Relevance of Magnetic Resonance
Imaging for Early Detection and
Diagnosis of Alzheimer Disease
Stefan J. Teipel, MDa,b,*, Michel Grothe, PhDb, Simone Lista, PhDc,
Nicola Toschi, PhDd, Francesco G. Garaci, PhDe,f,
Harald Hampel, MDc
Funding: H.H. was supported by the Katharina-Hardt-Foundation, Bad Homburg, Germany.
S.J.T. was supported by the Department AGIS of the University of Rostock.
aUniversity Medicine Rostock, Rostock, Germany;bDZNE, German Center for Neurodegener-
ative Diseases, Rostock, Germany;cDepartment of Psychiatry, Goethe University, Frankfurt,
Germany;dMedical Physics Section, Faculty of Medicine, University of Rome “Tor Vergata”
(Via Montpellier 1 - 00133 Rome), Rome, Italy;eDepartment of Diagnostic Imaging and Inter-
ventional Radiology, University of Rome “Tor Vergata”, Rome, Italy;fInstitute for Research
and Medical Care IRCCS San Raffaele (Via della Pisana 235), Rome, Italy
* Corresponding author.
E-mail address: email@example.com
? Alzheimer disease ? Preclinical diagnosis ? Magnetic resonance imaging
?Functional magnetic resonance imaging ?Diffusion tensor imaging
? Diffusion spectrum imaging ? Multimodal imaging ? Prediction
? Hippocampus volumetry currently is the best-established imaging biomarker for Alz-
heimer disease (AD).
? Imaging markers need further validation in respect of the underlying neurobiological
substrate and potential confounds such as vascular disease, inflammation, hydroceph-
alus, and alcoholism, and clinical outcomes such as cognition, but also as regards demo-
graphic and socioeconomic outcomes such as mortality and institutionalization.
? More advanced imaging protocols, including diffusion tensor imaging, diffusion spectrum
imaging, and functional magnetic resonance imaging (MRI), are presently being used in
monocenter and first multicenter studies.
? From a neurobiological point of view, the main determinants of cognitive impairment in AD
are the density of synapses and neurons in distributed cortical and subcortical networks.
? MRI-based measures of regional gray matter volume and associated multivariate analysis
techniques of regional interactions of gray matter densities provide insight into the onset
and temporal dynamics of cortical atrophy as a close proxy for regional neuronal loss and
as basis of functional impairment in specific neuronal networks.
Med Clin N Am 97 (2013) 399–424
0025-7125/13/$ – see front matter ? 2013 Elsevier Inc. All rights reserved.
Alzheimer disease (AD) is the most common cause of dementia in the elderly. Autopsy
case series on the sequential accumulation of neurofibrillary bundles and amyloid pla-
ques, as well as on the progression of neuronal loss through the cerebral cortex,
suggest that AD is a system-specific brain disease that affects discrete neuronal
systems in a fairly consistent temporospatial pattern while other brain regions are
widely spared or only affected in late stages of the disease.1,2The system specificity
of pathologic events in AD and the preclinical onset of these changes have fueled the
definition of new diagnostic criteria for preclinical and predementia AD that incorpo-
rate neuroimaging markers and biomarkers to define the presence of disease.3–5
According to current diagnostic criteria the diagnosis of AD is based on clinical
history, psychiatric and neurologic examination, neuropsychological testing, and
supportive measures including structural imaging and blood tests to exclude other
causes of dementia.6The inclusion of biomarkers from structural, functional, and
molecular imaging in the revised diagnostic criteria aims both to improve specificity
for the diagnosis of AD dementia7and to reach a diagnosis of AD before the onset
of manifest dementia. These criteria are primarily intended for use in research settings
but may also affect the clinical diagnosis of early stages of AD in the future.
Following the amyloid cascade hypothesis,8molecular lesions of neurotoxic
amyloid accumulation in the cerebral cortex are the first event of a pathologic cascade
involving synaptic dysfunction, axonal degeneration and, finally, neuronal loss.
Neuronal loss can be detected using measures of brain atrophy in structural magnetic
resonance imaging (MRI),9and already affects specific brain regions years before the
onset of clinical dementia.10A high-resolution structural MRI scan on a 1.5- or 3-T MRI
scanner requires only 5 to 10 minutes of acquisition time. The technique is widely
available, and some analysis methods have already been established since the early
1990s. Therefore, structural MRI has become a key imaging biomarker for prodromal
or predementia AD.
In the following sections this article discusses how structural MRI findings reflect the
systematic nature of AD progression through the brain, confirming and extending the
initial findings by Braak and Braak1on the temporospatial patterns of system-specific
brain changes associated with AD, and how these changes can be used to improve
diagnostic accuracy in early AD. The review proceeds from the most easily accessible
approaches of visual rating, through manual volumetry, to automated data-driven
analyses involving univariate or multivariate statistics.
Several criteria have been postulated for the use of biological measures as diag-
nostic markers or potential end points to assess treatment effects: (1) applicability
(ie, the test should be noninvasive, widely available, and pose low patient burden),
(2) reliability, both within one center (test-retest reliability) and across centers, and
(3) validity in respect to underlying pathology and clinical outcomes.11Structural
MRI is widely applicable and poses, in general, relatively low patient burden. The reli-
ability of volumetric measures obtained from repeated MRI scans is generally
high.12,13Less is known about the variability of volumetric measures obtained from
different MRI scanners. The variability of manual and automated volumetric measures
across 12 different MRI scanners was below 5% in one study,14for manual volumetric
measurement of the hippocampus only 3.5%, despite a rather liberal acquisition
protocol across centers. This variation is in the range of accuracy of manual or auto-
mated segmentation protocols tested against simple or complex phantoms in
a single-center approach,13suggesting that multicenter variability, once minimal
criteria for scanner quality are met by the participating centers, will not limit the
Teipel et al
application of structural MRI in clinical trials. Clinical validity in terms of prediction of
functional outcomes in predementia stages was a major focus of structural imaging
research in the last years. Autopsy diagnosis as gold standard end point has been
applied only in few studies so far, and a small number of analysis approaches have
been assessed with respect to pathologic validity. Clinicopathologic comparison
studies have shown that hippocampus volume obtained antemortem accounted for
at least 50% of variability in neuron numbers determined during autopsy.15The
amount of variation explained by MRI-based hippocampus volumetry was above
90% when MRI scans had been obtained post mortem.9Thus, hippocampus volume-
try can be considered as an in vivo surrogate measure of hippocampal neuronal
density. One should, however, be careful in simply interpolating these findings to
in vivo measures of cortical atrophy. In 27 antemortem cognitively intact subjects,
cortical thinning determined post mortem across age cohorts was not associated
with regional neuron numbers and density, but was suggested to reflect changes in
neuronal and dendritic architecture.16Therefore, the interpretation of imaging findings
is always founded on some prior concept of underlying disease mechanisms.
THE DIAGNOSTIC USE OF MRI IN AD
The neurodegeneration in AD leads to a marked reduction of brain tissue, which on
MRI scans primarily presents as signal loss in the medial temporal lobe (MTL) and
widening of cerebrospinal fluid (CSF) spaces. At the end of the last century, increasing
evidence from postmortem autopsy studies pointed to a temporospatial pattern of
progressive AD pathology that manifests at first in the perirhinal and entorhinal
cortices of the MTL and extends sequentially to include other allocortical regions fol-
lowed by neocortical association areas of the temporal, parietal, and frontal lobes.
Primary sensorimotor areas are relatively spared or affected only in highly advanced
stages of the disease.1Based on these observations, a neuropathologic staging
scheme of AD severity into 6 stages of cortical involvement was proposed. Of impor-
tance, this staging scheme predicted that the first stages of AD pathology, the “trans-
entorhinal” stages I and II, correspond to clinically silent cases, whereas the “limbic”
(III and IV) and “neocortical” (V and VI) stages are usually associated with incipient and
fully developed AD dementia, respectively (Fig. 1). An overview on MRI markers is
given in Table 1.
The simplest approach to determine regional changes of brain volumes is to use visual
rating scales based on digital images or hard copies of a 3-dimensional MRI
sequence.17These scales have a high accuracy in determining the extent of atrophy
in cross-sectional studies relative to more labor-intensive manual volumetry of the
hippocampus,17–19and reached an accuracy of about 89% in discriminating AD
patients from controls in a small sample of subjects.20By contrast, the use of a visual
rating scale to determine rates of atrophy over time only reached accuracy at chance
level relative to volumetric measures, suggesting that this approach may not be useful
as a secondary end point in clinical trials.19This finding illustrates that information on
reliability of new markers cannot simply be interpolated from cross-sectional to longi-
tudinal study designs.
Given the prominence of MTL atrophy on MRI scans, which corresponds to post-
mortem evidence of earliest and most severe neurofibrillary degeneration in this
MRI for Diagnosis of Alzheimer Disease
region, first attempts to quantify AD-related atrophy on in vivo MRI scans focused on
volumetric measurements of MTL structures, most notably the hippocampus.21,22
Manually measured hippocampus volumes in patients with AD dementia showed
a consistent reduction of up to 40% compared with healthy controls of the same
age, allowing for highly accurate group separations based on the MRI scan only.
Hippocampal volume as measured by MRI scans post mortem or in end stages of
disease was further shown to account for a high proportion of 50% to 80% of variance
in measures of neuronal loss and neurofibrillary abnormality at autopsy.9,23Whereas
subregions of the MTL showed comparably high degrees of atrophy even in mild
stages of AD,24,25neocortical involvement was more consistently detected in
advanced stages of AD dementia, and showed a pattern of declining atrophy severity
from temporal over parietal to frontal and occipital lobes.26,27
The Braak staging scheme of progressive neurofibrillary degeneration predicts
earliest structural changes to precede the onset of dementia by several years, and
thus AD-typical atrophy patterns on MRI should also be detectable in asymptomatic
or very mildly impaired subjects at high risk of developing AD. One of those risk groups
is given by subjects with a diagnosis of amnestic mild cognitive impairment (MCI),
which is defined as a relatively isolated memory impairment that is greater as what
is expected by age alone, but is not severe enough to interfere with activities of daily
living and does not suffice for a diagnosis of dementia.28Patients diagnosed with
MCI exhibit an increased risk for developing AD with incidence rates of 10% to 15%
per year, compared with 1% to 2% per year for healthy unimpaired subjects of the
same age. The syndrome is seen as a boundary or transitional stage between normal
aging and dementia, and often reflects prodromal AD pathology.29When comparing
temporal lobe volumes between healthy elderly subjects, individuals with a diagnosis
of MCI, and AD patients, a significant reduction of hippocampus volume was detect-
able in the MCI (?14%) and the AD groups (?22%), but atrophy of the temporal
neocortex was found only in the AD group.30Of interest is that in a clinical follow-up
Fig. 1. Cortical atrophy in MRI and Braak staging. (Upper row) Voxel-wise maps of gray
matter volume reductions in a group of subjects with mild cognitive impairment (MCI)
(n 5 69) and a group of AD patients (n 5 28), respectively, compared with the same group
of healthy age-matched controls (n 5 95). Unpublished data; MRI images retrieved from
publicly available database (www.oasis-brains.org). (Lower row) drawings of the Braak
staging scheme of neurofibrillary pathology in AD. Note that voxel-wise pattern of gray
matter atrophy in MCI and AD resemble the “limbic” and “neocortical” stages of neurofibril-
lary degeneration, respectively, as outlined by Braak and Braak. (Adapted from Braak H,
Braak E. Staging of Alzheimer’s disease-related neurofibrillary changes. Neurobiol Aging
1995;16(3):271–8; with permission from granted by Karger AG, Basel.)
