Amyloid-independent atrophy patterns predict time to progression to dementia in mild cognitive impairment
Kate et al. Alzheimer's Research & Therapy
Amyloid-independent atrophy patterns predict time to progression to dementia in mild cognitive impairment
Mara ten Kate 0 1
Frederik Barkhof 2 3
Pieter Jelle Visser 0 6
Charlotte E. Teunissen 5
Philip Scheltens 0
Wiesje M. van der Flier 0 4
Betty M. Tijms 0
0 Alzheimer Center & Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center , Amsterdam , The Netherlands
1 Alzheimer Center & Department of Neurology, VU University Medical Center , PO Box 7057, 1007 MB Amsterdam , The Netherlands
2 Institutes of Neurology and Healthcare Engineering, University College London , London , UK
3 Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center , Amsterdam , The Netherlands
4 Department of Epidemiology and Biostatistics, VU University Medical Center , Amsterdam , The Netherlands
5 Neurochemistry Laboratory and Biobank, Department of Clinical Chemistry, VU University Medical Center, Neuroscience Amsterdam , Amsterdam , The Netherlands
6 Department of Psychiatry & Neuropsychology, School for Mental Health and Neuroscience, Maastricht University , Maastricht , The Netherlands
Background: Amyloid pathology in subjects with mild cognitive impairment (MCI) is an important risk factor for progression to dementia due to Alzheimer's disease. Predicting the onset of dementia is challenging even in the presence of amyloid, as time to progression varies considerably among patients and depends on the onset of neurodegeneration. Survival analysis can account for variability in time to event, but has not often been applied to MRI measurements beyond singular predefined brain regions such as the hippocampus. Here we used a voxel-wise survival analysis to identify in an unbiased fashion brain regions where decreased gray matter volume is associated with time to dementia, and assessed the effects of amyloid on these associations. Methods: We included 276 subjects with MCI (mean age 67 ± 8, 41% female, mean Mini-Mental State Examination 26.6 ± 2.4), baseline 3D T1-weighted structural MRI, baseline cerebrospinal fluid (CSF) biomarkers, and prospective clinical follow-up. We fitted for each voxel a proportional Cox hazards regression model to study whether decreased gray matter volume predicted progression to dementia in the total sample, and stratified for baseline amyloid status. Results: Dementia at follow-up occurred in 122 (44%) subjects over an average follow-up period of 2.5 ± 1.5 years. Baseline amyloid positivity was associated with progression to dementia (hazard ratio 2.4, p < 0.001). Within amyloid-positive subjects, decreased gray matter volume in the hippocampal, temporal, parietal, and frontal regions was associated with more rapid progression to dementia (median (interquartile range) hazard ratio across significant voxels 1.35 (1.32-1.40)). Repeating the analysis in amyloid-negative subjects revealed similar patterns (median (interquartile range) hazard ratio 1.76 (1.66-1.91)). Conclusions: In subjects with MCI, both abnormal amyloid CSF and decreased gray matter volume were associated with future progression to dementia. The spatial pattern of decreased gray matter volume associated with progression to dementia was consistent for amyloid-positive and amyloid-negative subjects.
Mild cognitive impairment; Alzheimer's disease; Magnetic resonance imaging; survival analysis
Subjects with mild cognitive impairment (MCI) are at
increased risk of dementia with annual conversion rates
of 10–15% [
]. According to the NIA–AA research
criteria , a diagnosis of MCI due to AD requires the
presence of amyloid pathology as measured in
cerebrospinal fluid (CSF) or on amyloid PET. Since amyloid
reaches a plateau relatively early in the disease course
], it has limited prognostic value for the time to
onset of dementia. Neuronal injury markers, such as brain
atrophy measured with structural MRI, are more closely
related to cognitive impairment and could thus be useful
for estimating time to clinical progression [
Previous studies found that hippocampal atrophy can be used
to predict time to dementia in MCI patients with
positive amyloid markers [
]. However, other brain
regions may also be valuable for the prediction of
progression, as indicated by voxel-based morphometry
]. Two previous studies have used
hypothesis-free voxel-level survival analyses to show that
decreased gray matter in the medial temporal lobe and
posterior cingulate cortex can predict time to conversion
to AD-type dementia in nondemented subjects [
However, it is still unclear whether such predictive
atrophy patterns are specific for amyloid pathology.
In this study, we performed unbiased voxel-wise
survival analyses to detect brain regions where decreased
gray matter volume is associated with time to
progression to dementia, and examined whether patterns
associated with progression to dementia were dependent on
amyloid status in a large sample of subjects with MCI.
