Current Alzheimer Research, 2009, 6, 347-361 347
1567-2050/09 $55.00+.00 ©2009 Bentham Science Publishers Ltd.
Baseline MRI Predictors of Conversion from MCI to Probable AD in the
Shannon L. Risacher1,2, Andrew J. Saykin1,3,*, John D. West1, Li Shen1,4, Hiram A. Firpi1,
Brenna C. McDonald1 and the Alzheimer’s Disease Neuroimaging Initiative (ADNI)†
1IU Center for Neuroimaging, Division of Imaging Sciences, Department of Radiology, Indiana University School of
Medicine, 950 W Walnut St, R2 E124, Indianapolis, IN 46202, USA; 2Medical Neuroscience Program, Stark Neuros-
ciences Research Institute, Indiana University School of Medicine, 950 W Walnut St, R2 Building, Room 402, Indiana-
polis, IN 46202, USA; 3Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN
46202, USA; 4Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, 410 West
10th Street, Suite 5000, Indianapolis, IN 46202, USA;
Abstract: The Alzheimer’s Disease Neuroimaging Initiative (ADNI) is a multi-center study assessing neuroimaging in
diagnosis and longitudinal monitoring. Amnestic Mild Cognitive Impairment (MCI) often represents a prodromal form of
dementia, conferring a 10-15% annual risk of converting to probable AD. We analyzed baseline 1.5T MRI scans in 693
participants from the ADNI cohort divided into four groups by baseline diagnosis and one year MCI to probable AD
conversion status to identify neuroimaging phenotypes associated with MCI and AD and potential predictive markers of
imminent conversion. MP-RAGE scans were analyzed using publicly available voxel-based morphometry (VBM) and
automated parcellation methods. Measures included global and hippocampal grey matter (GM) density, hippocampal and
amygdalar volumes, and cortical thickness values from entorhinal cortex and other temporal and parietal lobe regions. The
overall pattern of structural MRI changes in MCI (n=339) and AD (n=148) compared to healthy controls (HC, n=206)
was similar to prior findings in smaller samples. MCI-Converters (n=62) demonstrated a very similar pattern of atrophic
changes to the AD group up to a year before meeting clinical criteria for AD. Finally, a comparison of effect sizes for
contrasts between the MCI-Converters and MCI-Stable (n=277) groups on MRI metrics indicated that degree of neurode-
generation of medial temporal structures was the best antecedent MRI marker of imminent conversion, with decreased
hippocampal volume (left > right) being the most robust. Validation of imaging biomarkers is important as they can help
enrich clinical trials of disease modifying agents by identifying individuals at highest risk for progression to AD.
Keywords: Alzheimer’s disease neuroimaging initiative (ADNI), magnetic resonance imaging (MRI), mild cognitive
impairment (MCI), hippocampus, cognition.
generative illness associated with aging, accounting for 60-
70% of age-related dementia cases. In 2000, approximately
25 million people over the age of 60 were diagnosed with
dementia worldwide, and the number afflicted is expected to
reach over 80 million by 2040 [1, 2]. Earlier diagnosis of AD
is widely considered to be an important goal for researchers.
Characterization of the earliest known clinical signs has led
to the development of the classification of Mild Cognitive
Impairment (MCI), which is thought to be a transitional
stage between normal aging and the development of AD
Alzheimer’s disease (AD) is the most common neurode-
*Address correspondence to this author at the IU Center for Neuroimaging,
Department of Radiology, Indiana University School of Medicine, 950 W
Walnut St, R2 E124, Indianapolis, Indiana 46202, USA; Tel: 317-278-6947;
Fax: 317-274-1067; Email: firstname.lastname@example.org
†Data used in the preparation of this article were obtained from the Alzhei-
mer’s Disease Neuroimaging
(www.loni.ucla.edu/ADNI). 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. For a complete
list of investigators involved in ADNI see: http://www.loni.ucla.edu/
Initiative (ADNI) database
. Patients with MCI, specifically those with primary
memory deficits or “amnestic MCI”, have a significantly
higher likelihood to progress to probable AD, with a conver-
sion rate of 10-15% per year . Therefore, MCI represents
an important clinical group in which to study longitudinal
changes associated with the development of AD. The detec-
tion of subtle changes in brain structure associated with di-
sease progression and the development of tools to detect
those who are most likely to convert from MCI to probable
AD is an important goal.
(ADNI) is a five-year public-private partnership 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 amnestic MCI and early proba-
ble AD [5-7]. One of the major goals of ADNI is to assess
selected neuroimaging and analysis techniques for sensitivity
and specificity for both cross-sectional diagnostic group
classification and longitudinal progression of MCI and AD.
The Alzheimer's Disease Neuroimaging Initiative
tural MRI data is voxel-based morphometry (VBM), which
A powerful technique for analyzing high resolution struc-
348 Current Alzheimer Research, 2009, Vol. 6, No. 4
Risacher et al.
allows specific tissue classes (i.e., grey matter (GM), white
matter (WM), or CSF) to be analyzed in an automated and
unbiased manner [8-10]. VBM analyses, particularly compa-
risons of GM density between groups, have been used to
examine diagnostic group differences in both cross-sectional
and longitudinal studies of brain aging and AD [11-30]. In
fact, VBM has been shown to accurately classify controls
and AD patients and to predict conversion from MCI to AD
and rate of progression in studies of brain aging [12, 13, 15,
18, 20, 23]. However, the small sample size of these studies
and minimal longitudinal monitoring has prevented VBM
from being established as a conclusive biomarker for MCI to
probable AD conversion.
(VOIs) have also been effective in measuring local atrophy
associated with AD and MCI and longitudinal monitoring of
neurodegeneration in studies of brain aging. Numerous
studies using manually defined ROIs have found that local
hippocampal and total brain volume are significantly redu-
ced in AD and MCI patients relative to healthy elderly indi-
viduals [17, 24, 25, 28, 31-46]. Rates and amount of hippo-
campal, medial temporal lobe (MTL), and total brain atrophy
have also been shown to correlate with MCI to AD conversi-
on [31-34, 37, 40-42, 44, 45, 47-52]. Recently, automated
methods for extraction of specific regional volumes have
been developed and found to provide similar reliability as
manually traced ROIs in AD [53-56]. Automated parcellati-
on methods have also demonstrated reliable cortical
thickness value estimations and decreased cortical thickness
in AD [56, 57].
