Joint assessment of structural, perfusion, and diffusion MRI in Alzheimer's disease and frontotemporal dementia.
ABSTRACT Most MRI studies of Alzheimer's disease (AD) and frontotemporal dementia (FTD) have assessed structural, perfusion and diffusion abnormalities separately while ignoring the relationships across imaging modalities. This paper aimed to assess brain gray (GM) and white matter (WM) abnormalities jointly to elucidate differences in abnormal MRI patterns between the diseases. Twenty AD, 20 FTD patients, and 21 healthy control subjects were imaged using a 4 Tesla MRI. GM loss and GM hypoperfusion were measured using high-resolution T1 and arterial spin labeling MRI (ASL-MRI). WM degradation was measured with diffusion tensor imaging (DTI). Using a new analytical approach, the study found greater WM degenerations in FTD than AD at mild abnormality levels. Furthermore, the GM loss and WM degeneration exceeded the reduced perfusion in FTD whereas, in AD, structural and functional damages were similar. Joint assessments of multimodal MRI have potential value to provide new imaging markers for improved differential diagnoses between FTD and AD.
[show abstract] [hide abstract]
ABSTRACT: Imaging plays an increasingly important role in both clinical practice and research in Alzheimer's disease. Clinically, there is growing appreciation that imaging provides not just exclusion of alternative pathologies, but also positive predictive, diagnostic, and prognostic information in dementia. Imaging can improve specificity of diagnosis in trial populations, facilitate research on the earlier stages of disease, and provide crucial information regarding drug safety and toxicity. With the advent of disease-modifying therapies, these properties acquire increasing importance. Furthermore, imaging biomarkers have the potential to serve as outcome measures of disease progression. A whole arsenal of imaging modalities has now been developed, each allowing a different aspect of the disease process to be explored. However, the limitations of each technique must also be appreciated. A multimodal approach, where imaging markers are combined, may be required to maximize the potential of imaging to enhance our understanding of Alzheimer's disease and to help find effective therapies.Annals of the New York Academy of Sciences 10/2009; 1180:20-7. · 3.15 Impact Factor
Article: In vivo mapping of gray matter loss with voxel-based morphometry in mild Alzheimer's disease.[show abstract] [hide abstract]
ABSTRACT: Up till now, the study of regional gray matter atrophy in Alzheimer's disease (AD) has been assessed with regions of interest, but this method is time-consuming, observer dependent, and poorly reproducible (especially in terms of cortical regions boundaries) and in addition is not suited to provide a comprehensive assessment of the brain. In this study, we have mapped gray matter density by means of voxel-based morphometry on T1-weighted MRI volume sets in 19 patients with mild AD and 16 healthy subjects of similar age and gender ratio and report highly significant clusters of gray matter loss with almost symmetrical distribution, affecting mainly and in decreasing order of significance the medial temporal structures, the posterior cingulate gyrus and adjacent precuneus, and the temporoparietal association and perisylvian neocortex, with only little atrophy in the frontal lobe. The findings are discussed in light of previous studies of gray matter atrophy in AD based either on postmortem or neuroimaging data and in relation to PET studies of resting glucose consumption. The limitations of the method are also discussed in some detail, especially with respect to the segmentation and spatial normalization procedures as they apply to pathological brains. Some potential applications of voxel-based morphometry in the study of AD are also mentioned.NeuroImage 09/2001; 14(2):298-309. · 5.89 Impact Factor
Article: Comparison of gray matter and metabolic reduction in mild Alzheimer's disease using FDG-PET and voxel-based morphometric MR studies.[show abstract] [hide abstract]
ABSTRACT: The aim of this study was to investigate regional differences between morphologic and functional changes in the same patients with mild Alzheimer's disease (AD) using statistical parametric mapping (SPM) and voxel-based morphometry (VBM). Thirty patients with very mild AD (mean age 66.8 years, mean MMSE score 24.0) and 30 age- and sex-matched normal volunteers underwent both( 18)F-fluorodeoxyglucose (FDG) positron emission tomography (PET) and three-dimensional spoiled gradient echo (SPGR) magnetic resonance imaging (MRI). Statistical parametric mapping was used to conduct VBM analysis of the morphological data, which were compared voxel by voxel with the results of a similar analysis of the glucose metabolic data. In AD patients, VBM data indicated a significant gray matter volume density decrease in bilateral amygdala/hippocampus complex (p < 0.05, corrected), while FDG-PET analysis showed significant glucose metabolic reductions in the posterior cingulate gyri and the right parietal lobule, compared with those in the normal control group. In very mild AD, morphological change occurs in the medial temporal lobes, while in contrast, metabolic changes occur in the posterior cingulate gyri and parietal lobule.European journal of nuclear medicine and molecular imaging 08/2005; 32(8):959-63. · 4.99 Impact Factor
SAGE-Hindawi Access to Research
International Journal of Alzheimer’s Disease
Volume 2011, Article ID 546871, 11 pages
JointAssessmentof Structural, Perfusion,and DiffusionMRI in
Yu Zhang,1,2Norbert Schuff,1,2Christopher Ching,1,2DuyguTosun,1,2Wang Zhan,1,2
Marzieh Nezamzadeh,1,2Howard J. Rosen,3Joel H. Kramer,3Maria Luisa Gorno-Tempini,3
Bruce L.Miller,3andMichaelW. Weiner1,2,3
1Center for Imaging of Neurodegenerative Diseases, Department of Veterans Affairs San Francisco VA, Medical Center,
4150, Clement Street, San Francisco, CA 94121, USA
2Department of Radiology, University of California, San Francisco, CA 94143, USA
3Department of Neurology, University of California, San Francisco, CA 94143, USA
Correspondence should be addressed to Yu Zhang, firstname.lastname@example.org
Received 29 November 2010; Accepted 26 April 2011
Academic Editor: Katsuya Urakami
Copyright © 2011 Yu Zhang et al. This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Most MRI studies of Alzheimer’s disease (AD) and frontotemporal dementia (FTD) have assessed structural, perfusion and
diffusion abnormalities separately while ignoring the relationships across imaging modalities. This paper aimed to assess brain
gray (GM) and white matter (WM) abnormalities jointly to elucidate differences in abnormal MRI patterns between the diseases.
Twenty AD, 20 FTD patients, and 21 healthy control subjects were imaged using a 4Tesla MRI. GM loss and GM hypoperfusion
tensor imaging (DTI). Using a new analytical approach, the study found greater WM degenerations in FTD than AD at mild
abnormality levels. Furthermore, the GM loss and WM degeneration exceeded the reduced perfusion in FTD whereas, in AD,
structuraland functional damages were similar.Jointassessments ofmultimodal MRIhave potential value to providenew imaging
markers for improved differential diagnoses between FTD and AD.
Alzheimer’s disease (AD) and frontotemporal dementia
(FTD) are two of the most common and devastating dis-
orders that result in dementia in the elderly population.
