A JOURNAL OF NEUROLOGY
Automated MRI measures identify individuals
with mild cognitive impairment and Alzheimer’s
Rahul S. Desikan,1,2Howard J. Cabral,3Christopher P. Hess,4William P. Dillon,4
Christine M. Glastonbury,4Michael W. Weiner,4,5Nicholas J. Schmansky,1Douglas N. Greve,1
David H. Salat,1Randy L. Buckner1,6,7,10and Bruce Fischl1,8,9; Alzheimer’s Disease
1 Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
2 Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA, USA
3 Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
4 Department of Radiology, University of California, San Francisco, CA, USA
5 Veteran Affairs Medical Center, San Francisco, CA, USA
6 Department of Psychology, Harvard University, Cambridge, MA, USA
7 Howard Hughes Medical Institute, Chevy Chase, MD, USA
8 Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology, Cambridge, MA, USA
9 Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
10 Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
Correspondence to: Bruce Fischl, PhD,
Massachusetts General Hospital,
NMR Center Rm. 2301,
Building 149, 13th Street,
Charlestown, MA 02129,
*Data used in the preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (Alzheimer’s disease NI) database
(http://www.loni.ucla.edu/Alzheimer’s disease NI). As such, the investigators within the Alzheimer’s disease NI contributed to the design and
implementation of Alzheimer’s disease NI and/or provided data but did not participate in analysis or writing of this report. Alzheimer’s disease
NI investigators include (complete listing available at www.loni.ucla.edu\Alzheimer’s disease NI\Collaboration\Alzheimer’s disease
Mild cognitive impairment can represent a transitional state between normal ageing and Alzheimer’s disease. Non-invasive
diagnostic methods are needed to identify mild cognitive impairment individuals for early therapeutic interventions. Our
objective was to determine whether automated magnetic resonance imaging-based measures could identify mild cognitive
impairment individuals with a high degree of accuracy. Baseline volumetric T1-weighted magnetic resonance imaging scans
of 313 individuals from two independent cohorts were examined using automated software tools to identify the volume and
mean thickness of 34 neuroanatomic regions. The first cohort included 49 older controls and 48 individuals with mild cognitive
impairment, while the second cohort included 94 older controls and 57 mild cognitive impairment individuals. Sixty-five patients
with probable Alzheimer’s disease were also included for comparison. For the discrimination of mild cognitive impairment,
entorhinal cortex thickness, hippocampal volume and supramarginal gyrus thickness demonstrated an area under the curve of
0.91 (specificity 94%, sensitivity 74%, positive likelihood ratio 12.12, negative likelihood ratio 0.29) for the first cohort and an
doi:10.1093/brain/awp123Brain 2009: 132; 2048–2057 |
Received January 23, 2009. Revised March 24, 2009. Accepted April 3, 2009. Advance Access publication May 21, 2009
? 2009 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/
2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
area under the curve of 0.95 (specificity 91%, sensitivity 90%, positive likelihood ratio 10.0, negative likelihood ratio 0.11) for
the second cohort. For the discrimination of Alzheimer’s disease, these three measures demonstrated an area under the curve of
1.0. The three magnetic resonance imaging measures demonstrated significant correlations with clinical and neuropsychological
assessments as well as with cerebrospinal fluid levels of tau, hyperphosphorylated tau and abeta 42 proteins. These results
demonstrate that automated magnetic resonance imaging measures can serve as an in vivo surrogate for disease severity,
underlying neuropathology and as a non-invasive diagnostic method for mild cognitive impairment and Alzheimer’s disease.
Keywords: MRI; mild cognitive impairment; Alzheimer’s disease; diagnostic marker
Abbreviations: AUC=area under curve; CDR=clinical dementia rating; MCI=mild cognitive impairment; OASIS=Open Access
Series of Imaging Studies; OC=older control; ROI=region of interest
Mild cognitive impairment (MCI) represents a transitional period
between normal ageing and clinically probable Alzheimer’s disease
(Petersen et al., 2001). Individuals classified with the amnestic
subtype of MCI are likely in the prodromal stage of Alzheimer’s
disease (Morris et al., 2001) and up to 80% of these individuals
progress to dementia after 6 years (Petersen et al., 1999). Overall,
the prevalence of MCI in the elderly is 19%, with estimates as
high as 29% amongst individuals greater than 85 years of age
(Lopez et al., 2003). The neuropathologic changes of this transi-
tional state are consistent with the density and distribution of the
tau-associated neurofibrillary and abeta-associated amyloid fea-
tures of very early Alzheimer’s disease (Bennett et al., 2005)
and present longbeforethe
Alzheimer’s disease (Gomez-Isla et al., 1996). As therapeutic inter-
ventions become available, there is a need for developing
methodologies that will serve as an in vivo surrogate for these
pathologic changes, and thus, accurately identify those cognitively
impaired individuals who are in the earliest stages of Alzheimer’s
Structural MRI provides visualization of the macroscopic tissue
Alzheimer’s disease. In order to be used as a diagnostic marker,
structural MRI measures should: (i) specifically detect and quantify
fundamental features of Alzheimer’s pathology in individuals at an
elevated risk for Alzheimer’s disease (i.e. individuals with amnestic
MCI) and in patients with a clinical diagnosis of Alzheimer’s
disease; (ii) demonstrate excellent discrimination accuracy between
normal elderly controls and individuals with MCI and Alzheimer’s
disease; (iii) exhibit a high degree of consistency and test–retest
reproducibility across multiple, independent cohorts; and (iv) cor-
relate strongly with clinical measures of decline as well as invasive
measures of cellular pathology.