Teipel et al
study on subjects with MCI, neocortical temporal lobe volume was already reduced in
those subjects who later declined to AD, which significantly distinguished them from
the MCI subjects who remained stable.31
In terms of diagnostic accuracies, volumes of the hippocampus and the entorhinal
cortex can separate AD patients and MCI subjects from healthy controls, with accu-
racies ranging from 70% for early stages of MCI to complete group separation for
advanced stages of AD dementia.21,25,32Furthermore, volumes of both hippocampus
and entorhinal cortex predict future conversion to AD in individuals with MCI at accu-
racies around 80% to 85%, whereas the predictive value of entorhinal cortex volume
seems to be slightly superior over hippocampus volume.33–35An important issue is the
added value of volumetric measurements over other predictors, such as age and neu-
ropsychological performance. In 139MCI subjects followed over an averageinterval of
5 years, age, Mini Mental State Examination score, delayed recall, and digit symbol
test performance predicted conversion to AD with almost 80% accuracy. Adding
volumes of hippocampus and entorhinal cortex to the prediction model increased
accuracy to 87%.36Although the diagnostic value of neuropsychological performance
may be slightly overestimated as it is at least an indirect criterion to define conversion
to dementia, these data indicate that the added value of MTL volumes may be signif-
icant, but clinically moderate.
Considering the diagnostic use of entorhinal cortex volume versus hippocampus
volume, cross-sectional studies suggest that entorhinal cortex volumetry is unlikely
to provide any additional benefit over hippocampus volume in identifying patients
with AD dementia in comparison with controls25,37–39; however, at the MCI stage it
may improve prognostic efficiency by a few percent compared with hippocampal
volumetry,32,38although one multicenter study suggests no additional benefit.40
Especially informative for the study of earliest structural changes in the course of AD
is MRI data of longitudinally followed elderly who were completely asymptomatic at
the time of the MRI scan and later developed cognitive impairments leading to diag-
noses of MCI or AD. Using such data, a few studies were able to show that volumetric
reduction of the anterior MTL, including the hippocampus, the amygdala, and the
entorhinal cortex, precedes the onset of cognitive deterioration by several years.41–43
Thus, volumes of the amygdala and the hippocampus in clinically declining subjects
were found to be on average 5% smaller than in stable controls as early as 6 years
before the diagnosis of dementia was made. An individual risk prediction, however,
is not possible based on these atrophy measures in cognitively healthy subjects.
Results on MTL volumetry are still heterogeneous, not the least because of different
measurement protocols with different anatomic criteria used to define and outline the
hippocampus. At present, a combined effort of the European Alzheimer’s Disease
Consortium (EADC) and the Alzheimer’s Disease Neuroimaging Initiative (ADNI) is
under way to harmonize protocols for the manual tracing of the hippocampus with
the goal of developing and validating a unified standard protocol.44This protocol
would eventually serve as gold-standard reference for the training of automated
The wide availability of high-field MRI at 3 T and the increasing availability of ultrahigh-
field MRI at 7 T rendered subfield measurements of the hippocampus a feasible diag-
nostic approach in selected samples. Pathologic evidence suggests a selective
vulnerability of hippocampal subfields in AD.10Both manual volumetric and atlas-
based automated measurements have been used to determine volumes of CA1
to CA4, dentate gyrus, and subiculum. The manual methods are extremely time
MRI for Diagnosis of Alzheimer Disease
Structural MRI markers for Alzheimer disease (AD)
Marker Diagnostic UseLongitudinal MarkerComments
Visual rating of hippocampus
High correlation with hippocampus volume
(R2w0.9, diagnostic accuracy for AD vs
controls between 80% and 90%,19,20
prediction of AD in MCI not assessed.
Reductions of hippocampus and adjacent
MTL areas in preclinical stages, but no
individual risk prediction
Detection of change at chance
Useful for diagnostic evaluation at
baseline,19,20no use for follow-up. So
far, no preclinical prediction in
asymptomatic subjects for individual
Manual volumetry of
Diagnostic accuracy for AD vs controls
between 80% and 90%,189prediction of
AD in MCI with 70%–80% accuracy190
Rates of atrophy about 4.7% per
annum in AD compared with
0.9%–1.4% in healthy
Already used in clinical trials as secondary
end point; multicenter variability of
longitudinal changes needs to be
Automated volumetry of
High correlation with manual volumetry
(R2> 0.8).78Group discrimination AD vs
controls 83%, MCI vs controls 73% (post
hoc probability only).5688% accuracy for
the discrimination of MCI converters from
stable MCI subjects45,58
Rate of atrophy predicts
subsequent cognitive decline in
nondemented elderly with
about 80% accuracy.193Baseline
volume correlates with
subsequent cognitive decline in
First data from large multicenter studies
suggest stable performance for
diagnostic and predictive accuracy
Manual and automated
In small sample studies, diagnostic accuracy
of subfield measurements was superior to
total hippocampus volume to discriminate
MCI from healthy controls.46,195,196One
study shows high predictive accuracy of
hippocampus shape in preclinical subjects,
but requires further confirmation51
Only little assessed in this respect
Approach that may prove useful in the
context of clinical trials for risk
stratification; no clinical application to
date because of complexity of method
and time-consuming delineation
(manual approaches). Very limited data
on predictive accuracy in preclinical
and predementia stages51
Teipel et al
Manual volumetry of
No additional benefit in identifying patients
with manifest AD. Accuracy of prediction
of AD in MCI increased by a few percent
compared with hippocampus volumetry in
monocenter studies,38,197,198no additional
use in first multicenter studies40
Rates of ERC atrophy 5-times
greater in AD than in
controls,199but only applied in
small-scale monocenter studies
Diagnostic use for prognosis of AD in MCI
seems to be superior to hippocampus;
use as secondary end point in RCT
seems not superior to hippocampus
considering the laborious
Automated measurement of
whole brain volume
Diagnostic use only for the rate of change,
not the baseline volume, but would
require second scan after 1 y before
diagnosis can be supported
Atrophy rate of 2.5% per year in
AD patients compared with
0.4%–0.9% in healthy
stability has not systematically
Already secondary end point in clinical
trials, but only limited heuristic value
because of global nature of the
Voxel-based morphometry Characteristic pattern of brain atrophy in AD
and MCI, but lacks an established statistical
model to determine individual risk for
a single subject. Combining VBM with ROIs
yields 78% sensitivity and 75% specificity
in predicting MCI in healthy subjects,202
but only post hoc probability
Accelerated rates of regional gray
matter atrophy in patients with
MCI who later converted to
AD,74but no established model
for effect size estimation of
The combination of VBM with an ROI
approach allows diagnostic use, but
abandons the advantage of mapping
changes across the entire brain40
Statistical models for individual risk
prediction have been proposed: predicts
AD in MCI with 80%–90% accuracy,177,178
discriminates between MCI and controls
with 90% accuracy.179
Characteristic spread of atrophy
through the brain in AD.203No
data from intervention studies,
no established statistical model
for evaluation of intervention
Allows individual risk prediction based on
information on atrophy pattern across
the entire brain. Awaits confirmation in
larger multicenter and longitudinal
90% accuracy in the discrimination between
AD patients and controls.60Reaches effect
sizes similar to manual hippocampus
volumetry when predicting AD in MCI40
Provides an easily accessible end
point such as an average rate of
0.18 mm atrophy per year in
Promising end point in clinical trials, as it
offers a direct assessment of effect sizes
expressed in a meaningful metric
Abbreviations: ERC, entorhinal cortex; MCI, mild cognitive impairment; MTL, medial temporal lobe; RCT, randomized controlled trial; ROI, region of interest; VBM,
MRI for Diagnosis of Alzheimer Disease
consuming, with several hours of tracing per scan. At the same time they are based on
the direct identification of anatomic boundaries, and therefore serve as gold standard
to assess the performance of automated methods.
Using total hippocampus volume as predictor for future conversion from MCI to AD,
the false-negative rate is still 30%.45In studies on a small number of subjects, the
assessment of CA1 and CA2 using manual delineation46and CA2 to CA3 using auto-
mated delineation47was more accurate than total hippocampus volume in discrimi-
nating MCI subjects from controls. Obviously the technique of analysis matters, and
thus subfields that were found most atrophic differ slightly between surface mapping,
automated atlas-based labeling, and manual measurement approaches. Sequences
at 7 T provide access to even finer substructures of the hippocampus, but the clinical
relevance of these measures is still unclear.48
A purely data-driven approach to determine within-hippocampus differences with
aging or disease is hippocampus shape analysis. Based on high parametric deforma-
tion algorithms, shape models of the hippocampus can automatically be determined.
Data-reducing approaches, such as principal component analysis, allow extraction of
characteristic parameters from these individual shape models that can be compared
between diagnostic groups. In a small-scale study of 18 AD patients and 26 controls,
this approach yielded 67% sensitivity and 85% specificity in the discrimination
between AD patients and controls.49A multivariate analysis based on support vector
machine classification of automatically extracted hippocampus shape features
yielded discrimination accuracy between AD patients and controls of 94%, and accu-
racy between MCI subjects and controls of 83%.50In a study on 139 subjects who
were free of cognitive impairment at baseline and were followed over 10 years, hippo-
campal shape analysis revealed a cluster at the ventromedial head of the right hippo-
campus that predicted conversion to AD with about 90% accuracy.51This interesting
result clearly requires further replication, as an overfitting of the data cannot always be
excluded with these complex models.
