Age Correction in Dementia – Matching to a Healthy Brain

Article (PDF Available)inPLoS ONE 6(7):e22193 · July 2011with45 Reads
DOI: 10.1371/journal.pone.0022193 · Source: PubMed
Abstract
In recent research, many univariate and multivariate approaches have been proposed to improve automatic classification of various dementia syndromes using imaging data. Some of these methods do not provide the possibility to integrate possible confounding variables like age into the statistical evaluation. A similar problem sometimes exists in clinical studies, as it is not always possible to match different clinical groups to each other in all confounding variables, like for example, early-onset (age<65 years) and late-onset (age≥65) patients with Alzheimer's disease (AD). Here, we propose a simple method to control for possible effects of confounding variables such as age prior to statistical evaluation of magnetic resonance imaging (MRI) data using support vector machine classification (SVM) or voxel-based morphometry (VBM). We compare SVM results for the classification of 80 AD patients and 79 healthy control subjects based on MRI data with and without prior age correction. Additionally, we compare VBM results for the comparison of three different groups of AD patients differing in age with the same group of control subjects obtained without including age as covariate, with age as covariate or with prior age correction using the proposed method. SVM classification using the proposed method resulted in higher between-group classification accuracy compared to uncorrected data. Further, applying the proposed age correction substantially improved univariate detection of disease-related grey matter atrophy using VBM in AD patients differing in age from control subjects. The results suggest that the approach proposed in this work is generally suited to control for confounding variables such as age in SVM or VBM analyses. Accordingly, the approach might improve and extend the application of these methods in clinical neurosciences.

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Age Correction in Dementia – Matching to a Healthy
Brain
Juergen Dukart
1,3
*, Matthias L. Schroeter
1,2,3
, Karsten Mueller
1
, The Alzheimer’s Disease Neuroimaging
Initiative
1Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany, 2Day Clinic of Cognitive Neurology, University of Leipzig, Leipzig, Germany, 3LIFE -
Leipzig Research Center for Civilization Diseases, University of Leipzig, Germany
Abstract
In recent research, many univariate and multivariate approaches have been proposed to improve automatic classification of
various dementia syndromes using imaging data. Some of these methods do not provide the possibility to integrate possible
confounding variables like age into the statistical evaluation. A similar problem sometimes exists in clinical studies, as it is not
always possible to match different clinical groups to each other in all confounding variables, like for example, early-onset
(age,65 years) and late-onset (age$65) patients with Alzheimer’s disease (AD). Here, we propose a simple method to control
for possible effects of confounding variables such as age prior to statistical evaluation of magnetic resonance imaging (MRI)
data using support vector machine classification (SVM) or voxel-based morphometry (VBM). We compare SVM results for the
classification of 80 AD patients and 79 healthy control subjects based on MRI data with and without prior age correction.
Additionally, we compare VBM results forthe comparison of three different groups of AD patients differing in age withthe same
group of control subjects obtained without including age as covariate, with age as covariate or with prior age correction using
the proposed method. SVM classification using the proposed method resulted in higher between-group classification accuracy
compared to uncorrected data. Further, applying the proposed age correction substantially improved univariate detection of
disease-related grey matter atrophy using VBM in AD patients differing in age from control subjects. The results suggestthat the
approach proposed in this work is generally suited to control for confounding variables such as age in SVM or VBM analyses.
Accordingly, the approach might improve and extend the application of these methods in clinical neurosciences.
Citation: Dukart J, Schroeter ML, Mueller K, The Alzheimer’s Disease Neuroimaging Initiative (2011) Age Correction in Dementia – Matching to a Healthy
Brain. PLoS ONE 6(7): e22193. doi:10.1371/journal.pone.0022193
Editor: Pedro Antonio Valdes-Sosa, Cuban Neuroscience Center, Cuba
Received February 18, 2011; Accepted June 17, 2011; Published July 29, 2011
Copyright: ß2011 Dukart et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: Juergen Dukart and Matthias L. Schroeter are supported by LIFE - Leipzig Research Center for Civilization Diseases at the University of Leipzig. LIFE is
funded by means of the European Union, by the European Regional Development Fund (ERFD) and by means of the Free State of Saxony within the framework of
the excellence initiative. Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging 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, AstraZeneca AB, Bayer Schering Pharma AG, Bristol-Myers Squibb, Eisai Global Clinical Development, Elan
Corporation, Genentech, GE Healthcare, GlaxoSmithKline, Innogenetics, Johnson and Johnson, Eli Lilly and Co., Medpace, Merck and Co., Novartis AG, Pfizer, F.
Hoffman-La Roche, Schering-Plough, Synarc, as well as non-profit partners the Alzheimer’s Association and Alzheimer’s Drug Discovery Foundation, with
participation from the U.S. Food and Drug Administration. Private sector contributions to ADNI are facilitated by the Foundation for the National Institutes of
Health (www.fnih.org). 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 for Neuro Imaging at the Universityof
California, Los Angeles. This research was also supported by NIH grants P30 AG010129, K01 AG030514, and the Dana Foundation. The funders had no role in study
design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: dukart@cbs.mpg.de
Introduction
In recent research, age-related changes have frequently been
reported in different imaging modalities investigating healthy
subjects and patients in advanced age [1]–[4]. When comparing
groups of younger and older subjects, most studies have reported
age-related decreases in grey matter (GM) densities in specific
brain structures [1],[4],[5] measured by magnetic resonance
imaging (MRI). Age-related changes have also been reported for
functional measurements like glucose utilization [6] measured by
[18F]fluorodeoxyglucose positron emission tomography. The use
of different univariate and multivariate statistical approaches for
the comparison of different groups of dementia patients with
healthy control subjects has led to the necessity to control for age-
related changes, as these might cover or lead to an overestimation
of group-specific differences.