Although the definitive diagnosis of each type of dementia
is not possible until autopsy, biomarkers based on magnetic
resonance imaging (MRI), providing measurements of brain
volume, perfusion, and white matter integrity, have been
promising for improved diagnosis and prediction of demen-
tia progression . In AD, which is a progressive dementing
disorder associated with cognitive impairments beginning
with episodic memory deficits, MRI measurements of brain
volume have shown characteristic gray matter (GM) loss pri-
marily in medial temporal lobe regions [2, 3] whereas func-
imaging have shown prominent changes primarily in the
eral temporoparietal areas) [4–7], though regions of struc-
tural, and functional alterations can overlap. Furthermore,
diffusion tensor imaging (DTI), a unique method to assess
the integrity of white matter microstructure, have revealed
white matter (WM) alterations in AD, involving the parietal,
temporal, and frontal brain regions [8–13]. In FTD, which is
associated with impairments of social behaviors, personality,
and executive functions, MRI has shown characteristic pat-
terns of structural GM loss [14, 15] and GM dysfunction
[16–20] primarily in frontal and anterior temporal lobes.
WM volume loss [21, 22] and WM degradation in FTD
[23–25] have also been reported in the frontal and temporal
Using biomarkers of neurocognitive measurements to
differentiate between AD and FTD are often difficult because
of overlapping symptoms. Several studies using imaging
markers compared differences in abnormal brain patterns
between AD and FTD directly. Structural MRI showed that
2 International Journal of Alzheimer’s Disease
AD was associated with greater GM loss than FTD in pos-
terior brain regions [26, 27] whereas FTD was associated
with more severe GM loss than was AD in frontal brain
regions [26, 28–30]. Similarly, functional imaging such as
PET/SPECT and perfusion MRI showed that AD was associ-
ated with greater reduced cerebral blood flow or metabolism
than FTD in parietal and occipital brain regions [16, 28,
30–32]; whereas FTD was associated with greater frontal
dysfunction than AD [16, 30, 32]. In addition to differences
in GM, we have previously reported differences in WM be-
tween AD and FTD . Specifically, measurements of
fractional anisotropy (FA)—a summary measure of DTI
indexing WM integrity—indicated greater WM degradation
(FA reduction) of frontal brain regions in FTD compared
to AD. Furthermore, no brain region in AD was shown to
have more WM degradation when compared to FTD. Taken
together, these findings suggest that AD and FTD are each
associated with disease-specific regional patterns of GM and
WM alterations. Recent multimodality strategies [33, 34]
of combined radiological markers such as analyzing brain
volume and perfusion or WM changes together have shown
superior power than that using conventional single modality
of AD. However, to our knowledge, rarely did MRI studies
evaluate GM and WM differences between AD and FTD
In this study we present a new approach to compare
structural, perfusion, and diffusion alterations between AD
and FTD using T1-weighted high-resolution structural MRI,
arterial spin-labeled perfusion MRI (ASL-MRI), and DTI.
The objective was to determine if a joint evaluation of
multimodal MRI could provide a biomarker for a differential
diagnosis between AD and FTD.
2.1. Subjects. Twenty AD patients (mean age and standard
deviation: 63.1±6.9 yrs) with a Mini-Mental State Examina-
tion (MMSE)  score of on average 21.9 ±5.6, 20 patients
with FTD (age: 60.7 ± 9.9 yrs; MMSE: 23.1 ± 5.6) and 21
29.6 ± 0.5) were included in this cross-sectional MRI study.
information are listed in Table 1. The patients with FTD and
AD were recruited from the Memory and Aging Center of
the University of California, San Francisco. All patients were
diagnosed based on information obtained from an extensive
clinical history and physical examination. The MR images
were used to rule out other major neuropathologies such as
tumors, strokes, or inflammation but not to diagnose de-
mentia. The subjects were included in the study if they
were between 30–80 years old and without history of brain
trauma, brain tumor, stroke, epilepsy, alcoholism, psychi-
atric illness, or other systemic diseases that affect brain
function. FTD was diagnosed according to the consensus
criteria established by Neary et al. . All FTD patients
were diagnosed with the frontal variant subtype, two of
which had combined motor neuron-related symptoms. AD
patients were diagnosed according to the criteria of the
National Institute of Neurological and Communicative Dis-
Association (NINCDS/ADRDA) . All subjects received
a standard battery of neuropsychological tests including
assessment of global cognitive impairment using MMSE and
global functional impairment using the Clinical Dementia
blood drawn for APOE genotyping. Reliable information
of 20 AD patients and from all 20 FTD patients. Because it is
not unusual for subjects in this age group to have WM signal
hyperintensities (WMSH) on MRI, subjects with WMSH
were included. An experienced radiologist reviewed all MRI
data, and the scores of WMSH were used as covariates in
the analysis. The severity of WMSH was classified as mild
(deep white matter lesions ≤3mm, and periventricular hy-
perintensities <5mm thickness), moderate (deep white
tensities between 6–10mm thickness), or severe (deep white
matter lesions >10mm, or periventricular hyperintensities
>10mm thickness), according to the Scheltens’ rating scale
. All subjects or their legal guardians gave written in-
formed consent before participating in the study, which was
approved by the Committees of Human Research at the
University of California and the VA Medical Center at San
2.2. Data Acquisition. All scans were preformed on a 4
Tesla (Bruker/Siemens) MRI system with a single housing
birdcage transmit and 8-channel receive coil. T1-weighted
images were obtained using a 3D volumetric magnetization
prepared rapid gradient echo (MPRAGE) sequence with TR/
TE/TI = 2300/3/950ms, 7-degree flip angle, 1.0 × 1.0
× 1.0mm3resolution, 157 continuous sagittal slices. In
with timing TR/TE/TI = 5000/355/1900ms were acquired to
facilitate the evaluation of WMSH. Perfusion images were
acquired using a continuous arterial spin labeling (cASL)
sequence  with a single-shot echo-planar imaging (EPI)
part to map the perfusion signal. cASL-MRI was performed
with TR/TE = 5200/9ms with 2-second long labeling pulses
slice thickness and 1.2mm interslice gap, 3.75 × 3.75mm2
in-plane resolution were acquired. DTI was acquired based
on a dual spin-echo EPI sequence supplemented with two-
fold parallel imaging acceleration (GRAPPA)  to reduce
susceptibility distortions. Other imaging parameters were
TR/TE = 6000/77ms, field of view 256 × 224cm, 128 ×
112 matrix size, yielding 2 × 2mm2in-plane resolution, 40
slices each 3mm thick. One reference image (b = 0) and
six diffusion-weighted images (b = 800s/mm2, along 6
noncollinear directions) were acquired.
2.3. Data Analyses. The assessment of brain volume changes
were performed using SPM8 software (http://www.fil.ion
.ucl.ac.uk/spm/software/spm8/) based on an “optimized”
VBM procedure described by Ashburner and Friston .