Automated Region-Driven Methods
Manual volumetric methods are associated with several drawbacks that limit their
utility for the assessment of brain atrophy in neurodegenerative diseases. Most
notably, manual delineation of regions of interest (ROIs) is rater dependent, relatively
labor intensive, and limited to brain structures that provide clearly delineable borders
defined by anatomic landmarks. Since the beginning of this century, automated
methods have been under development to determine regionally specific changes in
brain structure using hypothesis-driven and rater-independent approaches.52,53
Hypothesis-driven automated approaches focus on selected brain regions, such as
the hippocampus, the cholinergic basal forebrain, or hippocampus subfields (see
earlier discussion). Automated segmentations of the hippocampus exhibit high
anatomic accuracy when compared with manual delineations54,55and provide
comparable diagnostic power.56,57When tested for their diagnostic potential in large
multicenter data sets, such as provided by the ADNI, automatically segmented
volumes of the hippocampus achieve accuracies of around 70% and 80% for sepa-
rating MCI subjects and AD patients, respectively, from healthy controls, and predict
conversion to dementia in MCI subjects with 88% accuracy.45,58
However, diagnostic models based on single volumes do not account for the AD-
typical pattern of progressive atrophy that spreads from the MTL to the temporopar-
ietal neocortex early in the course of the disease. Modern image-processing software
allows the automated parcellation of the brain in neuroanatomic ROIs that can then be
tested separately and in combination for their diagnostic potential using logistic
Teipel et al
regression or discriminant analysis. Using such an approach, it could be shown in
a multicenter setting that a combined structural marker of the hippocampus, the ento-
rhinal cortex, and the temporoparietal junction could increase diagnostic accuracy for
the separation of MCI from controls to 90%.59Optimal combinations of automatically
extracted regional markers for distinguishing between AD patients and controls could
even yield complete group separations.59,60Accordingly, composite markers that
combined AD-typical regional measures, including the MTL, temporoparietal associ-
ation areas, and medial parietal regions, showed promising accuracies of around 75%
for the prediction of imminent conversion to AD in multicenter data of subjects with
Automated Data-Driven Methods
For purely data-driven analyses, the most commonly used automated morphometry
approach is voxel-based morphometry (VBM). In its original implementation the
method is based on a low-dimensional spatial transformation of brain scans into
a common reference space to control for global differences in brain size and shape.
After segmentation of the scans into brain tissue and CSF spaces, remaining local
differences in gray matter volumes, which are not modeled by the global spatial
normalization, are the parameters of interest that drive a voxel-based univariate
statistic. This approach has been criticized because the concept of global versus
regional effects cannot be operationalized, and modeled effects will depend on the
characteristics of the normalization algorithm. Furthermore, the accuracy of normali-
zation may vary with neurobiological differences, and thus effects in VBM may be
driven by group differences in normalization accuracy rather than the neurobiological
differences themselves.63These arguments are a matter of debate64and are still not
finally resolved. From a pragmatic perspective, VBM has widely been used and results
from these analyses have proved to be reproducible across different scanners and
processing approaches,14and to spatially agree with effects from other imaging tech-
niques and autopsy studies.65There is, however, still no validation of VBM in respect
of the underlying neurobiological changes, not the least because the counting of
neuron numbers, the most likely substrate of measures of atrophy in AD, is difficult
to perform for neocortical regions. Therefore, reference values for neuron numbers
in cortical regions of the human brain are lacking. The validity of VBM-based measure-
ments of cortical atrophy with respect to potential clinical outcomes such as memory
performance has begun to be established recently.66,67Combined with results from
other neurodegenerative dementias,68,69these data suggest that VBM reveals
a pattern of brain atrophy whose relation to specific cognitive impairments is consis-
tent with a priori models of the representation of cognitive function in cortical and
subcortical neuronal networks in the human brain.
When applied to demented AD patients, VBM analyses demonstrate the expected
patternofglobalcortical atrophythatismostpronounced inmedialandlateraltempor-
oparietal cortices, with a relative sparing of the sensorimotor cortex, the occipital pole,
and the cerebellum.70,71The spatial pattern of progressive neuropathology in AD as
suggested by the postmortem autopsy studies was impressively demonstrated using
VBM and serial MRI of MCI subjects who progressed to AD.72Hence, early atrophic
changes in subjects with MCI, approximately 3 years before the clinical diagnosis of
AD, were found to be confined to the MTL. At a later stage, but still with the diagnosis
more severe and widespread and by then also involved areas of the prefrontal cortex,
probably reflecting neurofibrillary-tangle abnormality of the neocortical Braak stages V
MRI for Diagnosis of Alzheimer Disease
and VI.65Accordingly, cross-sectional VBM studies on MCI consistently demonstrate
atrophy of the MTL, whereas the degree of involvement of areas of neocortical associ-
ation varies across studies, probably attributable to the heterogeneity ofMCI as a tran-
sitional state between normal aging and AD.73Thus, MCI subjects with an imminent
conversion to AD dementia show greater reductions in MTL volume but especially
greater atrophy of the temporoparietal neocortex and the posterior cingulate/precu-
neus when compared with MCI subjects who remain stable during clinical follow-
up.40,74Using serial MRI, it could be further shown that most of these regions not
only showed lower volumes at baseline but also higher longitudinal rates of volume
decline as MCI subjects progressed to a diagnosis of AD.74,75
Unbiased VBM studies couldalso complement manual volumetry findings ofearliest
structural changes in the (anterior) MTL in cognitively normal subjects who are
destined to develop AD, by providing evidence for concomitant atrophy outside the
MTL, including the basal forebrain76and the posterior cingulate/precuneus.51Analysis
of serial MRI data over a follow-up of up to 10 consecutive years further revealed that
age-related volume loss is apparent and widespread even in cognitively stable elderly,
although the regional brain changes associated with a conversion to MCI differ signif-
icantly in location and magnitude from the pattern associated with normal brain
Advanced Image Analysis
Based on improvements in hardware and software resources, increasingly sophisti-
cated computational methods for image processing and analysis are being developed
that allow the automated segmentation and detailed subfield analysis of the hippo-
campus and other ROIs in large imaging datasets.55,78,79Modern computational
methods demonstrate high anatomic accuracy of the segmented volumes when
compared with manual volumetry techniques54,80and even allow for the detailed
structural analysis of brain regions that are less amenable to manual delineation.
The overarching principle behind the majority of these approaches is the use of
high-dimensional image-deformation algorithms. Different to the classic VBM anal-
ysis, these deformation-based approaches use hundreds of thousands of nonlinear
parameters to warp the individual brain images into the reference space, yielding
a highly exact match to the template image and thereby eliminating spatial differences
among the individual scans. The information on interindividual spatial variability then
resides entirely in the deformation functions themselves, rendering the differentiation
in global and regional effects obsolete. This approach opens the way for the high-
resolution study of anatomic details by direct extraction of volumetric information
from the individual deformation fields or by automated segmentation of the individual
brains into structural or functional ROIs, based on inverse warping of detailed atlases
in the template space.
While MRI research on structural brain atrophy in AD generally focuses on the MTL
and neocortical regions, the involvement of subcortical nuclei across disease stages is
less thoroughly explored. This fact is surprising given the prevalent neuropathologic
evidence of selective involvement of specific subcortical areas, most notably the
cholinergic nuclei of the basal forebrain,81,82and may be partly explained by technical
issues associated with subcortical in vivo volumetry. The complex anatomy of the
basal forebrain and the indistinguishability of cholinergic cells on normal MRI scans
place important constraints on the in vivo structural analysis of cholinergic degenera-
tion in AD.83,84However, the recent development of cytoarchitectonic maps of the
cholinergic nuclei in MRI standard space, based on combined histology and post-
mortem MRI,85,86now renders the cholinergic basal forebrain amenable to in vivo
Teipel et al
volumetry by the use of advanced computational methods for automated regional
volume extraction. Using such an approach the selective involvement of different
cholinergic nuclei across disease stages could be studied in vivo. A progressive
and differential decline in volume of the cholinergic subnuclei was found across the
spectrum from normal aging over MCI to AD, resulting in severe atrophy of the nucleus
basalis Meynert in patients with mild to moderate AD that was comparable in magni-
tude with atrophy of the hippocampus (Fig. 2).87,88
BEYOND STRUCTURAL MRI ACQUISITION AND ANALYSIS: RESEARCH PERSPECTIVES
AND FUTURE CLINICAL APPLICATIONS
During the last decade, several advanced MR modalities have been applied to the
study of AD and have widened the field for the design of potential markers that may
aid in identifying individuals at risk of developing AD. Although such applications
have been primarily targeted to research (as opposed to clinical) applications, the
complementary information provided by multimodal MR imaging could eventually
be integrated (eg, through machine-learning techniques) to aid in diagnosis, disease
screening, and risk stratification.
Diffusion-Weighted Imaging, Gaussian and Non-Gaussian
Diffusion tensor imaging (DTI)89has been extremely successful in investigating white
matter microarchitecture, connectivity, and integrity, and has been widely used in
studiesinvestigating ADandMCI.90–92Modern echoplanar imaging (EPI)techniques93
Fig. 2. Volume reductions in the cholinergic basal forebrain. (I) Generation of a cytoarchitec-
tonic map of basal forebrain cholinergic nuclei in MRI standard space. Cholinergic cells are
stained in histologic slices and cholinergic nuclei are manually transferred to a correspond-
ing postmortem MRI, which is then automatically normalized into MRI standard space as
defined by the Montreal Neurological Institute (MNI). Figures based on data published in
Ref.88AC, anterior commissure; CH3, cholinergic cells corresponding to the horizontal
limb of the diagonal band of Broca; CH4 am, CH4 al, cholinergic cells corresponding to
the anterior medial and lateral parts of the nucleus basalis Meynert. (II) Left: voxel-wise
reductions in gray matter volume within the basal forebrain region of interest in MCI
subjects compared with healthy controls. Right: group means of extracted basal forebrain,
hippocampus, and total gray matter (GM) volumes in groups of MCI subjects and AD
patients plotted as percent volume reduction compared with a healthy elderly (HE) refer-
ence group. (Data from Bozzali M, Cherubini A. Diffusion tensor MRI to investigate demen-
tias: a brief review. Magn Reson Imaging 2007;25(6):969–77.)