Usually, when comparing imaging data of different groups of
subjects, these are matched for such confounding effects as age and
sex, and the confounding variable is usually integrated as a
covariate in the statistical model [7]. However, when using
multivariate approaches, for example for automatic detection or
differentiation of types of dementia, it is not always possible to
control for age-related changes or any other confounding
variables. Multivariate classification algorithms do not usually
provide the possibility to integrate covariates into between-group
classification.
As has been shown by Franke et al. [8], age estimation in
patients with mild Alzheimer’s disease (AD) based on T1-scans
results in an age gap of +10 years for these subjects compared to
healthy control subjects. Age of AD patients is overestimated using
T1-scans because age-related changes are equally directed with
changes associated with AD. As a result of the equal direction of
PLoS ONE | www.plosone.org 1 July 2011 | Volume 6 | Issue 7 | e22193
these changes, the classification algorithm might also lead to a
misclassification of younger AD patients and older control
subjects. Therefore, to avoid these misclassifications, groups used
for the training of the multivariate pattern classifier should ideally
be matched to each single subject who has to be classified in
clinical setting. This issue is highly important for clinical practice
as the use of all univariate and multivariate approaches has mainly
the aim of enabling accurate and early detection and differenti-
ation of various dementia syndromes in single subjects.
A similar problem arises when individual dementia patients or
two groups of dementia patients differing in age (e.g. early- and
late-onset AD) are compared with a specific group of control
subjects or to each other using univariate approaches. This is
because it is rarely possible to find sufficiently large groups of
control subjects, which match each individual patient in age and
other confounding variables. Otherwise, when a patient or a group
of patients differs from the control group in specific confounding
variables, including these into statistical evaluation might cover
disease-related changes. This issue might therefore be relevant for
group comparisons, as for example when comparing early- and
late-onset AD patients with healthy control subjects or to each
other it might be interesting to look for differential atrophy
patterns in these two groups. By definition, these two groups differ
in their mean age (early-onset AD: age,65years, late-onset AD:
age $65 years). In order to exclude confounding effects of age, two
different groups of healthy control subjects are usually used to
evaluate atrophy patterns in both AD patients groups [9].
However, this procedure restricts the quantitative and qualitative
comparison of atrophy patterns in both groups of AD patients as
there might be substantial differences between groups of control
subjects used for both comparisons. Therefore, it is highly
important to have methodical approaches enabling control for
such confounding effects.
In this work, we propose a linear detrending method in terms of
the general linear model (GLM) based only on control subjects to
control for the effects of age in single subjects and in groups of
subjects prior to statistical evaluation using support vector machine
classification (SVM) or voxel-based morphometry (VBM). A linear
model was chosen based on a study by Good et al. [1]. Thereby,
the authors compared linear vs. quadratic models in terms of
describing absolute and relative age related GM changes in a
healthy cohort consisting of 465 subjects. While the quadratic
coefficients failed to reach significance the linear coefficient was
highly significant. To evaluate this method, we compare SVM and
VBM results for differentiation of AD patients and healthy control
subjects with and without linear detrending of grey matter (GM)
values for age prior to statistical evaluation. We hypothesize that
applying linear age detrending prior to statistical evaluation should
increase the diagnostic accuracy for differentiation of dementia
patients and control subjects using SVM, and improve univariate
detection of GM changes when applying VBM for the evaluation
of groups of AD patients differing in age from the control group.
Methods
Age correction
To remove age-related effects, it is highly important to
differentiate between them and disease-related changes. For that
reason, when comparing single AD patients and control subjects
using SVM or VBM we propose performing additional GLMs
prior to the final statistical evaluation using the same methodical
approach as proposed by Friston et al. [7], but only for control
subjects. Thereby, GLMs are calculated for all GM voxels yCat
each coordinate separately. A constant and age are the only
columns in the matrix XCand only the group of healthy control
subjects is used to determine the regression coefficients b,
consisting of b0for the constant and bC, for age-related changes
at each voxel coordinate separately. In terms of GLM, the
following simple regression model has to be solved for bby
minimizing the sum of squared residuals , Pe2
C?min:
yC~XCbzeCð1Þ
Solving (1) for least squares estimates of bsatisfies the following
normal equations ([10], p. 91):
XT
CXCb~XT
CyCð2Þ
Solving linear equations system (2) for bresults in:
b0
bC

~XT
CXC

{1XT
CyC
To obtain age-corrected GM values yC, the calculated age
regression coefficients bCare then applied to corresponding voxels
in GM images of both healthy control subjects and AD patients.
Thereby, the residual amount explained by each subject’s individual
age XAis removed from the observed GM voxel values yAof this
subject at each coordinate using the determined coefficient bC, so:
yCOR~yA{bCXA
It is very important to use only control subjects to determine the
regression coefficient, as it has been reported that early- and late-
onset AD patients might show a differential pattern of atrophy in
MRI [9],[11,[12]. As a result, removing age-related effects using
regression coefficients determined in the AD group might also
remove disease-related changes due to their interaction with age. In
further statistical analyses, SVM and VBM results obtained using
GM images containing initial uncorrected GM values yAare
compared with results using age-corrected values yCOR .Theage
correction procedure was implemented in Matlab 7.7 (MathWorks
Inc., Sherborn, MA).
Subjects
To evaluate the effect of age correction, we extracted
multicenter MRI data of 80 patients with clinically validated AD
and 79 healthy control subjects (Table 1) from the Alzheimer’s
disease Neuroimaging Initiative (ADNI) database (www.adni-info.
org). AD patients were randomly selected from the database.