Regions of interest (ROIs) and volumes of interest
parisons using the 1.5T T1-weighted structural scans obtai-
ned from ADNI participants at baseline. Using VBM as
implemented in SPM5 (http://www.fil.ion.ucl.ac.uk/spm/),
we examined cross-sectional GM differences between
groups stratified by baseline diagnosis and one year conver-
sion from MCI to probable AD. Study groups included parti-
cipants diagnosed with AD at the screening, baseline, 6-, and
12-month follow-up visits (AD), participants designated as
healthy elderly controls at all four visits (HC), participants
who were diagnosed with MCI at all four visits (MCI-
Stable), and participants who were diagnosed with MCI at
baseline and converted from MCI to probable AD within the
first year (MCI-Converters). We extracted bilateral hippo-
campal GM density values, hippocampal and amygdalar
volumes, and entorhinal cortex, temporal lobe, and parietal
lobe cortical thickness values for between-group compari-
sons. We hypothesized that patients with AD would show
extensive GM reduction in medial and lateral temporal lobes
and other neocortical regions, and that both of the MCI
groups would demonstrate focal reduction in MTL structures
compared to HC. We also hypothesized that MCI partici-
pants who converted to AD within one year would show a
more extensive pattern of global GM reduction relative to
HC, particularly in regions of the MTL, than participants
with a stable diagnosis of MCI, but a less extensive pattern
than AD participants. We predicted that MCI-Converters
would show greater MTL and neocortical GM density reduc-
tions relative to MCI-Stable participants. Finally, we investi-
gated whether local hippocampal GM density and volume,
amygdalar volume, and entorhinal, temporal, and parietal
The goal of the present study was to perform group com-
cortical thickness values would reflect the same pattern of
group differences, and the relative ability of these MRI
metrics to detect differences between MCI-Converter and
Aging (NIA), the National Institute of Biomedical Imaging
and Bioengineering (NIBIB), the Food and Drug Administ-
ration (FDA), private pharmaceutical companies, and non-
profit organizations. More than 800 participants, ages 55-90,
have been recruited from 59 sites across the U.S. and Canada
to be followed for 2-3 years. The primary goal of ADNI is to
determine whether serial magnetic resonance imaging
(MRI), positron emission tomography (PET), other biologi-
cal markers, and clinical and neuropsychological assessment
can accurately measure the progression of MCI and early
AD. The identification of specific biomarkers of early AD
and disease progression will provide a useful tool for resear-
chers and clinicians in both the diagnosis of early AD and in
the development, assessment and monitoring of new treat-
ments. For additional information about ADNI, see
ADNI was launched in 2004 by the National Institute on
downloaded from the ADNI public website (http://www.loni.
ucla.edu/ADNI/) onto local servers at Indiana University
School of Medicine between January and April 2008. The
downloaded data initially included baseline scans from 229
HC, 403 patients with MCI, and 188 patients with AD.
Complete details regarding participant exclusion and catego-
rization are provided in Fig. (1). Scan data was acquired on
1.5T GE, Philips, and Siemens MRI scanners using a magne-
tization prepared rapid acquisition gradient echo (MP-
RAGE) sequence that was selected and tested by the MRI
Core of the ADNI consortium . Briefly, two high-
resolution T1-weighted MRI scans were collected for each
participant using a sagittal 3D MP-RAGE sequence with an
approximate TR=2400ms, minimum full TE, approximate
TI=1000ms, and approximate flip angle of 8 degrees (scan
parameters vary between sites, scanner platforms, and soft-
ware versions). Scans were collected with a 24cm field of
view and an acquisition matrix of 192 x 192 x 166 (x, y, z
dimensions), to yield a standard voxel size of 1.25 x 1.25 x
1.2 mm. Images were then reconstructed to give a 256 x 256
x 166 matrix and voxel size of approximately 1 x 1 x 1.2
mm. Additional scans included prescan and scout sequences
as indicated by scanner manufacturer, axial proton density
T2 dual contrast FSE/TSE, and sagittal B1-calibration scans
as needed. Further details regarding the scan protocol can be
found in  and at www.adni-info.org. Scans were collected
at either screening (n=845) or baseline visits (n=184)
between August 2005 and October 2007. If scans existed
from both sessions for a single participant, the scan from the
screening visit was used. Details of the ADNI design, parti-
cipant recruitment, clinical testing, and imaging methods,
have been published previously [5, 6, 7, http://www.adni-
Baseline 1.5T MRI scans from 820 participants were
MRI Predictors of MCI to AD Conversion Current Alzheimer Research, 2009, Vol. 6, No. 4 349
bed methods [8-10], as implemented in SPM5 (http://www.
fil.ion.ucl.ac.uk/spm/). Briefly, scans were converted from
DICOM to NIfTI format, co-registered to a standard T1
template image, bias corrected, and segmented into GM,
WM, and CSF compartments using standard SPM5 temp-
lates. GM maps were then normalized to MNI atlas space as
1x1x1 mm voxels and smoothed using a 10 mm FWHM
Gaussian kernel. In cases where the first MP-RAGE scan
could not be successfully segmented we attempted to use the
second MP-RAGE. This was successful for only 1 of 8
VBM: Analysis was performed using previously descri-
created by manual tracing of the left and right hippocampi in
an independent sample of 40 HC participants enrolled in our
study of brain aging and MCI at Dartmouth Medical School
[25, 58]. These ROIs were used to extract GM density values
from smoothed, unmodulated normalized and modulated
normalized GM maps for the ADNI cohort.
Region of Interest: A hippocampal ROI template was
campi and amygdalar nuclei, were extracted using FreeSur-
fer V4 [56, 59-62]. FreeSurfer was also used to extract cor-
tical thickness values from the left and right entorhinal cor-
tex, inferior, middle, and superior temporal gyri, inferior
parietal gyrus, and precuneus.
Automated Parcellation: VOIs, including bilateral hippo-
Core, and our internal quality control, and did not fail any
step of the processing pipeline (Fig. 1).
The final sample reported here passed site, ADNI MRI
logical test scores, and diagnosis were downloaded from the
ADNI clinical data repository (https://www.loni.ucla.edu/
ADNI/Data/ADCS_Download.jsp). The “10-27-08” version
of the ADNI clinical database was used for all analyses.
Participants were initially classified into groups based on
screening or baseline diagnosis as reported in the diagnosis
and conversion/reversion database.
