Vol.2, No.4, 135-147 (2013) Advances in Alzheimer’s Disease
Regional patterns of atrophy on MRI in Alzheimer’s
disease: Neuropsychological features and
progression rates in the ADNI cohort*
Ranjan Duara1,2,3,4#, David A. Loewenstein1,3, Qian Shen1,2, Warren Barker1, Maria T. Greig1,
Daniel Varon1,4, Melissa E. Murray5, Dennis W. Dickson5
1Wien Center for Alzheimer’s Disease and Memory Disorders, Mount Sinai Medical Center, Miami Beach, USA;
#Corresponding Author: duara@msmc.com
2Department of Neurology, Miller School of Medicine, University of Miami, Miami, USA
3Department of Psychiatry, University of Miami School of Medicine, Miami, USA
4Herbert Wertheim College of Medicine, Florida International University, Miami, USA
5Mayo Clinic, Jacksonville, USA
Received 26 August 2013; revised 30 September 2013; accepted 7 October 2013
Copyright © 2013 Ranjan Duara et al. This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background: Discrete clinical and pathological
subtypes of Alzheimer’s disease (AD) with vari-
able presentations and rates of progression are
well known. These subtypes may have specific
patterns of regional brain atrophy, which are
identifiable on MRI scans. Methods: To examine
distinct regions which had distinct underlying
patterns of cortical atrophy, factor analytic tech-
niques applied to structural MRI volumetric data
from cognitively normal (CN) (n = 202), amnestic
mild cognitive impairment (aMCI) (n = 333) or
mild AD (n = 146) subjects, in the Alzheimer’s
Disease Neuroimaging Initiative (ADNI) database
was applied. This revealed the existence of two
neocortical (NeoC-1 and NeoC-2), and a limbic
cluster of atrophic brain regions. The frequency
and clinical correlates of these regional patterns
of atrophy were evaluated among the three di-
agnostic groups, and the rates of progression
from aMCI to AD, over 24 months were evaluated.
Results: Discernable patterns of regional atro-
phy were observed in about 29% of CN, 55% of
aMCI and 83% of AD subjects. Heterogeneity in
clinical presentation and APOE ε4 frequency
were associated with regional patterns of atro-
phy on MRI scans. The most rapid progression
rates to dementia among aMCI subjects (n = 224),
over a 24-month period, were in those with
NeoC-1 regional impairment (68.2%), followed by
the Limbic regional impairment (48.8%). The
same pattern of results was observed when only
aMCI amyloid positive subjects were examined.
Conclusions: The neuroimaging results closely
parallel findings described recently among AD
patients with the hippocampal sparing and lim-
bic subtypes of AD neuropathology at autopsy.
We conclude that NeoC-1, Limbic and other
patterns of MRI atrophy may be useful markers
for predicting the rate of progression of aMCI
to AD and could have utility selecting indi-
viduals at higher risk for progression in clini-
cal trials.
Keywords: Subtypes; Mild Cognitive Impairment;
MCI; preMCI; Amnestic MCI; Alzheimer’s Disease;
Dementia; MRI; Hippocampal Volume; Algorithmic
Diagnosis; Clinical Diagnosis; Neuropsychological
Tests; Longitudinal Analysis; Regional Atr ophy
The earliest clinical manifestations of Alzheimer’s
disease (AD) occur well before the emergence of a de-
mentia syndrome and progression typically occurs at a
gradual pace. However, there is considerable variability
in clinical presentations and in progression rates [1],
which may be attributable to biological subtypes of AD,
as well as to the presence of comorbid conditions, meth-
odological limitations in the tests used to assess the fea-
*Disclosure: The authors report no conflicts of interest.
Statistical analysis performed by David Loewenstein, PhD.
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R. Duara et al. / Advances in Alzheimer’s Disease 2 (2013) 135-147
tures and severity of the disease and the disparate re-
sponses of subjects to these tests. Heterogeneity in pro-
gression rates in AD may reflect biological variability or
errors in the measurement of cognitive and functional
status, due to factors such as cognitive reserve, anxiety,
motivation and informant bias.
A recent pathological study of AD cases has shown
that the regional distribution of neurofibrillary pathology
in the brain appears to be the biological basis for some of
this heterogeneity, including variable presentations and
progression rates [2]. In this study, pathological subtypes
of AD included a limbic predominant subtype, with older
age of onset, female predominance and slow rate of pro-
gression, and a neocortical (NeoC) predominant subtype,
with younger age of onset, male predominance and a more
rapid rate of progression. Given that these neuropatho-
logic subtypes of AD were identified on the basis of the
regional distributions of neurofibrillary pathology, usu-
ally in very advanced stages of the disease, it seems rea-
sonable to assume that other pathologic subtypes of the
disease, with individual rates of progression, may exist
among living subjects with earlier stages of the dis-
Structural MRI has been shown to be one of the best
and most readily available measures to assess the bio-
logical heterogeneity and rate of progression of AD [3].
MRI, which has been used to evaluate the rates of atro-
phy in the hippocampus and the whole brain as well as
increases in ventricular volumes, has been found to be
reliable and accurate index of the rate of progression of
AD, correlating with cognitive and functional measures
of progression and potentially, of the response to dis-
ease-modifying treatments in clinical trials [4-6]. Be-
cause MRI measures of regional cerebral atrophy in AD
brains are highly correlated to the severity of regional
neurofibrillary pathology, which in turn is correlated to
severity of cognitive impairment, structural MRI may be
the best method to identify both pathological and clinical
subtypes of AD among living subjects.
While the prevailing emphasis has been to use MRI-
based measures, such as whole brain, hippocampal atro-
phy and ventricular enlargement, to assess disease sever-
ity and to predict and measure rates of progression, these
traditional measures do not fully capture the heterogene-
ity of atrophy patterns of the disease at baseline, [4].
Therefore, we performed a factor analysis on volumetric
data from subjects participating in the Alzheimer’s Dis-
ease Neuroimaging Initiative (ADNI), so as to identify
distinct patterns of regional atrophy on baseline struc-
tural MRI. We then stratified subjects into unique sub-
groupings based upon the patterns of atrophy present in
each individual, thereby enabling us to examine the as-
sociation of the presence of one or more regional patterns
of atrophy to demographic, clinical and neuropsycholo-
gical presentations and progression rates in each sub-
Data used in the preparation of this article were ob-
tained from the Alzheimer’s Disease Neuroimaging Ini-
tiative (ADNI) database (adni.loni.usc.edu). 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 Administra-
tion (FDA), private pharmaceutical companies and non-
profit organizations, as a $60 million, 5-year public-pri-
vate partnership. The primary goal of ADNI has been to
test whether serial magnetic resonance imaging (MRI),
positron emission tomography (PET), other biological
markers, and clinical and neuropsychological assessment
can be combined to measure the progression of mild cog-
nitive impairment (MCI) and early Alzheimer’s disease
(AD). Determination of sensitive and specific markers of
very early AD progression is intended to aid researchers
and clinicians to develop new treatments and monitor
their effectiveness, as well as lessen the time and cost of
clinical trials.