Control subjects were selected to match the AD patients for
gender, age and education. The ADNI is a partnership of the
National Institute of Aging, the National Institute of Biomedical
Imaging and Bioengineering, the Food and Drug Administration,
private pharmaceutical companies and non-profit organizations.
Diagnosis of AD patients was based on NINCDS/ARDRA
criteria [13]. Exclusion criteria for the ADNI data were the
presence of any significant neurological disease other than AD,
history of head trauma followed by persistent neurological deficits
or structural brain abnormalities, psychotic features, agitation or
behavioral problems within the last three months or history of
alcohol or substance abuse. For most subjects, multiple follow-up
MRI scans were available. For each subject only the first MRI
scan was used for further analysis. The study was conducted
Age Correction in Dementia
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according to the Declaration of Helsinki. Written informed
consent was obtained from all participants before protocol-specific
procedures were performed.
Image preprocessing and data analysis
All image-processing steps described below were carried out using
the SPM5 software package (Statistical Parametric Mapping
software: http://www.fil.ion.ucl.ac.uk/spm/) implemented in Ma-
tlab 7.7 (MathWorks Inc., Sherborn, MA). SVM classification was
conducted with the LIBSVM software [14] using the Matlab
interface.
MRI data
The MRI dataset included standard T1-weighted images
obtained with different scanner types using the volumetric
MPRAGE sequence varying in TR and TE with an in-plane
resolution of 1.2561.25 mm and 1.2 mm sagittal slice thickness.
Only images obtained using 1.5T scanners were used in this study.
All images were preprocessed as described on the ADNI website
(http://www.loni.ucla.edu/ADNI/Data/ADNI_Data.shtml), in-
cluding distortion correction and B1 non-uniformity correction.
Preprocessing
MRI data were interpolated to an isotropic resolution of
16161mm
3
, bias-corrected for inhomogeneity artifacts, segmented
and spatially normalized to an averaged size template created from
all subjects using the DARTEL (Diffeomorphic Anatomical
Registration Through Exponentiated Lie algebra) approach [15].
Within the normalization procedure, the data were modulated to
preserve the total amount of signal in the images. The data were
smoothed using a Gaussian kernel of 12 mm FWHM. This high
kernel was chosen because it has been shown in previous studies
investigating AD with VBM that large amount of smoothing of MRI
data results in an accurate statistical evaluation of GM atrophy
[16],[17]. The obtained GM images were masked twice: firstly to
avoid contamination by misclassified voxels, and secondly, after the
smoothing to avoid big edge effects. The mask was obtained after
extensive testing by excluding all voxels in the first and the last
template created by the DARTEL approach with a probability of
below 0.2 for belonging to GM and including only voxels that exceed
this threshold in both templates. Subsequently, all GM images were
corrected for age effects using the linear regression approach
described above. The statistical analysis using SVM and VBM was
performed twice, with and without correction for age effects.
SVM
Multivariate pattern classification, as described in [18], was
performed with a linear kernel by identifying a separating
hyperplane that maximizes the distance between different clinical
groups based on whole-brain information. The optimization and
cross-validation of the trained SVM was performed by using the
the split half method. This procedure splits the group into two
independent samples and trains the model on one of the samples
for subsequent class assignation of the sample that was not
included in the training procedure. Both samples are used once as
training sample and once as the sample that has to be classified.
This validation method enables the generalization of the trained
SVM to data that have never been presented to the SVM
algorithms previously. SVM classification was performed for the
whole group (Table 1) twice; once with and once without age
correction applied prior to SVM. The reported accuracy is the
percentage of subjects correctly assigned to the clinical diagnosis in
both samples. As it was expected that younger AD patients and
older control subjects tend to be misclassified using SVM, AD
patients and control subjects, which were misclassified with and
without age correction, were compared to each other in their
mean age using an independent samples t-test with a significance
threshold of p,0.05 (one-tailed). Additionally, to enable an
accurate evaluation of potential statistical differences in differen-
tiation accuracies when using age-corrected compared to uncor-
rected data the split half procedure was repeated 60 times by
randomly permuting the subjects to the training and testing groups
and calculating the SVM classification accuracies for both, age-
corrected and uncorrected data. The obtained distributions of
accuracy values were compared to each other using an
independent samples t-test with a significance threshold of
p,0.05.
VBM
To evaluate the effect of suggested age correction onto VBM
analyses, differences between groups of AD patients (Table 2) and
the group of healthy control subjects were assessed using voxel-wise
independent samples t-tests. Thereby, pair-wise group comparisons
with the control group were performed using only 25 youngest AD
patients, 25 oldest AD patients or 25 AD patients fitting the mean of
the control group. All three AD groups were subgroups of the AD
group used for SVM comparison. For all comparisons the t-test was
calculated three times: with age included as covariate, without age
as covariate and using age-corrected GM images. Sex was included
as covariate in all t-tests. Further, we wanted to evaluate if the
proposed method for age correction leads to an over- or
underestimate of atrophy in one of the AD groups. For this
purpose, the control group used for SVM was split into three groups
of 25 subjects each. The three groups of control subjects (Table 2)
were matched for age to the three groups of AD patients.
Subsequently, three VBM analyses using age-uncorrected GM
images were performed comparing each of the AD groups to the age
matched sample of control subjects. For these comparisons, age was
additionally included as covariate. Atrophic regions were investi-
gated with a threshold of p,0.001 (uncorrected) at the voxel level
and p,0.05 (FWE corrected for multiple comparison) at the cluster
level. The first threshold detects all voxels in the brain exceeding the
probability of 0.001 for being significantly different between both
groups. The second threshold removes all clusters smaller than a
cluster size expected by chance (accounted for the number of
comparisons) which additionally decreases the amount of false
positive errors. Additionally, to evaluate age-related changes in
healthy control subjects, voxel-wise correlations between GM
densities and age were calculated using only control subjects, with
sex included as covariate.