Demographic information, ApoE genotype, neuropsycho-
VBM Statistical Analyses
basis using a general linear model (GLM) approach imple-
mented in SPM5. A false discovery rate (FDR) adjustment
was used where appropriate to control for multiple compari-
sons, and a minimum cluster size (k) of 27 voxels was requi-
red for significance. Age, gender, years of education,
handedness, and total intracranial volume (ICV) were inclu-
ded as covariates, and an explicit GM mask was used to
restrict analyses to GM regions. A one-way ANOVA was
performed to compare the smoothed, unmodulated normali-
zed GM maps between groups to determine the effects of
Statistical analyses were performed on a voxel-by-voxel
Fig. (1). Flowchart of participant pool selection with group exclusion and inclusion criteria.
350 Current Alzheimer Research, 2009, Vol. 6, No. 4
Risacher et al.
diagnosis and one year conversion from MCI to AD on GM
density. The initial comparison was done using the entire
available sample of 693 participants. A second comparison
was completed using the same methods but with subgroups
of matched participants to correct for unequal group sizes
(n=248; 62 in each group). Matching was done on a case by
case basis using the best available match on age, gender,
education, and handedness, while preserving the relative
proportion of ApoE4+ participants within each subgroup.
After matching there were no significant group differences in
age, gender, education, or handedness.
Finally, a third set of analyses were performed with the
full available sample of 693 participants, adding a volume
preserving modulation step to the VBM method, yielding an
assessment of local GM volume differences instead of GM
Other Statistical Analyses
pal and amygdalar volumes, and cortical thickness values for
all 693 participants were compared between groups using a
one-way multivariate ANOVA in SPSS (version 16.0.1).
Age, gender, education, handedness, and ICV were included
as covariates in all ROI, VOI, and cortical thickness compa-
risons. One-way ANOVA and chi-square tests were used to
determine between-group differences in age, gender distribu-
tion, ApoE genotype, education, handedness distribution,
primary language distribution, and baseline global, functio-
nal, behavioral, neurological, neuropsychiatric, and neuro-
psychological test scores. All graphs were created using
SigmaPlot (version 10.0).
Mean left and right hippocampal GM density, hippocam-
and MCI-Stable participants were also calculated for selected
imaging biomarkers, including bilateral hippocampal GM
density and volume from the VBM images, bilateral hippo-
campal, amygdalar, accumbens, ventral dorsal column, infe-
rior lateral ventricle, lateral ventricle, cerebral cortex, and
cerebral white matter volumes extracted using automated
parcellation, and cortical thickness values from bilateral
entorhinal cortex, inferior, middle, and superior temporal
gyri, inferior parietal gyrus, and precuneus, which were also
extracted using automated parcellation. These values were
assessed due to significant differences between MCI-
Converter and MCI-Stable groups on pairwise comparisons
(p<0.05). Left and right adjusted means for each imaging
measure, covaried for age, gender, education, handedness,
and ICV, were averaged to give a bilateral estimate, and used
to calculate effect sizes (Cohen’s d) in SPSS and Microsoft
Excel (version 2007).
Effect sizes for the comparison between MCI-Converters
for all groups are presented in Table 1. Mean participant age
and handedness distribution did not differ across groups.
Years of education and percent of participants with either
one or two ApoE ?4 alleles (ApoE4+) were significantly
different between diagnosis groups (p<0.001). AD partici-
pants showed significantly fewer years of education than
Demographic information and mean baseline test scores
either HC (p<0.001) or MCI-Stable (p=0.003) participants.
Years of education did not differ significantly between any
other groups in pairwise comparisons. As expected, the HC
group had a lower percentage of ApoE4+ participants than
any of the clinical groups, while the AD group had the
highest percentage of ApoE4+ participants. The MCI-Stable
and MCI-Converter groups had different proportions of A-
poE4+ participants, with the MCI-Converter group showing
a higher percentage of ApoE4+ participants than the MCI-
Stable group. Neuropsychiatric test results, including scores
from the Geriatric Depression Scale (GDS)  and Neuro-
psychiatric Inventory Questionnaire (NPI-Q) , were
significantly different between groups (p<0.001). HC parti-
cipants showed significantly fewer depressive symptoms
than either AD or MCI-Stable participants (p<0.001), and
had a lower mean score on the NPI-Q than the AD, MCI-
Stable, and MCI-Converter groups (p<0.001). AD parti-
cipants also had a significantly higher mean NPI-Q score
than the MCI-Stable (p<0.001) and MCI-Converter
(p=0.008) groups. No significant differences in mean GDS
scores were found between the MCI-Stable, MCI-Converter,
and AD groups. No group showed clinically meaningful le-
vels of depressive symptoms. Ischemic events and/or risk
were not significantly different between groups as assessed
by the Modified Hachinski scale .
67], Clinical Dementia Rating (CDR) , and the Functio-
nal Assessment Questionnaire (FAQ) ) varied signifi-
cantly between groups (p<0.001). Pairwise comparisons
showed a similar pattern for the MMSE, Global CDR, and
CDR-Sum of Boxes. HC participants had significantly hig-
her MMSE and lower CDR scores relative to all other groups
(p<0.001). Additionally, MCI-Stable and MCI-Converter
participants showed significantly higher MMSE and lower
CDR scores compared to AD participants (p<0.001), but did
not differ from one another on these assessments. Mean FAQ
total scores were significantly different across groups and in
all pairwise comparisons (p<0.001).
As expected, neuropsychological test scores (MMSE [66,
Verbal Learning Test (RAVLT) , Boston Naming Test
(BNT) , and category verbal fluency tests (Fluency-
Animals, Fluency-Vegetables)  also showed significant
differences between groups (p<0.001). However, these
assessments showed a different pattern in pairwise compari-
sons than the MMSE, Global CDR, and FAQ. MCI-
Converters and AD participants showed similar scores on the
learning and verbal neuropsychological tests, with no signi-
ficant differences on RAVLT measures, BNT, or verbal
fluency tests. As expected, all of the clinical groups perfor-
med below HC participants for RAVLT, BNT, and verbal
fluency measures (p<0.001). MCI-Stable participants also
had significantly higher scores on all RAVLT measures and
Fluency-Vegetables than both the AD and MCI-Converter
groups (p<0.001). Finally, MCI-Stable participants had
significantly higher scores than AD participants but not
MCI-Converters on Fluency-Animals and BNT (p<0.001).