Subjects with a clinical diagnosis of MCI, a good-quality
structural MRI and CSF biomarker assessment at
baseline, and at least one clinical follow-up were selected
from the CODA (COnnectivity in DementiA) study,
which includes subjects from the Amsterdam Dementia
]. This cohort consists of subjects attending
the memory clinic of the VU University Medical Centre
Amsterdam since 2000. All subjects in this cohort
underwent routine dementia screening, including
physical and neurological examination, neuropsychological
testing, brain MRI scanning, and usually lumbar
puncture (unless contraindication or patient refusal).
The study protocol was approved by the VU University
Medical Centre institutional review board. All subjects
gave written informed consent for their clinical data to
be used for research purposes.
Baseline clinical diagnosis of MCI was established during
a consensus meeting from a multidisciplinary team
according to the Petersen criteria [
]. Subjects were
followed annually, and duration of follow-up ranged
from 1 to 11 years (mean 2.5 ± 1.5). A follow-up
diagnosis was made during a multidisciplinary consensus
meeting according to common clinical and research criteria
]. Time to dementia was defined as the time
between the baseline visit and the date of dementia diagnosis.
The primary analysis included all subjects, regardless of
follow-up diagnosis. Analyses were repeated for the
subsample of subjects who converted to AD-type dementia.
CSF was collected at baseline by lumbar puncture using
a 25-gauge needle in polypropylene tubes (Sarstedt,
Nümbrecht, Germany). CSF was centrifuged at 1800 × g
for 10 min at 4 °C and stored at – 20 °C until biomarker
analysis, within 2 months after collection. CSF Aβ1–42
was measured using InnoTest sandwich ELISAs
(Innogenetics, Fujirebio, Ghent, Belgium) [
]. Subjects were
classified as amyloid positive or negative with a cut-off
point of CSF Aβ1–42 < 640 ng/L [
Anatomical 3D T1-weighted images were acquired at
baseline as part of regular patient care with eight different
scanners using a spoiled gradient echo type of sequence
(e.g., MPRAGE, FSPGR, TFE). Details of scanners and
acquisition parameters can be found in Additional file 1.
The MRI protocol also included a 3D fluid attenuated
inversion recovery (FLAIR) sequence, dual-echo
T2weighted sequence, susceptibility weighted imaging (SWI),
and diffusion weighted imaging (DWI) to visually assess
brain pathology by an experienced neuroradiologist.
Structural 3D T1 images were segmented using Statistical
Parametric Mapping 12 (SPM12) software (Wellcome
Trust Centre for Neuroimaging, University College
London, UK) running in MATLAB 2011a (MathWorks
Inc., Natick, MA, USA). Diffeomorphic Anatomical
Registration Through Exponentiated Lie Algebra (DARTEL)
was used to create a custom study template by nonlinearly
aligning gray matter segmentations [
native space gray and white matter images were spatially
normalized to the template using the individual flow
fields. The resulting gray matter images (isotropic
1.5 mm3 voxels) were modulated to preserve the total
amount of gray matter from the native space image.
Images were smoothed with an isotropic Gaussian filter of
6 mm full-width at half-maximum (FWHM). After
processing, the quality of the segmentations was visually
checked and none had to be excluded. Total intracranial
volume (TIV) was calculated from segmented images in
TIV ¼ gray matter þ white matter
þ cerebrospinal fluid:
To limit the analysis to gray matter voxels, a mask was
created to include only voxels with a gray matter
probability of 0.1, resulting in 311,613 voxels included in
Independent-sample t tests, Mann–Whitney U tests, or
chi-square tests were used when appropriate to compare
the groups on demographic and clinical variables using
SPSS (version 22; IBM), with p < 0.05 considered
Prior to the imaging statistics, a linear regression was
performed at each voxel to correct the gray matter
volume for the effects of the nuisance variables age, gender,
scanner, and TIV. Baseline cross-sectional gray matter
differences between amyloid-positive and
amyloidnegative subjects were examined using a general linear
model. Proportional Cox hazards regressions to predict
disease progression were performed at each gray matter
voxel using the Coxphfit function implemented in
MATLAB 2011a (MathWorks Inc.). The outcome
measure was time to dementia onset. The independent
variable was residual gray matter at each voxel. At each
voxel, the residual gray matter volume was inverted and
Z-transformed. The hazard ratio (HR) then represents
the increased chance of progressing to dementia within
the next time point per standard deviation decrease in
gray matter volume. Proportional Cox hazards
regression was also used to assess the hazard ratio associated
with amyloid positivity in the whole sample and for
continuous CSF Aβ1–42 values in the amyloid subgroups,
while correcting for age and gender. Continuous amyloid
measures were inverted so that HRs were directly
First, the voxel-wise Cox regression analysis was
performed on the total sample. Next, analyses were
repeated after stratification by amyloid status. Statistical
significance was determined with nonparametric
permutation tests [
]. The event or group label was
reallocated randomly to each subject 10 times. For each of
these permutations, the voxel-wise Cox regression was
repeated. The results of all permutations at all voxels
were pooled to sample the permutation distribution
under the null hypothesis (=3,427,743 random tests).