The Principal Investigator of this initiative is Michael
W. Weiner, MD, VA Medical Center and University of
California—San Francisco. ADNI is the result of efforts
of many co-investigators from a broad range of academic
institutions and private corporations, and subjects have
been recruited from over 50 sites across the US and
Canada. The initial goal of ADNI was to recruit 800
subjects but ADNI has been followed by ADNI-GO and
ADNI-2. To date these three protocols have recruited
over 1500 adults, ages 55 to 90, to participate in the re-
search, consisting of cognitively normal older individuals,
people with early or late MCI, and people with early AD.
The follow up duration of each group is specified in the
protocols for ADNI-1, ADNI-2 and ADNI-GO. Subjects
originally recruited for ADNI-1 and ADNI-GO had the
option to be followed in ADNI-2. For up-to-date infor-
mation, see www.adni-info.org.
In 2011, we downloaded from the ADNI database
baseline demographic, clinical, neuropsychological, APOE
genotype and volumetric MRI data for 681 subjects [146
diagnosed with mild Alzheimer’s disease (AD), 333 with
amnestic MCI (aMCI), and 202 cognitively normal (CN)
cases]. MRI volumetric scans were performed using 1.5
T and 3.0 T Siemens, General Electric or Philips scan-
ners. Regional MRI volumes, normalized to total intrac-
ranial volume, were obtained from the ADNI database
using data derived by researchers at the University of
California, San Francisco who used FreeSurfer (FS) ver-
sion 4.3.0.
MRI data analysis for this study was done in two
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R. Duara et al. / Advances in Alzheimer’s Disease 2 (2013) 135-147
Copyright © 2013 SciRes.
phases, namely among the entire cohort described above
(Phase 1), and among only those aMCI and clinically
diagnosed AD subjects who were amyloid positive (Phase
2), using established standardized uptake value ratios
(SUVR) cut points (i.e., SUVR = 1.4+ for scans using
[C-11] Pittsburgh Compound B, and 1.11+ for scans us-
ing [F-18] Florbetapir).
greater were considered as loading on that particular
factor; 5) this resulted in the following factors being de-
rived from the left and right hemispheres (see Figure 1):
a Limbic Factor (entorhinal cortex, hippocampus, amyg-
dala, parahippocampal gyrus, temporal pole, fusiform
gyrus), a Neocortical Type 1 (NeoC-1) Factor (inferior
parietal, precuneus, middle and inferior temporal and
rostral middle frontal) and a Neocortical Type 2 (NeoC-
2) Factor (transverse temporal, superior temporal, in-
sula, supramarginal gyrus and posterior/isthmus cingu-
late); 6) factor scores were then calculated for Limbic,
NeoC-1, NeoC-2 brain regions for each subject, in the
left and right hemispheres, and those factor scores which
were 1.0 SD or below the entire sample as a whole were
designated as atrophic in a given individual and used to
categorize the specific pattern of atrophy present in each
individual (No atrophy, Limbic, NeoC-1, NeoC-2, or
the following combinations of Limbic, NeoC-1 and
NeoC-2, NeoC-1 + NeoC-2, Limbic + NeoC-1/NeoC-2.
Factor scores were then entered into predictive equations,
as described below.
We used regional brain volumes for 80 available su-
pratentorial brain regions among the 333 aMCI subjects
to identify for further analysis those regional volumes
which were 1.0 SD or below the mean volume for the
same regions in the CN group; 1) this resulted in 16 brain
regions in the aMCI group which met criteria for being
atrophic, including entorhinal cortex, hippocampus,
amygdala, parahippocampal gyrus, temporal pole, fusi-
form gyrus, inferior parietal lobule (excluding the su-
pramarginal gyrus), precuneus, middle and inferior
temporal, rostral middle frontal, posterior/isthmus cin-
gulate, transverse temporal, superior temporal, insula
and supramarginal gyrus; 2) a factor analysis, using the
entire group of 681 subjects (CN, aMCI and mild AD)
was conducted, separately for the left and right hemi-
spheres, using a Principal Components Approach (PCA)
with a varimax rotation to derive the most orthogonal
factors; 3) three factors were derived from this analysis,
using the criteria of an Eigen Value of 1.0 or greater after
inspection of the scree plot, with each factor representing
a unique pattern of intercorrelations among specific brain
regions (the total explained variance for the left hemi-
sphere was 55.8% and for the right hemisphere was
56.2%); 4) individual brain regions which were corre-
lated with the overall factor with a correlation of 0.4 or
2.1. Phase 2
To increase the likelihood that the derived factors in
the entire ADNI sample described in Phase 1 were asso-
ciated with AD pathology and not merely artifacts of
normal aging or other non-AD neurodegenerative pa-
thology, the following steps were taken: 1) We used re-
gional brain volumes for 80 available supratentorial brain
regions in the right and left hemispheres among the 83
aMCI subjects in ADNI who were amyloid positive and
Figure 1. Patterns of regional brain atrophy. Factor analysis of atrophic regions resulted in three subtypes depicted on an atlas cre-
ated by Desikan et al., 2006 [28]: Limbic pattern depicted in green includes the following regions: Entorhinal cortex, parahippocam-
pal gyrus, temporal pole, fusiform gyrus. Hippocampus and amygdala are part of this pattern of atrophy but are not seen in the figure.
Neocortical Type 1 (NeoC-1) pattern depicted in yellow includes the following regions: Inferior parietal, precuneus, middle and infe-
rior temporal and rostral middle frontal. Neocortical Type 2 (NeoC-2) Pattern depicted in blue includes the following regions:
Transverse temporal, superior temporal, insula, supramarginal gyrus and posterior/isthmus cingulate. Cortical regions based on De-
sikan et al., 2006 [28].