VBM Group Comparisons by Baseline Diagnosis and
One Year Conversion Status
Neuropsychological scores from the Rey Auditory
analyses. A one-way ANOVA indicated striking between-
All 693 participants were included in the initial VBM
MRI Predictors of MCI to AD Conversion Current Alzheimer Research, 2009, Vol. 6, No. 4 351
Table 1. ADNI Participants at Baseline (Adjusted Mean (SE))
AD MCI-Converters MCI-Stable HC
(n=148) (n=62) (n=277) (n=206)
Age (yrs.) 75.4 (0.6) 74.3 (0.9) 75.1 (0.4) 76.0 (0.5) NS No pairs significant
Gender (M, F) 77, 71 36, 26 178, 99 107, 99 0.02 MCI-S>HC
Education (yrs.) 14.8 (0.2) 15.2 (0.4) 15.8 (0.2) 16.1 (0.2) p<0.001 HC, MCI-S>AD
Handedness (R, L) 141, 7 57, 5 253, 24 189, 17 NS No pairs significant
% English Speaking 98.7% 98.4% 97.5% 99.0% NS No pairs significant
% ApoE ?4 Positive
(1 or 2 alleles)
65.5% 59.7% 53.1% 27.2% p<0.001
AD, MCI-C, MCI-S>HC
MMSEe 23.5 (0.1) 26.7 (0.2) 27.1 (0.1) 29.1 (0.1) p<0.001 HC>allf; MCI-S, MCI-C>AD
Global CDRe 0.75 (0.01) 0.50 (0.02) 0.50 (0.01) 0.00 (0.01) p<0.001 AD>allg; MCI-S, MCI-C>HC
CDR – Sum of Boxese 4.3 (0.1) 1.9 (0.1) 1.5 (0.1) 0.3 (0.7) p<0.001 AD>allg; MCI-S, MCI-C>HC
FAQa,e 13.0 (0.4) 6.4 (0.5) 3.2 (0.3) 0.1 (0.3) p<0.001 All pairs significant
1.6 (0.1) 1.3 (0.2) 1.6 (0.1) 0.8 (0.1) p<0.001
NPI-Qe 3.5 (0.2) 2.2 (0.3) 1.7 (0.2) 0.4 (0.2) p<0.001 AD>allg; MCI-S, MCI-C>HC
Modified Hachinskie 0.64 (0.06) 0.63 (0.09) 0.65 (0.04) 0.57 (0.05) NS No pairs significant
RAVLT (1-5)b,e 23.5 (0.7) 26.2 (1.1) 31.9 (0.5) 42.5 (0.6) p<0.001 HC>allf; MCI-S>MCI-C, AD
RAVLT 30min Recallc,e 0.8 (0.3) 1.2 (0.4) 3.1 (0.2) 7.5 (0.2) p<0.001 HC>allf; MCI-S>MCI-C, AD
7.4 (0.3) 8.1 (0.4) 10.0 (0.2) 13.0 (0.2) p<0.001
HC>allf; MCI-S>MCI-C, AD
Boston Naming Testd,e 22.8 (0.3) 24.4 (0.5) 25.5 (0.2) 27.9 (0.3) p<0.001 HC>allf; MCI-S>AD
Fluency - Animalse 12.7 (0.4) 14.3 (0.6) 16.2 (0.3) 20.1 (0.3) p<0.001 HC>allf; MCI-S>AD
Fluency - Vegetablese 7.8 (0.3) 9.3 (0.4) 11.2 (0.2) 14.7 (0.2) p<0.001 HC>allf; MCI-S>MCI-C, AD
No pairs significant
a 3 MCI-Stable participants removed due to incomplete scores
b 7 participants removed due to incomplete scores (3 AD, 4 HC)
c 1 HC participant removed due to an incomplete score
d 3 participants removed due to incomplete scores (1 AD, 1 MCI-Stable, 1 HC)
e Covaried for age, education, gender, and handedness
f HC>all is HC>MCI-S, MCI-C, AD
g AD>all is AD>MCI-S, MCI-C, HC (Note: greater scores on these measures (CDR, FAQ, GDS, NPI-Q, Modified Hachinski) signify more impairment)
group differences in smoothed, unmodulated normalized GM
maps (see Figs. 2 & 3, unless noted, all differences are
p<0.005 (FDR)). AD participants showed reduced density in
nearly all GM regions compared to the HC group, with the
maximum global difference in the left hippocampus (Fig. 2a,
HC>AD). Surface renderings of the comparison between the
HC and AD groups showed that the GM density of nearly
the entire cortical surface is significantly lower in AD (Fig.
2b, HC>AD), including significant differences in the tempo-
ral, frontal and parietal lobes. MCI-Converters also showed
reduced GM density compared to HC, with a global maxi-
mum in the left hippocampus (Fig. 2c, HC>MCI-
Converters). The pattern of significant voxels in the compa-
rison between HC and MCI-Converters was very similar to
352 Current Alzheimer Research, 2009, Vol. 6, No. 4
Risacher et al.
that seen in the HC>AD comparison, both in subcortical
regions and on the cortical surface (Fig. 2d, HC>MCI-
Converters). Selected sections (Fig. 2e, HC>MCI-Stable)
show a more focal distribution of differences in the compari-
son of GM maps from MCI-Stable and HC participants.
MCI-Stable participants showed reduced GM density in
focal bilateral MTL regions relative to HC, with a global
maximum in the right parahippocampal gyrus and additional
local maxima in bilateral amygdalar and hippocampal
regions. Surface renderings reflect the focal distribution pat-
tern of significant voxels in the HC>MCI-Stable contrast
(Fig. 2f, HC>MCI-Stable), with differences localized prima-
rily in the temporal and frontal lobes.
detected in the comparison between the MCI-Stable and AD
A widespread pattern of significant voxels was also
groups. MCI-Stable participants showed significantly higher
GM density than AD in the MTL, including a global maxi-
mum difference in the left hippocampus (Fig. 3a, MCI-
Stable>AD) and additional local maxima in bilateral amyg-
dalar and hippocampal regions. The extensive pattern of GM
differences between MCI-Stable and AD participants is
further reflected in the surface renderings, with AD partici-
pants having significant GM reduction on nearly the entire
cortical surface relative to MCI-Stable participants (Fig. 3b,
MCI-Stable>AD). A more focal pattern was observed when
comparing MCI participant groups. MCI-Converters had
significantly reduced GM density relative to MCI-Stable
participants in bilateral MTL regions, with a global maxi-
mum in the right insula and additional local maxima in bila-
teral amygdalar and hippocampal regions (Fig. 3c, MCI-
Fig. (2). Group comparisons of healthy control participants and patient groups using a one-way ANOVA of GM density maps.