The 2.5th and 97.5th percentiles of this null distribution
were used as the critical values for statistical significance
representing a two-sided test with a probability of type I
error of 0.05. Sampling of the null distribution by
permutation testing was repeated for all subgroup analyses.
We tested the assumption of proportional hazards
for each voxel-level test for the main analysis using
Schoenfeld residuals [
] using the cox.zph function
in R (R version 3.1.1; http://www.R-project.org) with
survival package version 2.37-7. We found no more
violations of the proportional hazards assumption than would
be expected by chance (3.24% of tests were significant at
p < 0.05).
Of the 276 subjects with MCI, 122 (44%) progressed to
dementia. Among those who progressed to dementia,
104 (85%) subjects progressed to AD-type dementia and
18 (15%) subjects to another type of dementia (four
fronto-temporal dementia, eight vascular dementia, one
mixed vascular and AD, three Lewy body dementia, and
two dementia unspecified). Clinical characteristics of
subjects are summarized in Table 1. Subjects who
progressed were on average older, had lower baseline
scores on the Mini-Mental State Examination (MMSE),
and had lower baseline CSF Aβ1–42 than subjects who
remained stable. The groups had similar follow-up times.
Subjects who were amyloid positive had a higher risk of
progressing to dementia compared to amyloid-negative
subjects (HR 2.4, p < 0.001). When stratifying for
amyloid status, 160 subjects were amyloid positive and 99
(62%) of them showed clinical progression. Of those
amyloid-positive subjects who progressed, most subjects
progressed to AD-type dementia (n = 94, 95%). A total
of 116 subjects were amyloid negative, of whom 23
(20%) subjects progressed. Amyloid-negative subjects
more often progressed to non-AD dementias (57%) than
AD-type dementia (43%). Within the amyloid-positive
group, continuous CSF Aβ1–42 levels were unrelated to
progression to dementia (HR 1.0, p = 0.8). Within the
amyloid-negative group, continuous CSF Aβ1–42 levels
were associated with an increased risk of progression to
dementia (HR 2.2, p = 0.01).
Brain regions predicting time to dementia
The voxel-wise proportional Cox hazards regressions
showed that lower gray matter volumes in widespread
cortical and subcortical areas were associated with time
to progression to dementia (Fig. 1). In addition to
decreased gray matter volume in well-known AD-related
areas (i.e., hippocampal and temporal regions), low gray
matter volume in the parietal and frontal regions was
also associated with progression to dementia (Table 2).
Repeating the analysis after excluding subjects
progressing to non-AD dementia did not substantially change
these results (data not shown).
Influence of amyloid pathology
First, we assessed the influence of amyloid status on gray
matter volume with standard voxel-based morphometry.
This revealed decreased gray matter volume in
amyloidpositive subjects when compared to amyloid-negative
subjects in the hippocampus, temporal, parietal, and
frontal regions (Additional file 2: Figure S1).
Amyloidnegative subjects had lower gray matter volume at
baseline in the cerebellum than amyloid-positive subjects.
Next, we repeated the survival analysis after stratifying
the subjects according to amyloid status.
Amyloidpositive subjects showed widespread decreases in gray
matter volume that were predictive of time to
progression to dementia, similar to the analysis in the whole
sample (Fig. 2). In comparison, the anatomical patterns
predicting progression in amyloid-negative subjects were
qualitatively similar to those observed in
amyloidpositive subjects, apart from bilateral anterior temporal
regions (only significant in amyloid-positive subjects)
and the right fusiform gyrus (only significant in
amyloidnegative subjects). To formally assess the difference
between amyloid-positive and amyloid-negative subjects,
we added an interaction term between voxel gray matter
volume and amyloid status to the voxel-level Cox
regression on the whole sample. For the majority of voxels
(97% of all voxels included in the analyses) this
interaction was not significant, supporting that the
predictive value of baseline gray matter volume is largely
similar for amyloid-positive and amyloid-negative subjects
(Additional file 2: Figure S2). Three somewhat larger
clusters of voxels showed significant interaction effects of
amyloid status: two ventromedial prefrontal regions,
which showed a higher hazard ratio in amyloid-negative
subjects compared to amyloid-positive subjects; and one
cluster in the right temporal fusiform gyrus that was only
significant in amyloid-negative subjects.