MRI for Diagnosis of Alzheimer Disease
allow relatively rapid (4–9 minutes) whole-brain estimation of the apparent water diffu-
sion tensor (DT) field, and DT-derived rotational invariants such as the single DT eigen-
values, mean diffusivity (MD) (the mean of DT eigenvalues), and fractional anisotropy
(FA) (normalized variance of DT eigenvalues94) can aid in quantifying fiber integrity
through ROI or voxel-based approaches.91In addition, DTI can provide information
about architecture of white matter tracts, which is commonly quantified through deter-
ministic and/or or probabilistic tractography approaches.95Several studies have
revealed a decrease in FA (commonly accompanied by an increase in MD or in 1 or
more eigenvalues) in AD and MCI.96–98This finding typically indicates unspecific
bundle degeneration, and correlations between white matter integrity and disease
severity have also been reported in AD,99,100suggesting that DTI measures may be
used as indices of disease progression.101Although early (MCI stage) alterations in
white matter have been detected within the parahippocampal gyrus, posterior
cingulum, and splenium of the corpus callosum,102–105most DTI-based investigations
of MCI and AD point to the uncinate fasciculus, the entire corpus callosum, and the
cingulum tract as the most affected regions. Of note, a recent study that included
both AD and MCI patients106found a circumscribed increase in FA in the MCI group.
The interpretation of these findings was aided by the introduction of a third tensor
invariant (tensor mode [MO]),107which is able to distinguish the “type” of anisotropy
(planar, eg, in regions of crossing fibers, vs linear, in regions where a single coherent
orientation dominates). In regions of increased FA, an accompanying increase in MO
(which indicates a transition toward a more linear anisotropy) pointed toward the
hypothesis of a selective degeneration of only 1 of 2 crossing fibers, in this case indi-
cating a relative preservation of motor-related projection fibers crossing the associa-
tion tracts of the superior longitudinal fasciculus in the early-stage MCI patients. Also,
DTI appears to be able to detect progressive white matter degeneration in AD as it
advances with aging. In agreement with the retrogenesis model (cortical regions
that mature earliest in infancy tend to degenerate last in AD), white matter abnormal-
ities seem to appear earliest in selected brain regions such as prefrontal cortex white
matter, the inferior longitudinal fasciculus, and temporoparietal areas.91,102,108,109It
should be noted that reproducibility and robustness play a prominent role in (compar-
atively noisy) DTI acquisitions, and a recent meta-analysis indicates high variability in
both the anatomy of regions studied and DTI-derived metrics.110In this context,
a recent European multicenter study111highlighted significant center-related effects
in DTI-derived measures. Also, the simplistic model of a Gaussian propagator, which
is at the base of DTI, may reach its limits in regions/voxels containing mixed tissue
types and/or where 2 or more fiber bundles cross.112To this end more advanced
protocols, such as diffusion spectrum imaging,113diffusional kurtosis imaging,114–116
higher-order tensor models,117compartment models,118,119and anomalous diffu-
sion,120,121have been developed to enhance their suitability in a clinical setting,122
and have already been successfully used in augmenting information about tissue
degeneration in several disorders including AD (Fig. 3).123–126
Functional Magnetic Resonance Imaging: Task-Based and Resting State
Neuronal activity can be studied noninvasively (albeit indirectly) through blood
oxygenation level dependent (BOLD) functional MRI (fMRI), and several fMRI studies
have been able to detect functional alterations that precede AD symptoms and quan-
tifiable AD-related structural neurodegeneration.127–129Standard task-based fMRI
protocols have used episodic memory tasks in studying memory-related activation
in the hippocampus and MTL, typically reporting a decrease in hippocampal or para-
hippocampal activity during information encoding.130–134Moreover, several studies
Teipel et al
have reported a decreased activation in the MTL in patients with MCI.135–137A
substantial body of fMRI research has focused on the “default network,” that is, the
interplay between a particular set of cortical regions and the hippocampal memory
system,138whose activity is thought to decrease when assisting encoding (eg, during
memory-intensive tasks) and to increase during information retrieval.139Accordingly,
several investigators have reported dysfunctional modulation of encoding-related
network activity in AD.135,140–143Also, resting-state fMRI (rsfMRI) is rapidly gaining
ground as a technique able to reveal subtle changes in brain functional connectivity,
and several rsfMRI studies have been able to demonstrate altered default-mode
activity in AD patients144–148as well as in MCI, in which case it has been associated
with a higher risk of converting to AD-related dementia.149A large (underexploited)
potential of fMRI or rsfMRI in AD and MCI may be the longitudinal prediction of disease
progression for devising novel treatment strategies. Although it is known that fMRI can
detect drug-induced modulation of memory-related networks,150to date only a few
studies have demonstrated altered activation after, for example, long-term treatment
with cholinesterase inhibitors in MCI and AD,151–154and the potential of rsfMRI studies
in elucidating acute and chronic drug-related alterations in function and connectivity
has yet to be fully explored.155
Complexity-Based Indicators of Disease
In the search for novel, synthetic descriptors of brain structure and function able to
extract additional information from data provided by existing MR protocols, the
concept of complexity (either structural or functional) seems particularly promising.
Physiologic systems manifest complex nonstationary and nonlinear behavior, and
in different physiologic systems.156,157Accordingly it has been shown that nonlinear
analyses using concepts such as entropy, fractality, and predictability can offer signif-
icant diagnostic and prognostic information on several systemic and central nervous
system disorders.158–160The brain cortex is statistically self-similar (fractal) down to
the scale of 2.5 mm, which approximately coincides with cortical thickness.161Hence,
Fig. 3. Reconstruction of intracortically projecting fiber tracts from diffusion spectrum
imaging of a healthy woman, 50 years of age, during a half-scheme acquisition of 256
gradient directions with an acquisition time of 30 minutes. Colors indicate the main direc-
tion of fiber tracts in left-right (red), superior-inferior (blue) and anterior-posterior (green)
directions. Fiber tracts have been reconstructed using DSI-Studio software (developed by
Fang-Cheng Yeh, Department of Biomedical Engineering, Carnegie Mellon University;
www.dsi-studio.labsolver.org) with default parameters.
MRI for Diagnosis of Alzheimer Disease
the fractal dimension (FD) of cortical gray matter (as estimated based on T1-weighted
MRI)canbe seenasamultiscaledescriptor oftheoverall sizeofthebrainaswellasthe
size of the folded cortex. FD has been found to be nonuniformly distributed between
genders and across brain hemispheres147and is significantly reduced in aging
patients,162those with multiple sclerosis,163and preterm infants with intrauterine
growth restriction.164A few recent studies found a reduction of FD in early AD,165,166
and multifractal analysis167unveiled age-related microstructural changes in the white
matter in the frontal region, whose degree was associated with executive cognitive
decline.168Significant differences in specific fractal descriptors of fMRI time series
have been revealed in several cortical brain areas when comparing patients with early
AD and controls,169and in a similar comparison higher regional fMRI signal entropy
was associated with better cognitive performance.170In rsfMRI studies, a decreased
mean level of functional connectivity as well as diminished fluctuations in the level of
synchronization have been demonstrated in AD,160and the application of graphic
theoretical analyses to the study of AD and other dementias has recently revealed
the disruption of small-world network characteristics typical of healthy functional
Multimodal Data-Processing Strategies and Machine-Learning Techniques
The integration of the complementary information afforded by multimodal imaging
protocols into a comprehensive analysis strategy is likely to aid in better discrimination
and staging of AD,172and technical efforts are under way to devise optimized imaging
protocols including state-of-the-art assessment of brain function, structure, micro-
architecture, and quantitative parameters within a clinically feasible protocol.173
Accordingly an increasing body of research has focused on methodological develop-
ments that combine multimodal brain-imaging protocols in a multiparametric
fashion174–176to formulate integrated hypotheses about structure-function links in
both healthy and diseased brains. Although conventional statistical approaches allow
articulate studies of statistical group differences as well as time-group interactions,
their stand-alone use cannot allocate a single subject to a particular group on MR
examination, hence limiting the overall diagnostic potential in a clinical setting. An
extension of univariate logistic regression and discriminant analysis approaches for
the construction of diagnostic models from region-driven or data-driven data is given
by multivariate pattern classification techniques, including principal component anal-
ysis and machine-learning algorithms such as support vector machines.177–179Such
techniques have recently been identified as promising tools in neuroimaging data
analysis,180especially because they are able to manage images from different modal-
ities simultaneously and, importantly, to classify a single subject into a predefined
group (eg, diseased or control group). Crucially, any type of data feature (eg, clinical,
genetic, neuropsychological, and structural/functional neuroimaging data) can be
integrated into the classification procedure, and the classification performance is
not significantly degraded if same-modality data are collected in different centers
and/or with different hardware.180In the context of atrophy quantification, machine-
learning techniques make optimal use of the data by extracting and recognizing
diagnosis-specific regional patterns of atrophy from preprocessed imaging data in
an unsupervised manner, and can distinguish between AD patients, MCI subjects,
and controls with highdiagnostic accuracy. When trained to recognize spatial patterns
of brain atrophy that best distinguish between AD patients and controls, pattern clas-
sifiers can detect prodromal AD pathology in MCI subjects with accuracies of around
80%,181and may be used to assess an individual’s risk for future cognitive decline.182
Preliminary studies based on multimodal neuroimaging have also obtained promising
Teipel et al
results in discriminating AD and MCI patients.183,184Machine-learning algorithms are
already now being developed for the software of scanner consoles as radiologic
expert systems based on anatomic MRI data. In the future, these algorithms may
be expanded to integrate diverse information from a multitude of image and biomarker
modalities to improve the automated detection of prodromal AD stages based on
high-dimensional pattern recognition.
Hippocampus volumetry currently is the best-established imaging biomarker for AD.
However, the effect of multicenter acquisition on measurements of hippocampus
volume needs to be explicitly considered when it is applied in large clinical trials, for
example by using mixed-effects models to take the clustering of data within centers
into account.185The marker needs further validation in respect of the underlying
neurobiological substrate and potential confounds such as vascular disease, inflam-
mation, hydrocephalus, and alcoholism, and with regard to clinical outcomes such
as cognition but also to demographic and socioeconomic outcomes such as mortality
and institutionalization. The use of hippocampus volumetry for risk stratification of pre-
dementia study samples will further increase with the availability of automated
measurement approaches. An important step in this respect will be the development
of a standard hippocampus tracing protocol that harmonizes the large range of pres-
ently available manual protocols.44In the near future, regionally differentiated auto-
mated methods will become available together with an appropriate statistical
model, such as multivariate analysis of deformation fields, or techniques such as
cortical-thickness measurements that yield a meaningful metrics for the detection of
treatment effects. More advanced imaging protocols, including DTI, DSI, and func-
tional MRI, are presently being used in monocenter and first multicenter studies. In
the future these techniques will be relevant for the risk stratification in phase IIa
type studies (small proof-of-concept trials). By contrast, the application of the broader
established structural imaging biomarkers, such as hippocampus volume, for risk
stratification and as surrogate end point is already today part of many clinical trial
protocols. However, clinical care will also be affected by these new technologies.