The procedure included several steps. (1) Tissue segmen-
tation: An expectation maximization segmentation (EMS)
International Journal of Alzheimer’s Disease3
Table 1: Demographic and clinical data summary.
20Number of subjects
Age range (years)
Years of Education (years)
APOE-ε4 (carriers: non-carriers)
Age of onset (years)
Symptom duration (years)
avalue for one subject is missing.
bvalue for two subjects is missing.
cvalue for 8 subjects is missing.
WMSH = white matter signal hyperintensities.
60.7 ± 9.9
16.2 ± 3.2
23.1 ± 5.6
55.1 ± 9.9
algorithm  was applied to obtain probabilistic maps
of GM, WM, and CSF from the T1-weighted MRI data.
(2) Spatial normalization: first, customized GM and WM
prior images were created by transforming GM and WM
probabilistic maps of all subjects into the standard MNI
(Montreal Neurological Institute) space . The segmented
GM probabilistic maps in their native spaces were then
using a nonlinear transformation with 16 interactions. (3)
Jacobian modulation: the spatially normalized GM images
were multiplied by the Jacobian determinants of the trans-
formation (modulation) to obtain volume differences. (4)
Smoothing: the modulated GM images were smoothed with
an 8mm full-width-at-half-maximum (FWHM) isotropic
Gaussian kernel to reduce variations from misregistrations
and to perform voxelwise image statistics.
ing steps. (1) Cerebral blood flow (CBF) image calculation:
a perfusion weighted (PWI) image was created by pairwise
subtraction of coregistered labeled from unlabeled ASL im-
ages. The PWI images were then scaled to obtain a quan-
titative CBF image based on a single compartment perfusion
model . (2) Intermodality coregistration: ALS perfusion
and the corresponding T2- and T1-weighted image were
coregistered using an affine alignment to establish anatomi-
cal correspondence between CBF and segmented GM images
and to compute partial volume-corrected CBF in GM.
(3) Partial volume correction: partial volume correction of
CBF images was performed by rescaling CBF in each voxel
proportionately to the GM and WM content, assuming that
perfusion of white matter is only 25% of that of GM, as
detailed by Du et al. . (4) Spatial normalization: the
partial volume-corrected CBF images in GM were spatially
normalized to the customized GM prior image that was
nonlinear transformation and smoothing parameters.
The assessment of white matter integrity was performed
based on DTI and computation of fractional anisotropy
(FA) maps, using the dTV.II software  and Volume-one
software package (URL: http://www.ut-radiology.umin.jp/
people/masutani/dTV.htm), supplemented by automatic
image denoising and eddy-current corrections. SPM8 soft-
ware was used for voxelwise analysis of the FA images as
outlined in detail elsewhere . In brief, the FA images
in the native space underwent the following procedures. (1)
Creation of a customized FA template: an averaged FA image
was first created from all subjects’ FA images that initially
transformed to the EPI-derived MNI template in SPM. This
image using affine alignment and manual adjustment  to
archive a customized FA template that accurately correspond
to the anatomical information in the WM prior image.
(2) Spatial normalization and smoothing: the FA image of
each subject in the native space was recursively normalized
to the customized FA template using the same nonlinear
transformation and smoothing parameters that were applied
in the previous VBM procedure.
2.4. Statistics. Paired group differences were evaluated voxel-
by-voxel using a general linear model with diagnosis as main
In addition, total intracranial volume (TIV) was used as a
covariate in test for volume differences and global mean
perfusion was used as a covariate in test for perfusion dif-
ferences. A threshold of at least 85% GM volume fraction
in voxels was applied to restrict GM volume and perfusion
restrict FA analysis to voxels containing predominantly WM.
The statistical significance for main effects of abnormalities,
which was only the prestep for joint analyses, was set to an
uncorrected voxel-level P value of .001.
To test if FTDand ADdiffer withrespecttoWM and GM
abnormalities,westarted bydetermining thenumber of vox-
els representing “abnormal” values for each patient and each
MRI modality. This was conducted by recording voxelwise
differences between an individual’s image value (GM vol-
ume, GM perfusion, or WM FA) and the respective mean
4International Journal of Alzheimer’s Disease
value of the control group. The difference was then nor-
malized to the standard error of the control mean value to
express abnormality as a T-score . High T-scores rep-
resent high abnormalities beyond the normal ranges. Next,
the number of “abnormal” voxels above a T-score threshold
was recorded and normalized to the total number of GM or
WM voxels to account for each patient and each modality
to quantify the extent of brain abnormality. We termed the
number of normalized “abnormal” voxels load. Differences
in loads between AD and FTD patients were evaluated sta-
tistically via permutation test with 1000-fold completely
random resampling of the diagnostic labels (AD and FTD),
on the loads were evaluated for T-score values ranging from
−2 (indicating a mild abnormality level) to −6 (indicating a
assessed whether the load from a specific MRI modality (e.g.,
WM FA) relative to another (e.g., GM loss) modality, termed
conditional load, differed within and across the diagnosis
groups. The level of significance for permutation tests was
set at P = .05.
3.1. Demographic Clinical Data. As shown in Table 1, there
were no significant differences in age, sex, years of education,
and severity of WMSH between each patient group (AD or
FTD) and controls. AD and FTD patients had significant
lower MMSE (P < .001, by ANOVA test) scores compared
with controls, as expected. Furthermore, AD patients had a
greater proportion of APOE-ε4 carriers than CN (P = .04
by χ2test) but the FTD patients did not (P = .35 by χ2
test) when compared to CN. AD and FTD patients did not
MMSE, CDR, age of onset, symptom duration, and severity
of WMSH. To avoid further reductions in sample size due
to the missing values, we did not perform the joint analysis
including the age of onset or the APOE-ε4 genotyping as
covariates across all MRI modalities, although symptom
duration was associated with GM volume loss within the
3.2. Spatial Distribution of MRI Abnormalities in AD and
FTD Compared to CN. Figures 1(a)–1(d) depict the regional
(in green color), and reduced WM FA (in blue color) in AD
and FTD, compared to CN, respectively, as well as the direct
prior to the joint analysis. For better visualization of regional
relations, the various distributions are overlaid on each other
in Figure 1(d).
Compared to CN, AD patients showed widespread GM
loss in bilateral parietal and temporal lobes. The left tem-
poroparietal lobes had the most prominent GM loss. Other
regions of GM loss in AD included the posterior cingulate
gyrus, thalamus, and bilateral occipital lobes. Compared to
CN, FTD patients showed GM loss predominantly in bi-
lateral frontal and temporal lobes. The right frontoinsular
gyrus showed the most prominent GM loss. Other regions
of GM loss included limbic lobes such as bilateral anterior
cingulate gyrus, uncus, subcortical nuclei including the bi-
lateral caudate and the thalamus, and the lateral parietal
lobes. Comparing FTD and AD directly, patients with AD
showed more GM loss than FTD in bilateral occipital gyrus,
left precuneus whereas FTD showed more GM loss in bi-
lateral frontal lobes, including the orbital gyrus, inferior and
medial frontal gyrus, and anterior cingulate gyrus.