Because within the amyloid-negative group continuous
CSF Aβ1–42 values were associated with progression to
dementia, we performed a voxel-wise Cox regression
after additionally correcting for continuous CSF Aβ1–42
values. This analysis resulted in HRs of similar size.
When using a voxel-wise threshold of p < 0.005 a similar
pattern of significant voxels emerged as in the analysis
without correction for CSF Aβ1–42, but these did not
survive correction for multiple testing (Additional file 2:
The main finding of this article is that a widespread
pattern of decreased gray matter volume, beyond the
hippocampal region, is predictive of time to progression
to dementia in subjects with MCI. The presence of
amyloid pathology was also a predictor of time to
progression to dementia. The pattern of decreased gray
matter volume that was predictive of progression was
mostly similar in amyloid-positive and amyloid-negative
The cortical pattern of decreased gray matter volume
predicting progression to dementia in subjects with
MCI included the temporal, parietal, and frontal
regions. Most of the voxels predictive of progression to
dementia are located in brain regions that typically
show atrophy and hypometabolism as a sign of
neurodegeneration in subjects with AD [
], and are
known to be pathologically involved in AD . Our
results are largely in line with previous voxel-based
morphometry studies comparing MCI subjects who
progress to dementia with those who remain stable
over time. These studies have identified the medial and
lateral temporal lobe [
] and also the parietal and
frontal areas [
] to be associated with progression.
Only two studies have so far also applied voxel-level
survival analysis to structural MRI data. Using this
method, intersubject variability in rates of clinical
progression and follow-up time can be taken into account.
Those studies also reported that atrophy in (mesial)
temporal and posterior cingulate structures is
predictive of cognitive decline [
]. The patterns of low
gray matter volume predictive of decline observed in
our study were more extensive than in these previous
studies and additionally revealed the frontal and more
widespread parietal regions to be involved. This could
partially be explained by our higher power due to the
larger sample size. Also, Zeifman et al.  examined
clinical progression from a cognitively normal stage,
whereas our sample comprised subjects with MCI at
baseline, who have more atrophy and a higher probability
to progress to dementia within a follow-up time of a
The regional patterns of decreased gray matter volume
predictive of progression to dementia in subjects with
evidence of amyloid pathology were largely similar to
those found in the whole sample, and were located in
regions known to be associated with Alzheimer pathology.
This is in line with the notion that subjects with MCI
and evidence of amyloid plaques are at an early stage of
]. In addition, we found that decreased gray matter
volume in most of these regions was also associated with
progression to dementia in MCI subjects with normal
amyloid at baseline. This suggests that the regions of
decreased gray matter volume we found to be predictive
for time to dementia are not specific for amyloid
pathology. These findings are in line with MRI studies using
a priori defined areas, which have also found that brain
regions typically atrophied in AD were predictive of
cognitive decline in MCI subjects both with and without
evidence of amyloid pathology [
7, 9, 11
]. We further
extend those results by showing that a more widespread
anatomical pattern of decreased gray matter volume is
related to cognitive decline, independent of the presence
of amyloid pathology.
A possible explanation for our results is that the
subjects who progressed within the amyloid-negative group
do have underlying AD pathology, but are above the
cut-off point for amyloid positivity. CSF Aβ1–42 levels,
although well above the threshold, were significantly lower
in subjects who progressed than in those who remained
stable (Table 1). Further characterizing the subjects who
progressed within the amyloid-negative group revealed
that those subjects who progressed to clinical AD type
dementia also had lower baseline CSF Aβ1–42 than those
who progressed to other dementias (740 ± 129 vs 930 ±
158, p < 0.01). Possibly, these subjects might have
reached abnormal CSF Aβ1–42 levels at the time of
dementia diagnosis (not tested), since several studies
have shown that higher amyloid burden at baseline, even
within the normal range, is associated with future
amyloid depositions [
10, 33, 34
]. An alternative explanation is
that subjects who progressed to clinical dementia in the
absence of amyloid pathology are akin to the concept of
suspected non-amyloid pathology (SNAP). This concept
has been constructed for subjects who present with
evidence of AD-like neurodegeneration in the absence of
amyloid pathology [
]. We studied subjects with MCI,
who suffer from cognitive impairment, suggesting that a
neurodegenerative process has already caused neuronal
damage, but the underlying cause might not be AD.