Selected slices (A) and surface renderings (B) of regions where HC>AD. Selected slices (C) and surface renderings (D) of regions where
HC>MCI-Converters. Selected slices (E) and surface renderings (F) of regions where HC>MCI-Stable. All comparisons are displayed at a
threshold of p<0.005 (FDR), k=27. Age, gender, years of education, handedness and ICV were included as covariates in all comparisons.
Selected sections for (A), (C), and (E) include left to right MNI coordinates: (0, -9, 0, coronal), (0, -23, -16, axial), (-26, -10, -15, sagittal),
and (26, -10, -15, sagittal).
MRI Predictors of MCI to AD Conversion Current Alzheimer Research, 2009, Vol. 6, No. 4 353
Stable>MCI-Converters). Surface renderings of the compari-
son between the MCI-Stable and MCI-Converter groups also
show a focal pattern of GM differences in the frontal and
temporal lobes (Fig. 3d, MCI-Stable>MCI-Converters). No
significant voxels were found in the comparison between
GM density maps from MCI-Converters and AD participants
(Fig. 3, MCI-Converters>AD). At a lower statistical thres-
hold (p<0.001 (uncorrected)), AD participants showed
reduced GM density in focal regions of the posterior parietal
and occipital lobes relative to MCI-Converters (data not
As noted above, similar contrasts were performed using
matched participants in equal sized groups to control for
power as a function of group size. This comparison resulted
in a highly similar pattern of between-group differences as in
the full sample but, as anticipated, at lower statistical thres-
holds due to attenuated power (data not shown). Results
from comparisons between groups using modulated normali-
zed GM maps from the full sample were also highly similar
to those using unmodulated images (data not shown).
ROI Grey Matter Density Comparisons
Mean left and right hippocampal GM density values from
the smoothed, unmodulated normalized GM maps of all 693
participants were extracted as described above. GM density
was significantly different between all groups for both the
left and right hippocampi (Fig. 4a, p<0.001). In pairwise
comparisons, HC participants showed significantly greater
hippocampal GM density bilaterally relative to all other
groups (p<0.001). MCI-Converters had significantly reduced
local GM density relative to MCI-Stable participants in both
the left (p=0.001) and right (p=0.034) hippocampi, as did
AD participants (p<0.001 bilaterally). Hippocampal GM
density did not differ significantly between AD participants
and MCI-Converters. Analyses using smoothed, modulated
normalized GM maps showed a similar pattern of results to
those using unmodulated images (data not shown).
FreeSurfer-Derived VOI and Cortical Thickness Com-
cal thickness values from the entorhinal cortex, inferior,
middle, and superior temporal gyri, inferior parietal gyrus
and precuneus were extracted from all 693 participants as
described above. Comparisons of mean bilateral hippocam-
pal (Fig. 4b) and amygdalar (Fig. 4c) volumes and entorhinal
cortex thickness (Fig. 4d) were significant across all groups
(p<0.001), and show similar results in pairwise comparisons.
Bilateral hippocampal and amygdalar volumes and corti-
Fig. (3). Group comparisons of patient groups based on baseline diagnosis and one year conversion status using a one-way ANOVA
of GM density maps.
Selected slices (A) and surface renderings (B) of regions where MCI-Stable>AD. Selected slices (C) and surface renderings (D) of regions
where MCI-Stable>MCI-Converters. No significant voxels were found in the comparison between MCI-Converters and AD participants. All
comparisons are displayed at a threshold of p<0.005 (FDR), k=27. Using a more lenient statistical threshold, differences were apparent in the
posterior parietal and occipital lobes (data not shown). Age, gender, years of education, handedness and ICV were included as covariates in
all comparisons. Selected sections for (A) and (C) include left to right MNI coordinates: (0, -9, 0, coronal), (0, -23, -16, axial), (-26, -10, -15,
sagittal), and (26, -10, -15, sagittal).
354 Current Alzheimer Research, 2009, Vol. 6, No. 4
Risacher et al.
All of the clinical groups (MCI-Stable, MCI-Converters,
AD) had decreased bilateral hippocampal and amygdalar
volumes and entorhinal cortex thickness compared to HC
(p<0.001). MCI-Converters also showed significant reducti-
ons relative to MCI-Stable participants, including reduced
bilateral hippocampal volumes (p<0.001), bilateral amygda-
lar volumes (p<0.001 left, p=0.01 right), and thinner bilateral
entorhinal cortex (p=0.006 left, p<0.001 right). AD partici-
pants had significant reductions in bilateral hippocampal and
amygdalar volumes and entorhinal cortex thickness relative
to MCI-Stable participants (p<0.001). However, MCI-
Converters and AD participants showed no significant diffe-
rences in any MTL measures (hippocampal and amygdalar
volume or entorhinal cortex thickness).
tices were also significantly different across groups (Fig. 5,
p<0.001). Similar to other ROI and VOI comparisons, HC
participants had significantly greater bilateral inferior (Fig.
5a), middle (Fig. 5b) and superior (Fig. 5c) temporal gyrus
cortical thickness relative to all other groups in pairwise
comparisons (p<0.001). MCI-Converters had significant
Mean cortical thickness values from lateral temporal cor-
cortical thinning bilaterally relative to MCI-Stable partici-
pants in the inferior (p<0.001), middle (p<0.001 left,
p=0.001 right), and superior (p=0.003 left, p=0.002 right)
temporal gyri. AD participants also had significantly thinner
bilateral inferior, middle, and superior temporal gyri relative
to MCI-Stable participants (p<0.001). MCI-Converters and
AD participants showed no temporal gyrus cortical thickness
ficant differences across all groups, specifically in the inferi-
or parietal gyrus (Fig. 6a, p<0.001) and precuneus (Fig. 6b,
p<0.001). Pairwise comparisons showed similar patterns as
those of other imaging biomarkers. HC participants had
significantly greater cortical thickness in bilateral inferior
parietal gyrus and precuneus relative to all other groups
(p<0.001). AD participants had significantly reduced cortical
thickness in bilateral inferior parietal and precuneus regions
relative to MCI-Stable participants (p<0.001), as did MCI-
Converters (inferior parietal gyrus p=0.006 left, p=0.009
Parietal lobe cortical thickness values also showed signi-
Fig. (4). Extracted GM density, volume, and cortical thickness values from medial temporal lobe structures.