Prior structural MRI studies have employed either manual region
of interest (ROI) (Killiany et al., 2000; Xu et al., 2000; Devanand
et al., 2007) or automated whole-brain approaches (Scahill et al.,
2002; Buckner et al., 2005; Dickerson et al., 2009) to identify
MCI and Alzheimer’s disease individuals. Though these methodol-
ogies offer several strengths, they are limited in their use as a
diagnostic marker due to variable discrimination accuracy and
decreased test–retest reliability with the manual ROI methods,
and an inability to evaluate the disease state in a single
individual with the whole-brain approaches.
Recent advances in image analysis algorithms have led to the
development of structural MRI-based software tools that can
automatically parcellate the brain into anatomic regions and quan-
tify the tissue atrophy in these regions for a single individual (Fischl
et al., 2002; Desikan et al., 2006). In this study, we investigated
the feasibility of utilizing these automated software tools as a
diagnostic marker for Alzheimer’s disease. Using structural MRI
scans from a cohort of 97 participants, we first identified a set
of anatomic regions that best differentiated MCI individuals from
elderly controls and examined the discrimination accuracy of these
regions. We then validated the accuracy and consistency of these
measures on a second, independent cohort of 216 participants.
Finally, we examined the relationship between these MRI-based
anatomic measures and clinical measures of decline and cerebrosp-
inal fluid (CSF) markers of cellular pathology.
A total of 313 individuals were examined in this study. The first cohort
of 97 participants (‘training cohort’) was selected from the Open
Access Series of Imaging Studies (OASIS) database (Marcus et al.,
2007). Informed consent for all participants was obtained in accor-
dance with guidelines of the Washington University Human Studies
Committee (St Louis, MO). Data from subsets of these participants
have been published in previous studies (Salat et al., 2004;
Buckner et al., 2004, 2005; Head et al., 2005; Dickerson et al.,
2009). The OASIS dataset reflects a collaborative effort of investiga-
tors from a single acquisition site supported by the National Institute
on Aging (NIA), the Howard Hughes Medical Institute, the Biomedical
Informatics Research Network (BIRN) and the Washington University
Alzheimer’s Disease Research Center [Alzheimer’s Disease Research
Center (ADRC)]. The dataset includes multiple (2–4) structural MRI
acquisitions from 416 adults, ages 18–96. For more information,
please see http://www.oasis-brains.org.
The second cohort of 216 participants (‘validation cohort’) was
(ADNI) database (www.loni.ucla.edu/ADNI). The ADNI is a large
multi-site collaborative effort launched in 2003 by the National
Institute on Aging, the National Institute of Biomedical Imaging and
Bioengineering, the Food and Drug Administration, private pharma-
ceutical companies and non-profit organizations as a public–private
partnership aimed at testing whether serial MRI, PET, other biological
markers and clinical and neuropsychological assessment can be
MRI as a diagnostic marker for MCI and Alzheimer’s diseaseBrain 2009: 132; 2048–2057 |
combined to measure the progression of MCI and early Alzheimer’s
disease. The Principal Investigator of this initiative is Michael Weiner,
MD, and ADNI is the result of many co-investigators from a broad
range of academic institutions and private corporations, with subjects
recruited from over 50 sites across the United States and Canada.
For more information, please see http://www.adni-info.org.
Cohort 1—training cohort
Potential participants underwent a multi-stage screening procedure
that has been described in detail elsewhere (Rubin et al., 1998).
Briefly, eligibility requirements included living independently in the
community, having an informant who could provide information
about the participants’ daily function, and absence of significant
underlying medical, neurologic or psychiatric illness. The degree of
clinical severity was evaluated by an annual semi-structured interview.
This interview generates both an overall Clinical Dementia Rating
(CDR) scoreanda measure
Boxes (CDR-SB) (Morris, 1993). Experienced clinicians conducted
independent semi-structured interviews with the participant and a
knowledgeable collateral source that included a health history and
neurological examination. The
(MMSE) (Folstein et al., 1975) and a complete neuropsychological
battery (Rubin et al., 1998) were also conducted.
Participants were selected from the OASIS database if they were
clinically classified as: (i) older controls (OCs) (n=49)—individuals
who were cognitively normal (CDR 0) or (ii) estimated to be MCI
(n=48)—individuals with a memory complaint who experienced very
mild cognitive decline and a CDR of 0.5. Note that a CDR score of 0.5
is not synonymous with a diagnosis of amnestic-type MCI and slight
differences in the sample may exist, although the two groups are
expected to largely be similar and capture individuals at the transitional
state between normal cognition and dementia. The mean age, gender
and mean MMSE, and mean CDR-SB scores are shown in Table 1.
As expected, the CDR-SB and MMSE showed a difference between
the groups (P50.05). No other demographic variables differed
between the groups.
Confirming our expectation that the samples are similar, based on
their CDR-SB scores, the individuals in this estimated MCI group are
comparable to amnestic MCI subjects used in epidemiological studies
and clinical trials (Davis and Rockwood, 2004; Petersen et al., 2005).