There are several disorders that show a spatially
overlapping atrophy pattern with regions that are typically
affected in AD. For example, the medial temporal lobe is
involved in hippocampal sclerosis and TDP43
pathology can be associated with widespread cortical
]. The association between regional amyloid
deposition in the brain and cortical atrophy is still
]. Our results support the idea that some
brain regions are selectively vulnerable to pathological
factors in general, and that amyloid pathology is one
possible initiator of a common neurodegenerative
Although the overall anatomical pattern of decreased
gray matter volume associated with time to progression
was largely similar in amyloid-positive and
amyloidnegative subjects, some subtle differences can be
appreciated in Fig. 2 and Additional file 2: Figure S2. In the
ventromedial prefrontal cortex, HRs were higher in
amyloid-negative subjects compared to amyloid-positive
subjects. Furthermore, the right fusiform gyrus was only
significantly associated with time to progression in
amyloid-negative subjects. This region has previously
been included in an AD signature cortical thickness
]. Whether decreased gray matter volume in
these regions could be an indication of non-AD
pathology in subjects with MCI will need to be examined in
A potential limitation of this study is that the data
were acquired at a clinical center over a relatively
long period of time, during which the dementia
workup protocols changed, diagnostic criteria evolved, and
higher field-strength MRI scanners were implemented.
Although we have corrected for the potential
influences where possible, we cannot exclude the
possibility that this variability might have led to an
underestimation of our results. Still, this can also be
considered a strong point of our study, as it supports
the robustness of our results. Another limitation is
that we defined the follow-up time when the clinical
diagnosis changed as the outcome parameter for the
survival analyses. Since follow-up times were not
strictly standardized but rather based on clinical
judgment and timing of a yearly appointment, this might
have biased the results. However, we think this is
unlikely since the time of follow-up, with an average
of 2.5 ± 1.5 and ranging up to 11 years, was similar
for subjects who progressed and those who remained
Widespread decreases in gray matter volume are
useful for the prediction of clinical progression and time
to dementia in subjects with MCI. Findings were
largely similar in subjects with and without evidence
of amyloid pathology. This leads us to consider that
although brain atrophy does not seem specific for the
underlying pathology, it is a useful marker that
reflects incipient dementia and thereby is valuable for
predicting clinical progression.
Additional file 1: Describes the MRI acquisition parameters for each of
the scanners. (PDF 57 kb)
Additional file 2: Figures S1–S3. Examining the differences between
amyloid-positive and amyloid-negative subjects. (PDF 2073 kb)
AD: Alzheimer’s disease; Aβ1–42: Beta-amyloid1–42; CDR: Clinical Dementia
Rating; CSF: Cerebrospinal fluid; MCI: Mild cognitive impairment; MMSE:
Mini-Mental State Examination; TIV: Total intracranial volume
Research of the VUmc Alzheimer Center is part of the neurodegeneration
research program of the Neuroscience Campus Amsterdam. The VUmc
Alzheimer Center is supported by Stichting Alzheimer Nederland and
Stichting VUmc fonds. The clinical database structure was developed with
funding from Stichting Dioraphte.
This work has received support from the EU/EFPIA Innovative Medicines
Initiative Joint Undertaking (EMIF grant: 115372) (MtK and PJV) and a
research grant from Boehringer Ingelheim Pharma GmbH Co KG, Germany
(WMvdF). Funding sources were not involved in data collection, data
analysis, interpretation, or writing of the manuscript.
MtK, BMT, CET, and WMvdF were involved in the study concept and the
design and collection of data. MtK and BMT analyzed the data. MtK, BMT,
PJV, FB, and WMvdF interpreted the data. MtK drafted the manuscript. BMT,
PJV, WMvdF, FB, PS, and CET critically revised the manuscript. All authors
read and approved the final manuscript.
Ethics approval and consent to participate
The study protocol was approved by the VU University Medical Centre
institutional ethics committee. All subjects gave written informed consent for
their clinical data to be used for research purposes.
Consent for publication
The authors declare that they have no competing interests.
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