R. Duara et al. / Advances in Alzheimer’s Disease 2 (2013) 135-147
compared them to 57 normal elderly subjects who were
amyloid negative; 2) we then conducted statistical
analyses and determined 16 regions among amyloid
positive subjects which were atrophic in the left and right
hemispheres at p 0.05 level of significance. These 16
regions were similar to those identified in Section A and
included the entorhinal cortex, hippocampus, amygdala,
parahippocampal gyrus, fusiform gyrus, inferior parietal
lobule (excluding the supramarginal gyrus), precuneus,
middle and inferior temporal gyri, rostral middle frontal
gyrus, posterior/isthmus cingulate gyrus, lateral orbital
frontal, superior frontal, superior temporal, insula cortex
and caudal middle frontal region; 3) a factor analysis,
was conducted using 230 subjects who were likely to
have AD pathology (amyloid positive subjects diagnosed
as aMCI [n = 85] and probable AD, diagnosed using
ADNI criteria [n = 145]); 4) factor analysis was con-
ducted, separately for the left and right hemispheres,
using a Principal Components Approach (PCA) with a
varimax rotation to derive the most orthogonal factors; 5)
three factors were derived from this analysis, using the
criteria of an Eigen Value of 1.0 or greater after inspec-
tion of the scree plot, with each factor representing a
unique pattern of intercorrelations among specific brain
regions; 6) three factors were derived from this analysis,
using the criteria of an Eigen Value of 1.0 or greater after
inspection of the scree plot, with each factor representing
a unique pattern of intercorrelations among specific brain
regions (the total explained variance for the left hemi-
sphere was 52.7% and for the right hemisphere was
53.4%); 7) individual brain regions were considered rep-
resentative of that factor if the correlations with the
overall factor was 0.4 or greater in both hemispheres; 8)
the following factors and associated regions (see Figure
1) were: a Limbic Factor (entorhinal cortex, hippo-
campus, amygdala, parahippocampal gyrus, fusiform
gyrus), a Neocortical Type 1 (NeoC-1) Factor (inferior
parietal, precuneus, middle and inferior temporal and
rostral middle frontal) and a Neocortical Type 2
(NeoC-2) Factor (superior frontal, superior temporal,
insula, lateral orbital frontal and posterior cingulate); 9)
among 57 normal elderly subjects in ADNI who were
amyloid negative, we examined volumetric values of
each region that comprised NeoC-1, NeoC-2 and Limbic
areas in both the left and right hemispheres; 10) any vol-
ume for a structure that was below that of the lowest
value obtained by any of the amyloid negative cogni-
tively normal subjects was then considered abnormal
when applied to the entire ADNI sample; 11) because
different structures within a left or right hemisphere
NeoC-1, NeoC-2 or Limbic region were of different sizes,
we considered a region as impaired if there were one or
more of the regions comprising that region that were
lower than any of the volumes for that region obtained
by amyloid negative normal controls; 12) By examining
impairment of structures within a region (empirically
derived from factor analyses), we were able to identify
those regions in which significant atrophy was present
and then proceeded to categorize the specific pattern of
atrophy present in each individual (No atrophy, Limbic,
NeoC-1, NeoC-2, or the following combinations of Lim-
bic, NeoC-1 and NeoC-2, NeoC-1 + NeoC-2, Limbic +
NeoC-1/NeoC-2) . Subsequently, we added the volumes
of all structures in the left and right hemispheres repre-
senting Limbic, NeoC-1 and NeoC-2 factors to employ
in regression equations investigating predictors of cogni-
tive and functional decline longitudinally.
2.2. Additional Clinical and MRI Variables
Because hippocampal volumes and ventricular dilata-
tion have commonly been used as predictors of cognitive
decline in longitudinal MRI studies, we included both
left and right-sided hippocampal and lateral ventricle
values as additional predictors of outcome, as described
below. We also examined baseline and follow-up values
for the Mini-Mental State Examination (MMSE) [8],
Clinical Dementia Rating Scale (CDR) [9] sum of boxes,
Immediate and Delayed recall of the Rey Auditory Ver-
bal Learning Test (RAVLT) [10], Delayed Memory for a
story passage (i.e., Logical Memory) (LM), Trail Making
Test Parts A and B and Category Fluency.
2.3. Longitudinal Follow-Up
Of the 681 ADNI subjects evaluated at baseline, 495
(166 mild AD, 224 aMCI and 105 EN subjects) had data
available at a 24-month follow-up, including a consensus
diagnostic evaluation (regarding “conversion” from CN
to aMCI, or aMCI to AD or another dementia) at the res-
pective ADNI sites. Progression from CN to aMCI oc-
curred in six (3.6%) of subjects and from aMCI to de-
mentia in 88 (39.3%) of subjects. Reversion from aMCI
to “CN” occurred in eight (3.6%) of subjects, while none
of the 98 subjects with AD had a change in diagnosis.
2.4. Statistical Analyses
Chi square (χ2) analyses were used to determine the
comparative distribution of subjects classified as limbic,
NeoC-1, NeoC-2, or a combination of these specific
(patterns of atrophy). Chi-square analyses and logistic
regression, using MRI derived factor scores, were also
employed to determine the extent to which progression
from aMCI to dementia could be predicted by different
patterns of regional atrophy. To determine the utility of a
given MRI factor score at baseline to predict scores on
tests of cognitive function at 24-month follow-up, we
employed linear regression-based approaches. The base-
line (T1) cognitive composite score of interest (e.g.,
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R. Duara et al. / Advances in Alzheimer’s Disease 2 (2013) 135-147 139
Trails B) was entered first into predictive models, fol-
lowed by simultaneous entry of baseline volumetric
scores for the hippocampus alone, ventricular volume,
NeoC-1, NeoC-2 and limbic factor scores, with the cog-
nitive composite score at T2 (i.e., Trails B) as an out-
come measure. These analyses allowed us to examine the
prevalence of different patterns of MRI atrophy among,
aMCI and mild AD patients as well as to predict the de-
gree that specific patterns of atrophy or combinations of
regional atrophy patterns were associated with disease
progression over time.
3.1. Phase 1 Analyses
There were no differences between CN, aMCI or mild
AD groups with regards to age [(F (2,678) = 1.70; p =
0.18], but as expected, there were differences with re-
gards to MMSE scores [(F (2,678) = 522.2; p < 0.001].
CN subjects had the highest MMSE scores (MMSE =
29.2; SD = 1.0) followed by the aMCI group (MMSE =
27.1; SD = 1.8) with the lowest scores being obtained by
the AD group (MMSE = 23.5; SD = 1.9). There were
statistically significant group differences with regards to
gender [(χ2 (df = 2) = 7.61; p < 0.031] with the lowest
percentage of females in the aMCI group (36.6%) versus
the CN (45.5%) and the mild AD group (48.6%). There
was a greater percentage of APOE ε4 carriers in the AD
(65.8%) and aMCI (53.2%) versus the CN groups (28.2%)
[(χ2 (df = 2) = 53.56; p < 0.001].
3.1.1. Frequencies of Different MRI Regional
Patterns of Atrophy among Diagnostic
The distribution of different regional patterns of MRI
atrophy (Table 1) was significantly different between
diagnostic groups [χ2 (df = 10) = 173.64 p < 0.001], with
83% of AD, 55% of aMCI and 29% of CN subjects
showing significant atrophy in NeoC-1, NeoC-2 or lim-
bic regions. The most frequent pattern of atrophy among
AD patients was NeoC-1 (24.7%), followed by Limbic
(21.2%) and NeoC-2 (6.2%); combined patterns of re-
gional atrophy comprised 30.8%, whereas 17.1% had no
atrophy. Among aMCI subjects the most frequent pattern
of atrophy was Limbic (18.0%), followed by NeoC-2
(12.6%) and NeoC-1 (10.5%); combined patterns of at-
rophy comprised 13.8%, whereas 45% had no atrophy.