Prior work using a larger cohort of the above-described mildly
impaired individuals has demonstrated that amnestic MCI represents
the prodromal stage of Alzheimer’s disease (Morris et al., 2001). For
simplicity, we use MCI in this article to refer to the entire group of
cognitively impaired subjects who did not meet criteria for probable
Alzheimer’s disease (CDR=1.0).
known astheCDR Sumof
Cohort 2—validation cohort
Each participant was selected using eligibility criteria that are described
indetail elsewhere (http://www.adni-info.org/index.php?option=
com_content&task=view&id=9&Itemid=43). Briefly, experienced clini-
cians conducted independent semi-structured interviews with the par-
ticipant and a knowledgeable collateral source that included a health
history, neurological examination, the MMSE (Folstein et al., 1975),
the CDR-Sum of Boxes (Morris, 1993) and a comprehensive neurop-
Participants were selected from the ADNI database if they were
clinically classifiedas:(i) OCs
cognitively normal (CDR 0) or (ii) MCI (n=57)—individuals with
MMSE scores between 24 and 30, a subjective memory complaint
verified by an informant, objective memory loss as measured by
education-adjusted performance on the Logical Memory II subscale
(delayed paragraph recall) of the Wechsler Memory Scale-Revised
(Wechsler, 1987), a CDR of 0.5, absence of significant levels of
impairment in other cognitive domains, essentially preserved activities
of daily living and an absence of dementia at the time of the baseline
MRI scan who within 2 years progressed to a diagnosis of probable
Alzheimer’s disease (CDR=1.0). Only individuals classified as the
amnestic subtype of MCI, based on the revised MCI criteria
(Petersen, 2004), were selected. In addition, 65 individuals who met
criteria for probable Alzheimer’s disease (McKhann et al., 1984)
(all were CDR 1) were also included for comparison. As expected,
the CDR-SB and MMSE showed a difference between the groups
(P50.05). No other demographic variables differed between the
(n=94)—individuals who were
MRI image acquisition
For the training cohort (OASIS subjects), the MRI scans were acquired
on a 1.5T Vision system (Siemens, Erlangen, Germany). T1-weighted
magnetization-prepared rapid gradient echo (MP-RAGE) scans were
obtained according to the following protocol: two sagittal acquisi-
tions, FOV=224, matrix=256?256, resolution=1?1?1.25mm3,
(Marcus et al., 2007). Two acquisitions were averaged together to
increase the contrast-to-noise ratio.
For the validation cohort (ADNI subjects), the MRI scans were
acquired at multiple sites using either a GE, Siemens or Philips 1.5T
system. Two high-resolution T1- weighted volumetric MP-RAGE scans
were collected for each subject and the raw DICOM images were
downloaded from the public ADNI site (http://www.loni.ucla.edu/
ADNI/Data/index.shtml). Parameter values vary depending on scan-
ning site and can be found at http://www.loni.ucla.edu/ADNI/
Table 1 Descriptive statistical information for the subjects in the study
Diagnostic groupTraining cohort (OASIS subjects)Validation cohort (ADNI subjects)
Means are listed with standard deviations in parentheses. OC=Older controls; MCI=individuals with mild cognitive impairment; Alzheimer’s disease=individuals with
clinically diagnosed Alzheimer’s disease; OASIS=Open Access Series of Imaging Studies; ADNI=Alzheimer’s Disease Neuroimaging Initiative.
Brain 2009: 132; 2048–2057R. S. Desikan et al.
Automated image analysis procedures
package, freely available at http://surfer.nmr.mgh.harvard.edu. Multi-
ple MPRAGE MRI acquisitions for each participant were motion cor-
rected, averaged and normalized for intensity inhomogeneities to
create a single image volume with relatively high contrast to noise
(Dale et al., 1999). This averaged volume was used to locate the grey/
white matter boundary (white matter surface) and this, in turn, was
then used to locate the grey/CSF boundary (grey matter surface)
(Fischl et al., 1999a; 2000). Cortical thickness measurements were
then obtained by calculating the distance between the grey and
the white matter surfaces at each point (per hemisphere) across
the entire cortical mantle (Fischl et al., 2000). This cortical thickness
measurement technique has been validated via histological (Rosas
et al., 2002) as well as manual measurements (Salat et al., 2004;
Dickerson et al., 2009). The reliability of the cortical thickness
measures as well as the other image analysis procedures presented
here has been demonstrated across different manufacturer types,
scanner upgrades, varying contrast-to-noise ratio, and the number
of MPRAGE MRI acquisitions used (Han et al., 2006; Fennema-
Notestine et al., 2007; Jovicich et al., 2009).
The neocortex of the brain on the MRI scans was then automati-
hemisphere). To accomplish this, a registration procedure was used
that aligns the cortical folding patterns (Fischl et al., 1999b) and
probabilistically assigns every point on the cortical surface to one
of the 32 ROIs (Desikan et al., 2006). In addition, two non-
neocortical regionsof the brain,
the hippocampus, were automatically delineated using an algorithm
that examines variations in voxel intensities and spatial relationships
to classify non-neocortical regions on MRI scans (Fischl et al., 2002).
The anatomic accuracy of the grey and white matter surfaces as well
as each of the individual ROIs was carefully reviewed by a trained
neuroanatomist (RSD), with particular attention to the medial temporal
lobe where non-brain tissue, such as dura mater and temporal bone,
often needs to be excluded. All of the MRI scans were processed on
a Linux cluster machine with 230 nodes, each with a 2GHz AMD
Opteron CPU (Advanced Micro Devices, Sunnyvale, CA, USA) and
4GB RAM. Processing time for each MRI scan was ?25–40h. The clus-
ter machine allows for the processing of 230 MRI scans simultaneously.
In total, 34 neocortical and non-necortical ROIs were used in this
study. For all of the analyses performed here, the mean thickness
(only neocortical regions) and the volume (both neocortical and
non-neocortical regions) of the right and the left hemispheres,
for each ROI, were added together. In order to account for
differences in head size, the total volume for each ROI was
corrected using a previously validated estimate of the total intracranial
volume (eTIV) (Buckner et al., 2004). Figure 1 shows all of the ROIs
used in this study.
