NeuroImage: Clinical 4 (2014) 508–516
Contents lists available at ScienceDirect
j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / y n i c l
Parallel ICA of FDG-PET and PiB-PET in three conditions with
underlying Alzheimer’s pathology
Robert Laforce Jr a , c , 1 , * , Duygu Tosun b , Pia Ghosh a , c , Manja Lehmann a , c , Cindee M. Madison a , Michael W.
Weiner b , Bruce L. Miller c , William J. Jagust a , d , Gil D. Rabinovici a , c , d
a Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA
b Center for Imaging of Neurodegenerative Diseases, Department of Radiology and Biomedical Imaging, University of California San Francisco, CA, USA
c Memory and Aging Center, Department of Neurology, University of California San Francisco, CA, USA
d Lawrence Berkeley National Laboratory, University of California, Berkeley, CA, USA
a r t i c l e i n f o
Received 15 August 2013
Received in revised form 12 March 2014
Accepted 13 March 2014
Multivariate data analysis
a b s t r a c t
The relationships between clinical phenotype, β-amyloid (A β) deposition and neurodegeneration in
Alzheimer’s disease (AD) are incompletely understood yet have important ramifications for future ther-
apy. The goal of this study was to utilize multimodality positron emission tomography (PET) data from a
clinically heterogeneous population of patients with probable AD in order to: (1) identify spatial patterns of
A β deposition measured by ( 11 C)-labeled Pittsburgh Compound B (PiB-PET) and glucose metabolism mea-
sured by FDG-PET that correlate with specific clinical presentation and (2) explore associations between
spatial patterns of A βdeposition and glucose metabolism across the AD population. We included all patients
meeting the criteria for probable AD (NIA–AA) who had undergone MRI, PiB and FDG-PET at our center
( N = 46, mean age 63.0 ± 7.7, Mini-Mental State Examination 22.0 ± 4.8). Patients were subclassified
based on their cognitive profiles into an amnestic / dysexecutive group (AD-memory; n = 27), a language-
predominant group (AD-language; n = 10) and a visuospatial-predominant group (AD-visuospatial; n = 9).
All patients were required to have evidence of amyloid deposition on PiB-PET. To capture the spatial distri-
bution of A β deposition and glucose metabolism, we employed parallel independent component analysis
(pICA), a method that enables joint analyses of multimodal imaging data. The relationships between PET
components and clinical group were examined using a Receiver Operator Characteristic approach, including
age, gender, education and apolipoprotein E ?4 allele carrier status as covariates. Results of the first set of
analyses independently examining the relationship between components from each modality and clinical
group showed three significant components for FDG: a left inferior frontal and temporoparietal component
associated with AD-language (area under the curve [AUC] 0.82, p = 0.011), and two components associated
with AD-visuospatial (bilateral occipito-parieto-temporal [AUC 0.85, p = 0.009] and right posterior cingulate
cortex [PCC] / precuneus and right lateral parietal [AUC 0.69, p = 0.045]). The AD-memory associated com-
ponent included predominantly bilateral inferior frontal, cuneus and inferior temporal, and right inferior
parietal hypometabolism but did not reach significance (AUC 0.65, p = 0.062). None of the PiB components
correlated with clinical group. Joint analysis of PiB and FDG with pICA revealed a correlated component pair,
in which increased frontal and decreased PCC / precuneus PiB correlated with decreased FDG in the frontal,
occipital and temporal regions (partial r = 0.75, p < 0.0001). Using multivariate data analysis, this study
reinforced the notion that clinical phenotype in AD is tightly linked to patterns of glucose hypometabolism
but not amyloid deposition. These findings are strikingly similar to those of univariate paradigms and provide
additional support in favor of specific involvement of the language network, higher-order visual network,
and default mode network in clinical variants of AD. The inverse relationship between A β deposition and
Abbreviations: AD or AD-memory, Alzheimer’s disease; AUC, area under the curve;
AD-language or LPA, logopenic variant primary progressive aphasia; PCA or AD-
visuospatial, posterior cortical atrophy; PCC, posterior cingulate cortex; PPC, posterior
1 Present address: Clinique Interdisciplinaire de M ´ emoire, D ´ epartement des Sciences
Neurologiques, CHU de Qu ´ ebec — H ˆ
opital de l’Enfant-J ´ esus 1401, 18i ` eme rue, Qu ´ ebec
G1J 1Z4, Canada.