Further, as the model obtained using the age correction
approach described above does not fulfill the criteria for a strict
Table 1. Subject group characteristics for SVM.
Controls AD t-test(df,t,p)
Number 79 80 -
Male/Female 41/38 40/40 -
Age (years6SD) 75.864.9 75.7.167.0 157,0.1,0.89
MMSE (score6SD) 28.761.7 23.662.2 157,16.6,,0.001
CDR (score6SD) 0.0460.13 0.8160.24 157,616.4,,0.001
AD Alzheimer’s disease, CDR Clinical Dementia Rating, MMSE Mini Mental State
Examination, SD standard deviation.
doi:10.1371/journal.pone.0022193.t001
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cross-validation because the model estimated on control subjects is
applied to the same data which were used to train the model we
repeated the VBM and SVM analyses using a stricter cross-
validation. Thereby, the control group was split into two equally
sized subgroups. The age effect was estimated in both subgroups
independently. The obtained betas for age from subgroup 1 were
applied to data of subgroup 2 and vice versa. Additionally, to
avoid a now possible confound in the AD group as in the control
group two models have now been applied to detrend for age while
in the AD only one model was used, we also split the AD group
randomly into two equally sized subgroups. A model which has
been estimated from only one of the control subgroups was applied
on subgroup 1 from AD. Correspondingly, for the 2
nd
AD
subgroup the second model from the other control subgroup was
used. This proceeding insures that the model used to detrend the
data for age is only applied on data which have not been seen by
the model before. For the VBM comparisons using this strict cross-
validation an additional covariate was added containing the
information if model 1 or 2 was used for the regression.
Statistical analysis
Group comparisons for age and severity of dementia as
measured by the MMSE (Mini Mental State Examination, [19])
and CDR (Clinical Dementia Rating Scale, [20]) between groups
used for SVM were performed using independent samples t-tests
(two-tailed). For the four groups used for VBM comparison on
age-corrected data (all control subjects, young AD, mean AD and
old AD), age, MMSE and CDR were compared by conducting
ANOVAs (analyses of variance). If an ANOVA revealed a
significant between-group effect, a Bonferroni t-test was calculated
with a significance threshold of p,0.05 (Bonferroni corrected for
multiple comparisons, two-tailed). The six groups used for pairwise
age-matched VBM comparisons (young controls vs. young AD,
mean controls vs. mean AD, old controls vs. old AD) were
compared to each other in age, MMSE and CDR using
independent samples t-tests.
Group differences for both, VBM and SVM groups, regarding
sex were evaluated using a chi-square test for independent
samples. The statistical analysis was performed using the
commercial software package SPSS 17.0 (http://www.spss.com/
statistics/).
Results
Clinical characteristics
The chi-square test for independent samples did not reveal a
statistical differences in sex between groups used for SVM
[x
2
(1) = 0.06;p = 0.811]. MMSE scores and CDR scores differed
significantly between AD patients and control subjects used for
SVM (Table 1). AD patients and control subjects used for SVM
did not differ in age.
There was no significant difference in sex between the three age
groups of AD patients and the group consisting of all control
subjects [x
2
(3) = 2.07;p = 0.56] or between the three age groups of
AD patients and the three age-matched groups of control subjects
(Table 2) [x
2
(5) = 3.44;p = 0.63] used for VBM comparisons.
The ANOVA revealed significant differences in MMSE
[F(150) = 85.99;p,0.001] and CDR [F(150) = 200.32;p,0.001]
scores between the three AD groups and the group consisting of all
control subjects used for VBM. The post-hoc tests revealed no
differences in the mean MMSE and CDR scores between the
three groups of dementia patients, indicating a similar severity of
dementia syndrome [young AD vs. mean AD: t(48) = 0.0;p = 1.0;
young AD vs. old AD: t(48) = 1.0;p = 1.0; mean AD vs. old AD:
t(48) = 1.0;p = 1.0]. All three groups of AD patients had signifi-
cantly lower MMSE scores compared to the control group [young
AD vs. control group: t(87) = -8.1;p,0.001; mean AD vs. control
group: t(87) = 28.1;p,0.001; old AD vs. control group:
t(87) = 29.8;p,0.001]. As expected, the ANOVA revealed a
significant group difference in age between groups used for VBM
comparisons. In post-hoc tests, all three groups of AD patients
differed significantly from each other [young AD vs. mean AD:
t(18) = 28.1;p,0.001; young AD vs. old AD: t(18) = 215.0;
p,0.001; mean AD vs. old AD: t(18) = 226.6; p,0.001]. Young
AD patients [t(87) = 27.6; p,0.001] and old AD patients [t(87) =
29.8; p,0.001] differed significantly from the control group.
There was no significant difference in age between mean AD
patients and control subjects [t(87) = 20.1; p = 1.0].
For the comparison of age-matched control subjects and AD
patients (young controls vs. young AD, mean controls vs. mean AD,
old controls vs. old AD) independent samples t-tests did not reveal
any significant differences in age [young controls vs. young AD:
t(48) = 1.55;p= 0.13, mean controls vs. mean AD: t(48) = 0.0,p = 1.0,
old controls vs. old AD: t(48) = 21.36;p = 0.18]. As expected
MMSE [young controls vs. young AD: t(48) = 7.22;p,0.001, mean
controls vs. mean AD: t(48)= 9.86,p,0.001, old controls vs. old AD:
t(48) = 10.48;p,0.001] and CDR [young controls vs. young AD:
t(48) = 12.41;p,0.001, mean controls vs. mean AD: t(48)=
13.3,p,0.001, old controls vs. old AD: t(48) = 15.32;p,0.001]
scores differed significantly for all comparisons of the age-matched
groups.