MRI scans wereprocessedusing the FreeSurfersoftware
gyral-basedROIs (in each
All ADNI subjects were administered a neuropsychological battery.
Two test scores were selected for analysis in the present study because
they had previously been shown to be sensitive predictors of progres-
sion from MCI to Alzheimer’s disease (Estevez-Gonzalez et al., 2003;
Blacker et al., 2007). These two test scores were 5min and 30min
recall fromthe ReyAuditory
(Lezak, 1995), and time to complete part B of the Trail Making Test
(Trails B) (Reitan, 1958).
From the current sample, a number of ADNI subjects (n=115) under-
went lumbar puncture for CSF biomarker evaluation. Three protein
samples were selected for analysis in the present study because they
had previously been shown to be sensitive predictors of progression
from MCI to Alzheimer’s disease (Hampel et al., 2008). These included
plasma samples of tau, abeta 42 and hyperphosphorylated tau (p-tau).
A series of logistic regression models were applied on the train-
ing cohort ROI data to identify those automated measures that best
discriminated the MCI individuals from the OCs. Age was included as
a covariate in each of the regression models. For each measure, the
P-value, odds ratio (OR) computed for a 1 SD difference in the
predictor and area under the curve (AUC) was computed. In these
analyses, the AUC functionally combines the sensitivity and specificity
of the regression analyses in classifying subjects as either MCI or OCs.
The value for the AUC varies from 0.5 representing no discrimination
to 1.0 representing perfect discrimination (Nam et al., 2002).
For the training cohort, the thickness and estimate of the total
intracranial volume-corrected volumes for each of the ROIs (total of
66 measures) were first entered into simple logistic regression models
and only those measures that demonstrated an AUC of 0.70 or better
were retained for further analysis. These retained ROI measures were
entered into a second logistic regression model but only those that
demonstrated an independent effect equal to an OR of 0.57 or lower
(equivalent to a 75% increase in risk with smaller volume or decreased
thickness) were selected. The resulting ROI measures were retained for
inclusion into a final multiple regression model and the discrimination
accuracy for the training cohort was derived. For the validation cohort,
only those ROI measures that best discriminated the MCI group in the
training cohort were entered into a multiple regression model and the
discrimination accuracy was derived. In order to assess consistency and
reproducibility, the logistic regression coefficients from the final model
(developed from the training cohort) were applied to the validation
cohort and the AUC was calculated.
Correlation coefficients were used to examine the relationship
between the automated MRI measures that best discriminated
the MCI group and clinical, neuropsychological and CSF biomarker
evaluations in the validation cohort (ADNI subjects). Spearman’s
rank correlation coefficients were utilized in order to avoid making
assumptions about the statistical distributions of the variables.
The simple logistic regressions on the training cohort (OASIS sub-
jects) revealed significant effects for entorhinal cortex thickness
(AUC=0.86, P50.00001) and volume (AUC=0.80, P50.0001),
inferior parietal lobule thickness (AUC=0.71, P50.0001) and
volume (AUC=0.70, P50.0001), inferior temporal gyrus thickness
(AUC=0.72, P50.0001) and volume (AUC=0.72, P50.0001),
isthmus of cingulate cortex thickness (AUC=0.71, P50.001),
lateral occipitalcortex thickness
lingual cortex thickness (AUC=0.71, P50.0001) and volume
(AUC=0.72, P50.0001) and volume (AUC=0.72, P50.0001),
parahippocampal gyrus (AUC=0.74, P50.0001) and volume
MRI as a diagnostic marker for MCI and Alzheimer’s diseaseBrain 2009: 132; 2048–2057 |
temporal gyrus thickness (AUC=0.78, P50.0001), supramarginal
gyrus thickness (AUC=0.75, P50.0001), temporal pole thickness
P50.0001) and hippocampal volume (AUC=0.82, P50.00001)
The final regression model, estimated from the training cohort,
P50.0001), hippocampal volume (OR=0.20, P50.0001) and
supramarginal gyrus thickness (OR=0.02, P50.0001) represented
the best set of discriminators for MCI. In the comparison between
the OCsand theMCIindividuals,
demonstrated an AUC=0.91.
For the validation cohort (ADNI subjects), in the comparison
between the OCs and the MCI individuals, entorhinal cortex
demonstrated an AUC=0.95. In the comparison between the
OCs and patients with Alzheimer’s disease, these three measures
these three measures
and supramarginal thickness
demonstrated an AUC=1.00. The application of the logistic
regression coefficients from the model based on the training
cohort to the validation cohort resulted in an AUC of 0.95. The
AUC, sensitivity and specificity, negative and positive predictive
values and negative and positive likelihood ratios for both the
training and validation cohorts are presented in Table 2.
The correlations between the automated MRI measures and the
clinical, neuropsychological and CSF measures are presented in
Table 3. Figure 3 illustrates in pictorial format the nature of the
relationship between one of the automated MRI measures,
entorhinal cortex thickness and the three CSF biomarkers.
The results demonstrate that automated MRI measures of entorh-
inal cortex thickness, hippocampal volume and supramarginal
Figure 1 Three-dimensional representations of all 34 ROIs examined in the current study (only one hemisphere is shown). All of
the neocortical ROIs visible in (A) lateral and (B) medial views of the grey matter surface and (C) the two non-neocortical regions
(i.e. the hippocampus and amygdala) visible in the coronal view of a T1-weighted MRI image.