Radiologic expert centers already offer “dementia screening” for well-off middle-
aged people who undergo an MRI scan with subsequent automated, typically VBM-
based analysis, and determination of z-score deviation from a matched control cohort.
Next-generation scanner software will likely include radiologic expert systems for
automated segmentation, deformation-based morphometry, and multivariate analysis
of anatomic MRI scans for the detection of a typical AD pattern. As these develop-
ments will start to change medical practice, first for selected subject groups that
can afford this type of screening but later eventually also for other cohorts, clinicians
must become aware of the potentials and limitations of these technologies. It is decid-
edly unclear to date how a middle-aged cognitively intact subject with a seemingly
AD-positive MRI scan should be clinically advised. There is no evidence for individual
risk prediction and even less for specific treatments. Thus, the development of preclin-
ical diagnostic imaging poses not only technical but also ethical problems that must
be critically discussed on the basis of profound knowledge.
From a neurobiological point of view, the main determinants of cognitive impairment
in AD are the density of synapses and neurons in distributed cortical and subcortical
networks.186MRI-based measures of regional gray matter volume and associated
multivariate analysis techniques of regional interactions of gray matter densities
provide insight into the onset and temporal dynamics of cortical atrophy as a close
MRI for Diagnosis of Alzheimer Disease
proxy for regional neuronal loss187and a basis of functional impairment in specific
neuronal networks.188From the clinical point of view, clinicians must bear in mind
that patients do not suffer from hippocampus atrophy or disconnection but from
memory impairment, and that dementia screening in asymptomatic subjects should
not be used outside of clinical studies.
1. Braak H, Braak E. Staging of Alzheimer’s disease-related neurofibrillary
changes. Neurobiol Aging 1995;16(3):271–8 [discussion: 278–84].
2. Thal DR, Capetillo-Zarate E, Del Tredici K, et al. The development of amyloid
beta protein deposits in the aged brain. Sci Aging Knowledge Environ 2006;
3. Albert MS, DeKosky ST, Dickson D, et al. The diagnosis of mild cognitive impair-
ment due to Alzheimer’s disease: recommendations from the National Institute
on Aging—Alzheimer’s Association workgroups on diagnostic guidelines for
Alzheimer’s disease. Alzheimers Dement 2011;7(3):270–9.
4. Dubois B, Feldman HH, Jacova C, et al. Revising the definition of Alzheimer’s
disease: a new lexicon. Lancet Neurol 2010;9(11):1118–27.
5. Sperling RA, Aisen PS, Beckett LA, et al. Toward defining the preclinical stages
of Alzheimer’s disease: recommendations from the National Institute on Aging—
Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s
disease. Alzheimers Dement 2011;7(3):280–92.
6. McKhann G, Drachman D, Folstein M, et al. Clinical diagnosis of Alzheimer’s
disease: report of the NINCDS-ADRDA Work Group under the auspices of the
Department of Health and Human Services Task Force on Alzheimer’s disease.
7. McKhann GM, Albert MS, Grossman M, et al. Clinical and pathological diag-
nosis of frontotemporal dementia: report of the Work Group on Frontotemporal
Dementia and Pick’s Disease. Arch Neurol 2001;58(11):1803–9.
8. Golde TE. Alzheimer disease therapy: can the amyloid cascade be halted?
J Clin Invest 2003;111(1):11–8.
9. Bobinski M, de Leon MJ, Wegiel J, et al. The histological validation of post mor-
tem magnetic resonance imaging-determined
Alzheimer’s disease. Neuroscience 2000;95(3):721–5.
10. Giannakopoulos P, Kovari E, Gold G, et al. Pathological substrates of cognitive
decline in Alzheimer’s disease. Front Neurol Neurosci 2009;24:20–9.
11. Hampel H, Frank R, Broich K, et al. Biomarkers for Alzheimer’s disease:
academic, industry and regulatory perspectives. Nat Rev Drug Discov 2010;
12. Giedd JN, Kozuch P, Kaysen D, et al. Reliability of cerebral measures in
repeated examinations with magnetic resonance imaging. Psychiatry Res
13. Byrum CE, MacFall JR, Charles HC, et al. Accuracy and reproducibility of brain
and tissue volumes using a magnetic resonance segmentation method. Psychi-
atry Res 1996;67(3):215–34.
14. Ewers M, Teipel SJ, Dietrich O, et al. Multicenter assessment of reliability of
cranial MRI. Neurobiol Aging 2006;27(8):1051–9.
15. Zarow C, Vinters HV, Ellis WG, et al. Correlates of hippocampal neuron number
in Alzheimer’s disease and ischemic vascular dementia. Ann Neurol 2005;57(6):
Teipel et al
16. Freeman SH, Kandel R, Cruz L, et al. Preservation of neuronal number despite
age-related cortical brain atrophy in elderly subjects without Alzheimer disease.
J Neuropathol Exp Neurol 2008;67(12):1205–12.
17. Scheltens P, Leys D, Barkhof F, et al. Atrophy of medial temporal lobes on MRI in
“probable” Alzheimer’s disease and normal ageing: diagnostic value and neuro-
psychological correlates. J Neurol Neurosurg Psychiatr 1992;55:967–72.
18. Wahlund LO, Julin P, Johansson SE, et al. Visual rating and volumetry of the
medial temporal lobe on magnetic resonance imaging in dementia: a compara-
tive study. J Neurol Neurosurg Psychiatr 2000;69(5):630–5.
19. Ridha BH, Barnes J, van de Pol LA, et al. Application of automated medial
temporal lobe atrophy scale to Alzheimer disease. Arch Neurol 2007;64(6):
20. Bresciani L, Rossi R, Testa C, et al. Visual assessment of medial temporal
atrophy on MR films in Alzheimer’s disease: comparison with volumetry. Aging
Clin Exp Res 2005;17(1):8–13.
21. Seab JP, Jagust WJ, Wong ST, et al. Quantitative NMR measurements of hippo-
campal atrophy in Alzheimer’s disease. Magn Reson Med 1988;8(2):200–8.
22. Killiany RJ, Moss MB, Albert MS, et al. Temporal lobe regions on magnetic reso-
nance imaging identify patients with early Alzheimer’s disease. Arch Neurol
23. Csernansky JG, Hamstra J, Wang L, et al. Correlations between antemortem
hippocampal volume and postmortem neuropathology in AD subjects.
Alzheimer Dis Assoc Disord 2004;18(4):190–5.
24. Lehericy S, Baulac M, Chiras J, et al. Amygdalohippocampal MR volume
measurements in the early stages of Alzheimer’s disease. AJNR Am J Neurora-
25. Teipel SJ,Pruessner JC,Faltraco F,etal. Comprehensivedissection ofthe medial
temporal lobe in AD: measurement of hippocampus, amygdala, entorhinal, peri-
rhinal and parahippocampal cortices using MRI. J Neurol 2006;253(6):794–800.
26. Rusinek H, de Leon MJ, George AE, et al. Alzheimer disease: measuring loss of
cerebral gray matter with MR imaging. Radiology 1991;178(1):109–14.
27. Teipel SJ, Bayer W, Alexander GE, et al. Regional pattern of hippocampus and
corpus callosum atrophy in Alzheimer’s disease in relation to dementia severity:
evidence for early neocortical degeneration. Neurobiol Aging 2003;24(1):85–94.
28. Petersen RC. Mild cognitive impairment as a diagnostic entity. J Intern Med
29. Guillozet AL, Weintraub S, Mash DC, et al. Neurofibrillary tangles, amyloid, and
memory in aging and mild cognitive impairment. Arch Neurol 2003;60(5):
30. Convit A, De Leon MJ, Tarshish C, et al. Specific hippocampal volume reduc-
tions in individuals at risk for Alzheimer’s disease. Neurobiol Aging 1997;
31. Convit A, de Asis J, de Leon MJ, et al. Atrophy of the medial occipitotemporal,
inferior, and middle temporal gyri in non-demented elderly predict decline to
Alzheimer’s disease. Neurobiol Aging 2000;21(1):19–26.
32. Du AT, Schuff N, Amend D, et al. Magnetic resonance imaging of the entorhinal
cortex and hippocampus in mild cognitive impairment and Alzheimer’s disease.
J Neurol Neurosurg Psychiatr 2001;71(4):441–7.
33. Jack CR Jr, Petersen RC, Xu YC, et al. Prediction of AD with MRI-based
hippocampal volume in mild cognitive impairment. Neurology 1999;52(7):
MRI for Diagnosis of Alzheimer Disease
34. Killiany RJ, Hyman BT, Gomez-Isla T, et al. MRI measures of entorhinal cortex vs
hippocampus in preclinical AD. Neurology 2002;58(8):1188–96.
35. deToledo-Morrell L, Stoub TR, Bulgakova M, et al. MRI-derived entorhinal
volume is a good predictor of conversion from MCI to AD. Neurobiol Aging
36. Devanand DP, Pradhaban G, Liu X, et al. Hippocampal and entorhinal atrophy in
mild cognitive impairment: prediction of Alzheimer disease. Neurology 2007;
37. Krasuski JS, Alexander GE, Horwitz B, et al. Volumes of medial temporal lobe
structures in patients with Alzheimer’s disease and mild cognitive impairment
(and in healthy controls). Biol Psychiatry 1998;43:60–8.
38. Pennanen C, Kivipelto M, Tuomainen S, et al. Hippocampus and entorhinal
cortex in mild cognitive impairment and early AD. Neurobiol Aging 2004;
39. Xu Y, Jack CR Jr, O’Brien PC, et al. Usefulness of MRI measures of entorhinal
cortex versus hippocampus in AD. Neurology 2000;54(9):1760–7.
40. Risacher SL, Saykin AJ, West JD, et al. Baseline MRI predictors of conversion
from MCI to probable AD in the ADNI cohort. Curr Alzheimer Res 2009;6(4):
41. Kaye JA, Swihart T, Howieson D, et al. Volume loss of the hippocampus and
temporal lobe in healthy elderly persons destined to develop dementia.