Comparisons of GM density values (A) were extracted from the unmodulated VBM GM maps using standard left and right hippocampal
ROIs traced on an independent sample of 40 HC participants [25, 58]. Bilateral hippocampal (B) and amygdalar (C) volume estimates and
entorhinal cortex thickness values (D) were extracted using automated parcellation. The comparisons of all four MRI metrics show a signifi-
cant difference (p<0.001) across all groups. In pairwise comparisons, hippocampal GM density, hippocampal and amygdala volumes, and
entorhinal cortex thickness show significant differences between HC and all clinical groups (p<0.001) bilaterally and MCI-Stable and AD
groups (p<0.001) bilaterally. Furthermore, MCI-Stable and MCI-Converter groups show significant differences in GM density and volume in
the left (GM (A), p=0.001; volume (B), p<0.001) and right (GM (A), p=0.034; volume (B), p<0.001) hippocampi, as well as significant
differences in amygdala volume on both the left (p<0.001) and right (p=0.01). MCI-Converters also showed significantly thinner entorhinal
cortices than MCI-Stable participants on both the left (p=0.006) and right (p<0.001). No significant differences were found in hippocampal
GM density, hippocampal or amygdalar volumes, or entorhinal cortex thickness values between MCI-Converter and AD groups. Age,
gender, years of education, handedness, and ICV were included as covariates in all comparisons.
MRI Predictors of MCI to AD Conversion Current Alzheimer Research, 2009, Vol. 6, No. 4 355
Fig. (5). Cortical thickness values from the temporal lobe extracted using automated parcellation.
Comparisons between cortical thickness values from three regions of the temporal lobe, including inferior (A), middle (B), and superior (C)
temporal gyri, demonstrated significant differences (p<0.001) across all groups. Pairwise comparisons demonstrated significant differences in
cortical thickness values for all temporal gyri bilaterally between HC and all other groups (p<0.001), as well as between the MCI-Stable and
AD groups (p<0.001). The MCI-Converter and MCI-Stable groups also showed significant differences in cortical thickness in bilateral inferi-
or temporal gyri (p<0.001), left (p<0.001) and right (p=0.001) middle temporal gyri, and left (p=0.003) and right (p=0.002) superior temporal
gyri. Cortical thickness values from bilateral inferior, middle, and superior temporal gyri were not significantly different between the MCI-
Converter and AD groups. Age, gender, years of education, handedness, and ICV were included as covariates in all comparisons.
Fig. (6). Parietal cortical thickness values extracted using automated parcellation.
Cortical thickness values from the inferior parietal gyrus (A) and precuneus (B) showed significant differences between groups (p<0.001).
Pairwise comparisons showed significant differences in bilateral inferior parietal gyrus and precuneus between HC and all clinical groups
(p<0.001), as well as between the MCI-Stable and AD groups (p<0.001). The MCI-Stable and MCI-Converter groups were significantly
different in the left (p=0.006) and right (p=0.009) inferior parietal gyri and left (p=0.012) and right (p=0.013) precuneus. No significant
difference was found between MCI-Converter and AD groups in either region. Age, gender, years of education, handedness and ICV were
included as covariates in all comparisons.
right; precuneus p=0.012 left, p=0.013 right). MCI-
Converters and AD participants showed no significant
differences in either inferior parietal gyrus or precuneus
cortical thickness values.
Effect Sizes of Imaging Biomarkers
and automated parcellation compared between MCI-
Imaging biomarkers extracted from both VBM GM maps
356 Current Alzheimer Research, 2009, Vol. 6, No. 4
Risacher et al.
Converters and MCI-Stable participants demonstrated large
effect sizes in MTL structures, as well as in temporal and
parietal lobar regions (Fig. 7). Bilateral mean hippocampal
volume was found to have the highest effect size, with a
Cohen’s d of 0.603. Cortical thickness values from the
inferior and middle temporal gyri, as well as the entorhinal
cortex, also showed strong effect sizes, with Cohen’s d
values of 0.535, 0.529, and 0.493, respectively. Amygdalar
volume (Cohen’s d=0.478), superior temporal cortical
thickness (Cohen’s d=0.448), inferior parietal cortical
thickness (Cohen’s d=0.417), precuneus cortical thickness
(Cohen’s d=0.408), and hippocampal GM density (Cohen’s
d=0.408) also showed high effect sizes. For illustrative
purposes, imaging metrics with the 20 largest effect sizes are
shown in Fig. (7).
from 693 participants in the ADNI cohort to (1) characterize
initial differences between the AD, MCI, and HC groups and
(2) detect anatomic features associated with imminent con-
version from MCI to probable AD within one year (MCI-
Converters). We hypothesized that cross-sectional baseline
differences would be consistent with the well-established
progression from MTL structures to neocortical involve-
ment, and that those individuals with MCI who are about to
convert to AD will appear more similar to AD prior to
conversion than those MCI patients who remain stable for at
We examined baseline 1.5T T1-weighted MRI scans
least one additional year. Publically available and widely
used semi-automated image analysis methodologies (VBM
in SPM5, automated parcellation in FreeSurfer) were
employed to assess these hypotheses.
results. First, the overall pattern of structural MRI changes in
MCI and AD patients observed at baseline in the ADNI co-
hort is similar to prior findings in other, typically smaller and
less intensively characterized samples [11-17, 19, 21, 25-36,
38-40, 43-46, 52, 53, 57, 73]. Second, MCI-Converters are
distinguishable from individuals with MCI who will not
show significant clinical progression over the next year
(MCI-Stable). Third, MCI-Converters show significantly
greater global and MTL-specific atrophy than MCI-Stable
participants, a pattern previously reported in earlier studies
with smaller samples [12, 13, 15, 18, 20, 22, 23, 31-36, 40-
45, 74, 75]. Fourth, MCI-Converters show a neuroimaging
profile more similar to that seen in the AD group than that of
the MCI-Stable group. The
demonstrated a pattern of atrophic changes nearly equivalent
to those of the AD group up to a year before meeting clinical
criteria for probable AD. Finally, a comparison of effect
sizes for contrasts between the MCI-Converter and MCI-
Stable groups on MRI metrics indicated that degree of neu-
rodegeneration of MTL structures is the best antecedent MRI
marker of imminent conversion, with decreased hippocampal
volume (left more than right) being the most robust struc-
tural MRI feature.
Several key conclusions can be drawn from the obtained
Fig. (7). Effect sizes of the comparison between MCI-Stable and MCI-Converter groups evaluated for selected imaging biomarkers.