Brain 2009: 132; 2048–2057R. S. Desikan et al.
gyrus thickness identify MCI and Alzheimer’s disease individuals
with excellent discrimination accuracy and specificity, exhibit a
high degree of consistency and reproducibility across multiple
independent cohorts, and correlate strongly with clinical measures
of decline as well as cellular biomarkers. Taken together, these
findings suggest the feasibility of using automated, MRI-based
software tools as a diagnostic marker for Alzheimer’s disease.
The regression analyses presented here indicate that automated
MRI measures can differentiate MCI and Alzheimer’s disease from
normal ageing with excellent discrimination accuracy. In the
comparisons between the MCI individuals and OCs, entorhinal
cortex thickness, hippocampal volume and supramarginal gyrus
thickness demonstrated an average AUC of 0.91 in the training
cohort and an AUC of 0.95 in the validation cohort. Using these
same MRI measures, patients with mild Alzheimer’s disease could
(AUC=1.0). These AUC values are more accurate than prior
MRI (Xu et al., 2000; Devanand et al., 2007; Colliot et al.,
2008; Kloppel et al., 2008), FDG–PET (Mosconi et al., 2008;
Jack et al., 2008) or amyloid-binding PET studies (Jack et al.,
2008; Li et al., 2008). The MCI discrimination accuracies pre-
sented here are comparable to one prior PET study utilizing a
radioactive amyloid and tau protein tracer (Small et al., 2006)
with perfect discrimination
and two prior MRI studies where a smaller number of subjects
were examined from a single cohort (Killiany et al., 2000;
Davatzikos et al., 2008). Further studies are needed to determine
whether combining structural MRI measures with other imaging
modalities will improve diagnostic and prediction accuracy and
whether the benefits of using multiple methods outweigh the
To the best of our knowledge, this is the first study to demon-
strate that automated software tools can be utilized to quantify
the atrophy of individual anatomic regions in a highly specific and
precise fashion. The fact that entorhinal cortex thickness and
hippocampal volume were two of the best discriminators of MCI
indicates the specificity of these automated MRI methods for
identifying the two regions implicated in the earliest stages of
Alzheimer’s pathology (Braak and Braak, 1991; Kemper, 1994).
Consistent with prior MRI studies (Scahill et al., 2002; Buckner
et al., 2005; Dickerson et al., 2009), these results also highlight
the relative importance of examining lateral parietal regions, such
as the supramarginal gyrus, as important discriminators for the
earliest stages of Alzheimer’s disease.
The regression results further illustrate that these automated
MRI measures are highly consistent and reproducible. In the com-
parison between the MCI individuals and OCs, both the training
Table 2 Discrimination results for automated MRI measures from final stepwise regression model
Training cohort (OASIS subjects)
(n=98; MCI 48, OC 49)
Validation cohort (ADNI subjects)
(n=151; MCI 57, OC 94)
Area Under Curve (AUC)a(95% CI)
Negative predictive valuea
Positive predictive valuea
Negative likelihood ratioa
Positive likelihood ratioa
SE=standard error; CI=confidence interval; Odds ratio is for a 1 SD difference in the independent variable.
a Derived from entorhinal thickness, hippocampal volume and supramarginal thickness.
Table 3 Correlation results from the validation cohort (ADNI subjects) between the automated MRI measures that best
discriminated the MCI group and clinical, neuropsychological and CSF biomarker evaluations
Region of interestCDR-SB MMSEAVLT 5min recall AVLT 30min recallTrails BTau P-Tau Abeta 42
Entorhinal cortex thickness
Supramarginal gyrus thickness
Spearman’s rank correlation coefficients listed with P-values in parenthesis.
MRI as a diagnostic marker for MCI and Alzheimer’s diseaseBrain 2009: 132; 2048–2057 |
and the validation cohorts demonstrated similar AUC values indi-
cating the reliability of these measures across multiple independent
cohorts. Furthermore, for the validation cohort, the application of
the training cohort logistic regression coefficients resulted in an
AUC value of 0.95, the same as the value derived without the
application of these coefficients. This shows that the model based
on the training cohort using the three temporoparietal measures is
clinically applicable and can be reproduced in populations other
than that from which the training cohort were drawn.
measures of clinical severity (i.e. CDR-SB and MMSE) suggest
the potential for using these measures as surrogate markers
of underlying disease. Correlations between tests of episodic
memory function (AVLT 5 and 30min recall) and measures of
entorhinal cortex thickness and hippocampal volume are consistent
with the fact that declines in episodic memory function are
reported as predictors of disease progression. Future studies will
examine whether combining these automated MRI measures with
neuropsychological assessments will better predict which MCI
individuals eventually progress to Alzheimer’s disease.
(i.e. entorhinal cortexthickness,
Figure 2 AUC results (neocortical thickness and non-neocortical volumes) from the first regression model (MCI versus older controls)
for all of the automated ROIs from the training cohort (OASIS subjects) displayed on the grey matter surface (only one hemisphere is
shown) in (A) lateral, (B) medial views and (C) the two non-neocortical regions (i.e. the hippocampus and amygdala) in the coronal
view of a T1-weighted MRI image. The colour scale at the bottom represents the discrimination accuracy (AUC value), with green
indicating regions of lowest discrimination and brown/red indicating regions of highest discrimination (please see text for specific
AUC values for each ROI).
Brain 2009: 132; 2048–2057R. S. Desikan et al.
supramarginal gyrus thickness) and CSF measures of tau, p-tau
and abeta 42 suggest that these MRI measures are likely to be
a reflection of known underlying Alzheimer’s disease pathology.
