Alzheimer’s Disease Neuroimaging Initiative: A one-year follow up study
using Tensor-Based Morphometry correlating degenerative rates, biomarkers
Alex D. Leow, Igor Yanovsky, Neelroop Parikshak, Xue Hua, Suh Lee,
Arthur W. Toga, Clifford R. Jack Jr., Matt A. Bernstein, Paula J. Britson,
Jeffrey L. Gunter, Chadwick P. Ward, Bret Borowski, Leslie M. Shaw, John
Q. Trojanowski, Adam S. Fleisher, Danielle Harvey, John Kornak, Norbert
Schuff, Gene E. Alexander, Michael W. Weiner, Paul M. Thompson
To appear in:
11 April 2008
4 October 2008
12 January 2009
Please cite this article as: Leow, Alex D., Yanovsky, Igor, Parikshak, Neelroop, Hua,
Xue, Lee, Suh, Toga, Arthur W., Jack Jr., Clifford R., Bernstein, Matt A., Brit-
son, Paula J., Gunter, Jeffrey L., Ward, Chadwick P., Borowski, Bret, Shaw, Leslie
M., Trojanowski, John Q., Fleisher, Adam S., Harvey, Danielle, Kornak, John, Schuff,
Norbert, Alexander, Gene E., Weiner, Michael W., Thompson, Paul M., Alzheimer’s
Disease Neuroimaging Initiative: A one-year follow up study using Tensor-Based Mor-
phometry correlating degenerative rates, biomarkers and cognition, NeuroImage (2009),
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Laboratory of Neuro Imaging, Dept. of Neurology,
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Alzheimer’s Disease Neuroimaging Initiative: A One-year Follow up Study Using
Tensor-based Morphometry Correlating Degenerative Rates, Biomarkers and
Alex D. Leow MD PhD12, Igor Yanovsky PhD3, Neelroop Parikshak1, Xue Hua1, Suh Lee1, Arthur W. Toga PhD1,
Clifford R. Jack Jr MD4, Matt A. Bernstein PhD5, Paula J. Britson BS4, Jeffrey L. Gunter PhD4,
Chadwick P. Ward BA4, Bret Borowski RTR4, Leslie M. Shaw PhD5, John Q. Trojanowski MD PhD5,
Adam S. Fleisher MD6, Danielle Harvey PhD7, John Kornak PhD8, Norbert Schuff PhD9,
Gene E. Alexander PhD11, Michael W. Weiner MD9,10, Paul M. Thompson PhD1
and the Alzheimer's Disease Neuroimaging Initiative*
1Laboratory of Neuro Imaging, Dept. of Neurology, UCLA School of Medicine, Los Angeles, CA
2Resnick Neuropsychiatric Hospital at UCLA, Los Angeles, CA
3Department of Mathematics, University of California, Los Angeles, CA
4Mayo Clinic, Rochester, MN
5Dept. Pathology and Laboratory Medicine, andInstitute on Aging,
University of Pennsylvania School of Medicine, Philadelphia, PA
6Dept. of Neurosciences, UC San Diego, La Jolla, CA
7Dept. of Public Health Sciences, UC Davis School of Medicine, Davis, CA
8Dept. of Radiology and Dept. of Epidemiology and Biostatistics, and
9Dept. Radiology, and 10Dept. Medicine andPsychiatry, UC San Francisco, San Francisco, CA
11Dept. Psychology and Evelyn F. McKnight Brain Institute, University of Arizona, Tucson, AZ
To be submitted to NeuroImage: April 11, 2008
Please address correspondence to:
Alex D. Leow MD PhD
*Acknowledgments and Author Contributions: Data used in preparing this article were obtained from the
Alzheimer's Disease Neuroimaging Initiative database (www.loni.ucla.edu/ADNI). Many ADNI investigators therefore
contributed to the design and implementation of ADNI or provided data but did not participate in the analysis or
writing of this report. A complete listing
www.loni.ucla.edu/ADNI/Collaboration/ADNI_Citation.shtml. This work was primarily funded by the ADNI
(Principal Investigator: Michael Weiner; NIH grant number U01 AG024904). ADNI is funded by the National Institute
of Aging, the National Institute of Biomedical Imaging and Bioengineering (NIBIB), and the Foundation for the
National Institutes of Health, through generous contributions from the following companies and organizations: Pfizer
Inc., Wyeth Research, Bristol-Myers Squibb, Eli Lilly and Company, GlaxoSmithKline, Merck & Co. Inc.,
AstraZeneca AB, Novartis Pharmaceuticals Corporation, the Alzheimer’s Association, Eisai Global Clinical
of ADNI investigators is available at
status exam (MMSE) scores, clinical dementia rating (CDR), and logical/verbal learning
Development, Elan Corporation plc, Forest Laboratories, and the Institute for the Study of Aging (ISOA), with
participation from the U.S. Food and Drug Administration. 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. Algorithm development for this study was also funded by the NIA, NIBIB, the
National Library of Medicine, and the National Center for Research Resources (AG016570, EB01651, LM05639,
RR019771 to PT). Author contributions were as follows: AL, IY, NP, XH, SL, AT, NS, and PT performed the image
analyses; CJ, MAB, PB, JG, CW, BB, LS, JT, AF, DH, JK, GA, and MW contributed substantially to the image and
data acquisition, study design, quality control, calibration and pre-processing, databasing and image analysis. We thank
Anders Dale for his contributions to theimage pre-processing and the ADNI project.
Abstract: (231 words; max 250 words)
Tensor-based morphometry can recover three-dimensional longitudinal brain changes
over time by nonlinearly registering baseline to follow-up MRI scans of the same subject.