Among CN subjects, NeoC-2 (18.3%) was the most fre-
quent pattern of atrophy, followed by NeoC-1 (5%) and
Limbic (3.5%); combined regional patterns of atrophy
comprised 2.0%, whereas 71.3% had no atrophy.
3.1.2. Volumes among aMCI and Cognitively
Normal Subjects with Different Patterns
of MRI Atrophy
Table 2 shows regional volumes of limbic and non-
limbic structures representing distinct patterns of atrophy
among cognitively normal and aMCI subjects in the
ADNI subject groups. The table demonstrates the fre-
quency of the various predominant patterns of atrophy
among normal and aMCI. These findings demonstrate
that: the hippocampus, amygdala and ERC have the most
severe atrophy in the Limbic region (as would be ex-
pected), whereas the precuneus and the posterior cingu-
late regions show the most severe atrophy among NeoC-
1 and NeoC-2 groups. Volumes of the ERC region were
relatively greater in the NeoC-1 group than in the normal
group (emphasizing that the ERC region, specifically, is
likely to be neuropathologically uninvolved in the NeoC-
1 group).
3.1.3. Demographic, Clinical, ApoΕ4
Genotype and Neuropsychological
Characteristics of Different Regional
Patterns of MRI Atrophy
The comparative demographic, clinical and cognitive
features of different MRI patterns of atrophy were ex-
amined for CN, aMCI and AD subjects, contrasted with a
control group of CN subjects with scans showing no at-
rophy (CNMRI) (n = 144) (Table 3). There were no sta-
tistically significant differences with regards to age [F
(5,442); p = 0.056], although there was a trend for the
Table 1. Frequencies of different MRI-based regional patterns of atrophy by diagnostic groups.
MRI-Based Pattern CN aMCI AD
Limbic Only 3.5% (L = 3.0%) (R = 2.0%) 18.0% (L = 15.3%) (R = 14.1%) 21.2% (L = 18.5%) (R = 24.0%)
NeoC-1 Only 5.0% (L = 4.0%) (R = 3.0%) 10.5% (L = 10.8%) (R = 9.3%) 24.7% (L = 21.2%) (R = 25.3%)
NeoC-2 Only 18.3% (L = 11.4%) (R = 13.4%) 12.6% (L = 9.9%) (R = 9.6%) 6.2% (L = 8.2%) (R = 5.5%)
NeoC-1 + NeoC-2 1.5% (L = 0.5%) (R = 1.0%) 6.6% (L = 2.7%) (R = 3.3%) 8.9% (L = 3.4%) (R = 4.1%)
Limbic+ (NeoC-1/NeoC-2) 0.5% (L = 0%) (R = 0%) 7.2% (L = 3.3%) (R = 2.7%) 21.9% (L = 11.0%) (R = 11.0%)
Unimpaired 71.3% (L = 81.2%) (R = 80.7%) 45.0% (L = 58.0%) (R = 48.9%) 17.1% (L = 37.7%) (R = 30.1%)
Abbreviations: CN—cognitively normal, aMCI—amnestic mild cognitive impairment, AD—Alzheimer’s disease, NeoC—neocortical. χ2 (df = 10) = 173.64; p <
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R. Duara et al. / Advances in Alzheimer’s Disease 2 (2013) 135-147
Table 2. Normalized regional volumes in aMCI and cognitively normal subjects with different MRI-based patterns of atrophy.
MRI-Based Patterns MRI Normal
(n = 294)
Limbic Only
(n = 60)
NeoC-1 Only
(n = 35)
NeoC-2 Only
(n = 42)
NeoC-1 + NeoC-2
(n = 22)
NeoC-2) (n = 24)
Hippocampal Volume 0.0043a
(SD = 0.0006)
(SD = 0.0004)
(SD = 0.0005)
(SD = 0.0005)
(SD = 0.0005)
(SD = 0.0003)
Amygdala 0.0014a
(SD = 0.0002)
(SD = 0.0002)
(SD = 0.0002)
(SD = 0.0002)
(SD = 0.0002)
(SD = 0.0002)
ERC 0.0025a
(SD = 0.0004)
(SD = 0.0004)
(SD = 0.0004)
(SD = 0.0004)
(SD = 0.0003)
(+Inferior Parietal)
(SD = 0.0010)
(SD = 0.0010)
(SD = 0.0008)
(SD = 0.0009)
(SD = 0.0007)
(SD = 0.0012)
Posterior Cingulate 0.0039a
(SD = 0.0004)
(SD = 0.0003)
(SD = 0.0004)
(SD = 0.0004)
(SD = 0.0004)
(SD = 0.0005)
26. 36***
Middle Frontal
(SD = 0.0008)
(SD = 0.0009)
(SD = 0.0008)
(SD = 0.0008)
(SD = 0.0009)
(SD = 0.0008)
+ Superior Temporal
(SD = 0.0005)
(SD = 0.0005)
(SD = 0.0004)
(SD = 0.0004)
(SD = 0.0004)
(SD = 0.0004)
Temporal + Insula
(SD = 0.0002)
(SD = 0.0002
(SD = 0.0002)
(SD = 0.0001
(SD = 0.0002)
(SD = 0.0002)
Note: Means with different alphabetic superscripts are statistically significant at p < 0.05 by the Tukey HSD Procedure; Values in parentheses are Standard
Deviations. In Table 2, it is evident that, as compared to those who had no regional atrophy (“MRI Normal”), the Limbic pattern had significant atrophy in the
limbic structures (hippocampus, Limbic structures only).
Table 3. Clinical and neuropsychological features in aMCI and AD subjects with various MRI-based patterns of atrophy.
