used approach in large clinical trials
applying automated neuroimaging processing techniques.
Second, the primary goal of the ADNI is to optimize clinical
trials, and the sample is not representative of the general
population (e.g., highly educated); therefore the generaliz-
ability of the current results is limited. Third, we did not
include longitudinal CSF data, thus the timing of biomarker
effects cannot be fully disentangled with the current set of
analyses. A longer duration of follow-up would likely be
necessary to optimally complete such analyses, as there is
evidence that little change in CSF biomarkers can be mea-
sured over relatively short intervals such as those included
in this study (Vemuri et al. 2010). This is particularly nota-
ble for Aβ42, as change in this CSF biomarker has not been
significantly associated with annual decline in cognitive and
functional scores in MCI and AD groups despite evidence of
clear cognitive and functional decline (Vemuri et al. 2010),
leading some to propose that CSF load is nearly disconnect-
ed from the disease stage (Caroli and Frisoni 2010). Finally,
the sample size varied across the diagnostic subgroups, with
nearly twice as many subjects in the MCI sample relative to
the NC and AD subgroups. This limits the extent to which
we can make conclusions regarding the significance or lack
thereof of effects across subgroups. Again, this is why we
chose to focus primarily on the results collapsed across
In summary, the current results provide at least partial
support for the Jack et al. (2010) dynamic biomarker model,
although the current analyses alone are insufficient to fully
test this model. Results suggest that there may be an early
affect of amyloid in the Alzheimer’s pathological cascade
process, which appears to be most detectable in its effects on
the rate of hippocampal atrophy in normal older people.
Despite this early effect, amyloid does not appear to directly
Brain Imaging and Behavior
affect atrophy in later disease stages. Results also raise
questions about how the precuneus is affected by AD since
evidence of relationships between Aβ and rates of atrophy
are weak. Finally, in conjunction with results from Desikan
et al. (2011), these results highlight the importance of con-
sidering the additive effect of Aβ42and p-tau181pin the
progression of atrophy over time across the Alzheimer’s
disease spectrum, and provide further support of the possi-
bility that amyloid deposition alone may be insufficient for
emergence of significant morphometric changes and clinical
the 2011 Friday Harbor Advanced Psychometrics Workshop, funded
by the National Institute on Aging R13 AG030995. Data collection and
sharing for this project was funded by the Alzheimer’s Disease Neuro-
imaging Initiative (ADNI) (National Institutes of Health 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: Abbott; Alz-
heimer’s Association; Alzheimer’s Drug Discovery Foundation; Amor-
fix Life Sciences Ltd.; AstraZeneca; Bayer HealthCare; BioClinica,
Inc.; Biogen Idec Inc.; Bristol-Myers Squibb Company; Eisai Inc.;
Elan Pharmaceuticals Inc.; Eli Lilly and Company; F. Hoffmann-
LaRoche Ltd and its affiliated company Genentech, Inc.; GE Health-
care; Innogenetics, N.V.; Janssen Alzheimer Immunotherapy Research
& Development, LLC.; Johnson & Johnson Pharmaceutical Research
& Development LLC.; Medpace, Inc.; Merck & Co., Inc.; Meso Scale
Diagnostics, LLC.; Novartis Pharmaceuticals Corporation; Pfizer Inc.;
Servier; Synarc Inc.; and Takeda Pharmaceutical Company. The Cana-
dian Institutes of Health Research is providing funds to support ADNI
clinical sites in Canada. Private sector contributions are facilitated by
The grantee organization is the Northern California Institute for Research
and Education, and the study is coordinated by the Alzheimer’s Disease
CooperativeStudyatthe University of California,San Diego.ADNI data
are disseminated by the Laboratory for Neuro Imaging at the University
of California, Los Angeles. This research was also supported by NIH
grants P30 AG010129, K01 AG030514, P30 AG008017, R01
AG029672-01A1 and the Dana Foundation.
This manuscript was a collaborative effort from
any of the authors.
There were no actual or potential conflicts of interest for
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