Here, we compared the anatomical distribution of longitudinal brain structural changes,
over 12 months, using a subset of the ADNI dataset consisting of 20 patients with
Alzheimer’s disease (AD), 40 healthy elderly controls, and 40 individuals with mild
cognitive impairment (MCI). Each individual longitudinal change map (Jacobian map)
was created using an unbiased registration technique, and spatially normalized to a
geometrically-centered average image based on healthy controls. Voxelwise statistical
analyses revealed regional differences in atrophy rates, and these differences were
correlated with clinical measures and biomarkers. Consistent with prior studies, we
detected widespread cerebral atrophy in AD, and a more restricted atrophic pattern in
MCI. In MCI, temporal lobe atrophy rates were correlated with changes in mini-mental
memory scores. In AD, temporal atrophy rates were correlated with several biomarker
indices, including a higher CSF level of p-tau protein, and a greater CSF tau/beta amyloid
1-42 (Abeta42) ratio. Temporal lobe atrophy was significantly faster in MCI subjects
who converted to AD than in non-converters. Serial MRI scans can therefore be analyzed
with nonlinear image registration to relate ongoing neurodegeneration to a variety of
Neuroimaging Initiative, with the goal of developing improved methods based on
pathological biomarkers, cognitive changes, and conversion from MCI to AD, tracking
disease progression in 3-dimensional detail.
Alzheimer’s disease is the most common form of dementia, afflicting over 24 million
people worldwide. In early AD, short-term memory function is typically among the first
to be impaired, followed by a progressive decline in other cognitive functions (such as
language, attention, orientation, visuospatial skills, and executive function) along with
emotional/behavioral disturbances. At present, there is no cure for AD, whose natural
course is insidious yet gradually debilitating, and is typically fatal at its most advanced
stage, usually due to medical complications. In recent years, scientific interest has also
focused on mild cognitive impairment (MCI), a pre-dementia stage that carries a 4-6-fold
increased risk of future diagnosis of dementia, relative to the general population (Petersen
et al., 1999; Petersen, 2000; Petersen et al., 2001).
Many investigators have used MRI and PET imaging to measure longitudinal progression
of brain changes in normal aging, MCI and AD, with varying results. As drug candidates
that might slow the progression of Alzheimer's pathology began to be developed, the
need to develop robust and sensitive imaging methods to quantify progression of
Alzheimer’s disease has become increasingly important. To this end, the National
Institute of Aging and pharmaceutical industry funded the Alzheimer’s Disease
imaging and other biomarkers, for AD treatment trials.
A variety of methods have been used to quantify the longitudinal changes in structural
brain MRI including: region-of-interest measurements (especially of the hippocampus
(Frisoni et al., 1999), the "boundary shift integral" technique which quantifies differences
between two successive co-registered 3D MRIs (Fox et al., 2000), voxel-based
2006; Studholme and Cardenas, 2007; van de Pol et al., 2007; Barnes et al., 2008; Ridha
morphometry (Good et al., 2001; Whitwell et al., 2007), and tensor-based morphometry
(Studholme et al., 2004, 2006; Leow et al., 2007).
Tensor-based morphometry (TBM) is a relatively new image analysis technique that
identifies regional structural differences in the brain, across groups or over time, from the
gradients of the deformation fields that align, or ‘warp’, images to a common anatomical
template (reviewed in Ashburner and Friston, 2003). Highly automated methods such as
TBM are being tested to examine their utility in large-scale clinical trials, and in studies
to identify factors that influence disease onset and progression (Leow et al., 2005b;
Cardenas et al., 2007), or normal development (Thompson et al., 2000a; Chung et al.,
2001; Hua et al., 2007).
In this paper, TBM is applied to a longitudinal ADNI dataset by using a nonlinear
registration algorithmto match 3D baseline structural MR images with follow-up images
acquired one year later (for related approaches, see Leow et al. 2005; Studholme et al.,
et al., 2008).
Color-coded Jacobian maps – which show the local expansion or compression factor at
each point in the image – can be used to indicate local volume loss or gain relative to the
baseline image (Freeborough and Fox, 1998; Chung et al., 2001; Fox et al., 2001;
considered exploratory rather than hypothesis-based, for purposes of statistical inference,
Ashburner and Friston, 2003; Riddle et al., 2004). Here we examined longitudinal brain
changes, using these Jacobian maps, in groups of AD and MCI subjects relative to
controls. We also investigated, at a voxelwise level, how ongoing brain atrophy
correlated with clinical measures including MMSE scores, and the global Clinical
Dementia Rating (CDR), as well as biomarkers of AD pathology including CSF levels of
tau protein, 181-phosphorylated tau protein (p-tau), beta amyloid (ABeta 42), and
tau/Abeta42 ratio (Andreasen et al. 2001; Itoh et al., 2001; Verbeek et al., 2003; Clark et
al., 2003; Hampel et al., 2004; Lee and Trojanowski, 2006).
Our hypotheses were as follows: (1) brain atrophic rates would be greater in AD and MCI
than in controls, with MCI-control differences restricted primarily to the temporal lobe;
(2) MCI converters (who transitioned to AD during the 1-year follow-up interval) would
have faster atrophic rates than non-converters, but slower atrophic rates than those with
AD; (3) longitudinal temporal lobe atrophy would be significantly correlated with
progression of cognitive impairment in AD and MCI; and (4) higher CSF tau protein
level, lower CSF ABeta42 level, and higher p-tau/Abeta42 ratio would be significantly
associated with higher rates of temporal lobe atrophy. In further tests that were
we also determined which additional CSF biomarkers might correlate best with atrophic
rates in each diagnostic group, and in all subjects combined.