* Corresponding author:
E-mail address: firstname.lastname@example.org (R. Laforce Jr).
2213-1582/ $ - see front matter c
?2014 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license ( http: // creativecommons.org /
licenses / by-nc-nd / 3.0 / ).
R. Laforce Jr et al. / NeuroImage: Clinical 4 (2014) 508–516
glucose metabolism in partially overlapping brain regions suggests that A β may exert both local and re-
mote effects on brain metabolism. Applying multivariate approaches such as pICA to multimodal imaging
data is a promising approach for unraveling the complex relationships between different elements of AD
?2014 The Authors. Published by Elsevier Inc.
This is an open access article under the CC BY-NC-ND license
( http: // creativecommons.org / licenses / by-nc-nd / 3.0 / ).
The relationships between amyloid, metabolism and clinical phe-
notype in Alzheimer’s disease (AD) are incompletely understood. Pre-
vious studies have yielded mixed results within typical amnestic AD
and across different AD phenotypes. For example, in three clinical
variants of AD (AD-memory, AD-language, AD-visuospatial), clinical
syndromes were strongly linked to patterns of glucose metabolism,
whereas ( 11 C)-labeled Pittsburgh Compound B (PiB-PET) binding was
similar across clinical phenotypes ( Cohen et al., 2009 ; de Souza et al.,
2011 ; Lehmann et al., 2013a ; Leyton et al., 2011 ; Rabinovici et al.,
2008 ; Rosenbloom et al., 2011 ). Correlations between increased β-
amyloid and decreased metabolism have been found in some studies
( Cohen et al., 2009 ; Edison et al., 2007 ; Engler et al., 2006 ) but not in
others ( Furst et al., 2012 ; Li et al., 2008 ). Some studies have suggested
that the relationships between amyloid and glucose metabolism vary
by brain region and disease state ( Cohen et al., 2009 ; La Joie et al.,
To date, most studies have investigated these relationships us-
ing univariate analyses, but this approach may fail to capture dis-
tributed variations across brain networks. A number of recent stud-
ies have demonstrated that multivariate statistical paradigms (e.g.,
principal component analysis or independent component analysis
[ICA]), where distributed variations in multiple neuroimaging data
and their inter-relationships are assessed together, provide a better
framework for integrative analysis of imaging data. Multivariate tech-
niques have been shown to be more sensitive for early diagnosis of AD
and capture patterns of normal age-associated atrophy ( Brickman et
al., 2007 ). Parallel independent component analysis (pICA; Calhoun et
al., 2006 ), a variation of ICA which allows estimation of independent
components as well as multimodal patterns or mixed coefficients,
has recently been used to study the mechanisms by which amyloid- β
deposition leads to neurodegeneration and cognitive decline ( Tosun
et al., 2011 ). This is particularly relevant in light of possible distant
( Bourgeat et al., 2010 ) rather than local ( Cohen et al., 2009 ) effects of
β-amyloid on glucose metabolism.
In this study we applied a multivariate approach to explore the
relationships between metabolism and amyloid accumulation across
AD phenotypes. To this end, we recruited patients with three pheno-
types of AD cited in the new clinical diagnostic guidelines ( McKhann
et al., 2011 ): 1) a group of prototypical AD, or AD-memory, charac-
terized by predominant episodic memory impairment and executive
dysfunction ( Dubois et al., 2007 ), 2) a group with language variant
AD (AD-language, also called logopenic variant primary progressive
aphasia ) characterized by progressive word-finding difficulties and
deficits in sentence repetition ( Gorno-Tempini et al. , 2004 , 2011 ),
and 3) a group with the visuospatial variant of AD (AD-visuospatial,
also referred to as posterior cortical atrophy ) marked by predominant
visuospatial and visuoperceptual dysfunctions. We then applied pICA
to 1) identify specific components from each modality that correlated
with clinical presentation and 2) identify relationships between spa-
tial patterns of PiB and FDG across AD patients. Based on previous
results applying univariate statistics from our group and others, we
hypothesized that FDG but not PiB would generate individual com-
ponents that correlated with diagnosis. We further aimed to capture
relationships between spatial patterns of glucose metabolism and
amyloid deposition in this clinically and anatomically diverse cohort
which may not be apparent using traditional univariate methods.