SVM results
Applying SVM to uncorrected data resulted in a high overall
classification accuracy of 83.0%, which was calculated using the
split half procedure. However, as expected, there was a significant
difference in age between misclassified control subjects and AD
patients (Figure 1). Misclassified AD patients were significantly
Table 2. Subject group characteristics for VBM.
Young Controls Mean Controls Old Controls Young AD Mean AD Old AD ANOVA (df,F,p)
Number 25 25 25 25 25 25 -
Male/Female 15/10 12/13 9/16 12/13 12/13 14/11 -
Age (years6SD) 70.763.1 75.761.5 81.162.6 69.263.7 75.661.3 82.263.0 5,87.8,,0.001
MMSE (score6SD) 28.462.4 28.661.2 28.961.1 23.862.1* 23.862.1* 23.562.3* 5,48.3,,0.001
CDR (score6SD) 0.0460.1 0.0460.1 0.0460.1 0.7660.3* 0.860.3* 0.8660.2* 5,112.0,,0.001
*significant difference compared to the group of age-matched control subjects. AD Alzheimer’s disease, ANOVA analysis of variance, CDR Clinical Dementia Rating,
MMSE Mini Mental State Examination, SD standard deviation.
doi:10.1371/journal.pone.0022193.t002
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younger than misclassified control subjects [t(25) = 2.1;p = 0.02]
indicating that age has a substantial effect onto classification
outcome. When SVM was applied to age-corrected data, the
overall classification accuracy further increased to 85.0%.
Additionally, there was no further difference in mean age between
misclassified AD patients and control subjects [t(22) =
20.69;p = 0.75].
Permutation statistic using the split half method revealed a
mean classification accuracy of 81.9% using uncorrected data.
Applying age-correction prior to SVM resulted in a significantly
improved mean classification accuracy of 83.2% [t(118) = 2.7;
p = 0.004] (Figure 2). The split half accuracy using the data
obtained after the strict cross-validation (by applying an age
correction model on data which have not been used to train the
model) revealed a similar improvement from 82.4% using
uncorrected data to 84.3% after age-correction for the same split
half groups.
VBM results
The comparison of the three groups of AD patients differing in
age with the same group of control subjects without including age
as covariate resulted in a differential qualitative and quantitative
pattern of GM atrophy in all three groups (Figure 3 and 4). There
were no voxels in the young AD group that exceeded the
significance threshold. In the mean AD group, differences relative
to control subjects were detected in the right hippocampus and in
the right middle and inferior temporal lobe. Old AD patients
showed bilaterally an extensive frontotemporal, cingulate, hippo-
campal and thalamic atrophy pattern compared to control
subjects. Including age as covariate into VBM analyses resulted
in a substantially different atrophy pattern. Young AD patients
showed a bilateral atrophy in the hippocampus and middle and
inferior temporal lobe. For mean AD patients, the atrophy pattern
detected was very similar to the pattern detected without including
age as covariate, with GM atrophy in the right hippocampus and
in the right middle and inferior temporal lobe. However, there
were no significant changes detected for old AD patients when age
was included as covariate.
Applying age correction prior to statistical evaluation using
VBM resulted in the detection of substantial differences between
young AD patients and healthy control subjects, in particular
parietotemporal, frontal, cuneal and hippocampal GM atrophy. A
comparison of mean AD patients with control subjects after age
correction resulted in a highly similar pattern of atrophy compared
to VBM using uncorrected data. For old AD patients, GM atrophy
after age correction were observed in the right hippocampus and
in the right polar region of the temporal lobe. The comparison of
the three different age groups of AD patients with age-matched
control subjects revealed a similar pattern of atrophy extension to
that detected using age-corrected data with younger AD patients
showing the strongest GM atrophy and older AD having
substantially less pronounced atrophy compared to age-matched
control subjects. In these older groups a small gender imbalance
might have partially biased the statistical results in this group
comparison.
VBM results using the data obtained after the strict cross-
validation models for age correction did not differ substantially
from the results obtained using a single model to detrend for age.
Voxel-wise correlations between age and GM atrophy in
healthy control subjects revealed an age-related decrease
(Figure 5a) in bilateral cingulate, temporal and hippocampal
regions. A further age-related decrease was observed in the right
prefrontal cortex.
Discussion
When comparing imaging data of groups of patients with
healthy control subjects to investigate disease-related changes,
control subjects are usually selected to match the patient groups in
possible confounding variables that are expected to have an
impact onto imaging data. In the further statistical evaluation,
possible confounding variables are then additionally included as
Figure 1. Age characteristics of missclassified subjects using SVM. Mean age of misclassified subjects in AD and control group using SVM
classification with (middle two bars) and without (right two bars) age correction priorly applied. Left two bars represent the mean of all subjects in
each group used for SVM. Error bars represent the standard errors of mean. AD Alzheimer’s disease, SVM support vector machine classification,
* significant difference between conditions.
doi:10.1371/journal.pone.0022193.g001
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PLoS ONE | www.plosone.org 5 July 2011 | Volume 6 | Issue 7 | e22193
covariates. This straightforward procedure, although sufficient to
exclude major effects of possible confounding variables, is not
always applicable in clinical studies for several reasons.