this paper, we studied longitudinal brain structural changes in 100 subjects, divided into 3
The Alzheimer’s Disease Neuroimaging Initiative (ADNI) (Mueller et al., 2005a; Mueller
et al., 2005b) is a large multi-center longitudinal MRI and FDG-PET
(fluorodeoxyglucose positron emission tomography) study of 800 adults, ages 55 to 90,
including 200 elderly controls, 400 MCI subjects, and 200 AD patients. The ADNI was
launched in 2003 by the National Institute on Aging (NIA), the National Institute of
Biomedical Imaging and Bioengineering (NIBIB), the Food and Drug Administration
(FDA), private pharmaceutical companies and non-profit organizations, as a $60 million,
5-year public-private partnership. The primary goal of ADNI has been to test whether
serial MRI, PET, other biological markers, and clinical and neuropsychological
assessments acquired in a multi site manner mirroring enrollment methods used in
clinical trials, can replicate results from smaller single site studies measuring the
progression of MCI and early 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 effectiveness, as well as lessen the time and cost of clinical
At the time of writing this report, data collection for the ADNI project was in progress. In
groups: 40 healthy elderly individuals, 40 individuals with amnestic MCI, and 20
individuals with probable AD. All groups were well matched for gender and age: each
group included 50% men and 50% women; mean ages for the control, MCI and AD
groups were, respectively, 75.27 years (standard deviation (SD) = 5.33 years), 75.43
years (SD = 7.02), and 75.70 years (SD = 7.36), with no significant age differences
(Wechsler, 1987). All AD patients met NINCDS/ADRDA criteria for probable AD
among the three groups. We included twice as many subjects in the MCI and control
groups versus the AD group, based on the availability of one-year follow-up scans at the
time of writing this paper.
All subjects underwent thorough clinical/cognitive assessment at the time of both the
baseline and the follow-up scan acquisitions. As part of each subject’s cognitive
evaluation, the Mini-Mental State Examination (MMSE) was administered to provide a
global measure of mental status based on evaluation of five cognitive domains (Folstein
et al., 1975; Cockrell and Folstein, 1988); scores of 24 or less (out of a maximum of 30)
are generally consistent with dementia. The Clinical Dementia Rating (CDR) was also
assessed as a measure of dementia severity (Hughes et al., 1982; Morris, 1993). A global
CDR of 0, 0.5, 1, 2 and 3, respectively, indicate no dementia, very mild, mild, moderate,
and severe dementia. The elderly normal subjects had MMSE scores between 28 and 30
(inclusive), all had a global CDR of 0, and no symptoms of depression, MCI, or other
forms of dementia. The MCI subjects had MMSE scores in the range of 24 to 30, all had
a global CDR of 0.5, and mild memory complaints, with memory impairment assessed
via education-adjusted scores on the Wechsler Memory Scale - Logical Memory II
(McKhann et al., 1984) with baseline MMSE scores as high as 26, and a lower limit of 20.
In this study, 16 AD patients had a CDR of 0.5, and the remainder had a CDR of 1.
Detailed exclusion criteria, e.g., regarding concurrent use of psychotropic medications,
can be found in the ADNI protocol (Mueller et al., 2005a; Mueller et al., 2005b). Briefly,
subjects were excluded if they had any serious neurological disease other than incipient
AD, any history of brain lesions or head trauma, or psychotropic medication use
(including antidepressants, neuroleptics, chronic anxiolytics or sedative hypnotics, etc.).
MRI Acquisition and Image Correction
All subjects were scanned with a standardized MRI protocol, developed after a
substantial effort evaluating and comparing 3D T1-weighted sequences for morphometric
analyses (Leow et al., 2006; Jack et al., 2008). High-resolution structural brain MRI scans
were acquired at multiple ADNI sites using 1.5 Tesla MRI scanners from General
Electric Healthcare and Siemens Medical Solutions (ADNI also collects a smaller subset
of data at 3 Tesla but it was not analyzed here to avoid the additional complications of
combining data across scanner field strengths). All scans were collected according to the
standard ADNI MRI protocol. For each subject, two T1-weighted MRI scans were
collected using a sagittal 3D MP-RAGE sequence. As described in (Jack et al., 2008)
typical 1.5T acquisition parameters are repetition time (TR) of 2400 ms, minimum full
TE, inversion time (TI) of 1000 ms, flip angle of 8°, 24 cm field of view, with a
192x192x166 acquisition matrix in the x-, y-, and z- dimensions yielding a voxel size of
1.25x1.25x1.2 mm3. In-plane, zero-filled reconstruction yielded a 256x256 matrix for a
reconstructed voxel size of 0.9375x0.9375x1.2 mm3.
Additional image corrections were also applied, using a processing pipeline at the Mayo
Clinic, consisting of: (1) a procedure termed GradWarp for correction of geometric
distortion due to gradient non-linearity (Jovicich et al., 2006), (2) a “B1-correction”, to
adjust for image intensity inhomogeneity due to B1 non uniformity using calibration
scans (Jack et al., 2008), (3) “N3” bias field correction, for reducing residual intensity
the same baseline transformation that carries baseline scans to the ICBM space (for
inhomogeneity (Sled et al., 1998), and (4) geometrical scaling, according to a phantom
scan acquired for each subject (Jack et al., 2008), to adjust for scanner- and session-
specific calibration errors. In addition to the original uncorrected image files, images with
all of these corrections already applied (GradWarp, B1, phantom scaling, and N3) are
available to the general scientific community (at www.loni.ucla.edu/ADNI).
To adjust for global differences in brain positioning and scale across individuals, all scans
were linearly registered to the stereotactic space defined by the International Consortium
for Brain Mapping (ICBM-53) (Mazziotta et al., 2001) with a 9-parameter (9P)
transformation (3 translations, 3 rotations, 3 scales) using the Minctracc algorithm
(Collins et al., 1994). Globally aligned images were resampled in an isotropic space of
220 voxels along each axis (x, y, and z) with a final voxel size of 1 mm3.