MRI-Based Patterns Limbic Only
(n = 91)
NeoC-1 Only
(n = 71)
NeoC-2 Only
(n = 51)
NeoC-1 + NeoC-2
(n = 35)
Limbic +
(n = 56)
(n = 144) F or χ2p-value
Age, Years 75.99 (6.3) 73.86 (8.8) 76.02 (6.5) 75.40 (7.6) 77.57 (6.3) 74.51 (4.9) 2.180.056
Gender, % Females 46.2% 43.7% 25.5% 37.1% 33.9% 50.0% 11.990.35
APOE 4+ 58.2% 70.4% 39.2% 57.1% 66.1% 25.7% 56.84<0.001
MMSE 25.76bc (2.6) 25.21cd (2.0) 26.39b (1.9)25.14cd (2.6) 24.30de (2.8) 29.13a (1.0) 77.16<0.001
CDR Sum of Boxes 2.68bc (1.8) 2.85cd (1.5) 2.01b (1.4) 2.60bc (1.7) 3.46d (2.0) 0.03a (0.11) 80.23<0.001
2.41cd (2.7) 2.28cd (2.5) 4.20b (2.6) 3.03bc (2.3) 1.50d (2.2) 12.98a (3.5) 267.74<0.001
AVLT Total Score 30.60bcd (9.6) 28.00d (10.3) 33.69b (12.3)29.83 bcd (9.8) 26.29d (10.1) 52.12a (10.7) 91.88<0.001
AVLT-Delayed Recall 1.42bc (2.4) 1.44bc (2.4) 2. 65b (3.2)2.34b (2.3) 0.84c (1.8) 7.45a (3.5) 85.44<0.001
Trails A 43.75b (15.0) 66.31cd (35.7) 55.22bc (31.7)65.86cd (37.4) 58.04cd (34.4) 36.25a (12.5) 19.02<0.001
Trails B 137.00b (70.1) 188.88cd (85.4)156.20bc (78.5)201.73d (86.1) 191.56cd (88.2) 85.24a (36.8) 36.74<0.001
Category Fluency 24.30b (7.1) 22.55b (7.1) 25.06b (7.1)25.86cd (9.3) 21.39b (8.3) 35.12a (7.9) 48.36<0.001
All data are presented as mean (standard deviation), unless noted otherwise. Abbreviations: NeoC—neocortical, CN—cognitively normal. Note: Means with
different alphabetic superscripts are statistically significant at p < 0.05 by the Tukey HSD procedure; Values in parentheses are Standard Deviations.
NeoC-1 subjects to be the youngest group and the Lim-
bic + (NeoC-1/NeoC-2) subjects to be the oldest group.
The NeoC-2 subjects had the lowest percentage of fe-
males (25%), whereas the CNMRI subjects had the high-
est percentage of females (50%). There were also group
differences with regards to APOE ε4 frequency ([χ2 (df =
5) = 56.84; p < 0.001] with NeoC-1 (70.4%) and com-
bined Limbic + (NeoC-1/NeoC-2) subjects (66.1%) hav-
ing the highest ε4 frequencies, which were significantly
higher than CNMRI (25.7%) and NeoC-2 subjects (39.2%)
who had the lowest APOE ε4 frequencies.
In Table 3, it can be seen that the control group,
namely (CNMRI) subjects, scored higher on all neuro-
psychological and functional measures than subjects with
any pattern of atrophy including NeoC-2. NeoC-2 sub-
jects generally had the least impairment on all tests
Copyright © 2013 SciRes. OPEN ACCESS
R. Duara et al. / Advances in Alzheimer’s Disease 2 (2013) 135-147 141
scores, with the exception of scores on Trails A and B
and Category Fluency tests, whereas subjects with a pre-
dominant Limbic + (NeoC-1/NeoC-2) pattern generally
had the most impaired scores on all tests, with the excep-
tion of Trails A and B tests. Subjects with a predominant
NeoC-2 pattern had significantly better scores than par-
ticipants with the NeoC-1 and the Limbic + (NeoC-1/
NeoC-2) regional patterns of atrophy on CDR sum of
box scores, MMSE, delayed memory for passages and
AVLT total scores. In addition, the NeoC-2 subjects
had the least impaired scores on tests of memory
(WMS and AVLT, overall cognition (MMSE score)
and functional ability (CDR score) relative to NeoC-1,
NeoC-1 + NeoC-2 and Limbic + (NeoC-1/NeoC-2) sub-
3.1.4. Rates of Progression of aMCI to
Dementia among Different Subjects
with Different MRI Patterns of Atrophy
The rate of progression to dementia over 24 months
was evaluated among aMCI subjects, so as to provide a
measurable rate of progression to dementia among sub-
jects with different MRI subtypes of atrophy. At baseline,
aMCI subjects exhibited no MRI predominant regional
patterns of atrophy in age [F (5,218) = 1.35; p > 0.24] or
gender [χ2 (df = 5) = 5.73; p > 0.33]. MMSE scores dif-
fered among groups [F (5,218); p < 0.005], with the
aMCI non-atrophic subjects obtaining higher mean MMSE
scores (M = 27.6; SD = 1.6) than those with predominant
NeoC-1 regional atrophy (M = 26.2; SD = 1.7), but among
the different MRI groupings there was no significant
difference in MMSE scores.
As indicated in Table 4, the rate of progression from
aMCI to dementia over 24 months was different accord-
ing to the type of regional pattern of atrophy [χ2 (df = 5)
= 21.29; p < 0.001], with about two thirds of NeoC-1
(68.2%) and NeoC-1 + NeoC-2 (64.3%) subjects, half of
Limbic + (NeoC-1/NeoC-2) (50%) and Limbic (48.8%),
one third of NeoC-2 (34.5%), and about a quarter
(26.0%) of the non-atrophic group progressing to de-
3.1.5. Predictors of Progression of aMCI to
Dementia among Subjects with Different
Predominant MRI Patterns of Atrophy
We used step-wise logistic regression to predict pro-
gression to dementia in 24 months among aMCI subjects,
including left and right hemisphere factor scores for
Limbic, NeoC-1 and NeoC-2 groupings, right and left
hippocampal volumes, as well as ApoΕ4 status and base-
line MMSE score. For the right hemisphere, variables
that entered into the model was NeoC-1 factor score (B =
0.86; SE = 0.18; Wald = 21.67; p < 0.001), hippocam-
pal volume (B = 8.24; SE = 3.1 Wald = 7.31; p < 0.016
and Limbic factor score (B = 0.45; SE = 0.17; Wald =
6.96; p < 0.008), correctly classifying 51.1% of progres-
sors and 84.3% of non-progressors, with an overall cor-
rect classification rate of 71.2%. For the left hemisphere,
variables that entered into the model were left hippo-
campal volume (B = 1445.6; SE = 511.8; Wald = 7.98;
p < 0.006), NeoC-1 (B = 0.47; SE = 0.17; Wald = 7.08;
p < 0.006 and baseline MMSE (B = 0.193; SE = 0.09;
Wald = 7.98; p < 0.04). These predictors correctly classi-
fied 46.6% of progressors and 78.4% non-progressors,
with an overall correct classification rate of 64.4%.
As indicated in Table 5, baseline MMSE score was
the strongest predictor of the MMSE score at the
24-month endpoint. However, Limbic, NeoC-1, NeoC-2
volumes all provided additional and statistically signifi-
cant added explained variance (14.9%) in predicting
24-month MMSE scores beyond the baseline MMSE
score alone. Similar findings were observed for CDR
sum of boxes, total recall score of the AVLT and cate-
gory fluency test. NeoC-1 and Limbic factor scores pre-
dicted Trails B, Delay Memory for Passages (Delayed)
and AVLT-Delay scores at the 24-month follow-up even
after adjusting for baseline cognitive performance on
these measures. After controlling for baseline perform-
ance on Trails A, Trails A performance at the 24-month
follow-up could be predicted by NeoC-1. The strongest
MRI-based subtype predictor of cognitive performance
for delayed recall tasks was the Limbic factor score,
while the strongest predictor of immediate learning and
Table 4. Progression to dementia among aMCI subjects after 24 months.