When considered together with the regression results, these data
suggest the hierarchical fashion in which pathology affects the
earliest stages of Alzheimer’s disease, with tau-associated neurofi-
brillary changes in medial temporal regions and abeta-associated
amyloid changes in the entorhinal cortex and neocortical regions
(Arnold et al., 1991; Braak and Braak, 1991; Kemper, 1994).
The methods we have described here can be implemented in
clinical practice for the diagnosis of MCI and Alzheimer’s disease.
Using these software tools a single volumetric T1-weighted MRI
scan can be completely processed, with little to no manual
intervention, in a relatively short amount of time. The training
cohort regression coefficients presented here can then be applied
to the final output values of entorhinal cortex thickness, hippo-
campal volume and supramarginal gyrus thickness to calculate
the predictive probability of a single individual being diagnosed
as either MCI or Alzheimer’s disease.
The present study has limitations. Since the MCI individuals in
the two cohorts were diagnosed using slightly different criteria,
differences between the two MCI groups could have affected
the ability to independently assess the discrimination accuracy of
the automated MRI measures. Another limitation is that the two
MCI cohorts had differing percentages of males and females, with
the training cohort comprised of a larger number of females and
the validation cohort comprised of a larger number of males. The
fact that the AUC values were comparable between the two
cohorts and that the application of the training cohort logistic
regression coefficients resulted in the same AUC value as without
the application of these coefficients, suggests that the differences
observed between the two cohorts did not play a major role in
affecting the main findings of this study.
One concern is that although the procedures demonstrated here
generalized across clinically diagnosed Alzheimer’s disease and
MCI populations, these procedures may be less accurate in the
clinical setting where a range of cognitive disorders and dementia
subtypes are present. The fact that the current results show
complete discrimination suggests that these tools would be
additionally powerful in the clinical setting. Future work will
examine the application of these automated MRI measures to a
larger, community-based, volunteer cohort that would be more
representative of a clinical setting. Another concern is regarding
clinical utility and whether these automated MRI measures can
predict progression from MCI to Alzheimer’s disease. Recently,
we have completed a study examining the feasibility of using
these automated MRI measures to identify those MCI individuals,
within a larger MCI cohort, at greatest risk for Alzheimer’s disease.
automated MRI measures can identify MCI converters from MCI
non-converters with a high degree of accuracy and have signifi-
cant benefit when compared to clinical and neuropsychological
assessments alone for predicting progression from MCI to
Alzheimer’s disease (Desikan et al., 2009).
The identification of individuals in a transitional phase is critical
for testing disease-modifying therapies and for the development of
novel medications to prevent or delay Alzheimer’s disease. The
results from this study demonstrate that automated MRI-based
neuroanatomic measures provide one cost-effective and efficient
method to identify individuals in the earliest stages of Alzheimer’s
disease and may further serve as a quantitative and biologically
meaningful endpoint in therapeutic trials.
study indicates that these
The authors would like to thank the Washington University
Alzheimer’s Disease Research Center directed by John C. Morris,
Figure 3 Scatter plots illustrating the relationship between
total entorhinal cortex thickness and CSF measures of (A) tau
protein, (B) abeta 42 protein and (C) p-tau protein for 33
Alzheimer’s disease (green circles), 30 MCI (red circles) and
52 older controls (blue circles) individuals. Cortical thickness
values are expressed in mm and CSF measures are expressed in
picograms per mm.
MRI as a diagnostic marker for MCI and Alzheimer’s diseaseBrain 2009: 132; 2048–2057 |
for providing clinical and imaging data and Daniel Marcus for his
contributions to the OASIS project (www.oasis-brains.org).
Medical Student Training in Aging Research Program from the
American Federation for Aging Research (RSD); National Center
for Research Resources grants (P41-RR14075, R01 RR 16594-
01A1 and the NCRR BIRN Morphometric Project BIRN002,
U24 RR021382); the National Institute for Biomedical Imaging
and Bioengineering (R01 EB001550); the Mental Illness and
Instituteon Aging(P50 AG05681,
AG021910). Data collection and sharing for this project was
(Alzheimer’s disease NI; Principal Investigator: Michael Weiner;
NIH grant U01 AG024904) and the Howard Hughes Medical
Institute (OASIS project). Alzheimer’s disease NI is funded by
the National Institute on Aging, the National Institute of
Biomedical Imaging and Bioengineering (NIBIB), and through
generous contributions from the following: Pfizer Inc., Wyeth
GlaxoSmithKline, Merck & Co. Inc., AstraZeneca AB, Novartis
Laboratories and the Institute for the Study of Aging, with parti-
cipation from the US Food and Drug Administration. Industry
partnerships are coordinated through the Foundation for the
National Institutes of Health. The grantee organization is the
Northern California Institute for Research and Education, and the
study is coordinated by the Alzheimer’s Disease Cooperative Study
at the University of California, San Diego. Alzheimer’s disease NI
data are disseminated by the Laboratory of Neuro Imaging at the
University of California, Los Angeles.
Institute and the National
Eli Lillyand Company,
Arnold SE, Hyman BT, Flory J, Damasio AR, Van Hoesen GW. The
topographical and neuroanatomical distribution of neurofibrillary
tangles and neuritic plaques in the cerebral cortex of patients with
Alzheimer’s disease. Cereb Cortex 1991; 1: 103–16.
Bennett DA, Schneider JA, Bienias JL, Evans DA, Wilson RS. Mild
cognitive impairment is related to Alzheimer disease pathology and
cerebral infarctions. Neurology 2005; 64: 834–41.
Blacker D, Lee H, Muzikansky A, Martin EC, Tanzi R, McArdle JJ, et al.