2. Subjects and methods
2.1. Subject selection and characteristics
We identified all patients seen at the University of California San
Francisco (UCSF) Memory & Aging Center who met the criteria for
probable AD according to the National Institute on Aging–Alzheimer’s
Association (NIA–AA) guidelines ( McKhann et al., 2011 ), were PiB-
positive and had available FDG and MRI scans. Patients were excluded
if they had clinical or imaging evidence of previous stroke, or had a
high burden of white matter hyperintensities (defined as Scheltens
grade ≥ 4) ( Scheltens et al., 1998 ). All patients were recruited be-
tween April 2005 and July 2011. Patients underwent a history and
physical examination by a behavioral neurologist, a structured care-
giver interview by a nurse, and a battery of neuropsychological tests
( Kramer et al., 2003 ). All patients had mild-to-moderate dementia
based on the Mini-Mental State Examination (MMSE; Folstein et al.,
1975 ) and the Clinical Dementia Rating (CDR; Morris, 1993 ) scale.
Diagnosis was made in a consensus clinical conference incorporat-
ing clinical and neuropsychological profiles but blinded to imaging
data. Patients were subclassified as AD-memory, AD-language, and
AD-visuospatial using published criteria ( Gorno-Tempini et al., 2011 ;
McKhann et al., 2011 ; Tang-Wai et al., 2004 ). The AD-memory group
was composed of patients meeting the NIA–AA criteria for probable
AD but not AD-language or AD-visuospatial criteria. The final cohort
consisted of 27 patients with probable AD-memory, ten with AD-
language and nine with AD-visuospatial (see Table 1 ).
Acquisition parameters for all scanners have been described in
previous publications ( Mormino et al., 2012 ; Mueller et al., 2009 ;
Rabinovici et al., 2007 ; Rosen et al., 2002 ; Zhou et al., 2012 ).
2.3. Structural imaging
T 1 -weighted scans were collected at UCSF or Lawrence Berkeley
National Laboratory (LBNL) on different MRI units, including two 1.5 T
units (Magnetom Avanto System, Siemens Medical Systems, Erlangen,
Germany; Magnetom VISION system, Siemens Inc., Iselin, NJ), a 3 T
unit (Siemens Tim Trio scanner), and a 4 T unit (BrukerMedSpec). The
proportions of subjects studied on each scanner were balanced across
the three AD groups. In patients with multiple MRIs, the MRI closest
to the date of the PET scan was used for data preprocessing.
R. Laforce Jr et al. / NeuroImage: Clinical 4 (2014) 508–516
AD-memory ( n = 27)
16 / 11
AD-language ( n = 10)
5 / 5
AD-visuospatial ( n = 9)
6 / 3
Age (mean, SD)
M / F
Education (mean, SD)
ApoE ?4 status
- Absent ?4
- 1 ?4
- 2 ?4
MMSE / 30
Abbreviations: Alzheimer’s disease (AD); standard deviation (SD); apolipoprotein E ?4 allele (ApoE ?4); Mini-Mental State Examination (MMSE); Clinical Dementia Rating (CDR);
Clinical Dementia Rating Sum of Boxes (CDR-SB).
∗p < 0.05.
2.4. Positron emission tomography radiochemistry and acquisition
[ 11 C]PiB was synthesized at the LBNL Biomedical Isotope Facility
using a previously published protocol. [ 18 F]FDG was purchased from
a commercial vendor (IBA Molecular). PET scans were performed at
LBNL using a Siemens ECAT EXACT HR PET scanner in 3-dimensional
acquisition mode. 90 min of dynamic PiB data and 30 min of FDG data
( t = 30–60 min post-injection, minimum of 2 h after PiB injection)
were obtained. Ten-minute transmission scans for attenuation correc-
tion were obtained either immediately before or after each [ 11 C]PiB
and [ 18 F]FDG scan. PET data were reconstructed using an ordered sub-
set expectation–maximization algorithm with weighted attenuation.
Images were smoothed with a 4 mm Gaussian kernel with scatter cor-
rection. All images were evaluated before analysis for patient motion
and adequacy of statistical counts.
2.5. Image processing
All image pre-processing was performed in Statistical Paramet-
ric Mapping version 8 (SPM8; http: // www.fil.ion.ucl.ac.uk / spm ).
Reference regions were defined in native MRI space for
each subject using subcortical parcellations from FreeSurfer
4.5 ( http: // surfer.nmr.mgh.harvard.edu ). FDG-PET frames were
summed and standardized uptake value ratios (SUVR) were calcu-
lated by normalizing the summed FDG image to mean activity in the
pons for each subject. For PiB, voxel-wise distribution volume ratios
(DVRs) were calculated using Logan graphical analysis ( Logan et al.,
1996 ) with the gray matter cerebellum time–activity curve used as a
reference tissue input function ( t = 35–90 min) ( Price et al., 2005 ).