MRI-Based Pattern Progressed to Dementia Did Not Progress to Dementia
Limbic Only 48.8% 51.2%
NeoC-1 Only 68.2% 31.8%
NeoC-2 Only 34.5% 65.5%
NeoC-1 + NeoC-2 64.3% 35.7%
Limbic + (NeoC-1/NeoC-2) 50.0% 50.0%
Non-Atrophic 26.9% 73.1%
Abbreviations: NeoC—neocortical. Note:
2 (df = 5) = 21.29; p < 0.001.
Copyright © 2013 SciRes. OPEN ACCESS
R. Duara et al. / Advances in Alzheimer’s Disease 2 (2013) 135-147
Table 5. Predictors of cognitive and functional decline at 24-month follow among aMCI subjects.
Score (Time 2) Predictors Standardized Betat-Value R2 (Baseline Cognitive Score) R2 (Baseline Cognitive Score +
MRI Measures)
MMSE-T1 0.342 6.05*** 21.6% 36.5%
Left Ventricle 0.025 0.39
Left Limbic 0.210 3.52**
Left NeoC-1 0.328 5.57***
Left NeoC-2 0.205 3.43**
CDRSOB T1 0.393 6.89*** 22.2% 34.3%
Left Ventricle 0.003 0.05
Left Limbic 0.236 3.88***
Left NeoC-1 0.233 3.93***
Left NeoC-2 0.189 3.13**
Pass Delay T1 0.017 0.28 NA 9.9%
Left Ventricle 0.091 1.31
Left Limbic 0.291 4.44***
Left NeoC-1 0.241 3.91***
Memory for
Left NeoC-2 0.064 1.00
AVLT-Tot T1 0.693 18.10*** 57.3% 62.6%
Left Ventricle 0.032 0.72
Left Limbic 0.162 3.97***
Left NeoC-1 0.172 4.31***
AVLT-Total Recall
Left NeoC-2 0.133 3.23**
AVLT-Del T1 0.556 11.85*** 41.0% 46.4%
Left Ventricle 0.070 1.31
Left Limbic 0.224 4.44***
Left NeoC-1 0.186 3.91***
Left NeoC-2 0.049 1.00
Trails A T1 0.670 15.67*** 52.7% 54.8%
L Ventricle 0.003 1.31
Left Limbic 0.052 1.20
Left NeoC-1 0.163 3.71***
Trails A
Left NeoC-2 0.075 1.67
Trails B T1 0.544 11.18*** 39.4% 44.9%
Left Ventricle 0.064 1.17
Left Limbic 0.111 2.24*
Left NeoC-1 0.209 4.19***
Trails B
Left NeoC-2 0.044 0.87
Cat Fluency T1 0.562 12.29*** 40.1% 46.6%
Left Ventricle 0.041 0.77
Left Limbic 0.111 2.29*
Left NeoC-1 0.194 4.09***
Category Fluency
Left NeoC-2 0.126 2.54*
Abbreviations: MMSE = Mini-Mental State Examination; CDR SOB = Clinical Dementia Rating Scale Sum of Boxes; AVLT = Rey Auditory Verbal Learning
Test. Note: *p < 0.05; **p < 0.01; ***p < 0.001.
Copyright © 2013 SciRes. OPEN ACCESS
R. Duara et al. / Advances in Alzheimer’s Disease 2 (2013) 135-147
Copyright © 2013 SciRes. OPEN ACCESS
non-amnestic measures was NeoC-1 factor score. Ven-
tricular dilatation was not predictive of outcome after
other variables were entered into regression models.
3.2. Phase 2 Analyses
As indicated in the Methods, we re-analyzed our data
so that factor derived regional patterns of atrophy were
derived from amyloid positive aMCI and clinically di-
agnosed AD subjects in the ADNI sample. This approach,
in which MRI patterns of atrophy were derived only on
subjects thought to have underlying AD pathology, pro-
vided factor structures which were very similar to those
obtained from the entire ADNI cohort. Subsequently,
cut-offs for impairment were determined relative to the
performance of normal elderly subjects in the ADNI
sample who were amyloid negative. There were statisti-
cally significant differences in regional patterns of atro-
phy among the different diagnostic groups [χ2 (df = 14) =
215.21; p < 0.001]. Among aMCI subjects, the limbic
subtype of atrophy was most frequent (13.5%), followed
by combined Limbic + NeoC-1 + NeoC-2 (10.8%) and
combined NeoC-1 + NeoC-2. Other predominant pat-
terns of atrophy were NeoC-1 alone (6.6%) and NeoC-2
alone (4.2%) and another 10.5% had other combinations
or predominant atrophy. Among AD subjects, the most
frequent patterns of atrophy were: Combined Limbic +
NeoC-1 + NeoC-2 (27.4%) followed by limbic alone
(17.1%), NeoC-1 + Limbic (14.4%), NeoC-1 alone (9.6%)
and other combinations of regional atrophy (14.3%).
Among cognitively normal subjects in the ADNI cohort
the most frequent pattern of atrophy was NeoC-2 (6.9%)
while additional subjects (10.5%) had other patterns of
Similar to the analyses we conducted in Phase 1, in
Phase 2 analyses we used simultaneous entry of the total
volumes of the factorially derived Limbic, NeoC-1 and
NeoC-2 regions in the left hemisphere for amyloid posi-
tive aMCI subjects and Probable AD patients in the
ADNI sample. Results indicated that after controlling for
baseline performance and NeoC-2 and Limbic regional
volumes, lower total volumes of NeoC-1 regions were
predictive of poorer performance at 24 months on the
MMSE, CDR sum of boxes, memory (delayed memory
for passages), AVLT immediate recall and AVLT de-
layed recall, Trails A and B and Category Fluency. After
controlling for the entry of other variables, lower scores
in the Limbic region were associated with lower memory
scores at the 24-month follow-up period. Lower volumes
in NeoC-2 region were associated with lower scores on
Trails B; the severity of ventricular dilatation was not
predictive of cognitive or functional decline.
Taken together, these results among amyloid positive
aMCI and clinically diagnosed AD subjects replicate the
existence of neocortical and limbic subtypes of AD
found in Phase 1 results, and demonstrate that atrophy in
NeoC-1 regions is especially predictive of decline on a
broad array of neuropsychological and functional meas-
ures at 24 months when adjusting for baseline perform-
ance and NeoC-2, Limbic and ventricular volumes.