42. den Heijer T, Geerlings MI, Hoebeek FE, et al. Use of hippocampal and amyg-
dalar volumes on magnetic resonance imaging to predict dementia in cogni-
tively intact elderly people. Arch Gen Psychiatry 2006;63(1):57–62.
43. Martin SB, Smith CD, Collins HR, et al. Evidence that volume of anterior medial
temporal lobe is reduced in seniors destined for mild cognitive impairment. Neu-
robiol Aging 2010;31(7):1099–106.
44. Frisoni GB, Jack CR. Harmonization of magnetic resonance-based manual
hippocampal segmentation: a mandatory step for wide clinical use. Alzheimers
45. Chupin M, Gerardin E, Cuingnet R, et al. Fully automatic hippocampus segmen-
tation and classification in Alzheimer’s disease and mild cognitive impairment
applied on data from ADNI. Hippocampus 2009;19(6):579–87.
46. Mueller SG, Weiner MW. Selective effect of age, Apo e4, and Alzheimer’s
disease on hippocampal subfields. Hippocampus 2009;19(6):558–64.
47. Hanseeuw BJ, Van Leemput K, Kavec M, et al. Mild cognitive impairment: differ-
ential atrophy in the hippocampal subfields. AJNR Am J Neuroradiol 2011;32(9):
48. Kerchner GA, Deutsch GK, Zeineh M, et al. Hippocampal CA1 apical neuropil
atrophy and memory performance in Alzheimer’s disease. Neuroimage 2012;
49. Wang L, Beg F, Ratnanather T, et al. Large deformation diffeomorphism and
momentum based hippocampal shape discrimination in dementia of the Alz-
heimer type. IEEE Trans Med Imaging 2007;26(4):462–70.
50. Gerardin E, Chetelat G, Chupin M, et al. Multidimensional classification of hippo-
campal shape features discriminates Alzheimer’s disease and mild cognitive
impairment from normal aging. Neuroimage 2009;47(4):1476–86.
51. Tondelli M, Wilcock GK, Nichelli P, et al. Structural MRI changes detectable up to
ten years before clinical Alzheimer’s disease. Neurobiol Aging 2012;33(4):
Teipel et al
52. Ashburner J, Friston KJ. Voxel-based morphometry—the methods. Neuroimage
2000;11(6 Pt 1):805–21.
53. Fischl B, van der Kouwe A, Destrieux C, et al. Automatically parcellating the
human cerebral cortex. Cereb Cortex 2004;14(1):11–22.
54. Klein A, Andersson J, Ardekani BA, et al. Evaluation of 14 nonlinear deformation
algorithms applied to human brain MRI registration. Neuroimage 2009;46(3):
55. Leung KK, Barnes J, Ridgway GR, et al. Automated cross-sectional and longi-
tudinal hippocampal volume measurement in mild cognitive impairment and
Alzheimer’s disease. Neuroimage 2010;51(4):1345–59.
56. Colliot O, Chetelat G, Chupin M, et al. Discrimination between Alzheimer
disease, mild cognitive impairment, and normal aging by using automated
segmentation of the hippocampus. Radiology 2008;248(1):194–201.
57. Mak HK, Zhang Z, Yau KK, et al. Efficacy of voxel-based morphometry with
DARTEL and standard registration as imaging biomarkers in Alzheimer’s disease
patients and cognitively normal older adults at 3.0 Tesla MR imaging.
J Alzheimers Dis 2011;23(4):655–64.
58. Calvini P, Chincarini A, Gemme G, et al. Automatic analysis of medial temporal
lobe atrophy from structural MRIs for the early assessment of Alzheimer
disease. Med Phys 2009;36(8):3737–47.
59. Desikan RS, Cabral HJ, Hess CP, et al. Automated MRI measures identify indi-
viduals with mild cognitive impairment and Alzheimer’s disease. Brain 2009;
60. Lerch JP, Pruessner J, Zijdenbos AP, et al. Automated cortical thickness
measurements from MRI can accurately separate Alzheimer’s patients from
normal elderly controls. Neurobiol Aging 2008;29(1):23–30.
cortical thickness: impact of cognitive reserve. Brain 2009;132(Pt 8):2036–47.
62. Bakkour A, Morris JC, Dickerson BC. The cortical signature of prodromal AD:
regional thinning predicts mild AD dementia. Neurology 2009;72(12):1048–55.
63. Bookstein FL. “Voxel-based morphometry” should not be used with imperfectly
registered images. Neuroimage 2001;14(6):1454–62.
64. Ashburner J, Friston KJ. Why voxel-based morphometry should be used. Neuro-
65. Whitwell JL, Josephs KA, Murray ME, et al. MRI correlates of neurofibrillary
tangle pathology at autopsy: a voxel-based morphometry study. Neurology
66. Di Paola M, Macaluso E, Carlesimo GA, et al. Episodic memory impairment in
patients with Alzheimer’s disease is correlated with entorhinal cortex atrophy.
A voxel-based morphometry study. J Neurol 2007;254(6):774–81.
67. Grossman M, McMillan C, Moore P, et al. What’s in a name: voxel-based morpho-
ral dementia and corticobasal degeneration. Brain 2004;127(Pt 3):628–49.
68. Josephs KA, Whitwell JL, Duffy JR, et al. Progressive aphasia secondary to
Alzheimer disease vs FTLD pathology. Neurology 2008;70(1):25–34.
69. Gee J, Ding L, Xie Z, et al. Alzheimer’s disease and frontotemporal dementia
exhibit distinct atrophy-behavior correlates: a computer-assisted imaging study.
Acad Radiol 2003;10(12):1392–401.
70. Frisoni GB, Testa C, Zorzan A, et al. Detection of grey matter loss in mild
Alzheimer’s disease with voxel based morphometry. J Neurol Neurosurg Psy-
MRI for Diagnosis of Alzheimer Disease
71. Karas GB, Burton EJ, Rombouts SA, et al. A comprehensive study of gray matter
loss in patients with Alzheimer’s disease using optimized voxel-based morphom-
etry. Neuroimage 2003;18(4):895–907.
72. Whitwell JL, Przybelski SA, Weigand SD, et al. 3D maps from multiple MRI illus-
trate changing atrophy patterns as subjects progress from mild cognitive
impairment to Alzheimer’s disease. Brain 2007;130(Pt 7):1777–86.
73. Ries ML, Carlsson CM, Rowley HA, et al. Magnetic resonance imaging charac-
terization of brain structure and function in mild cognitive impairment: a review.
J Am Geriatr Soc 2008;56(5):920–34.
74. Chetelat G, Landeau B, Eustache F, et al. Using voxel-based morphometry to
map the structural changes associated with rapid conversion in MCI: a longitu-
dinal MRI study. Neuroimage 2005;27(4):934–46.
75. Risacher SL, Shen L, West JD, et al. Longitudinal MRI atrophy biomarkers:
relationship to conversion in the ADNI cohort. Neurobiol Aging 2010;31(8):
76. Hall AM, Moore RY, Lopez OL, et al. Basal forebrain atrophy is a presymptomatic
marker for Alzheimer’s disease. Alzheimers Dement 2008;4(4):271–9.
77. Driscoll I, Davatzikos C, An Y, et al. Longitudinal pattern of regional brain volume
change differentiates normal aging from MCI. Neurology 2009;72(22):1906–13.
78. Csernansky JG, Wang L, Swank J, et al. Preclinical detection of Alzheimer’s
disease: hippocampal shape and volume predict dementia onset in the elderly.
79. Heckemann RA, Keihaninejad S, Aljabar P, et al. Automatic morphometry in
Alzheimer’s disease and mild cognitive impairment. Neuroimage 2011;56(4):
80. Barnes J, Foster J, Boyes RG, et al. A comparison of methods for the automated
calculation of volumes and atrophy rates in the hippocampus. Neuroimage
81. Bartus RT, Dean RL, Pontecorvo MJ, et al. The cholinergic hypothesis: a histor-
ical overview, current perspective, and future directions. Ann N Y Acad Sci
82. Mesulam M. The cholinergic lesion of Alzheimer’s disease: pivotal factor or side
show? Learn Mem 2004;11(1):43–9.
83. Huckman MS. Where’s the chicken? AJNR Am J Neuroradiol 1995;16(10):
84. Hanyu H, Asano T, Sakurai H, et al. MR analysis of the substantia innominata in
normal aging, Alzheimer disease, and other types of dementia. AJNR Am J Neu-
85. Teipel SJ, Flatz WH, Heinsen H, et al. Measurement of basal forebrain atrophy in
Alzheimer’s disease using MRI. Brain 2005;128(Pt 11):2626–44.
86. Zaborszky L, Hoemke L, Mohlberg H, et al. Stereotaxic probabilistic maps of the
magnocellular cell groups in human basal forebrain. Neuroimage 2008;42(3):
87. Grothe M, Heinsen H, Teipel SJ. Atrophy of the cholinergic basal forebrain over
the adult age range and in early stages of Alzheimer’s disease. Biol Psychiatry
88. Teipel SJ, Meindl T, Grinberg L, et al. The cholinergic system in mild cognitive
impairment and Alzheimer’s disease: an in vivo MRI and DTI study. Hum Brain
89. Basser PJ, Jones DK. Diffusion-tensor MRI: theory, experimental design and
data analysis—a technical review. NMR Biomed 2002;15(7–8):456–67.
Teipel et al
90. Bozzali M, Cherubini A. Diffusion tensor MRI to investigate dementias: a brief
review. Magn Reson Imaging 2007;25(6):969–77.
91. Chua TC, Wen W, Slavin MJ, et al. Diffusion tensor imaging in mild cognitive
impairment and Alzheimer’s disease: a review. Curr Opin Neurol 2008;21(1):
92. Hess CP. Update on diffusion tensor imaging in Alzheimer’s disease. Magn Re-
son Imaging Clin N Am 2009;17(2):215–24.
93. Jones DK, Leemans A. Diffusion tensor imaging. Methods Mol Biol 2011;711:
94. Giannelli M, Belmonte G, Toschi N, et al. Technical note: DTI measurements of
fractional anisotropy and mean diffusivity at 1.5 T: comparison of two radiofre-
quency head coils with different functional designs and sensitivities. Med
95. Jones DK. Studying connections in the living human brain with diffusion MRI.
96. Medina D, DeToledo-Morrell L, Urresta F, et al. White matter changes in mild
cognitive impairment and AD: a diffusion tensor imaging study. Neurobiol Aging
97. Liu Y, Spulber G, Lehtimaki KK, et al. Diffusion tensor imaging and tract-based
spatial statistics in Alzheimer’s disease and mild cognitive impairment. Neuro-
biol Aging 2011;32(9):1558–71.