Specifically, baseline scans were first linearly normalized with a 9-parameter registration
to the ICBM space. Follow-up scans, on the other hand, were 9-parameter registered to
their corresponding baseline scans, followed by normalization to the ICBM space using
discussions on the effect of global scaling in longitudinal studies, see Paling et al., 2004;
Whitwell et al., 2004).
Three-dimensional Jacobian Maps quantifying Structural Changes over Time
et al., 2008), an unbiased average template image in the ICBM space representing
- 10 -
To quantify 3D patterns of volumetric brain atrophy over time for each subject, an
individual change map, or Jacobian map, was computed by non-linearly registering the
follow-up scan to the baseline scan with an unbiased registration algorithm we developed
(Leow et al., 2007). The unbiased image registration technique computes deformation
fields by penalizing statistical bias in the resulting Jacobian maps, thus eliminating skew
from the distribution of Jacobian determinants, and has been shown to perform favorably
in recovering true physiological changes in serial MRI data (Yanovsky et al., 2007).
Non-positive Jacobians are prevented when using unbiased registration, as a
regularization term is penalized based on the logarithmic transform of the local Jacobian
values. Moreover, unbiased registration is inherently symmetric (i.e., inverse-consistent),
so there is no methodologically-induced bias towards detecting gain versus loss (see
Leow et al., 2005, 2007, for examination of the Jacobian statistical distributions).
Unbiased Group Average Template - Minimal Deformation Target (MDT)
To facilitate voxelwise comparisons between groups, we followed our previous
approaches and further nonlinearly registered all individual brains and their
corresponding Jacobian maps to the Minimal Deformation Target (MDT) created in (Hua
common anatomical features on a voxel level for the group of control ADNI subjects
(Good et al., 2001; Kochunov et al., 2002; Joshi et al., 2004; Studholme and Cardenas,
2004; Kovacevic et al., 2005; Christensen et al., 2006; Lorenzen et al., 2006; Lepore et al.,
significance of group differences (see, e.g., Bullmore et al., 1999; Nichols and Holmes
- 11 -
After this final step, these spatially normalized Jacobian maps now share a common
anatomical coordinate as defined by the MDT. Thus, statistical analyses may be
conducted at each voxel to assess the magnitude and significance of deficits in MCI and
AD versus the healthy controls.
Using these spatially normalized Jacobian maps that encode longitudinal brain changes,
we carried out voxel-wise statistical tests between the Jacobian maps in each group. The
Jacobian maps in MCI and AD were compared to those from normal controls, using both
a spatial average of the Jacobian values within specific regions of interest (ROIs; defined
below), and voxel-wise tests controlled for multiple comparisons. In the latter, at each
voxel, we evaluated the significance level of group differences using a two-sample t test
with unequal variance. The resulting p-values were displayed as maps to visualize
patterns of significant differences throughout the brain.
To correct for multiple comparisons, we used permutation testing to assess the overall
2002; Thompson et al. 2003; Chiang et al. 2007). A null distribution for the group
differences in Jacobian at each voxel was constructed using 10,000 random permutations
of the data. The number of permutations N was chosen to be 10,000, to control the
standard error SEp of the omnibus probability p, which follows a binomial distribution
B(N, p) with known standard error(Edgington, 1995). WhenN = 10,000, the
statistical map, for a range of thresholds.
- 12 -
approximate margin of error (95% confidence interval) for pis approximately 5% of p.
For each test, the subjects’ diagnosis was randomly permuted and voxel-wise t tests were
conducted to identify voxels more significant than p = 0.05. The volume of voxels in the
brain more significant than p = 0.05 was computed for the real experiment and for the
random assignments. Finally, a ratio, describing the fraction of the time the
suprathreshold volume was greater in the randomized maps than the real effect (the
original labeling), was calculated to give an overall P-value for the significance of the
Cumulative distribution function (CDF) plots based on the above two-sample t-tests were
used to compare the effect sizes of group differences and effects of covariates of interest
in all three groups. These CDF plots are commonly generated when using false discovery
rate methods to assign overall significance values to statistical maps (Benjamini and
Hochberg, 1995; Genovese et al., 2002; Storey, 2002); they may also be used to compare
effect sizes of different methods, subject to certain caveats (Lepore et al., 2007b; Hua et
al., 2008; Morra et al., 2008), as they show the proportion of supra-threshold voxels in a
Regions of interest (ROIs)
Regions of interest, including frontal, parietal, temporal, and occipital lobes, were defined
by manually labeling the normal group MDT. The MDT was traced by a trained
anatomist to generate binary masks for each lobe, which were subsequently used to
patient's cognitive and functional performance in six areas on a scale of 0 (no impairment)
- 13 -
summarize brain atrophy at a regional level in each group. Within each lobe, tissue types
were distinguished by creating maps of gray and white matter, CSF, and non-brain tissues
using the partial volume classification (PVC) algorithm from the BrainSuite software
package (Shattuck and Leahy, 2002). One single voxel was eroded from the boundary of
each tissue class to avoid the inclusion of partial volumed voxels at tissue interfaces (e.g.,
CSF, gray and white matter mixtures) where the Jacobian values may not be
representative of the rest of the region. To avoid confounding the average values in
regions where tissue atrophy was assessed, CSF was excluded from the masks for the
ROI-based Jacobian averages, but was included for voxel-wise comparisons (i.e., maps of
Correlations of Structural Brain Differences (Jacobian Values) with Clinical
Measurements and Biomarkers
At each voxel, correlations were assessed for each group, using the general linear model,
between the Jacobian values and several clinical measures at baseline - MMSE scores
(Folstein, 1975), the geriatric depression scale (GDS), Clinical Dementia Rating
summary scores (Morris, 1993), and CDR sum-of-boxes scores. The CDR assesses a
to 3 (impaired): memory, orientation, judgment & problem solving, community affairs,
home & hobbies, and personal care. As there is a significant range restriction with global
CDR scores, we also assessed correlations with the CDR ‘sum-of-boxes’ scores, which
has a greater dynamic range (0-18), and may provide more useful information than the
CDR global score, especially in mild cases (Lynch et al., 2006). In addition, several
- 14 -
biomarkers obtained from CSF were also included for assessing correlations, including
beta amyloid 1-42 (ABeta42), tau protein, phosphorylated-tau protein 181 (p-tau), the tau
and ABeta42 ratio (tau/ABeta42), and p-tau ABeta42 ratio (p-tau/ABeta42). Biomarker
measurements were performed by Drs. Leslie Shaw and John Trojanowski of the ADNI
Biomarker Core at the University of Pennsylvania School of Medicine, which collects
and banks biological samples (DNA, blood, urine and CSF) from all participating sites,
and conducts studies of selected AD biomarkers, including apolipoprotein E (ApoE)
genotype, isoprostanes, tau, Abeta, sulphatides and homocysteine levels (Shaw et al.,
As individual Jacobian maps encode structural differences between baseline and follow-
up scans, correlations werealso assessed between the Jacobian values and the differences
in clinical measures between the two time points. All correlations were corrected for
multiple comparisons as described above.