Using structural MRI data from the ADNI public-ac-
cess database, and factor analytic and principal compo-
nent analyses methods, we have identified three distinct
patterns of regional cerebral atrophy among subjects di-
agnosed with AD, aMCI and elderly normals. In the en-
tire cohort of 333 subjects, among those diagnosed with
AD, 83% of subjects had one or more of these regional
patterns of atrophy (Table 1). Among subjects diagnosed
as aMCI, 55% had one or more regional patterns of at-
rophy, the most frequent one being Limbic (46%). Among
CN subjects, 29% had one or more regional pattern of
atrophy, the most frequent being NeoC-2.
Among the three core MRI-based patterns of atrophy
(NeoC-1, NeoC-2 and Limbic), there were certain dis-
tinguishable demographic, genetic and cognitive features,
as well as rates of clinical progression (see Tables 3 and
4). Subjects with the NeoC-1 pattern of impairment tend-
ed to be younger, about equally likely to be male as fe-
male, had the highest AP OE ε4 frequencies, the most
severe cognitive (especially, non-amnestic) and func-
tional impairment, as well as the most rapidly progres-
sive course. The regions encompassed by the NeoC-1
region included primarily those structures in the default
mode network (DMN) [11], namely the precuneus, infe-
rior parietal cortex, the rostral middle frontal cortex and
the lateral temporal cortices, which show abnormal func-
tional connectivity early in the course of AD [12]. Re-
gional hypometabolism on fluorodeoxyglucose PET
scans and amyloid deposition on amyloid PET scans also
occurs early and most severely in the regions comprising
the NeoC-1 and the DMN. There is also evidence that
disruption in functional connectivity in these regions is
present in asymptomatic APOE ε4 carriers, antedating
measurable amyloid deposition in the brain [13].
Subjects with the limbic patterns of atrophy tended to
be older than NeoC-1 subjects, equally likely to be male
as female, have APOE ε4 frequencies and rates of pro-
gression to dementia intermediate between subjects with
NeoC-1 and NeoC-2 patterns, and to be marginally less
impaired on global cognitive, memory and functional
measures as NeoC-1 subjects, but significantly less im-
paired on non-amnestic measures. The more severe me-
mory impairment and the relatively mild non-amnestic
impairment relative to NeoC-1 associated with the Lim-
bic subtype of atrophy, may be associated with the
greater frequency of this subtype among aMCI subjects.
Also, the slower progression rates of these subjects, rela-
R. Duara et al. / Advances in Alzheimer’s Disease 2 (2013) 135-147
tive to those with NeoC-1 subtype of atrophy may be
associated with the higher frequency (and lack of pro-
gression to dementia) among aMCI subjects. The regions
encompassed by the limbic region (hippocampus, entor-
hinal cortex, amygdala, fusiform gyrus and anterior tem-
poral pole are closely linked and co-activated with the
DMN [11].
Subjects with the NeoC-2 pattern of atrophy also tend
to be older than NeoC-1 subjects, are more likely to be
male than female, have the lowest ε4 frequencies, the
mildest amnestic and functional impairment, relatively
greater nonamnestic impairment than among the other
subtypes, and the slowest rate of progression among the
three subtypes. The very mild cognitive and functional
impairment and slow rates of progression to dementia
among NeoC-2 subjects may account for our finding that
NeoC-2 is the most frequently identified atrophic pattern
among CN subjects and the least common atrophic pat-
tern among AD subjects. The regions encompassed by
the NeoC-2 pattern of atrophy (superior and transverse
temporal, insula, the supramarginal gyrus and posterior
and isthmus cingulate regions) are located in the “Tem-
poroparietal Junction Area” [14]. Atrophy in these re-
gions would likely be associated with disruption of a
core network of functionally connected regions resulting
in non-amnestic cognitive dysfunction [15]. ADNI does
not have a separate category of subjects with non-am-
nestic MCI (naMCI), but our prediction would be that
the NeoC-2 subtype of atrophy would be most closely
associated with naMCI.
The heterogeneity in patterns of atrophy on MRI scans,
observed in the current investigation, corresponds, to
some extent, to the subtypes of AD pathology reported
recently by Murray et al. [2]. In that study, 25% of pri-
marily end-stage AD patients who came to autopsy had
atypical patterns of distribution of regional neuropathol-
ogy, including a “limbic predominant” (LP) subtype (ac-
counting for 14% of AD) and a “hippocampal sparing”
(HpSp) subtype i.e., a predominantly neocortical (NeoC)
subtype, which accounted for 11% of AD. However, the
vast majority of cases (75%) had a “typical” distribution
of pathology, including a combination or limbic and neo-
cortical neurofibrillary pathology. In this study we also
found that combinations of neocortical and limbic sub-
types accounted for the most frequent atrophy patterns
among aMCI and AD subjects. It would also appear that
the heterogeneity in progression rates may be associated
with the relative proportion of subjects harboring the
NeoC-1 or NeoC-2 patterns of atrophy we have identi-
fied. Similar to the NeoC-1 subtype found on antemor-
tem MRI scans, the HpSp (i.e., neocortical) subtype de-
fined pathologically, had faster rates of progression, as
well as earlier age-of-onset. The limbic predominant
subtype identified at autopsy had a slower rate of pro-
gression and later age of onset, which bears some simi-
larity to the MRI-based Limbic subtype we have defined
It is highly likely that the pathological subtypes, de-
scribed in autopsied subjects, typically with end-stage
disease, may also be present in much earlier stages of
AD, detectable as regional atrophy in MRI scans. Volu-
metric measurements are highly reliable and accurate
markers of regional brain atrophy and have been shown
to be highly correlated with regional neurodegenerative
pathology, even in the preclinical stage of the disease
[7,16-18]. Further, regional cerebral atrophy is known to
occur early in Alzheimer’s disease (AD), and the pattern
of atrophy seen in cortical thickness measures (i.e., the
“AD signature”) can be detected among elderly, cogni-
tively normal subjects (CN) who later go on to develop
mild cognitive impairment (MCI) and/or Alzheimer’s
disease (AD) [19].
In this study we derived three distinct patterns of at-
rophy on the entire ADNI cohort based on a factor
analysis of 16 brain regions, which were selected be-
cause they were shown to have significant atrophy
among aMCI subjects in the ADNI cohort. These distinct
patterns of atrophy were also observed when the sample
was restricted to AD and aMCI subjects who were amy-
loid positive on amyloid PET scans. In both cases, a
subtype with limbic regional atrophy was identified, as
well as two distinct patterns of predominant neocortical
involvement. Our findings in this cohort of aMCI and
mildly demented AD subjects show similarity to the
pathological findings among end-stage AD patients in
the Murray et al. study (2). There were, of course, dif-
ferences in the findings between the in- vi vo ADNI study
and the work of Murray and colleagues, as follows: a) in
the Murray et al. study, three neocortical regions and two
hippocampal regions (CA-1 and subiculum) were se-
lected, a priori, to define the subtypes, whereas in this
MRI-based study, 6 limbic regions and 8 neocortical
regions were used to derive the atrophic areas; b) the
subjects in the pathological study were diagnosed to have
AD, on the basis of hallmark neuropathologic criteria for
this disease, whereas in the MRI study, subjects were
diagnosed using clinical criteria, which are likely to be
nearly 100% accurate for a pathological diagnosis among
amyloid positive Probable AD subjects and somewhat
less accurate among subjects with amyloid positive
aMCI subjects; c) subjects in the pathological study gen-
erally had end-stage AD and were very demented by the
time they came to autopsy, whereas subjects in the
MRI-based study had mild AD, aMCI or were cogni-
tively normal.