Regarding perfusion, AD patients showed reductions re-
lative to CN in bilateral temporoparietal lobes, including su-
perior temporal gyrus, precuneus and posterior cingulate
gyrus. Hypoperfusion in AD was most pronounced in the
left temporal gyrus. Compared to CN, FTD patients showed
reduced perfusion in bilateral frontal lobes, including infe-
rior, medial, and superior frontal gyrus, anterior cingulate
gyrus, and thalamus. Hypoperfusion in FTD was most pro-
nounced in the right inferior frontal gyrus. Compared to
FTD, AD patients showed significant hypoperfusion in the
left superior temporal gyrus, claustrum; whereas compared
to AD, FTD patients had significant hypoperfusion in right
superior, middle, and bilateral medial frontal gyrus.
Regarding WM FA, AD patients had FA reductions
relative to CN bilaterally in WM regions in parietal, tem-
poral, and some frontal lobe regions. The periventricular
deep WM, posterior corpus callosum and the left posterior
cingulum exhibited the most prominent FA reductions. In
to CN bilaterally in frontal and temporal lobes, and the
anterior corpus callosum, and bilateral anterior cingulum
were prominently involved. Compared to AD, FTD patients
had lower FA values bilaterally in frontal deep WM, anterior
corpus callosum and bilateral anterior cingulum, whereas
AD patients showed no region with significantly lower FA
values when compared to the FTD group.
3.3. Differences between AD and FTD in GM Volume,
Perfusion, or WM Damage. We first tested if loads differed
between AD and FTD. Figure 2(a) displays the loads of GM
loss (2A-I), GM hypoperfusion (2A-II), and WM FA (2A-
III) reduction, respectively, in AD and FTD over a range of
T-score levels (deviation from normal). The significance of
differences in the loads between AD and FTD as a function
of T-scores is plotted in Figure 2(b), and separately for each
type (GM Vol, GM Perf,WM FA) of load. Note, theloads and
the P values are plotted on a logarithmic scale and increasing
negative T-scores indicate increasing deviation from normal
values. This demonstrates that there is a significantly greater
load of WM FA reduction in FTD compared to AD at mild
abnormality levels (up to T-scores of about −2.3) while the
significance gradually vanishes at more severe abnormality
levels. There is a trend (P
GM loss in FTD compared to AD at moderate to severe
abnormalitylevels(T-scores< −4).However,theload ofGM
hypoperfusion does not differ significantly between AD and
FTD across the range of T-scores.
= .07 to .1) towards more
3.4. Joint Assessment of GM Volume, Perfusion, and WM FA
(the load in one MRI modality relative to another) over
International Journal of Alzheimer’s Disease5
FTD< CNAD< CN
FTD< CN AD< CN
Figure 1: Significance maps of systematic brain abnormalities in FTD and AD patients relative to control subjects (AD < CN and FTD <
CN), and direct comparisons between AD and FTD (AD < FTD and FTD < AD). (a) GM loss (warm color) and (b) GM hypoperfusion
(green color) overlaid on a surface rendered brain template. (c) Reduced WM FA (blue color) in AD or FTD overlaid on an axial brain
template. (d) Overlay of the abnormal distributions together. The significant threshold was Puncorrected < .001 for all voxelwise tests. Color
scales indicated ranges of significance (T-scores) upon the P threshold.
a range of abnormality levels (T-scores) separately for AD
(Figure 3(a)) and for FTD (Figure 3(b)). Note, the plots in
Figures 3(a) and 3(b) are on a logarithmic scale. Accordingly,
a conditional load larger than 1 indicates that the load of a
particular modality is greater than the load of the reference
modality, whereas a value smaller than 1 indicates that the
load of the reference is greater than particular modality. The
extent to which differences in one load (on particular mo-
dality) relative to another (on reference modality) are above
chance is depicted in Figures 3(c) and 3(d) for AD and FTD,
plotted on a logarithmic scale of the base-10 logarithm. The
loads of GM volume loss relative to GM hypoperfusion as a
function of severity (T-scores) are displayed in brown lines,
reduced WM FA relative to GM hypoperfusion in purple
lines, and GM volume loss relative to WM FA in red lines.
Figures 3(a) and 3(c) show that in AD, the loads of the
different modalities are not significantly different compared
to each other across the level of abnormalities (T-scores)
levels. In FTD, by contrast (Figures 3(b) and 3(d)), the
load of GM volume loss as well as the load of reduced
WM FA are each significantly greater relative to the load
of GM hypoperfusion at lower levels of abnormality (up
to T-scores = −4). However, as abnormality increases (T-
scores < −4), the significance of the difference in the load
of GM volume loss or WM FA reductions relative to GM
hypoperfusion gradually disappears. There are no significant
differences between the load of GM loss and the load of WM
3.5. Differences between AD and FTD in Joint Modalities.
Finally, we also tested if AD and FTD differed with regard
to their conditional loads from a particular modality relative
to that from reference modality but found no significant
difference between the groups (P > .1) across all ranges of
the abnormalities (T-score) levels.
6International Journal of Alzheimer’s Disease
−6 −5 −4 −3 −2
−6 −5 −4
−6 −5 −4 −3 −2
L oad of GM hypoperfusion
Load of GM volume loss
Load of WM FA reduction
P values (log)
Figure 2: Differences in loads of GM loss (orange lines), GM hypoperfusion (green lines), and reduced WM FA (blue lines) between FTD
and AD across a range of T-scores and significant levels. (a) Mean loads of GM loss (I), GM hypoperfusion (II), and reduced WM FA (III)
along a range of abnormality levels (T-scores) in AD (solid lines) and FTD (dash lines) patients. (b) Variations in significance of the load
differences between FTD and AD as a function of T-scores. Note, the vertical axis of all plots in (a, b) uses a base-10 logarithmic scale. The
horizontal dotted line indicates P = .05 significance.
We have two main findings. First, FTD patients exhibited
more WM damage than AD patients at mild abnormality
for more severe abnormality levels (i.e., larger T-scores).
Second, FTD patients had greater GM loss and WM damage
relative to GM hypoperfusion, although the differences of
damage between modalities gradually vanished with increas-
ing levels of abnormality. In contrast to FTD, AD patients
had equal damage in all three modalities, irrespective of the
level of abnormality. Taken together, the results suggest that
FTD and AD differ in amount of WM and GM structural
and functional damages, in addition to their characteristic
regional patterns of brain alterations than healthy controls.