GM density, volume, and cortical thickness were extracted using VBM and automated parcellation and compared between sub-groups based
on MCI to AD conversion status after one year. Effect sizes (Cohen’s d) of comparisons between MCI-Stable and MCI-Converter groups
showed that imaging biomarkers from the temporal lobe, including hippocampal and amygdalar volume and cortical thickness values from
the entorhinal cortex and inferior, middle, and superior temporal gyri, provided the greatest statistical difference. Age, gender, handedness,
education, and ICV were included as covariates and adjusted bilateral means were used to calculate effect size.
MRI Predictors of MCI to AD Conversion Current Alzheimer Research, 2009, Vol. 6, No. 4 357
that warrant comment. This report is among the first, in the
fully enrolled ADNI cohort, to assess group differences
between AD, MCI, and controls at baseline, as well as to
examine antecedent imaging predictors of future change in
clinical status (i.e., conversion to probable AD, in patients
with amnestic MCI). Our comparisons of the three baseline
diagnostic groups using VBM are similar to previous reports
using alternative methods to compare global atrophy
between AD, MCI, and HC participants in the ADNI cohort
[76-78]. One recent study from Hua et al.  found signi-
ficant MTL atrophy in both AD and MCI subjects in the
ADNI cohort using tensor-based morphometry (TBM), simi-
lar to our results using VBM. Furthermore, our results using
the one year MCI to AD converter population from the
ADNI cohort provided congruent results with those of Hua
et al., in which temporal lobe atrophy as assessed using
TBM correlated with MCI to AD conversion in a subset of
the ADNI MCI-Converters (n=40) . A recent study using
another imaging analysis technique (RAVENS) also found a
similar pattern of distinctive atrophy in MCI-Converters re-
lative to MCI-Stable participants in a sub-sample (27 MCI-
Converter, 76 MCI-Stable) of the ADNI cohort, which could
be used to predict MCI to AD conversion using a pattern
classification technique . In the present study, we were
able to substantially extend the results of earlier partial
cohort analyses by including the largest possible set of
ADNI participants with usable data, since one year outcomes
were only recently completed. Further, our multi-method
approach included VBM-based analyses of GM density and
volume and FreeSurfer-derived ROI analyses of volume and
cortical thickness, which together provide a more detailed
picture of anatomical differences between groups.
There are several aspects to these results and analyses
including a volume conserving step referred to as “modulati-
on” [8-10]. Briefly, unmodulated GM maps are typically
interpreted as indicating differences in GM density or
concentration. By contrast, VBM performed on modulated
GM maps are interpreted as local GM volume estimates. At
present there is no strong consensus in the literature regar-
ding which approach is more appropriate for a given applica-
tion. Furthermore, the pathophysiological significance of
differences detected by one method versus the other has not
been conclusively determined. Our primary VBM analyses
were performed without modulation. We then repeated the
analyses with the modulation step for comparison, and found
highly similar patterns of GM differences between groups
(Figs. 2 & 3, modulated data not shown). Specifically, the
overall pattern of GM reduction for all patient groups (AD,
MCI-Converters, MCI-Stable) compared to HC participants
remained significant using both VBM methods, with the
greatest differences remaining in bilateral MTL. Similarly,
the pattern for MCI-Converters relative to the MCI-Stable
participants was largely unaffected by analytic methodology.
Analysis of GM values extracted from left, right and combi-
ned hippocampal ROIs defined in an independent cohort of
healthy older adult controls [25, 58] showed similar group
differences, and the effect size for MCI-Converters versus
MCI-Stable participants was nearly identical (Figs. 4 & 7,
modulated data not shown). Overall, inclusion of a volume
conserving modulation step in the VBM analyses had little
Studies employing VBM methods differ with regard to
influence on the pattern or magnitude of group differences.
This may in part be related to our inclusion of intracranial
volume as a covariate in all analyses. To eliminate the possi-
bility of bias due to markedly unequal group sizes when
comparing MCI-Converters and MCI-Stable participants, we
repeated the main VBM analyses on four matched groups of
equal size. Despite slightly attenuated power to detect group
differences, the additional matched group analyses did not
alter the overall pattern of results (data not shown).
changes in AD, MCI and controls entailed examining Free-
Surfer parcellation derived ROIs, selected on the basis of
their status as important regions for AD pathology. Group
differences were evaluated for left, right and combined
hippocampal volume and GM density, additional MTL
ROIs, and regional cortical thickness estimates. Significant
differences between groups were found in hippocampal, a-
mygdalar, and other MTL regions, as well as widespread
neocortical regions. These results are consistent with prior
ROI and VOI studies in AD and MCI, in which hippocampal
volumes [17, 25, 31-36, 38-40, 44, 48, 52, 53, 79], hippo-
campal GM density [19, 24, 25], and other regions [19, 31-
35, 38, 39, 43-47, 50, 51, 74, 75], were found to be signifi-
cantly decreased relative to HC. As in our VBM results, ROI
measures indicated that participants who convert from MCI
to AD within one year show significant atrophy relative to
MCI participants who remain clinically stable, and also have
a generally equivalent degree of atrophy to AD participants.
Decreased hippocampal GM density and volume, amygdalar
volume, and cortical thickness in entorhinal cortex, inferior,
middle, and superior temporal gyri, inferior parietal gyrus,
and precuneus reflect the antecedent structural characteristics
of MCI-Converters compared to individuals with MCI who
remained clinically stable for at least a year. Similar to the
global atrophy detected using VBM, local measures of
volume and cortical thickness detected significant degenera-
tion in MCI-Converters up to one year prior to the point at
which they meet clinical criteria for an AD diagnosis,
suggesting an accelerated rate of neuropathological changes
in these individuals which is not well captured by the MCI
diagnosis alone. Furthermore, these results, obtained from
the largest group assessment of one year conversion from
MCI to AD to date, extend the findings of previous smaller
studies which have reported local atrophy in MCI to AD
converters using measures of hippocampal, amygdalar,
entorhinal cortex, and other MTL volume estimates [12, 13,
15, 18, 20, 23, 31-34, 36, 37, 40, 42-45, 50, 74, 75, 80].