In this study, we propose a methodical approach to control for
effects of confounding variables like age in imaging data prior to
univariate or multivariate statistical evaluation by calculating
linear regression models. This is approach is similar to a method
applied in some earlier studies [21],[22]. In these studies a linear
regression model was applied on regional volumes and on total
brain volume to control for the effect of intracranial volume prior
to statistical evaluation. Thereby, age-related effects on MRI data
are estimated voxel-wise using only healthy control subjects to
calculate the regression coefficient. In the second step, the amount
of GM atrophy explained by the age factor is removed from all
data on a single subject level. To investigate the effect of the
proposed age-correction method, we compared VBM and SVM
results for differentiation of AD patients and healthy control
subjects, with and without age correction applied prior to statistical
evaluation.
SVM classification using GM values without correction resulted
in very high accuracy for differentiation of AD patients and
healthy control subjects, consistent with results of previous studies
applying this method [18]. However, applying SVM without prior
age correction resulted in a significant misclassification of younger
AD patients and older control subjects indicating that age has a
major impact onto differentiation accuracy using SVM. Applying
age correction before SVM further increased the classification
accuracy. In addtion, the two groups of misclassified patients and
control subjects did not further show a difference in mean age.
Although an increase of only about 2% might appear not to be
noteworthy, when dealing with already very high accuracies it is
more important to decrease the percentage of misclassified subjects
which is in our case 17% (100%–83%: e.g. when already obtaining
accuracies of 98% an additional improvement of only 1% to 99%
would mean that the amount of misclassified subjects would
Figure 3. Visualization of VBM results for different age groups. GM atrophy projected onto an averaged brain detected with VBM in three
groups of AD patients (columns) differing in age compared to the same group of healthy control subjects without age as covariate (upper row), with
age as covariate (middle row) and after the proposed age correction (lower row). Color bars indicate the t-values. Images are thresholded with
p = 0.001 on voxel level (uncorrected) and p = 0.05 on cluster level (FWE corrected). AD Alzheimer’s disease, VBM voxel-based morphometry.
Anatomical convention. Radiological convention.
doi:10.1371/journal.pone.0022193.g003
Figure 2. Results of permutation statistics using SVM. Results of
permutation statistics using SVM split half cross-validation on
uncorrected (red) and age-corrected data (blue). Observed frequency
is the cumulative number of accuracies observed for a specific accuracy
range.
doi:10.1371/journal.pone.0022193.g002
Age Correction in Dementia
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Age Correction in Dementia
PLoS ONE | www.plosone.org 7 July 2011 | Volume 6 | Issue 7 | e22193
decrease by 50% which is clinically important despite the fact that
the accuracy is increased only by 1%). This error rate decreased to
15% after applying age correction which means a decrease by
12% (taking the initial 17% as baseline) in the amount of
misclassified subjects which makes the improvement highly
relevant for clinical application.
The improved classification accuracy after age correction and
the absence of age differences between misclassified AD patients
and control subjects after age correction indicate that some
subjects were misclassified due to their large deviation from the
mean age of the corresponding group. In younger AD patients,
smaller age-related changes might have covered the disease-related
effect while in older healthy control subjects, the normal age-
related GM atrophy might have been misrecognized as a disease-
specific alteration.
The results of VBM comparisons complement and provide
further support for this interpretation. Here, three groups of AD
patients differing in age were compared to the same group of
control subjects. Generally, results confirmed previously reported
regional atrophy patterns for AD patients [23]. When age was not
included as covariate, old AD subjects showed an extensive
atrophy in frontotemporal, cingulate, thalamic and hippocampal
regions. In contrast, young AD patients showed substantially less
extended disease-specific reductions. However, when age was
correlated with GM changes in healthy control subjects, similar
regions to those detected in old AD patients showed an age-related
GM atrophy. Calculating an overlay of atrophy regions detected
in old AD patients without including age as covariate and regions
showing a normal age related decline resulted in a large overlap of
changes detected in both analyses (Figure 5b). This result indicates
that as expected, changes detected in old AD patients whithout
including age as a covariate substantially overestimate the real
amount of atrophy in this patient group. The opposite effect was
observed when age was included as covariate – less disease-related
GM atrophy was detected in old AD patients. Young AD patients
now showed a strong decrease in GM densities in hippocampal
and inferior and middle temporal regions. The amount of GM
atrophy was comparable in young and mean AD patiens.
Applying the proposed age correction method prior to VBM
analyses substantially improved the detection of GM atrophy in
young AD patients. This group showed the most extensive age-
corrected GM atrophy compared to mean AD and old AD
patients. VBM performed after age correction also detected GM
atrophy in old AD patients in hippocampal, inferior temporal,
parietal and frontal regions although these were less extensive than
in the two other groups of AD patients. For the mean AD group,
GM atrophy detected without age as covariate, with age as
covariate and with age correction prior to statistical evaluation did
not show any substantial qualitative or quantitative differences.
Additionally, the quantative and qualitative pattern detected using
age-corrected data was highly similar to differences detected in
comparisons of age-matched AD patients and control subjects.
This similarity indicates that the application of the proposed age
correction method provides a sufficient control for the effect of
possible covariates like age and therefore enables a direct
comparison of clinical groups with substantial initial differences
in potential confounding variables. The higher cluster extension
for all comparisons using age-corrected data compared to age-
matched comparisons are rather a statistical artifact resulting due
to different group sizes used for these comparisons. Further, the
results obtained using age-corrected data are in line with previous
studies indicating that less severe disease-related pathology is
required with increased age to induce a similar decline in cognitive
performance [11],[12]. Furthermore, as all three groups of AD
patients showed a similar stage of cognitive impairment, the
amounts of atrophy detected in all three groups in our study
suggest a negative linear relationship between age and GM
atrophy which are sufficient to induce similar cognitive impair-
ment.