Our first main finding of greater WM damage in FTD
than AD at mild levels of abnormality suggests that WM
may be more sensitive to the pathology of FTD than to the
pathology of AD at their early disease stages. The hallmark of
FTD is tauopathies or ubiquitin immunoreactive inclusions
by the presences of neuronal and glial inclusions [48, 49] in
gray and white matter. WM pathologies in FTD have been
reported with astrocytic gliosis and oligodendroglial apopto-
sis, which may ultimately result in axonal degeneration [50–
53]. A recent study  also reported that oligodendroglial
pathology can be predominant in FTD despite severe GM
damage. On the other hand, degeneration of frontostriatal
networks, which is a characteristic feature of FTD , sug-
gests that WM denegation in anterior brain could also be a
primary FTD pathology. In contrast to FTD, the hallmark of
AD is the deposition of amyloid plaques and neurofibrillary
tangles that are associated with loss of neurons and synapses
[55, 56]. WM pathology in AD has been suggested to occur
secondarily to GM pathology and may include reduction
of myelin, axons, and oligodendrocytes [57, 58]. A vascular
origin of WM pathology in AD has also been suggested .
that differences in WM damage between AD and FTD dis-
that AD and FTD undergo similar WM pathologies at a
severe brain damage level. One explanation is that severe
WM abnormalities are an outcome of irreversible vascular
damage, such as appearance of WMSH, which affects AD
and FTD similarly . Another possible explanation is that
WM changes resulting from degeneration of corticocortical
connections may inevitably occur in both AD and FTD.
In addition, we found a trend of more GM loss in FTD
than AD at moderate to severe levels of abnormality.
This observation is consistent with histopathological studies
showing substantial loss of spindle neurons in the cortex in
FTD but not in AD . The finding is also in agreement
with several MRI studies [29, 30, 63, 64] which compared
FTD and AD directly, showing regionally greater GM loss in
FTD when compared to AD but no greater GM loss in AD
when compared to FTD. Similarly, several longitudinal stu-
pared to AD [65, 66]. However, some MRI studies [26, 67]
reported greater GM loss in AD compared to FTD, though
the extent of the GM losses varied regionally. The discrep-
ancy may result from notorious difficulties to adequately
match impairment severity in FTD and AD given substantial
differences in symptomatology, although our current study
attempted to match the severities (such as the measures of
CDR, MMSE) of AD and FTD groups as closely as possible.
The second major finding that GM loss and WM damage
exceed perfusion damage in FTD may be explained by loss
of brain tissue other than neurons contributing to GM atro-
phy in FTD. Information about differential loss of various
types of brain tissue may be particularly relevant in earlier
International Journal of Alzheimer’s Disease7
Conditional load (log)
GM vol relative to GM perf
WM FA relative to GM perf
GM vol relative to WM FA
Conditional load (log)
GM vol relative to GM perf
WM FA relative to GM perf
GM vol relative to WM FA
P values (log)
Significance of the conditional loads
Significance of the conditional loads
Figure 3: Conditional load of one modality relative to another as a function of the level of abnormality (T-score) for AD (a) and FTD (b).
modality was higher than the load from reference modality. Conditional load lower than 1 indicated opposite relations between modalities.
The extent to which differences in the conditional loads among modalities are above chance (P < .05) across the level of abnormality is
indicated in plot (c) for AD and in plot (d) for FTD. Note, P values in (c) and (d) are also plotted on a logarithmic scale. The horizontal dash
lines indicate P = .05 significance.
and potentially reversible stages of the disease. Our findings
are supported by several histopathological studies demon-
strating that the earliest cellular changes in FTD occur in as-
trocytes without neuron loss and that neuronal damage
becomes more prominent in later stages of the disease
[50, 68]. Therefore, at mild abnormality levels of FTD, the
surviving neurons may still function normally as reflected by
the normal levels of perfusion. Our findings are consistent
with other studies carried out in our laboratory  and on
different cohorts of FTD and AD patients which have
implied dissociation between GM atrophy and perfusion.
Specifically, these studies indicated that FTD was associated
with greater GM atrophy in absence of significant reduction
the abnormalities across the three modalities were all similar.
Our voxelwise analysis demonstrates the regional pat-
terns of GM loss, hypoperfusion, and reduced WM FA in
this sample of AD and FTD patients are consistent with the
disease-specific patterns that have been reported in previous
MRI studies of brain structure [14, 70], perfusion [5, 6], and
DTI [25, 71–73]. Furthermore, the similarity between the
regional patterns of GM and WM alterations which appear
in the same lobe together for each disease, implies that WM
degradation mirrors that of GM damages and is consistent
with previous reports . Taken together, these results can
demonstrate the well-documented pathological bases of AD
 and FTD .
The new joint analysis approach allows for the investiga-
tion of different abnormalities across multiple MRI modal-
ities such as structural,perfusion and diffusion MRI between
groups. The approach can be used to test whether groups
measures. The approach was augmented by nonparametric
statistical tests via permutations and carried out across
8International Journal of Alzheimer’s Disease
a range of T-scores to reduce measurement bias toward the
various brain conditions. The concept can be expanded in
principle to conduct a voxelwise joint analysis of multiple
MRI measures to determine regional variations in GM loss
and hypoperfusion. Other statistical methods for joint anal-
yses of multiple image modalities such as joint independent
component analysis (jICA)  may provide alternative
solutions. The findings with multimodal MRI could poten-
tially be useful to improve the design of AD and FTD clinical
ures, potentially boosting sensitivity and specificity, could
lead to reduced sample sizes. Second, the finding revealed
that FTD presents more white matter involvement relative
to AD, thus providing a new biological feature of FTD, and
could be used to relax the need to match disease severity in
studies recruiting AD and FTD patients.
Limitations of the current study include a small sample
size that was reliant on clinical diagnoses which were not
sufficiently confirmed by autopsies. Therefore, confidence
in the generalization of the results is limited, and potential
misdiagnosis of patients may have resulted in spurious find-
ings. Furthermore, we cannot completely rule out that other
factors than disease etiology, such as genetic profiles, dura-
tion of symptoms, and cardiovascular conditions, which
contributed to MRI differences between the patients and
thus contaminated the findings. Second, diffusion encoding
was limited to the minimum of 6 directions at the time this
protocol was initiated, although it is known that many more
encoding directions improve the characterization of diffu-
sion such as fewer ambiguities in regions of crossing fibers
and better spatial invariance of the noise pattern. Therefore,
fiber crossings and the DTI noise pattern may potentially
mimic regional differences in FA between these groups.
Third, we ignored relationships between brain regions in
our joint analysis and therefore under-utilized information
from the multimodal MRI data. A more powerful statistical
framework  that takes spatial relations between mul-
tivariate measures into account may provide more power.
Finally, the data was artificially scaled to provide a uniform
resolution for all MRI modalities, which may have induced
a spatial bias as well as altered selectively the sensitivity of
each modality. Other approaches that do not require a uni-
form resolution but can operate on variable spatial scales,
such as information theoretic formalisms , may lead to
differences in results.