3D Maps of Brain Atrophy in MCI and AD
We first visualized the mean brain structural change map for each group (AD, MCI, and
CTL) by averaging individual Jacobian maps within each group. These maps may be
considered as showing the mean percent tissue loss over the one-year interval, or, if
(p values, based on 1-sample t-test, < 0.01 for all ROIs) in the frontal lobes (annual
- 15 -
changes are assumed to be approximately linear, they provide a regional estimate of the
atrophy rate. The resulting statistical maps, shown in Figure 1, suggest widespread
progressive atrophy throughout the entire brain and expansion of ventricular and CSF
spaces in AD (compared to controls), and a more restricted pattern of atrophy in MCI. To
test whether there were statistically significant volume changes, over this 12-month time
period, for each group, and whether these changes differ between groups, we conducted
regional (ROI) analyses as well as voxel-wise tests controlled for multiple comparisons
as follows (Figures 2 and 3).
Mean Jacobian values within each ROI, normalized to indicate annualized rates of
atrophy (in percent per year) were computed to represent overall differences in the rates
of atrophy for each region (Figure 3). Here, we also separated MCI converters (N=7)
from MCI non-converters (N=32). (These MCI subgroups excluded the one MCI subject
whose diagnosis was changed to control at follow-up). In general, a consistent trend was
observed for atrophy rates, with AD>MCI converters>MCI non-converters. MCI non-
converters showed comparable rates to controls (see bars colored camel and white,
respectively, in Figure 3). The AD group (N=40) showed significant progressive atrophy
atrophy rate in AD: 2.41%), occipital lobes (2.07%), parietal lobes (2.40%), and temporal
lobes (2.22%). The corresponding mean atrophy rates were 0.37%, 0.63%, 0.22%, and
0.74% for the control group, with corresponding p-values exhibiting significance for the
occipital lobes (p=0.02) and the temporal lobes (p=0.05) (the MCI non-converter group
showed a similar atrophy profile; see Figure 3 for more explanations). The small group
greater atrophy rates in MCI versus controls were confirmed in the left/right/total
- 16 -
of MCI converters (N=7) showed an atrophy profile resembling AD (no statistical tests
were conducted in this sub-group due to a smaller sample size, although the MCI
converters/AD combined group, with a sample size of 27, also exhibited significant
volume loss for all ROIs). These data are consistent with earlier reports assessing atrophy
rates using 2 MRI brain scans separated by a 12-month interval: using the boundary shift
integral method, Fox et al. (2000) found that mean (SD) rate of brain atrophy for the
patients with mild to moderate AD was 2.37% (1.11%) per year, while in the control
group it was 0.41% (0.47%) per year.
Permutation tests, using suprathreshold percentages, were conducted to assess the
presence of statistically significant local volume change differences between groups,
based on the voxel-wise two-sample t-test on the rates of change (Fig. 2). This step
provides a detailed 3-D visualization of the local atrophy profile, in terms of voxel-wise
mean change rates and their significance level). Here, significantly greater atrophy rates
in AD versus controls were confirmed for all ROIs, including left/right/both temporal
lobes (p=0.0021/0.0015/0.0015, corrected), occipital lobes (p=0.0014, corrected), parietal
lobes (p=0.0015, corrected), and frontal lobes (p=0.005, corrected). Also, significantly
temporal lobe (p=0.03/0.03/0.03, corrected), and parietal lobe (p=0.046, corrected) but
not in the occipital or frontal lobes (non-converters were not separated in this permutation
testing, due to a relatively small sample size of 7). This is consistent with previous
findings that parietal and temporal lobes are among the first to be involved in AD.
Although mean Jacobian values (Fig. 3) – when spatially averaged across the temporal
AD or MCI groups separately, correlations were also examined across the entire sample
- 17 -
lobe - were similar in MCI (both converters and non-converters) and controls (annual
atrophy rate 0.73% and 0.74%), locally faster atrophy was detected in the MCI group
versus controls using a voxelwise permutation test. This is partly due to contributions
from the converters, who have smaller Jacobian values in general - denoting faster
atrophy - compared to non-converters. This also shows that permutation testing, on the
suprathreshold volume of statistics in a map may be more powerful for detecting group
differences than performing univariate tests on regional averages (numeric summaries)
derived from the maps.