However, some very important aspects have to be considered
prior to application of the proposed method to control for possible
confounding variables in VBM or SVM studies. One of these
major points is the group size used for the comparisons. On the
one hand, the group size of the control group used for the
calculation of the regression coefficients shoud be sufficiently large
to provide a robust estimate of age-related changes in the total
population. On the other hand, the application of the pre-
regression for age does change the degrees-of-freedom in the final
statistical model for VBM studies which might lead to differences
in results when using smaller sample sizes. Therefore, studies using
lower sample sizes should take care or account for these altered
degrees-of-freedom when using the proposed method.
Figure 4. VBM results for different age groups. VBM results for the comparison of three groups of AD patients differing in age compared to the
same group of control subjects without age as covariate (upper two diagrams), with age as covariate (middle two diagrams) and after the proposed
age correction (lower two diagrams). Diagrams on the left represent the number of voxels detected with VBM in the three groups of AD patients.
Diagrams on the right represent the peak t-values of clusters exceeding the threshold (p = 0.001 uncorrected on voxel level and p= 0.05 FWE
corrected on cluster level) detected in each group of AD patients. AD Alzheimer’s disease, FWE family-wise error, VBM voxel-based morphometry.
doi:10.1371/journal.pone.0022193.g004
Figure 5. Effect of healthy aging on VBM results in AD.
a) Regions showing an age-related GM atrophy in healthy control
subjects detected in a correlational analysis using VBM (p = 0.001
uncorrected on voxel level and p = 0.05 FWE corrected on cluster level)
plotted onto an averaged brain. Color bar represents the t-values for
the correlation. b) Overlap of regions (in red) showing GM atrophy in
old AD patients compared to the control group, without age included
as covariate, and regions detected in the correlational analysis showing
an age-related GM decline in the control group. Results are plotted
onto an averaged brain. Only regions which exceeded the significance
threshold (p = 0.001 uncorrected on voxel level and p = 0.05 FWE
corrected on cluster level) in both analyses are shown. AD Alzheimer’s
disease, FWE family-wise error, GM grey matter, VBM voxel-based
morphometry. Radiological convention.
doi:10.1371/journal.pone.0022193.g005
Age Correction in Dementia
PLoS ONE | www.plosone.org 8 July 2011 | Volume 6 | Issue 7 | e22193
A further important point which has to be considered prior to
application of the proposed method is the potential mutual
correlation between the variable used for pre-regression and other
covariates used for subsequent analysis. In our study we only used
sex as an additional covariate in the subsequent VBM analysis.
Furthermore, it has been shown by Good et al. [1] that the
interaction between age and sex does not reach significance even
in a substantially larger cohort. Therefore, we ignored this
potential mutual effect in our study. Nonetheless, when applying
the proposed method using any other covariates in the subsequent
analysis the potential effect between these covariates and age (or
any other variable for pre-regression) has to be carefully
investigated. A possible option to account for mutual correlation
between the covariates would be that a more complex pre-
regression model is used including more than one covariate in a
multiple linear regression model. However, this option has first to
be carefully investigated.
Finally, the proposed method is not meant to replace classical
VBM analyses which simply include possible covariates into the
design matrix. The proposed option is rather meant for the studies
when matching is not possible for any reason, as might be the case
for example when comparing patient groups differing in more
than one factor. Another option for the application of the
proposed method would be a pre-regression for possible
confounding effects prior to application of SVM classification.
Conclusion and perspectives
In our study, we suggest an easily applicable approach providing
the possibility to compare groups of subjects differing in specific
confounding variables or to control for the effect of confounding
variables in different imaging modalities in a separate step before
multivariate pattern classification algorithms are applied. Using
age as an example of a confounding variable in comparisons of
patients with AD and healthy control subjects, we showed that
applying the proposed method improves the between-group
classification using SVM and the detection of univariate
differences using MRI data in groups of AD patients of differing
age. However, the proposed approach is not limited to age or to
between-group evaluation. It can be easily applied at a group or
single subject level to remove effects of any other confounding
variables which might affect the statistical evaluation. However,
the proposed method is not meant to replace the usual statistical
approach of including possible confounding variables directly into
the statistical analyses of VBM studies. If matching is easily
possible which is usually the case in studies investigating healthy
volunteers common statistical methods should be prefered.
Acknowledgments
Data used in preparation of this article were obtained from the Alzheimer’s
Disease Neuroimaging Initiative (ADNI) database (www.loni.ucla.edu/
ADNI). ADNI investigators include (complete listing available at http://
www.loni.ucla.edu/ADNI/Collaboration/ADNI_Manuscript_Citations.
pdf).
Author Contributions
Conceived and designed the experiments: JD MS KM. Performed the
experiments: JD MS KM. Analyzed the data: JD MS KM. Contributed
reagents/materials/analysis tools: JD MS KM. Wrote the paper: JD MS
KM.