In conclusion, our findings suggest that FTD and AD
functional abnormalities, in addition to differences between
thermore, the joint assessment of multimodal MRI measures
employed in this study has potential value to improve the
differential diagnosis between FTD and AD.
This research was funded in part by National Institutes of
Health Grants (P01AG19724, P50 AG23501) and a Grant
from the National Center for Resource Research (P41
RR23953). This material is the result of work supported
with resources and the use of facilities at the Veterans
Administration Medical Center, San Francisco California.
The authors thank all the participants in this study. The
authors also thank Mr. Shannon Buckley and Mr. Pouria
Mojabi for assistance with image processing, Mr. Philip Insel
for the help with statistics, and Dr. Susanne Mueller for the
advice in data analysis.
 N. S. Ryan and N. C. Fox, “Imaging biomarkers in Alzheimer’s
disease,” Annals of the New York Academy of Sciences, vol. 1180,
pp. 20–27, 2009.
 J. C. Baron, G. Chetelat, B. Desgranges et al., “In vivo mapping
of gray matter loss with voxel-based morphometry in mild
Alzheimer’s disease,” NeuroImage, vol. 14, no. 2, pp. 298–309,
 K. Ishii, H. Sasaki, A. K. Kono, N. Miyamoto, T. Fukuda,
and E. Mori, “Comparison of gray matter and metabolic
reduction in mild Alzheimer’s disease using FDG-PET and
voxel-based morphometric MR studies,” European Journal of
Nuclear Medicine and Molecular Imaging, vol. 32, no. 8, pp.
 P. Bartenstein, S. Minoshima, C. Hirsch et al., “Quantitative
assessment of cerebral blood flow in patients with Alzheimer’s
disease by SPECT,” Journal of Nuclear Medicine, vol. 38, no. 7,
pp. 1095–1101, 1997.
 N. A. Johnson, G. H. Jahng, M. W. Weiner et al., “Pattern
of cerebral hypoperfusion in Alzheimer disease and mild
cognitive impairment measured with arterial spin-labeling
MR imaging: initial experience,” Radiology, vol. 234, no. 3, pp.
 D. C. Alsop, J. A. Detre, and M. Grossman, “Assessment of
cerebral blood flow in Alzheimer’s disease by spin-labeled
1, pp. 93–100, 2000.
 E. J. Kim, S. S. Cho, Y. Jeong et al., “Glucose metabolism in
early onset versus late onset Alzheimer’s disease: an SPM anal-
ysis of 120 patients,” Brain, vol. 128, no. 8, pp. 1790–1801,
 S. Xie, J. X. Xiao, G. L. Gong et al., “Voxel-based detection
of white matter abnormalities in mild Alzheimer disease,”
Neurology, vol. 66, no. 12, pp. 1845–1849, 2006.
 O. Naggara, C. Oppenheim, D. Rieu et al., “Diffusion tensor
imaging in early Alzheimer’s disease,” Psychiatry Research—
Neuroimaging, vol. 146, no. 3, pp. 243–249, 2006.
 R. Stahl, O. Dietrich, S. J. Teipel, H. Hampel, M. F. Reiser, and
S. O. Schoenberg, “White matter damage in Alzheimer disease
and mild cognitive impairment: assessment with diffusion-
tensor MR imaging and parallel imaging techniques,” Radiol-
ogy, vol. 243, no. 2, pp. 483–492, 2007.
 S. J. Teipel, R. Stahl, O. Dietrich et al., “Multivariate network
analysis of fiber tract integrity in Alzheimer’s disease,” Neu-
roImage, vol. 34, no. 3, pp. 985–995, 2007.
 J. Huang, R. P. Friedland, and A. P. Auchus, “Diffusion tensor
imaging of normal-appearing white matter in mild cognitive
of axonal degeneration in the temporal lobe,” American
 L. Wang, F. C. Goldstein, E. Veledar et al., “Alterations in
cortical thickness and white matter integrity in mild cogni-
tive impairment measured by whole-brain cortical thickness
International Journal of Alzheimer’s Disease9
mapping and diffusion tensor imaging,” American Journal of
Neuroradiology, vol. 30, no. 5, pp. 893–899, 2009.
 H. J. Rosen, M. L. Gorno-Tempini, W. P. Goldman et al.,
“Patterns of brain atrophy in frontotemporal dementia and
semantic dementia,” Neurology, vol. 58, no. 2, pp. 198–208,
voxel-based morphometric analyses of MRI and naming
corticobasal degeneration,” Brain, vol. 127, no. 3, pp. 628–649,
 A. T. Du, G. H. Jahng, S. Hayasaka et al., “Hypoperfusion in
frontotemporal dementia and Alzheimer disease by arterial
spin labeling MRI,” Neurology, vol. 67, no. 7, pp. 1215–1220,
 J. Diehl, T. Grimmer, A. Drzezga, M. Riemenschneider, H.
Forstl, and A. Kurz, “Cerebral metabolic patterns at early
stages of frontotemporal dementia and semantic dementia: A
PET study,” Neurobiology of Aging, vol. 25, no. 8, pp. 1051–
 T. Grimmer, J. Diehl, A. Drzezga, H. F¨ orstl, and A. Kurz,
“Region-specific decline of cerebral glucose metabolism in
patients with frontotemporal dementia: a prospective F-FDG-
PET study,” Dementia and Geriatric Cognitive Disorders, vol.
18, no. 1, pp. 32–36, 2004.
 K. Ishii, S. Sakamoto, M. Sasaki et al., “Cerebral glucose
metabolism in patients with frontotemporal dementia,” Jour-
nal of Nuclear Medicine, vol. 39, no. 11, pp. 1875–1878, 1998.
 Y. Jeong, S. S. Cho, J. M. Park et al., “18F-FDG PET findings
in frontotemporal dementia: an SPM analysis of 29 patients,”
Journal of Nuclear Medicine, vol. 46, no. 2, pp. 233–239, 2005.
 V. A. Cardenas, A. L. Boxer, L. L. Chao et al., “Deformation-
based morphometry reveals brain atrophy in frontotemporal
dementia,” Archives of Neurology, vol. 64, no. 6, pp. 873–877,
 L. L. Chao, N. Schuff, E. M. Clevenger et al., “Patterns of
white matter atrophy in frontotemporal lobar degeneration,”
Archives of Neurology, vol. 64, no. 11, pp. 1619–1624, 2007.
 B. Borroni, S. M. Brambati, C. Agosti et al., “Evidence of white
matter changes on diffusion tensor imaging in frontotemporal
dementia,” Archives of Neurology, vol. 64, no. 2, pp. 246–251,
 E. S. Matsuo, R. W. Shin, M. L. Billingsley et al., “Biopsy-
derived adult human brain tau is phosphorylated at many of
the same sites as Alzheimer’s disease paired helical filament
tau,” Neuron, vol. 13, no. 4, pp. 989–1002, 1994.