Correlations of Jacobian values with clinical measures and biomarkers
Any quantitative measure of brain atrophy has greater value if it can be shown to
correlate with established measures of cognitive or clinical decline, or with future
outcome measures. Here, we investigated whether longitudinal temporal lobe atrophy
over this 12-month period correlates with cognitive decline over the same interval. Our
results in Tables 1 and 2 show corrected p-values for these correlations (although all
markers and clinical measures were correlated for each diagnostic group, only significant
results are reported here). To avoid reductions in power due to restricting the range to the
of 100 subjects (which we will refer to as the “pooled” group). In this case, the normal
subjects, who tend to score in the normal range for all clinical measures, drive these
associations to some extent. Diagnostic groups were not pooled together for these
cognitive tests since the groups are stratified according to their test scores.
Interestingly, in our analysis, longitudinal temporal lobe atrophy was not correlated with
- 18 -
Our results in Table 1 and Figure 4a indicate that higher CSF tau protein level, lower
CSF ABeta42 level, and higher p-tau/Abeta42 ratio are significantly associated with
higher rates of temporal lobe atrophy only in the pooled group (p=0.003, 0.001 and 0.002,
corrected), but not in any of the 3 individual diagnostic groups. On the other hand,
correlations between temporal lobe atrophy rates and the CSF p-tau level and
tau/Abeta42 ratio were significant for both the pooled data (p=0.01 and 0.0003) and
within the AD group (p=0.02 and 0.02).
For clinical measures (Table 2; Figure 4b), longitudinal temporal lobe atrophy was
significantly correlated with progression of cognitive impairment in the MCI group,
including an increase over time in CDR score (p=0.03), a decrease over time in MMSE
(p=0.02), a worsening in immediate logical memory performance (p=0.04), and
worsening scores on the 30-minute delayed auditory verbal learning test (AVLT)
(p=0.04). Moreover, a higher baseline Geriatric Depression Score (i.e., more severe
depression) and a lower baseline score on the delayed logical memory test also correlated
with a greater ongoing rate of temporal lobe atrophy (p=0.02 and 0.04). Lastly,
converters exhibited significantly faster rates of temporal lobe atrophy (p=0.03) than non-
converters (i.e., atrophy rates differentiated the group of converters from non-converters).
baseline MMSE, CDR, or sum-of boxes scores. Nor does it correlates with the change in
sum-of-boxes scores over the one-year interval. However, in the AD group, a higher
baseline sum-of-boxes score, i.e., greater cognitive impairment at baseline, correlated
with less ongoing atrophy (i.e., larger Jacobian values) (p=0.02). In AD, the sum-of-
boxes score may therefore better reflect the disease severity in advanced AD, at a point
- 19 -
when advanced temporal atrophy has already occurred, and progressive atrophy has
CDF Curves. The cumulative distribution curves in Figure 4 show relative effect sizes
for the associations between rates of brain atrophy and different pathological markers and
clinical correlates. In general, curves that rise more sharply at the origin denote statistical
maps with greater effect sizes, and those curves that intersect the line y=20x at points
other than the origin, pass the conventional criterion of controlling the false discovery
rate at an expected rate of 5%, and are regarded as significant after multiple comparisons
correction. This approach allows an approximate ranking of the effect sizes for different
correlates: higher tau/Abeta42 ratio was the pathological biomarker most highly
correlated with higher rates of temporal lobe atrophy when all subjects were combined;
other biomarkers showed comparable but slightly lower effect sizes. The clinical score
that correlated most with higher rates of temporal lobe atrophy was 1-year change in
MMSE in MCI, with many other cognitive measures showing comparable but slightly
lower effect sizes.
In this paper, our hypotheses were largely confirmed regarding longitudinal brain
structural changes in three groups of subjects including normal controls, those with MCI,
and patients with Alzheimer’s disease. Alzheimer’s disease was associated with
significantly faster ongoing atrophy in the temporal and parietal lobes, relative to
matched healthy controls. There was also significantly faster expansion in the CSF spaces,
technical: (1) the deformations are spatially smooth and partial volume averaging
- 20 -
consistent with previous studies (e.g., Boyes et al., 2006), and significant progressive
tissue loss in frontal and occipital lobes, indicating that ongoing atrophy is widely
distributed in AD.
Prior studies of the disease trajectory in AD (e.g., Scahill et al., 2002; Thompson et al.,
2003), show a shift in the distribution of atrophy with advancing disease. In line with the
trajectory of neurofibrillary pathology (Braak and Braak, 1991), the entorhinal and
medial temporal lobes show the earliest signs of atrophy in MCI, with frontal atrophy
typically occurring later, and primary sensory and motor cortices spared until late in the
illness (see Thompson and Apostolova, 2008, for a review of this trajectory mapped with
different imaging modalities). Consistent with this, in our MCI group, progressive
atrophy was detected only in the temporal and parietal lobes, in line with evidence that
ongoing changes are more anatomically restricted at this pre-dementia stage.
One notable aspect of the topography of brain matter loss shown in Figures 1 and 2
is that the greatest proportion of brain matter loss appears to lie in the white matter
rather than at the voxels on the cortical surface. There are two reasons for this, both
effects occur and diminish the signal somewhat at tissue boundaries, such as the
cortex/CSF interface, and (2) the registration accuracy of TBM is poorer at the
cortical surface, at least relative to some approaches that explicitly model the
cortical surface. To clarify this, note that Figures 1 and 2 visualize group differences
by averaging rates of volumetric changes (i.e., J acobian maps), after nonlinearly
directly from the meshes (Lerch and Evans, 2005), or by tissue classification of the
- 21 -
aligning individual maps of change to the minimal deformation template (MDT).
This deformation field is spatially smooth, so some partial volume effects between
cortex and CSF are inevitable and are more pronounced along the MDT boundary.