98. O’Dwyer L, Lamberton F, Bokde AL, et al. Multiple indices of diffusion identifies
white matter damage in mild cognitive impairment and Alzheimer’s disease.
PLoS One 2011;6(6):e21745.
99. Fjell AM, Amlien IK, Westlye LT, et al. Mini-mental state examination is sensitive
to brain atrophy in Alzheimer’s disease. Dement Geriatr Cogn Disord 2009;
100. Heo JH, Lee ST, Kon C, et al. White matter hyperintensities and cognitive
dysfunction in Alzheimer disease. J Geriatr Psychiatry Neurol 2009;22(3):
101. Teipel SJ, Meindl T, Wagner M, et al. Longitudinal changes in fiber tract integrity
in healthy aging and mild cognitive impairment: a DTI follow-up study. J Alz-
heimers Dis 2010;22(2):507–22.
102. Chua TC, Wen W, Chen X, et al. Diffusion tensor imaging of the posterior cingu-
late is a useful biomarker of mild cognitive impairment. Am J Geriatr Psychiatry
103. Takahashi S, Yonezawa H, Takahashi J, et al. Selective reduction of diffusion
anisotropy in white matter of Alzheimer disease brains measured by 3.0 Tesla
magnetic resonance imaging. Neurosci Lett 2002;332(1):45–8.
104. Zhang L, Dean D, Liu JZ, et al. Quantifying degeneration of white matter in
normal aging using fractal dimension. Neurobiol Aging 2007;28(10):1543–55.
105. Zhuang L, Wen W, Zhu W, et al. White matter integrity in mild cognitive impair-
ment: a tract-based spatial statistics study. Neuroimage 2010;53(1):16–25.
106. Douaud G, Jbabdi S, Behrens TE, et al. DTI measures in crossing-fibre areas:
increased diffusion anisotropy reveals early white matter alteration in MCI and
mild Alzheimer’s disease. Neuroimage 2011;55(3):880–90.
107. Ennis DB, Kindlmann G. Orthogonal tensor invariants and the analysis of diffu-
sion tensor magnetic resonance images. Magn Reson Med 2006;55(1):136–46.
108. Stricker NH, Schweinsburg BC, Delano-Wood L, et al. Decreased white matter
integrity in late-myelinating fiber pathways in Alzheimer’s disease supports ret-
rogenesis. Neuroimage 2009;45(1):10–6.
MRI for Diagnosis of Alzheimer Disease
109. Teipel SJ, Stahl R, Dietrich O, et al. Multivariate network analysis of fiber tract
integrity in Alzheimer’s disease. Neuroimage 2007;34(3):985–95.
110. Sexton CE, Kalu UG, Filippini N, et al. A meta-analysis of diffusion tensor
imaging in mild cognitive impairment and Alzheimer’s disease. Neurobiol Aging
111. Teipel SJ, Wegrzyn M, Meindl T, et al. Anatomical MRI and DTI in the diagnosis
of Alzheimer’s disease: a European multicenter Study. J Alzheimers Dis 2012;
112. Alexander AL, Hasan KM, Lazar M, et al. Analysis of partial volume effects in
diffusion-tensor MRI. Magn Reson Med 2001;45(5):770–80.
113. Wedeen VJ, Wang RP, Schmahmann JD, et al. Diffusion spectrum magnetic
resonance imaging (DSI) tractography of crossing fibers. Neuroimage 2008;
114. Fieremans E, Jensen JH, Helpern JA. White matter characterization with diffu-
sional kurtosis imaging. Neuroimage 2011;58(1):177–88.
115. Hui ES, Cheung MM, Qi L, et al. Advanced MR diffusion characterization of
neural tissue using directional diffusion kurtosis analysis. Conf Proc IEEE Eng
Med Biol Soc 2008;2008:3941–4.
116. Jensen JH, Helpern JA. Progress in diffusion-weighted imaging: concepts,
techniques and applications to the central nervous system. NMR Biomed
117. Alexander DC. Multiple-fiber reconstruction algorithms for diffusion MRI. Ann N
Y Acad Sci 2005;1064:113–33.
118. Assaf Y, Blumenfeld-Katzir T, Yovel Y, et al. AxCaliber: a method for measuring
axon diameter distribution from diffusion MRI. Magn Reson Med 2008;59(6):
119. Assaf Y, Basser PJ. Composite hindered and restricted model of diffusion
(CHARMED) MR imaging of the human brain. Neuroimage 2005;27(1):48–58.
120. Alexander DC, Hubbard PL, Hall MG, et al. Orientationally invariant indices of
axon diameter and density from diffusion MRI. Neuroimage 2010;52(4):
121. De Santis S, Gabrielli A, Bozzali M, et al. Anisotropic anomalous diffusion as-
sessed in the human brain by scalar invariant indices. Magn Reson Med
122. De Santis S, Gabrielli A, Palombo M, et al. Non-Gaussian diffusion imaging:
a brief practical review. Magn Reson Imaging 2011;29(10):1410–6.
123. Mintun MA, Larossa GN, Sheline YI, et al. [11C]PIB in a nondemented popula-
tion: potential antecedent marker of Alzheimer disease. Neurology 2006;67(3):
124. Iraji A, Davoodi-Bojd E, Soltanian-Zadeh H, et al. Diffusion kurtosis imaging
discriminates patients with white matter lesions from healthy subjects. Conf
Proc IEEE Eng Med Biol Soc 2011;2011:2796–9.
125. Falangola MF, Jensen JH, Babb JS, et al. Age-related non-Gaussian diffusion
patterns in the prefrontal brain. J Magn Reson Imaging 2008;28(6):1345–50.
126. Wang JJ, Lin WY, Lu CS, et al. Parkinson disease: diagnostic utility of diffusion
kurtosis imaging. Radiology 2011;261(1):210–7.
127. Bookheimer SY, Strojwas MH, Cohen MS, et al. Patterns of brain activation in
people at risk for Alzheimer’s disease. N Engl J Med 2000;343(7):450–6.
128. Borghesani PR, Johnson LC, Shelton AL, et al. Altered medial temporal lobe
responses during visuospatial encoding in healthy APOE*4 carriers. Neurobiol
Teipel et al
129. Filippini N, MacIntosh BJ, Hough MG, et al. Distinct patterns of brain activity in
young carriers of the APOE-epsilon4 allele. Proc Natl Acad Sci U S A 2009;
130. Golby A, Silverberg G, Race E, et al. Memory encoding in Alzheimer’s disease:
an fMRI study of explicit and implicit memory. Brain 2005;128(Pt 4):773–87.
131. Gron G, Bittner D, Schmitz B, et al. Hippocampal activations during repetitive
learning and recall of geometric patterns. Learn Mem 2001;8(6):336–45.
132. Hamalainen A, Pihlajamaki M, Tanila H, et al. Increased fMRI responses during
encoding in mild cognitive impairment. Neurobiol Aging 2007;28(12):1889–903.
133. Sperling R. Functional MRI studies of associative encoding in normal aging,
mild cognitive impairment, and Alzheimer’s disease. Ann N Y Acad Sci 2007;
134. Sperling RA, Dickerson BC, Pihlajamaki M, et al. Functional alterations in
memory networks in early Alzheimer’s disease. Neuromolecular Med 2010;
135. Celone KA, Calhoun VD, Dickerson BC, et al. Alterations in memory networks in
mild cognitive impairment and Alzheimer’s disease: an independent component
analysis. J Neurosci 2006;26(40):10222–31.
136. Johnson SC, Schmitz TW, Moritz CH, et al. Activation of brain regions vulnerable
to Alzheimer’s disease: the effect of mild cognitive impairment. Neurobiol Aging
137. Petrella JR, Wang L, Krishnan S, et al. Cortical deactivation in mild cognitive
impairment: high-field-strength functional MR imaging. Radiology 2007;245(1):
138. Satterthwaite TD, Green L, Myerson J, et al. Dissociable but inter-related
systems of cognitive control and reward during decision making: evidence
from pupillometry and event-related fMRI. Neuroimage 2007;37(3):1017–31.
139. Vannini P, Hedden T, Becker JA, et al. Age and amyloid-related alterations in
default network habituation to stimulus repetition. Neurobiol Aging 2012;33(7):
140. Fleisher AS, Sherzai A, Taylor C, et al. Resting-state BOLD networks versus task-
associated functional MRI for distinguishing Alzheimer’s disease risk groups.
141. Pihlajamaki M, O’Keefe K, O’Brien J, et al. Failure of repetition suppression and
memory encoding in aging and Alzheimer’s disease. Brain Imaging Behav 2011;
142. Pihlajamaki M, O’Keefe K, Bertram L, et al. Evidence of altered posteromedial
cortical FMRI activity in subjects at risk for Alzheimer disease. Alzheimer Dis As-
soc Disord 2010;24(1):28–36.
143. Pihlajamaki M, DePeau KM, Blacker D, et al. Impaired medial temporal repeti-
tion suppression is related to failure of parietal deactivation in Alzheimer
disease. Am J Geriatr Psychiatry 2008;16(4):283–92.
144. Greicius MD, Srivastava G, Reiss AL, et al. Default-mode network activity distin-
guishes Alzheimer’s disease from healthy aging: evidence from functional MRI.
Proc Natl Acad Sci U S A 2004;101(13):4637–42.
145. Greicius MD, Supekar K, Menon V, et al. Resting-state functional connectivity
reflects structural connectivity in the default mode network. Cereb Cortex
146. Sorg C, Riedl V, Muhlau M, et al. Selective changes of resting-state networks in
individuals at risk for Alzheimer’s disease. Proc Natl Acad Sci U S A 2007;
MRI for Diagnosis of Alzheimer Disease
147. Wang K, Liang M, Wang L, et al. Altered functional connectivity in early
Alzheimer’s disease: a resting-state fMRI study. Hum Brain Mapp 2007;28(10):
148. Wermke M, Sorg C, Wohlschlager AM, et al. A new integrative model of cerebral
activation, deactivation and default mode function in Alzheimer’s disease. Eur J
Nucl Med Mol Imaging 2008;35(Suppl 1):S12–24.
149. Petrella JR, Sheldon FC, Prince SE, et al. Default mode network connectivity
in stable vs progressive mild cognitive impairment. Neurology 2011;76(6):
150. Kukolja J, Thiel CM, Fink GR. Cholinergic stimulation enhances neural activity
associated with encoding but reduces neural activity associated with retrieval
in humans. J Neurosci 2009;29(25):8119–28.