While hippocampal volume has been used, in genetic
and other studies, as a biomarker for AD [7,20], the
variable clinical presentations and rates of progression in
Copyright © 2013 SciRes. OPEN ACCESS
R. Duara et al. / Advances in Alzheimer’s Disease 2 (2013) 135-147 145
AD are not likely to be predicted by atrophy in structures
located solely in the medial temporal lobe [21] among
Probable AD subjects and perhaps even more so among
subjects with aMCI and naMCI. Hippocampal volumes
have been found to be much weaker biomarkers of AD
pathology among subjects with aMCI and especially
those with PreMCI, than in those with Probable AD
[7,18,22]. In fact, the lack of amnestic deficits on neuro-
psychological evaluation among PreMCI subjects who
go on to develop aMCI or AD [18,22], suggests the pre-
sence of neocortical rather than limbic pathology, in this
early stage of disease. Nevertheless, the regional pattern
of atrophy, especially in NeoC-1 areas of the neocortex
appears to predict the future course of disease.
In Table 4, it can be seen that most of the variance in
the rate of progression among aMCI cases is accounted
for by baseline cognitive and functional (CDR-sum of
boxes) scores themselves. Although MRI measures pre-
dict an additional 14% to 15% of this variance over that
predicted by MMSE or CDR-SB scores, hippocampal
volume is not a predictor, ventricular volume accounts
for a very minor proportion of the variance and the main
MRI predictors are volumes of left sided NeoC-1 regions.
The preceding suggests that in the very early preclinical
stages of the disease, before cognitive scores have shown
any major decline, these MRI measures may be early
predictors of disease course.
The findings in mostly late stage AD cases who had
come to autopsy and the current findings on MRI scans
in CN, aMCI and mild-moderate AD cases, together
suggest that over the course of the disease, there may be
an evolution from a single pattern of atrophy (reflecting
localized distribution of neurofibrillary pathology) to a
combination of various patterns of atrophy in the later
stages of the disease. While it is less than clear that the
NeoC-2 pattern of atrophy reflects early AD pathology,
as opposed to some other degenerative or age-related
atrophy, the data presented in Table 3 suggest that the
initial clinical features are related to regional patterns of
atrophy detectable early in the disease course. As the dis-
ease evolves, the regional pattern of atrophy at any par-
ticular stage of disease may allow prediction of the sub-
sequent disease course. Specifically, our data seems to
suggest that if a patient presents with predominant
NeoC-2 atrophy, the course of the disease is likely to be
very slow; however, subsequent development of either
NeoC-1 or Limbic pattern of atrophy may signal a
change in the course to a more rapid rate of clinical pro-
The limitation of the current study lies in the unique
characteristics of subjects who volunteer to participate in
ADNI, which is rigorous, with the requirement for lon-
gitudinal clinical, neuropsychological and imaging stud-
ies, and more recently lumbar puncture. Such subjects
may not be representative of the typical patient seen in
memory clinics or in a doctor’s office. For example,
about 25% - 30% of CN elderly subjects in most studies
have been shown to be amyloid positive on PET scans,
whereas 45% - 50% of ADNI subjects, who are cogni-
tively normal, have been found to be amyloid positive on
PET scans [23]. The strengths of the current study also
lie in its use of the large, multimodal and longitudinal
database available in ADNI. This has made it possible to
show that regional cortical changes in temporal, parietal
and frontal cortex have greater utility as outcome meas-
ures in predicting the course of AD than traditional hip-
pocampal and ventricular volumes. While other studies
have also provided similar evidence [19,21,24-27], our
findings are the first to investigate the possibility of using
individual patterns of cortical atrophy for predicting
progression rates unique to a particular subject.
The findings in this study, especially with regards to
predicting future rates of progression according to re-
gional patterns of predominant atrophy, need to be con-
firmed and generalized to more representative and typi-
cal clinic populations. This may then set the stage for
more effective planning of patient care and enable strati-
fication of subjects into relatively homogenous sub-
groups in AD clinical treatment trials, based on expected
rates of progression. Moreover, MRI technology is wide-
ly available and provides reliably quantifiable measures,
which may be used to provide valuable insights into the
pathophysiology of cognitive decline in aging and very
early neurodegenerative diseases.
Data collection and sharing for this project was funded by the Alz-
heimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes
of Health Grant U01 AG024904) and DOD ADNI (Department of
Defense award number W81XWH-12-2-0012). ADNI is funded by the
National Institute on Aging, the National Institute of Biomedical Imag-
ing and Bioengineering, and through generous contributions from the
following: Alzheimer’s Association; Alzheimer’s Drug Discovery
Foundation; BioClinica, Inc.; Biogen Idec Inc.; Bristol-Myers Squibb
Company; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Com-
pany; F. Hoffmann-La Roche Ltd and its affiliated company Genentech,
Inc.; GE Healthcare; Innogenetics, N.V.; IXICO Ltd.; Janssen Alz-
heimer Immunotherapy Research & Development, LLC.; Johnson &
Johnson Pharmaceutical Research & Development LLC.; Medpace,
Inc.; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx
Research; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal
Imaging; Servier; Synarc Inc.; and Takeda Pharmaceutical Company.
The Canadian Institutes of Health Research is providing funds to sup-
port ADNI clinical sites in Canada. Private sector contributions 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 Cali-
Copyright © 2013 SciRes. OPEN ACCESS
R. Duara et al. / Advances in Alzheimer’s Disease 2 (2013) 135-147
fornia, San Diego. ADNI data are disseminated by the Laboratory for
Neuro Imaging at the University of California, Los Angeles.
This work was supported by the State of Florida Alzheimer’s Disease
Initiative, Department of Elder Affairs, Tallahassee, Florida.
Data used in preparation of this article were obtained from the Alz-
heimer’s Disease Neuroimaging Initiative (ADNI) database (adni.lo-
ni.usc.edu). As such, the investigators within the ADNI contributed to
the design and implementation of ADNI and/or provided data but did
not participate in analysis or writing of this report. A complete listing of
ADNI investigators can be found at:
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