As a result, some signal spillover from outside of the brain tissue may be present,
explaining the reduction in the atrophy signal along the boundary. This effect has
been noted in our prior work (Hua et al., 2008), where the disease-related expansion
in the ventricles spills over into the subcortical white matter by about 1-2 mm, in the
average maps. Similarly, the cortical atrophy signal is partially canceled by the
signal in the CSF outside the brain, which may not show the same level of atrophy,
and if anything, may show slight expansion over time. Second, and perhaps more
importantly, the deformation fields are based on automated matching of intensities
in the images, and the spatial smoothness of the fields makes it difficult to register
the entire cortical mantle within subjects from one time-point to the next, as would
be required to gauge the atrophy of cortical gray matter. Alternative approaches
may be used that compute thickness at each point, but these are typically more time-
consuming as they generally require extraction of explicit models of the cortical
surface as geometric meshes, prior to computing the cortical thickness either
images and voxel coding (Thompson et al., 2004; Aganj et al., 2008).
There are at least two possible solutions to better sensitizing our TBM approach for
detecting cortical gray matter loss. The first is to use a method termed voxel-based
morphometry (VBM; Ashburner and Friston, 2000) or a related approach termed
changes from underlying white matter changes, we seek to assign signals to the
- 22 -
RAVENS (Davatzikos et al., 2001). In VBM, the deformation-based compression
signal at each point is multiplied by maps of gray matter density, which are based
on smoothing maps of gray matter voxels derived from an explicit tissue
classification into gray and white matter and CSF. An additional modulation step is
also included that preserves information on the volume of gray matter in the
baseline images after warping. When gray matter density and deformation signals
are multiplied together in this way, VBM maps in AD do typically show progressive
cortical gray matter atrophy in a temporal-to-frontal pattern that matches the
spread of neurofibrillary tangle pathology (Baron et al., 2001). A second approach
to identifying cortical gray matter atrophy with TBM was developed by (Studholme
et al., 2003), in which deformation-based compression signals at each point are
smoothed adaptively depending on the amount of gray matter lying under the filter
kernel. This is a way to avoid some of the signal depletion that occurs when
atrophying gray matter is partial-volumed with CSF. A third solution is to run the
deformation maps at a very high spatial resolution and with less spatial
regularization, or with a regularization term that enforces continuity but not
smoothness. Because of the complexity of differentiating cortical gray matter
cortex without surface-based modeling, which can be time consuming for larger
analyses (Thompson et al., 2004), Thus, in this paper, we decided to combine both
gray and white matter for each region of interest (ROI) in our analyses, instead of
matter loss and ventricular expansion, but no change in the white matter (Fox et al.,
- 23 -
Although much of the literature has suggested that gray matter loss is the primary
change in AD that is observable on MRI (see Thompson and Apostolova, 2008, for a
review), there has been substantial theoretical and empirical evidence supporting
white matter pathology in AD (Bartzokis et al., 2003). For example, in (Rose et al.,
2008), the authors summarized recent DTI studies in AD, and many have reported
reduced FA (fractional anisotropy) in temporal, frontal, and parietal lobes,
especially in the internal capsule and limbic association fibers, the corticothalamic
pathway, superior longitudinal fasciculus, and posterior cingulate bundle (Yoshiura
et al., 2002). White matter degeneration in AD has also been detected with MR
relaxometry (Bartzokis et al., 2003) and myelin and oligodendrocyte reductions have
been detected in neuropathological studies of AD. Future MRI studies, using state-
of-the-art techniques such as diffusion-weighted MRI (Rose et al., 2000; Choi et al.,
2005; Medina et al., 2006) or High Angular Resolution Diffusion Imaging (HARDI),
are likely to further elucidate white matter pathology in AD.
A further notable feature, which requires some explanation, is that a related
technique (the voxel-compression method) has shown unequivocally temporal gray
2001) in serial MRI studies of AD. As already noted, we did indeed detect subtle and
diffuse changes in the subcortical white matter, but the ability to detect them
depends to some degree on the level of regularization used in TBM. In TBM, there is
a smoothness term, which causes correlations in the deformation signals at
neighboring voxels. In general, for simplicity and practicality, an elastic (Leow et al.,
2005a) or fluid (Fox et al., 2001) model of registration is used, in which the Green’s
normal subjects (Andreasen et al. 2001; Itoh et al., 2001; Verbeek et al., 2003; Clark et al.,
- 24 -
function of the governing operator is spatially uniform and fixed. If the correlations
are assumed to be long-range (i.e., the deformations are spatially quite smooth),
there is more signal enhancement in large homogeneous regions such as the white
matter, whereas if the correlations are assumed to be short-range (i.e., the
deformations are spatially quite rough, as in the fluid registration model of Fox et
al., 2001), there is typically more sensitivity to finer-scale differences (as found in the
gray matter in the Fox et al. study), while sacrificing some power to detect broader-
scale differences (e.g., the failure to detect white matter atrophy in Fox et al., 2001).
In future, the differential sensitivity of both approaches could be combined by
estimating these spatially varying correlations empirically from anatomical
landmarks using 6-dimensional covariance tensors (Fillard et al., 2008) and
incorporating them into a statistically-based adaptive registration model as we have
begun to do (Brun et al., 2007 and 2008).
In this aper, the correlations between atrophy and CSF biomarkers are also of significant
interest. Prior literature has indicated that there is lower ABeta42, but higher tau and p-
tau protein, in the CSF of AD patients versus those with other dementia subtypes or
2003; Hampel et al., 2004). More recently, researchers have also investigated the utility
of using these markers for predicting conversion from MCI to AD (Fagan et al., 2007; Li
et al., 2007). In our results, progressive temporal lobeatrophy was highly correlated with
baseline p-tau, tau/ABeta42 ratio (for both AD groups and all subjects pooled), Abeta42,
and tau (the latter two only for data pooled across all diagnostic groups). This suggests
somewhat restricted in disease severity to reflect relatively mild AD; by contrast, in
- 25 -
that p-tau and tau/Abeta42 may be more clinically useful than Abeta42 or tau in
predicting ongoing atrophy (in Figure 4, CDF curves rise more rapidly for the
correlations with p-tau and with the tau/Abeta42 ratio).