151. Goekoop R, Scheltens P, Barkhof F, et al. Cholinergic challenge in Alzheimer
patients and mild cognitive impairment differentially affects hippocampal activa-
tion—a pharmacological fMRI study. Brain 2006;129(Pt 1):141–57.
152. Rombouts SA, Barkhof F, Van Meel CS, et al. Alterations in brain activation
during cholinergic enhancement with rivastigmine in Alzheimer’s disease.
J Neurol Neurosurg Psychiatr 2002;73(6):665–71.
153. Saykin AJ, Wishart HA, Rabin LA, et al. Cholinergic enhancement of frontal lobe
activity in mild cognitive impairment. Brain 2004;127(Pt 7):1574–83.
154. Shanks MF, McGeown WJ, Forbes-McKay KE, et al. Regional brain activity after
prolonged cholinergic enhancement in early Alzheimer’s disease. Magn Reson
155. Sperling R. Potential of functional MRI as a biomarker in early Alzheimer’s
disease. Neurobiol Aging 2011;32(Suppl 1):37–43.
156. Goldberger AL, Amaral LA, Hausdorff JM, et al. Fractal dynamics in physiology:
alterations with disease and aging. Proc Natl Acad Sci U S A 2002;99(Suppl 1):
157. Goldberger AL, Peng CK, Lipsitz LA. What is physiologic complexity and how
does it change with aging and disease? Neurobiol Aging 2002;23(1):23–6.
158. Andrzejak RG, Widman G, Lehnertz K, et al. The epileptic process as nonlinear
deterministic dynamics in a stochastic environment: an evaluation on mesial
temporal lobe epilepsy. Epilepsy Res 2001;44(2–3):129–40.
159. Osowski S, Swiderski B, Cichocki A, et al. Epileptic seizure characterization by
Lyapunov exponent of EEG signal. Comput Methods Programs Biomed 2007;
160. Stam CJ, Montez T, Jones BF, et al. Disturbed fluctuations of resting state EEG
synchronization in Alzheimer’s disease. Clin Neurophysiol 2005;116(3):708–15.
161. Kiselev VG, Hahn KR, Auer DP. Is the brain cortex a fractal? Neuroimage 2003;
162. Zhang L, Liu JZ, Dean D, et al. A three-dimensional fractal analysis method for
quantifying white matter structure in human brain. J Neurosci Methods 2006;
163. Esteban FJ, Sepulcre J, de Mendizabal NV, et al. Fractal dimension and white
matter changes in multiple sclerosis. Neuroimage 2007;36(3):543–9.
164. Esteban FJ, Padilla N, Sanz-Cortes M, et al. Fractal-dimension analysis detects
cerebral changes in preterm infants with and without intrauterine growth restric-
tion. Neuroimage 2010;53(4):1225–32.
165. King RD, George AT, Jeon T, et al. Characterization of atrophic changes in the
cerebral cortex using fractal dimensional analysis. Brain Imaging Behav 2009;
Teipel et al
166. King RD, Brown B, Hwang M, et al. Fractal dimension analysis of the cortical
ribbon in mild Alzheimer’s disease. Neuroimage 2010;53(2):471–9.
167. Takahashi T, Murata T, Narita K, et al. Multifractal analysis of deep white matter
microstructural changes on MRI in relation to early-stage atherosclerosis. Neu-
168. Takahashi T, Murata T, Omori M, et al. Quantitative evaluation of age-related
white matter microstructural changes on MRI by multifractal analysis. J Neurol
169. Maxim V, Sendur L, Fadili J, et al. Fractional Gaussian noise, functional MRI and
Alzheimer’s disease. Neuroimage 2005;25(1):141–58.
measurements in old age. IEEE Trans Biomed Eng 2011;58(11):3206–14.
171. Pievani M, de Haan W, Wu T, et al. Functional network disruption in the degen-
erative dementias. Lancet Neurol 2011;10(9):829–43.
172. Ewers M, Frisoni GB, Teipel SJ, et al. Staging Alzheimer’s disease progression
with multimodality neuroimaging. Prog Neurobiol 2011;95(4):535–46.
173. Landman BA, Huang AJ, Gifford A, et al. Multi-parametric neuroimaging repro-
ducibility: a 3-T resource study. Neuroimage 2011;54(4):2854–66.
174. Casanova R, Srikanth R, Baer A, et al. Biological parametric mapping: a statis-
tical toolbox for multimodality brain image analysis. Neuroimage 2007;34(1):
175. Oakes TR, Fox AS, Johnstone T, et al. Integrating VBM into the General Linear
Model with voxelwise anatomical covariates. Neuroimage 2007;34(2):500–8.
176. Yang X, Beason-Held L, Resnick SM, et al. Biological parametric mapping with
robust and non-parametric statistics. Neuroimage 2011;57(2):423–30.
177. Teipel SJ, Born C, Ewers M, et al. Multivariate deformation-based analysis of
brain atrophy to predict Alzheimer’s disease in mild cognitive impairment. Neu-
178. Plant C, Teipel SJ, Oswald A, et al. Automated detection of brain atrophy
patterns based on MRI for the prediction of Alzheimer’s disease. Neuroimage
179. Davatzikos C, Fan Y, Wu X, et al. Detection of prodromal Alzheimer’s disease via
pattern classification of MRI. Neurobiol Aging 2008;29(4):514–23.
180. Orru G, Pettersson-Yeo W, Marquand AF, et al. Using support vector machine to
identify imaging biomarkers of neurological and psychiatric disease: a critical
review. Neurosci Biobehav Rev 2012;36(4):1140–52.
181. Misra C, Fan Y, Davatzikos C. Baseline and longitudinal patterns of brain
atrophy in MCI patients, and their use in prediction of short-term conversion
to AD: results from ADNI. Neuroimage 2009;44(4):1415–22.
182. Davatzikos C, Xu F, An Y, et al. Longitudinal progression of Alzheimer’s-like
patterns of atrophy in normal older adults: the SPARE-AD index. Brain 2009;
183. Zhang D, Shen D. Alzheimer’s disease neuroimaging I. Multi-modal multi-task
learning for joint prediction of multiple regression and classification variables
in Alzheimer’s disease. Neuroimage 2012;59(2):895–907.
184. Zhang D, Shen D. Alzheimer’s disease neuroimaging I. Predicting future clinical
changes of MCI patients using longitudinal and multimodal biomarkers. PLoS
185. Teipel SJ, Ewers M, Wolf S, et al. Multicentre variability of MRI-based medial
temporal lobe volumetry in Alzheimer’s disease. Psychiatry Res 2011;182(3):
MRI for Diagnosis of Alzheimer Disease
186. Giannakopoulos P, Herrmann FR, Bussiere T, et al. Tangle and neuron numbers, Download full-text
but not amyloid load, predict cognitive status in Alzheimer’s disease. Neurology
187. Smith AD. Commentary: imaging the progression of Alzheimer pathology
through the brain. Proc Natl Acad Sci U S A 2002;99(7):4135–7.
188. Teipel SJ, Bokde AL, Born C, et al. The morphological substrate of face match-
ing in healthy aging and mild cognitive impairment: a combined MRI-fMRI study.
Brain 2007;130(Pt 7):1745–58.
189. Jack CR Jr, Petersen RC, Xu YC, et al. Medial temporal atrophy on MRI in normal
aging and very mild Alzheimer’s disease. Neurology 1997;49:786–94.
190. Wang PN, Lirng JF, Lin KN, et al. Prediction of Alzheimer’s disease in mild cogni-
tive impairment: a prospective study in Taiwan. Neurobiol Aging 2006;27(12):
191. Barnes J, Bartlett JW, van de Pol LA, et al. A meta-analysis of hippocampal
atrophy rates in Alzheimer’s disease. Neurobiol Aging 2009;30(11):1711–23.
192. Raz N, Rodrigue KM, Head D, et al. Differential aging of the medial temporal
lobe: a study of a five-year change. Neurology 2004;62(3):433–8.
193. Rusinek H, De Santi S, Frid D, et al. Regional brain atrophy rate predicts future
cognitive decline: 6-year longitudinal MR imaging study of normal aging. Radi-
194. Kovacevic S, Rafii MS, Brewer JB. High-throughput, fully automated volumetry
for prediction of MMSE and CDR decline in mild cognitive impairment. Alz-
heimer Dis Assoc Disord 2009;23(2):139–45.
195. Antharam V, Collingwood JF, Bullivant JP, et al. High field magnetic resonance
microscopy of the human hippocampus in Alzheimer’s disease: quantitative
imaging and correlation with iron. Neuroimage 2012;59(2):1249–60.
196. Pluta J, Yushkevich P, Das S, et al. In vivo analysis of hippocampal subfield
atrophy in mild cognitive impairment via semi-automatic segmentation of T2-
weighted MRI. J Alzheimers Dis 2012;31(1):85–99.
197. Stoub TR, Bulgakova M, Leurgans S, et al. MRI predictors of risk of incident Alz-
heimer disease: a longitudinal study. Neurology 2005;64(9):1520–4.
198. Devanand DP, Liu X, Tabert MH, et al. Combining early markers strongly
predicts conversion from mild cognitive impairment to Alzheimer’s disease.
Biol Psychiatry 2008;64(10):871–9.
199. Ezekiel F, Chao L, Kornak J, et al. Comparisons between global and focal brain
atrophy rates in normal aging and Alzheimer disease: boundary Shift Integral
versus tracing of the entorhinal cortex and hippocampus. Alzheimer Dis Assoc
200. Fox NC, Cousens S, Scahill R, et al. Using serial registered brain magnetic reso-
nance imaging to measure disease progression in Alzheimer disease: power
calculations and estimates of sample size to detect treatment effects. Arch Neu-
201. Schott JM, Price SL, Frost C, et al. Measuring atrophy in Alzheimer disease:
a serial MRI study over 6 and 12 months. Neurology 2005;65(1):119–24.
202. Smith CD, Chebrolu H, Wekstein DR, et al. Brain structural alterations before
mild cognitive impairment. Neurology 2007;68(16):1268–73.
203. Thompson PM, Hayashi KM, Dutton RA, et al. Tracking Alzheimer’s disease. Ann
N Y Acad Sci 2007;1097:183–214.
204. Lerch JP, Pruessner JC, Zijdenbos A, et al. Focal decline of cortical thickness in
Alzheimer’s disease identified by computational neuroanatomy. Cereb Cortex
Teipel et al