2.6. Spatial normalization
PiB and FDG data were co-registered to the subject’s skull
stripped T 1 -weighted MRI. To allow across-subject comparisons, each
subject’s T 1 -weighted MRI was normalized to MNI (Montreal Neuro-
logical Institute) space using the skull stripped ch2 template, and
the derived normalization parameters were applied to the subject’s
co-registered PiB and FDG volumes. All normalized images were
smoothed with a 12-mm Gaussian kernel.
2.7. Visual inspection
Voxel-wise PiB DVR images from all subjects were qualitatively
assessed by an experienced PET researcher (W.J.J.) blinded to clinical
diagnosis. Scans were read visually as positive or negative for cortical
PiB. A positive scan was defined as a DVR image in which uptake was
substantially greater in the cortex than in the white matter. Visual
inspection based on these criteria has been validated previously as
a reproducible and reliable estimate of increased PiB uptake when
compared with quantitative analysis ( Mormino et al., 2012 ; Ng et al.,
2007 ; Rabinovici et al., 2011 ).
2.8. Parallel ICA
We analyzed PiB and FDG data jointly and took all image voxels
into account simultaneously using pICA (Fusion ICA Toolbox: Calhoun
et al., 2006 ; Rachakonda et al., 2008 ). The mathematical foundations
of pICA are described in detail in Liu et al. (2009) . In this frame-
work, pICA applied to multimodality imaging data aims to identify
independent components in each image modality as well as the rela-
tionships of these independent components across image modalities.
Briefly, ICA is run on each modality and a correlation measure is
enforced between the mixing coefficients of modalities during the
analysis. In the context of this study, pICA identified spatially inde-
pendent components of PiB and FDG while simultaneously revealing
the largest variations across patients that PiB and FDG had in common.
The number of significant independent components in each modality
was estimated using both the Akaike information criterion (AIC) and
the minimum description length criterion. As a well-accepted order
selection criterion, AIC maximizes the log-likelihood of the observed
data based on the independent component set, with a penalty term
that is directly proportional to the total number of independent com-
ponents. The independent component set with the lowest AIC value
is selected for a balance between the accuracy of fitting and the com-
plexity of the independent component model. For each modality, the
loading parameters expressing the contribution of each independent
component to the variance across subjects were estimated. Each in-
dependent component for each modality was scaled to unit standard
deviation, yielding z -score maps in the MNI template space. Because
FDG-PET data is interpreted in terms of hypometabolism and PiB-PET
data in terms of increased tracer retention, all FDG data were inverted
(sign reversed). All component maps were thresholded at a z -score
level of | z | ≥ 2.5 (99.4% cumulative probability) for visualization pur-
Based on these loading parameters, we computed Pearson’s cor-
relation coefficients for all pairs of PiB and FDG independent com-
ponents while further accounting for variations in age, gender, ed-
ucation and apolipoprotein E ?4 allele (ApoE ?4) carrier status. The
Pearson’s correlation coefficients were then used to identify signif-
icant relationships between brain amyloid- β accumulation and hy-
pometabolism after correction for multiple comparisons using a false
discovery rate (FDR) at a significance level of p < 0.05. Relationship to
clinical group was examined using a Receiver Operator Characteristic
approach, including age, gender, education, and ApoE ?4 (present /
absent) as covariates. The ROC analyses assessed the contribution of
each component separately to the classification of one clinical group
from the other two (e.g., AD-memory versus AD-language and AD-
visuospatial) within the general linear model framework, using the
R. Laforce Jr et al. / NeuroImage: Clinical 4 (2014) 508–516
logit function as link between the linear predictor variable (i.e., load-
ing parameters of a component) and group as binomial outcome vari-
able (clinical group of interest = 1 versus rest = 0).
2.9. Statistical analysis
Group differences in demographic variables were examined us-
ing one-way analysis of variance or the Mann–Whitney U test. As
appropriate statistical analyses were implemented in R Software
( http: // www.r-project.org / ). This study was approved by the Univer-
sity of California, Berkeley, University of California, San Francisco, and
Lawrence Berkeley National Laboratory institutional review boards
for human research.