We could not demonstrate significant correlations between biomarkers and ongoing
temporal lobe atrophy in the MCI group. This is perhaps not surprising due to the
heterogeneous nature of MCI, and the relatively small sample of 40 subjects.
Clinical measures correlated more strongly with atrophy rates in MCI than in AD,
supporting the use of serial neuropsychiatric testing in monitoring disease progression in
MCI. In AD, atrophy rates exceeded those in MCI, but did not correlate so strongly with
interval changes in neuropsychiatric test scores. This may suggest that (1) decline in
cognition is more tightly linked with atrophy rates early in the illness, or (2) in late AD,
atrophy rates may eventually plateau or slow down, which may disrupt any correlation
between the absolute rate of tissue loss and further changes in cognition, or (3)
correlations may only be detectable in samples that are larger and/or have a broader range
of disease severity. Our AD sample was only half the size of our MCI sample, and was
recent study of 52 subjects with mild-to-moderate AD (Ridha et al., 2008), there was a
strong association between brain atrophy rate and MMSE decline (r = 0.59, p < 0.0001).
In addition, there is some evidence that atrophy rates do not slow down as AD progresses;
Chan et al. (2003) found that in 12 patients with mild dementia (MMSE=23), scanned
from a presymptomatic stage through to moderately severe dementia, mean yearly loss of
features and boundaries are well-preserved in the MDT, and the hippocampus is
- 26 -
brain volume was 2.8% (95% CI: 2.3-3.3), but rose by 0.32% per year (0.15-0.50). In 39
healthy control subjects, Scahill et al. (2003) also found rates of atrophy accelerated
nonlinearly with increasing age, with the most marked changes occurring after the age of
To summarize, our results further support the value of serial MRI scanning, combined
with quantitative nonlinear registration, for tracking disease progression in Alzheimer’s
and MCI. Our detailed 3D Jacobian maps, reflecting regional brain atrophy, correlated
well with disease progression and conversion to AD, as well as with various biomarkers
and clinical measures. Moreover, groups of MCI converters and non-converters were
differentiated by measures of temporal lobe atrophy over time.
Lastly, instead of separating hippocampus in our analysis, it was included as part of the
temporal lobe. Any TBM study is limited by the accuracy with which deformable
registration can match anatomical boundaries between individual brains and
corresponding regions on the template. Our mean deformation template (MDT) was
created after rigorous nonlinear registration, and geometric centering. Most anatomical
sufficiently discernible to be labeled by hand. Even so, it may not always be possible to
achieve accurate regional measurements of atrophy in small regions such as the
hippocampus, since that would require a locally highly accurate registration. Some
research groups have successfully computed hippocampal atrophy rates from fluid
registration methods (e.g., Crum et al., 2001), and found that they can be superior to
Accepted Manuscript, Available online 24 August 2007.
- 27 -
manual delineations in separating AD from controls (P < 0.0001; Barnes et al., 2007) and
more reliable (van de Pol et al., 2007).
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with boundary shift integral measures (Barnes et al., 2007), may offer additional
statistical power to detect subregional differences. We are currently investigating
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Figure 1. Unbiased registration was performed on 100 pairs of serial MR images,
acquired 12 months apart, from the Alzheimer's Disease Neuroimaging Initiative (ADNI)
dataset. The selected sample consisted of 20 patients with Alzheimer's disease (AD), 40
individuals with mild cognitive impairment (MCI), and 40 healthy elderly controls
(CTL). The mean of the resulting Jacobian maps in each group is superimposed on a
20) versus Controls (N = 40) on the top, and MCI (N = 40) versus Controls (N = 40) on
the bottom. Corresponding color-coded P maps also show the local significance of these
differences (bottom row). There is widespread progressive atrophy in AD, at a faster
mean rate than in normals – this difference in rates reaches the voxelwise significance
level of 0.05 in most regions of the brain, and remains significant after corrected for
multiple comparisons in ROIs including the temporal lobes, parietal lobes, occipital lobes,
and frontal lobes. By contrast, for MCI versus Controls, only the parietal and temporal
lobes reach ROI significance. Please refer to the results section for more detailed
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Figure 2 Voxel-wise Z-statistics (top row) comparing mean Jacobian values for AD (N =
0.01 for the comparision. Here, p values for MCI to AD group are not given, as there
were only 7 subjects. (Significance levels were as follows: p < 0.01 for all lobes in AD
and AD at 12Mo; p = 0.012, 0.054, and 0.02 in MCI for the temporal, frontal and
occipital lobes; p = 0.02 and 0.055 in controls for the occipital and temporal lobes; p =
0.068 and 0.045 in MCI at 12Mo for occipital and temporal lobes; regions not reported
here do not reach significance at the 0.05 level).
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Figure 3. Percent brain tissue loss from baseline to follow-up as determined by the
average Jacobian value within each lobe (with CSF excluded). The diagnosis for the AD,
MCI, and CTL groups was determined at baseline. Here, MCI at 12Mo denotes subjects
diagnosed with MCI at baseline who did not convert to AD at 12-month follow-up,
whereas MCI to AD signifies those who had converted to AD at 12-month follow-up.
One MCI subject’s diagnosis converted back to control at follow-up, and thus was
excluded from the MCI subgroups. The first bar in each group (colored white), shows
mild but significant progressive atrophy in controls. The 2ndbar (turquoise) denotes MCI
subjects at baseline, and is followed by bars denoting converters and non-converters.
Mean rates are typically higher in the converters, comparable to subjects diagnosed as
AD at both time-points, or AD at the last time point (last two bars). A * indicates p <
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