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Age Correction in Dementia
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    • "To control for this effect, we correct the metabolite data by taking the residuals of linear regression models on storage time at -20° [27]. We build the regression model on healthy controls only, and then apply the model to all subjects in order to avoid the removal of some disease-related effects as suggesied in [28]. Specifically, let X be one column of the metabolite feature vector (one metabolite) to be corrected, and T be the column vector of the storage time at -20°. "
    [Show abstract] [Hide abstract] ABSTRACT: Background Major depressive disorder (MDD) is a heterogeneous disease at the level of clinical symptoms, and this heterogeneity is likely reflected at the level of biology. Two clinical subtypes within MDD that have garnered interest are “melancholic depression” and “anxious depression”. Metabolomics enables us to characterize hundreds of small molecules that comprise the metabolome, and recent work suggests the blood metabolome may be able to inform treatment decisions for MDD, however work is at an early stage. Here we examine a metabolomics data set to (1) test whether clinically homogenous MDD subtypes are also more biologically homogeneous, and hence more predictiable, (2) devise a robust machine learning framework that preserves biological meaning, and (3) describe the metabolomic biosignature for melancholic depression. ResultsWith the proposed computational system we achieves around 80 % classification accuracy, sensitivity and specificity for melancholic depression, but only ~72 % for anxious depression or MDD, suggesting the blood metabolome contains more information about melancholic depression.. We develop an ensemble feature selection framework (EFSF) in which features are first clustered, and learning then takes place on the cluster centroids, retaining information about correlated features during the feature selection process rather than discarding them as most machine learning methods will do. Analysis of the most discriminative feature clusters revealed differences in metabolic classes such as amino acids and lipids as well as pathways studied extensively in MDD such as the activation of cortisol in chronic stress. Conclusions We find the greater clinical homogeneity does indeed lead to better prediction based on biological measurements in the case of melancholic depression. Melancholic depression is shown to be associated with changes in amino acids, catecholamines, lipids, stress hormones, and immune-related metabolites. The proposed computational framework can be adapted to analyze data from many other biomedical applications where the data has similar characteristics.
    Full-text · Article · Aug 2016
    • "These results are in line with recent studies that showed AD-like MRI-based indices in pMCI subjects [4, 57], increased GM atrophy of approximately 2% per year in AD [58] , accelerated changes in whole brain volume in MCI [18], acceleration in atrophy rates as subjects progress from MCI to AD [59], and greater GM loss in certain regions in pMCI subjects [60, 61]. Furthermore, our results also support the assumption of AD being a form of or at least being associated with accelerated aging [18, 62, 63]. Taking into account the patients APOE genotype revealed significant differences within the MCI groups at baseline and follow-up Table 5. Cox Regression values for cumulative AD incidence in APOE ε4 carriers and non-carriers in the BrainAGE, MMSE, CDR-SB, and ADAS scores alone and in combination with the APOE ε4 carrier status, based on a median split. "
    [Show abstract] [Hide abstract] ABSTRACT: In our aging society, diseases in the elderly come more and more into focus. An important issue in research is Mild Cognitive Impairment (MCI) and Alzheimer’s Disease (AD) with their causes, diagnosis, treatment, and disease prediction. We applied the Brain Age Gap Estimation (BrainAGE) method to examine the impact of the Apolipoprotein E (APOE) genotype on structural brain aging, utilizing longitudinal magnetic resonance image (MRI) data of 405 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. We tested for differences in neuroanatomical aging between carrier and non-carrier of APOE ε4 within the diagnostic groups and for longitudinal changes in individual brain aging during about three years follow-up. We further examined whether a combination of BrainAGE and APOE status could improve prediction accuracy of conversion to AD in MCI patients. The influence of the APOE status on conversion from MCI to AD was analyzed within all allelic subgroups as well as for ε4 carriers and non-carriers. The BrainAGE scores differed significantly between normal controls, stable MCI (sMCI) and progressive MCI (pMCI) as well as AD patients. Differences in BrainAGE changing rates over time were observed for APOE ε4 carrier status as well as in the pMCI and AD groups. At baseline and during follow-up, BrainAGE scores correlated significantly with neuropsychological test scores in APOE ε4 carriers and non-carriers, especially in pMCI and AD patients. Prediction of conversion was most accurate using the BrainAGE score as compared to neuropsychological test scores, even when the patient’s APOE status was unknown. For assessing the individual risk of coming down with AD as well as predicting conversion from MCI to AD, the BrainAGE method proves to be a useful and accurate tool even if the information of the patient’s APOE status is missing.
    Full-text · Article · Jul 2016
    • "Although the performance improvement did not reach significance according to the corrected repeated t-test, the average t over all the classifiers was significantly different from zero, verifying the findings in the AD vs. NC classification of Dukart et al (2011) and in the MCI-to-AD conversion prediction of Moradi et al (2015). The rationale for age-removal stemmed from strong evidence of overlapping effects of normal aging and dementia on brain atrophy (Fjell et al 2013; Dukart et al 2011). We note that there was no stratification according to age or gender when dividing the data into two sets A i and B i . "
    [Show abstract] [Hide abstract] ABSTRACT: We present a comparative split-half resampling analysis of various data driven feature selection and classification methods for the whole brain voxel-based classification analysis of anatomical magnetic resonance images. We compared support vector machines (SVMs), with or without filter based feature selection, several embedded feature selection methods and stability selection. While comparisons of the accuracy of various classification methods have been reported previously, the variability of the out-of-training sample classification accuracy and the set of selected features due to independent training and test sets have not been previously addressed in a brain imaging context. We studied two classification problems: 1) Alzheimer’s disease (AD) vs. normal control (NC) and 2) mild cognitive impairment (MCI) vs. NC classification. In AD vs. NC classification, the variability in the test accuracy due to the subject sample did not vary between different methods and exceeded the variability due to different classifiers. In MCI vs. NC classification, particularly with a large training set, embedded feature selection methods outperformed SVM-based ones with the difference in the test accuracy exceeding the test accuracy variability due to the subject sample. The filter and embedded methods produced divergent feature patterns for MCI vs. NC classification that suggests the utility of the embedded feature selection for this problem when linked with the good generalization performance. The stability of the feature sets was strongly correlated with the number of features selected, weakly correlated with the stability of classification accuracy, and uncorrelated with the average classification accuracy.
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