 Y. Zhang, N. Schuff, A. T. Du et al., “White matter damage in
diffusion MRI,” Brain, vol. 132, no. 9, pp. 2579–2592, 2009.
 G. D. Rabinovici, W. W. Seeley, E. J. Kim et al., “Distinct
MRI atrophy patterns in autopsy-proven Alzheimer’s disease
and frontotemporal lobar degeneration,” American Journal of
Alzheimer’s Disease and other Dementias, vol. 22, no. 6, pp.
 T. Fukui and A. Kertesz, “Volumetric study of lobar atrophy
in Pick complex and Alzheimer’s disease,” Journal of the
Neurological Sciences, vol. 174, no. 2, pp. 111–121, 2000.
 A. R. Varma, W. Adams, J. J. Lloyd et al., “Diagnostic patterns
of regional atrophy on MRI and regional cerebral blood flow
change on SPECT in young onset patients with Alzheimer’s
disease, frontotemporal dementia and vascular dementia,”
Acta Neurologica Scandinavica, vol. 105, no. 4, pp. 261–269,
 C. Bocti, C. Rockel, P. Roy, F. Gao, and S. E. Black, “Top-
ographical patterns of lobar atrophy in frontotemporal
dementia and Alzheimer’s disease,” Dementia and Geriatric
Cognitive Disorders, vol. 21, no. 5-6, pp. 364–372, 2006.
 T. Kanda, K. Ishii, T. Uemura et al., “Comparison of grey
matter and metabolic reductions in frontotemporal dementia
using FDG-PET and voxel-based morphometric MR studies,”
European Journal of Nuclear Medicine and Molecular Imaging,
vol. 35, no. 12, pp. 2227–2234, 2008.
 C. Tranfaglia, B. Palumbo, D. Siepi, H. Sinzinger, and L. Par-
netti, “Semi-quantitative analysis of perfusion of Brodmann
areas in the differential diagnosis of cognitive impairment
in Alzheimer’s disease, fronto-temporal dementia and mild
cognitive impairment,” Hellenic Journal of Nuclear Medicine,
vol. 12, no. 2, pp. 110–195, 2009.
 A. Varrone, S. Pappata, C. Caraco et al., “Voxel-based com-
parison of rCBF SPET images in frontotemporal dementia
cortical networks,” European Journal of Nuclear Medicine, vol.
29, no. 11, pp. 1447–1454, 2002.
 C. Luckhaus, M. Janner, M. Cohnen et al., “A novel MRI-
biomarker candidate for Alzheimer’s disease composed of
regional brain volume and perfusion variables,” European
Journal of Neurology, vol. 17, no. 12, pp. 1437–1444, 2010.
 K.B. Walhovd, A. M.Fjell, I. Amlienet al., “Multimodal imag-
ing in mild cognitive impairment: metabolism, morphometry
and diffusion of the temporal-parietal memory network,”
NeuroImage, vol. 45, no. 1, pp. 215–223, 2009.
 M. F. Folstein, S. E. Folstein, and P. R. McHugh, “’Mini mental
state’: a practical method for grading the cognitive state of
patients for the clinician,” Journal of Psychiatric Research, vol.
12, no. 3, pp. 189–198, 1975.
 D. Neary, J. S. Snowden, L. Gustafson et al., “Frontotemporal
lobar degeneration: a consensus on clinical diagnostic crite-
ria,” Neurology, vol. 51, no. 6, pp. 1546–1554, 1998.
 G. McKhann, D. Drachman, M. Folstein, R. Katzman, D.
Price, and E. M. Stadlan, “Clinical diagnosis of Alzheimer’s
disease: report of the NINCDS- ADRDA Work Group under
the auspices of Department of Health and Human Services
Task Force on Alzheimer’s Disease,” Neurology, vol. 34, no. 7,
pp. 939–944, 1984.
 J. C. Morris, “The Clinical Dementia Rating (CDR): current
 P. Scheltens, F. Barkhof, D. Leys et al., “A semiquantitative
rating scale for the assessment of signal hyperintensities on
magnetic resonance imaging,” Journal of the Neurological
Sciences, vol. 114, no. 1, pp. 7–12, 1993.
 J. A. Detre, J. S. Leigh, D. S. Williams, and A. P. Koretsky,
“Perfusion imaging,” Magnetic Resonance in Medicine, vol. 23,
no. 1, pp. 37–45, 1992.
 M. A. Griswold, P. M. Jakob, R. M. Heidemann et al.,
“Generalized autocalibrating partially parallel acquisitions
(GRAPPA),” Magnetic Resonance in Medicine, vol. 47, no. 6,
pp. 1202–1210, 2002.
 J. Ashburner and K. J. Friston, “Voxel-based morphometry—
the methods,” NeuroImage, vol. 11, no. 6 I, pp. 805–821, 2000.
 K. Van Leemput, F. Maes, D. Vandermeulen, A. Colchester,
and P. Suetens, “Automated segmentation of multiple sclerosis
lesions by model outlier detection,” IEEE Transactions on
Medical Imaging, vol. 20, no. 8, pp. 677–688, 2001.
 J. Wang, Y. Zhang, R. L. Wolf, A. C. Roc, D. C. Alsop,
and J. A. Detre, “Amplitude-modulated continuous arterial
10International Journal of Alzheimer’s Disease
spin-labeling 3.0-T perfusion MR imaging with a single coil:
feasibility study,” Radiology, vol. 235, no. 1, pp. 218–228, 2005.
 Y. Masutani, S. Aoki, O. Abe, N. Hayashi, and K. Otomo, “MR
diffusion tensor imaging: recent advance and new techniques
for diffusion tensor visualization,” European Journal of Radiol-
ogy, vol. 46, no. 1, pp. 53–66, 2003.
 W. Zhan, Y. Zhang, S. G. Mueller et al., “Characterization
of white matter degeneration in elderly subjects by magnetic
resonance diffusion and FLAIR imaging correlation,” Neu-
roImage, vol. 47, supplement 2, pp. T58–T65, 2009.
 M. Signorini, E. Paulesu, K. Friston et al., “Rapid assessment
of regional cerebral metabolic abnormalities in single subjects
with quantitative and nonquantitative [18F]FDG PET: a clini-
cal validation of statistical parametric mapping,” NeuroImage,
vol. 9, no. 1, pp. 63–80, 1999.
 G. M. McKhann, M. S. Albert, M. Grossman, B. Miller,
D. Dickson, and J. Q. Trojanowski, “Clinical and patho-
logical diagnosis of frontotemporal dementia: report of the
work group on frontotemporal dementia and Pick’s disease,”
Archives of Neurology, vol. 58, no. 11, pp. 1803–1809, 2001.
 J. Q. Trojanowski, K. Duff, H. Fillit et al., “New directions
for frontotemporal dementia drug discovery,” Alzheimer’s and
Dementia, vol. 4, no. 2, pp. 89–93, 2008.