Neuropsychologicalmeasures in normal individuals that predict
subsequent cognitive decline. Arch Neurol 2007; 64: 862–71.
Braak H, Braak E. Neuropathological stageing of Alzheimer-related
changes. Acta Neuropathology 1991; 82: 239–59.
Buckner RL, Head D, Parker J, Fotenos AF, Marcus D, Morris JC, et al.
A unified approach for morphometric and functional data analysis
in young, old,and demented
based head size normalization: reliability and validation against
manual measurement of total intracranial volume. Neuroimage 2004;
Buckner RL, Snyder AZ, Shannon BJ, LaRossa G, Sachs R, Fotenos AF,
Alzheimer’s disease: evidence for a relationship between default
activity, amyloid, and memory. J Neurosci 2005; 25: 7709–17.
adultsusing automated atlas-
Colliot O, Che ´telat G, Chupin M, Desgranges B, Magnin B, Benali H,
et al. Discrimination between Alzheimer disease, mild cognitive
impairment, and normal aging by using automated segmentation of
the hippocampus. Radiology 2008; 248: 194–201.
Dale AM, Fischl B, Sereno MI. Cortical surface-based analysis. I.
Segmentation and surface reconstruction. Neuroimage 1999; 9:
Davatzikos C, Fan Y, Wu X, Shen D, Resnick SM. Detection of prodro-
mal Alzheimer’sdisease via pattern
resonance imaging. Neurobiol Aging 2008; 29: 514–23.
Davis H, Rockwood K. Conceptualization of mild cognitive impairment: a
review. Internatl J Geriatr Psychiatr 2004; 19: 313–19.
Desikan RS, Se ´gonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D,
et al. An automated labeling system for subdividing the human
cerebral cortex on MRI scans into gyral based regions of interest.
Neuroimage 2006; 31: 968–80.
Desikan RS, Cabral HJ,Settecase
Glastonbury CM, et al. Use of automated MR imaging cortical
thickness and volume measurements with neuropsychological testing
for predicting progression from mild cognitive impairment to Alzheimer
disease. American Society for Neuroradiology 47th Annual Meeting
2009; (poster presentation).
Devanand DP, Pradhaban G, Liu X, Khandji A, De Santi S, Segal S, et al.
Hippocampal and entorhinal atrophy in mild cognitive impairment:
prediction of Alzheimer disease. Neurology 2007; 68: 828–36.
Dickerson BC, Bakkour A, Salat DH, Feczko E, Pacheco J, Greve DN,
et al. The cortical signature of Alzheimer’s disease: regionally specific
cortical thinning relates to symptom severity in very mild to mild
Alzheimer’s disease dementia and is detectable in asymptomatic
amyloid-positive individuals. Cereb Cortex 2009; 19: 497–510.
Este ´vez-Gonza ´lez A, Kulisevsky J, Boltes A, Otermı ´n P, Garcı ´a-
Sa ´nchez C. Rey verbal learning test is a useful tool for differential
diagnosis in the preclinical phase of Alzheimer’s disease: comparison
with mild cognitive impairment and normal aging. Int J Geriatr
Psychiatry 2003; 18: 1021–28.
Fennema-Notestine C, Gamst AC, Quinn BT, Pacheco J, Jernigan TL,
Thal L, et al. Feasibility of multi-site clinical structural neuro-
imaging studies of aging using legacy data. Neuroinformatics 2007;
Fischl B, Sereno MI, Dale AM. Cortical surface-based analysis. II:
inflation, flattening,anda surface-based
Neuroimage 1999a; 9: 195–207.
Fischl B, Sereno MI, Tootell RB, Dale AM. High-resolution intersubject
averaging and a coordinate system for the cortical surface. Hum Brain
Map 1999b; 8: 272–84.
Fischl B, Dale AM. Measuring the thickness of the human cerebral cortex
from magnetic resonance images. Proc Natl Acad Sci USA 2000; 97:
Fischl B, Salat DH, Busa E, Albert M, Dieterich M, Haselgrove C, et al.
Whole brain segmentation: automated labeling of neuroanatomical
structures in the human brain. Neuron 2002; 33: 341–55.
Folstein M, Folstein S, McHugh P. ‘‘Mini-Mental State’’. A practical
method for grading the cognitive state of patients for the clinician.
J Psychiatr Res 1975; 12: 189–98.
Gomez-Isla T, Price JL, McKeel DW Jr, Morris JC, Growdon JH,
Hyman BT. Profound loss of layer II entorhinal cortex neurons
occurs in very mild Alzheimer’s disease. J Neurosci 1996; 16:
Hampel H, Bu ¨rger K, Teipel SJ, Bokde AL, Zetterberg H, Blennow K.
Alzheimer’s disease. Alzheimers Dement 2008; 4: 38–48.
Han X, Jovicich J, Salat D, van der Kouwe A, Quinn B, Czanner S, et al.
Reliability of MRI-derived measurements of human cerebral cortical
thickness: the effects of field strength, scanner upgrade and manufac-
turer. Neuroimage 2006; 32: 180–94.
Head D, Snyder AZ, Girton LE, Morris JC, Buckner RL. Frontal-
Alzheimer’s disease. Cereb Cortex 2005; 15: 732–9.
F, HessCP,Dillon WP,
betweennormal aging and
Brain 2009: 132; 2048–2057R. S. Desikan et al.
Jack CR Jr, Lowe VJ, Senjem ML, Weigand SD, Kemp BJ, Shiung MM, Download full-text
et al. 11C PiB and structural MRI provide complementary information
in imaging of Alzheimer’s disease and amnestic mild cognitive
impairment. Brain 2008; 131: 665–80.