The second major approach to assessing morphological
structural MRI as a biomarker for assessing prodromal and
early AD related changes. An important implication of the
analyses performed in this report is that although many
regions and measurements are sensitive to early AD patholo-
gy, MRI markers have differential sensitivities for detection
of those individuals who are at greatest risk of short-term
progression to probable AD. The MRI measures with the
largest effect sizes (far left, Fig. 7) for MCI-Converters ver-
sus MCI-Stable contrasts appear to be important biomarker
candidates for prediction of MCI to AD conversion. Previous
studies have investigated the use of MTL density and volu-
me in the prediction of MCI to AD conversion, with some
reports finding significantly greater sensitivity and speci-
Taken together, the present findings support the use of
358 Current Alzheimer Research, 2009, Vol. 6, No. 4
Risacher et al.
ficity achieved by adding imaging biomarkers to clinical test
prediction algorithms, while others suggested minimal utility
of including additional imaging variables [31-34, 37, 42-45,
47, 48, 51, 74, 75, 79, 81]. However, the majority of these
studies included modest participant pools and manually
drawn ROIs. The time-consuming nature of manual ROI
tracing limits the utility of these endpoints as biomarkers in
studies with large numbers of participants, as well as in rou-
tine clinical settings. Automated or semi-automated extracti-
on of volume and cortical thickness values from ROIs in the
MTL requires minimal manual intervention. The largely au-
tomated nature and wide availability and use of this and
other methods (e.g., [53, 82]) in assessing local and global
atrophy will facilitate incorporation of these measures as key
variables in pharmacological efficacy and neuroprotection
A limitation of the present report is the inclusion of only
baseline scans in characterizing anatomic changes. Additio-
nal information, including changes in imaging measures over
time and rate of atrophy, has been shown to be useful in
assessing and accurately predicting rapid conversion [41,
42]. As a cross-sectional assessment of structural neuroima-
ging measures, the present study does not capture the dyna-
mic processes associated with MCI to AD conversion. Futu-
re studies assessing multiple timepoints, including two and
three year MCI to AD conversion patterns, will be needed to
determine the diagnostic and predictive value of dynamic
measures of global and local atrophy. Furthermore, the cur-
rent participant pool includes 182 participants diagnosed
with MCI at baseline who were on AD-indicated medicati-
ons during the first year of the study. Pharmacological treat-
ments, such as AChE inhibitors and memantine, have been
shown to reduce or delay MCI to AD conversion [83-87].
The impact of medications was not assessed in the current
study. Future studies should focus on including this variable
in predicting and assessing conversion from MCI to AD.
Another limitation of this report is the inclusion of only
structural imaging. It is possible that FDG PET, obtained on
approximately half of the ADNI cohort, could enhance the
detection and characterization of antecedent changes alone or
in combination with MRI and other measures. Targeted mo-
lecular PET imaging for amyloid deposition with [11C]PiB
is also being investigated in a smaller add-on study in the
ADNI cohort . Future studies will undoubtedly clarify
the contribution of FDG and PiB PET to understanding early
changes and predicting clinical trajectory. Finally, the role of
genetic factors was only considered to a limited degree in the
present study by controlling for APOE genotype where
appropriate. A genome wide association study employing a
high density microarray with over 620,000 single nucleotide
polymorphisms is underway by the ADNI Genetics Working
Group and these forthcoming results will permit inclusion of
data on individual differences in important biological
pathways in predictive models.
biomarkers that could be used for early detection and predic-
tion of longitudinal changes in MCI and AD. Two semi-
automated, widely used and publically available image ana-
lysis methods (VBM, automated parcellation) revealed signi-
ficant global and local atrophy in AD and MCI patients in a
large cohort from the ADNI sample at baseline relative to
In summary, a major goal of ADNI is to identify imaging
HC. These techniques were also successful at detecting dif-
ferences at baseline between participants who would convert
from MCI to AD within one year and those who would
remain stable with an MCI diagnosis for at least one year.
The results of these analyses suggest that VBM and automa-
ted parcellation are useful tools for characterization of
atrophy in MCI and AD and prediction of disease course.
Employed with repeated scans for longitudinal monitoring of
brain degeneration, these methods will be useful for clinical
trials in MCI and AD. With further refinement, MRI coupled
with advanced image analysis approaches appears to have
potential for individualized prediction of risk of progression
and enhancement of clinical trials by including those at grea-
test risk of conversion.
mer’s Disease Neuroimaging Initiative (ADNI; Principal
Investigator: Michael Weiner; NIH grant U01 AG024904).
ADNI is funded by the National Institute on Aging (NIA),
the National Institute of Biomedical Imaging and Bio-
engineering (NIBIB), and through generous contribution
from the following: Pfizer Inc., Wyeth Research, Bristol-
Myers Squibb, Eli Lilly and Company, GlaxoSmithKline,
Merck & Co. Inc., AstraZeneca AB, Novartis Pharma-
ceuticals Corporation, the Alzheimer’s Association, Eisai
Global Clinical Development, Elan Corporation plc, Forest
Laboratories, and the Institute for the Study of Aging, with
parti-cipation by the U.S. Food and Drug Administration.
Industry partnerships are coordinated through the Foundation
for the National Institutes of Health. The grantee organizati-
on is the Northern California Institute for Research and Edu-
cation, and the study is coordinated by the Alzheimer’s
Disease Cooperative Study at the University of California,
San Diego. ADNI data are disseminated by the Laboratory of
Neuro Imaging at the University of California, Los Angeles.
Data collection and sharing was funded by the Alzhei-
grants from the National Institutes of Health: NIA R01
AG19771 to AJS and P30 AG10133 to Bernardino Ghetti,
MD and NIBIB R03 EB008674 to LS, and by the Indiana
Economic Development Corporation (IEDC #87884 to AJS).
Data analysis was supported in part by the following
University, Sungeun Kim, PhD of Indiana University School
of Medicine, Nick Schmansky, MA, MSc and Bruce Fischl,
PhD of Harvard Medical School, and Randy Heiland, MA,
MS of Indiana University for their help. We also thank Ber-
nardino Ghetti, MD, Guest Editor and organizer of the
Indiana ADC MCI Symposium in which initial results were
The authors thank Aaron Cannon of Brigham Young
MRI = Magnetic resonance imaging
ADNI = Alzheimer’s Disease Neuroimaging Initia-
Alzheimer’s Disease AD =
MCI = Mild Cognitive Impairment
MRI Predictors of MCI to AD Conversion Current Alzheimer Research, 2009, Vol. 6, No. 4 359
HC = Healthy elderly controls
MTL = medial temporal lobe
= Grey matter
Magnetization prepared rapid acquisition
Statistical parametric mapping
ROI = Region of interest
Volume of interest
False discovery rate
ApoE = Apolipoprotein E
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Received: December 22, 2008 Revised: December 22, 2008 Accepted: December 23, 2008