 M. Broe, J. Kril, and G. M. Halliday, “Astrocytic degeneration
relates to the severity of disease in frontotemporal dementia,”
Brain, vol. 127, no. 10, pp. 2214–2220, 2004.
 J. M. Powers, N. P. Byrne, M. Ito et al., “A novel leukoen-
cephalopathy associated with tau deposits primarily in white
matter glia,” Acta Neuropathologica, vol. 106, no. 2, pp. 181–
 E. Englund and A. Brun, “Frontal lobe degeneration of non-
Alzheimer type IV: white matter changes,” Archives of Geron-
tology and Geriatrics, vol. 6, no. 3, pp. 235–243, 1987.
 E. M. Larsson, E. Englund, M. Sjobeck, J. Latt, and S.
Brockstedt, “MRI with diffusion tensor imaging post-mortem
at 3.0 T in a patient with frontotemporal dementia,” Dementia
and Geriatric Cognitive Disorders, vol. 17, no. 4, pp. 316–319,
 G. G. Kovacs, K. Majtenyi, S. Spina et al., “White matter
tauopathy with globular glial inclusions: a distinct sporadic
and Experimental Neurology, vol. 67, no. 10, pp. 963–975,
 M. E. Calhoun, P. Burgermeister, A. L. Phinney et al., “Neu-
ronal overexpression of mutant amyloid precursor protein
results in prominent deposition of cerebrovascular amyloid,”
Proceedings of the National Academy of Sciences of the United
States of America, vol. 96, no. 24, pp. 14088–14093, 1999.
 H. Braak and E. Braak, “Staging of Alzheimer’s disease-related
neurofibrillary changes,” Neurobiology of Aging, vol. 16, no. 3,
pp. 271–278, 1995.
 M. Bozzali, A. Falini, M. Franceschi et al., “White matter
damage in Alzheimer’s disease assessed in vivo using diffusion
tensor magnetic resonance imaging,” Journal of Neurology
Neurosurgery and Psychiatry, vol. 72, no. 6, pp. 742–746, 2002.
 H. Hanyu, H. Sakurai, T. Iwamoto, M. Takasaki, H. Shindo,
and K. Abe, “Diffusion-weighted MR imaging of the hip-
pocampus and temporal white matter in Alzheimer’s disease,”
Journal of the Neurological Sciences, vol. 156, no. 2, pp. 195–
 E. Englund, “Neuropathology of white matter changes in
Alzheimer’s disease and vascular dementia,” Dementia and
Geriatric Cognitive Disorders, vol. 9, supplement 1, pp. 6–12,
 C. M. Filley and B. K. Kleinschmidt-DeMasters, “Toxic
leukoencephalopathy,” The New England Journal of Medicine,
vol. 345, no. 6, pp. 425–432, 2001.
 A. R. Varma, R. Laitt, J. J. Lloyd et al., “Diagnostic value
of high signal abnormalities on T2 weighted MRI in the
differentiation of Alzheimer’s, frontotemporal and vascular
dementias,” Acta Neurologica Scandinavica, vol. 105, no. 5, pp.
 W. W. Seeley, D. A. Carlin, J. M. Allman et al., “Early
frontotemporal dementia targets neurons unique to apes and
humans,” Annals of Neurology, vol. 60, no. 6, pp. 660–667,
 H. Kitagaki, E. Mori, S. Yamaji et al., “Frontotemporal demen-
tia and Alzheimer disease: evaluation of cortical atrophy with
automated hemispheric surface display generated with MR
images,” Radiology, vol. 208, no. 2, pp. 431–439, 1998.
“Dementia induces correlated reductions in white matter
integrity and cortical thickness: a multivariate neuroimaging
study with sparse canonical correlation analysis,” NeuroImage,
vol. 50, no. 3, pp. 1004–1016, 2010.
 J. L. Whitwell, C. R. Jack Jr., V. S. Pankratz et al., “Rates of
brain atrophy over time in autopsy-proven frontotemporal
dementia and Alzheimer disease,” NeuroImage, vol. 39, no. 3,
pp. 1034–1040, 2008.
 D. Chan, N. C. Fox, R. Jenkins, R. I. Scahill, W. R. Crum, and
M. N. Rossor, “Rates of global and regional cerebral atrophy
in AD and frontotemporal dementia,” Neurology, vol. 57, no.
10, pp. 1756–1763, 2001.
 C. Davatzikos, S. M. Resnick, X. Wu, P. Parmpi, and C. M.
Clark, “Individual patient diagnosis of AD and FTD via high-
dimensional pattern classification of MRI,” NeuroImage, vol.
41, no. 4, pp. 1220–1227, 2008.
 C. Kersaitis, G. M. Halliday, and J. J. Kril, “Regional and
stage of disease in cases with and without Pick bodies,” Acta
Neuropathologica, vol. 108, no. 6, pp. 515–523, 2004.
 S. Shimizu, Y. Zhang, J. Laxamana et al., “Concordance
and discordance between brain perfusion and atrophy in
frontotemporal dementia,” Brain Imaging and Behavior, vol.
4, no. 1, pp. 46–54, 2010.
 J. L. Whitwell, K. A. Josephs, M. N. Rossor et al., “Magnetic
resonance imaging signatures of tissue pathology in fron-
totemporal dementia,” Archives of Neurology, vol. 62, no. 9, pp.
 S. Takahashi, H. Yonezawa, J. Takahashi, M. Kudo, T. Inoue,
and H. Tohgi, “Selective reduction of diffusion anisotropy in
magnetic resonance imaging,” Neuroscience Letters, vol. 332,
no. 1, pp. 45–48, 2002.
 A. Fellgiebel, M. J. Muller, P. Wille et al., “Color-coded
diffusion-tensor-imaging of posterior cingulate fiber tracts in
8, pp. 1193–1198, 2005.
 D. H. Salat, D. S. Tuch, A. J. W. van der Kouwe et al.,
“White matter pathology isolates the hippocampal formation
in Alzheimer’s disease,” Neurobiology of Aging, vol. 31, no. 2,
pp. 244–256, 2010.
 G. Bartzokis, P. H. Lu, and J. Mintz, “Human brain myeli-
nation and amyloid beta deposition in Alzheimer’s disease,”
Alzheimer’s and Dementia, vol. 3, no. 2, pp. 122–125, 2007.
International Journal of Alzheimer’s Disease 11
in memory networks in mild cognitive impairment and
Alzheimer’s disease: an independent component analysis,”
 K. J. Worsley, J. E. Taylor, F. Tomaiuolo, and J. Lerch, “Unified
vol. 23, supplement 1, pp. S189–S195, 2004.
 K. Young, A. T. Du, J. Kramer et al., “Patterns of structural
tia,” Human Brain Mapping, vol. 30, no. 5, pp. 1667–1677,