Jovicich J, Czanner S, Han X, Salat D, van der Kouwe A, Quinn B, et al.
MRI-derived measurements of human subcortical, ventricular and
intracranial brain volumes: reliability effects of scan sessions, acquisi-
tion sequences, data analyses, scanner upgrade, scanner vendors
and field strengths. Neuroimage 2009; 46: 177–92.
Kemper TL. Neuroanatomical and neuropathological changes in normal
aging and in dementia. In: Albert M, Knoefel J, editors. Clinical
neurology of aging. New York: Oxford University Press; 1994.
Killiany R, Gomez-Isla T, Moss M, Kikinis R, Sandor T, Jolesz F, et al. Use
of structural magnetic resonance imaging to predict who will get
Alzheimer’s disease. Ann Neurol 2000; 47: 430–39.
Klo ¨ppel S, Stonnington CM, Chu C, Draganski B, Scahill RI, Rohrer JD,
et al. Automatic classification of MR scans in Alzheimer’s disease. Brain
2008; 131: 681–9.
Lezak MD. Neuropsychological Assessment. Oxford: Oxford University
Li Y, Rinne JO, Mosconi L, Pirraglia E, Rusinek H, Desanti S, et al.
Regional analysis of FDG and PIB-PET images in normal aging, mild
cognitive impairment, and Alzheimer’s disease. Eur J Nucl Med Mol
Imaging 2008; 35: 2169–81.
Lopez OL, Jagust WJ, DeKosky ST, Becker JT, Fitzpatrick A, Dulberg C,
et al. Prevalence and classification of mild cognitive impairment in
the cardiovascular health study cognition study: part 1. Arch Neurol
2003; 60: 1385–89.
Marcus DS, Wang TH, Parker J, Csernansky JG, Morris JC, Buckner RL.
Open Access Series of Imaging Studies (OASIS): cross-sectional MRI
data in young, middle aged, nondemented, and demented older
adults. J Cogn Neurosci 2007; 19: 1498–1507.
McKhann G, Drachman D, Folstein MF, Katzman R, Price D, Stadlan E.
Clinical diagnosis of Alzheimer’s disease: report of the NINCDS-
Department of Health and Human Services Task Force. Neurology
1984; 34: 939–44.
Morris JC. The Clinical Dementia Rating (CDR): current version and
scoring rules. Neurology 1993; 43: 2412–14.
Morris JC, Storandt M, Miller JP, McKeel DW, Price JL, Rubin EH, Berg L.
Mild cognitive impairment represents early-stage Alzheimer disease.
Arch Neurol 2001; 58: 397–405.
groupunder the auspicesof
Mosconi L, Tsui WH, Herholz K, Pupi A, Drzezga A, Lucignani G, et al.
Multicenter standardized 18F-FDG PET diagnosis of mild cognitive
impairment, Alzheimer’s disease, and other dementias. J Nucl Med
2008; 49: 390–98.
Nam B-H, D’Agostino R. Discrimination index, the area under the ROC
curve. In: Huber-Carol C, Balakrishnan N, Nikulin MS, Mesbah M,
editors. Goodness-of-fit tests and model validity. Boston: Birkha ¨user;
2002. p. 273–77.
Petersen R, Smith G, Waring S, Ivnik R, Tangalos E, Kokmen E. Mild
cognitive impairment: clinical characterization and outcome. Arch
Neurol 1999; 56: 303–8.
Petersen RC, Doody R, Kurz A, Mohs RC, Morris JC, Rabins PV, et al.
Arch Neurol 2001; 58: 1985–92.
Petersen R. Mild cognitive impairment. J Intern Med 2004; 256:
Petersen R, Thomas R, Grundman M, Bennett D, Doody R, Ferrris S,
et al. Vitamin E and donepezil for the treatment of mild cognitive
impairment. N Engl J Med 2005; 352: 2379–88.
Reitan RM. Validity of the Trail Making Test as an indicator of organic
brain damage. Percept Mot Skills 1958; 8: 271–76.
Rosas HD, Liu AK, Hersch S, Glessner M, Ferrante RJ, Salat DH, et al.
Huntington’s disease. Neurology 2002; 58: 695–701.
Rubin EH, Storandt M, Miller JP, Kinscherf DA, Grant EA, Morris JC,
et al. A prospective study of cognitive function and onset of
dementia in cognitively healthy elders. Arch Neurol 1998; 55:
Salat DH, Buckner RL, Snyder AZ, Greve DN, Desikan RS, Busa E, et al.
Thinning of the cerebral cortex in aging. Cereb Cortex 2004; 14:
Scahill RI, Schott JM, Stevens JM, Rossor MN, Fox NC. Mapping
the evolution of regional atrophy in Alzheimer’s disease: unbiased
analysis of fluid-registered serial MRI. Proc Natl Acad Sci USA 2002;
Small GW, Kepe V, Ercoli LM, Siddarth P, Bookheimer SY, Miller KJ,
et al. PET of brain amyloid and tau in mild cognitive impairment.
N Engl J Med 2006; 355: 2652–63.
Psychological Corporation; 1987.
Xu Y, Jack CR Jr, O’Brien PC, Kokmen E, Smith GE, Ivnik RJ, et al.
Usefulness of MRI measures of entorhinal cortex versus hippocampus
in Alzheimer’s disease. Neurology 2000; 54: 1760–7.
of the corticalribbonin
scale-revised.San Antonio: The
MRI as a diagnostic marker for MCI and Alzheimer’s disease Brain 2009: 132; 2048–2057 |