3.1. Patient characteristics
Patient characteristics are shown in Table 1 . Patients with AD-
memory were significantly older at PET and were more impaired
on CDR and CDR Sum-of-Boxes (CDR-SB) ( p = 0.03). No significant
differences were found in gender, education, ApoE ?4 status or MMSE.
3.2. Neuropsychological evaluations
Neuropsychological test batteries were available for most patients
(see Table 2 ). The mean interval between cognitive testing and PET
was 131 days (SD 207.6 days). As expected, AD-memory patients per-
formed poorly on verbal and visual memory tasks ( p = 0.03 on the Cal-
ifornia Verbal Learning Test 10-minute delayed recall, and p = 0.01 on
the modified Rey 10-minute delayed recall), whereas AD-visuospatial
patients showed lower performance on visuospatial tasks ( p = 0.02 for
modified Rey copy). The AD-language group performed significantly
worse on sentence repetition ( p = 0.003), consistent with the deficits
in auditory working memory previously reported in this group.
3.3. Individual FDG and PiB components
The number of estimated components using the AIC in our ob-
served data from 46 subjects was eight for the FDG-PET feature and
seven for the PiB-PET feature. Three of the eight FDG components
were associated with a particular clinical group with significant pre-
dictor accuracy. A left inferior frontal and left temporoparietal hy-
pometabolism component was associated with AD-language with
an area under the curve (AUC) of 0.82 ( p = 0.011) ( Fig. 1 A). Two
components correlated with AD-visuospatial, one involving bilateral
occipito-parieto-temporal hypometabolism ( Fig. 1B ) and another in-
volving right posterior cingulate cortex (PCC) / precuneus and right
lateral parietal hypometabolism ( Fig. 1C ) with AUC measures of 0.85
( p = 0.009) and 0.69 ( p = 0.045), respectively. A fourth component
correlated at a trend level with AD-memory ( Fig. 1D ). This compo-
nent included predominantly bilateral inferior frontal, cuneus and
inferior temporal, and right inferior parietal hypometabolism (AUC
of 0.65, p = 0.062). The remaining FDG components showed no as-
sociation with clinical presentation (data not shown). These included
two components showing bilateral cerebellar hypometabolism, one
with bilateral medial orbito-frontal, inferior frontal, and right supe-
rior frontal hypometabolism, and one with cerebellar and superior
frontal hypometabolism. Supplementary Figs. S1–S4 illustrate the as-
sociations with clinical groups.
None of the seven estimated PiB components (left PCC and lateral
parietal; right parieto-temporal; bilateral cerebellar; bilateral inferior
frontal; bilateral temporal, frontal, parietal, PCC / precuneus, and cere-
bellar; bilateral fronto-orbital and PCC / precuneus; bilateral fronto-
orbital and middle frontal) were significant predictors in classifying
clinical groups after adjusting for age, sex, education, and ApoE ?4.
Mean FDG and PiB images of each of the three groups are provided
as Supplementary Figs. S5–S10 to allow for qualitative comparison of
PET patterns across groups.
3.4. Joint FDG and PiB components
We also explored the joint predictive value of FDG and PiB inde-
pendent components in correctly classifying a given diagnostic group
from the rest and among all possible pair combinations identified,
with paired glucose metabolism and amyloid deposition components
providing significant improvement in classification accuracy relative
to the ones based on unimodal components. The AD-memory re-
lated hypometabolism component (shown in Fig. 1D ) jointly with a
PiB component with amyloid deposition in the left posterior parietal
cortex and lateral parietal regions provided an AUC measure of 0.76
( Supplementary Fig. S11 ), significantly larger than single modality
(AUC of 0.65, p < 0.01). The same PiB component when considered
jointly with the AD-language related hypometabolism component
( Fig. 1A ) significantly ( p < 0.01) improved the classification accuracy
from AUC = 0.82 to AUC = 0.87 ( Supplementary Fig. S12 ). A more
diffuse PiB component including temporal, parietal, PCC / precuneus,
and lateral and medial frontal amyloid deposition when considered
jointly with the first AD-visuospatial related hypometabolism compo-
nent shown in Fig. 1B in a multimodal principal component analysis
diagnosis prediction model increased the unimodal hypometabolism
AUC measure of 0.85–0.88 ( p < 0.01) ( Supplementary Fig. S13 ).
3.5. Correlated FDG and PiB components
We found a significant and spatially distributed component pair
across all subjects, depicting an association between FDG and PiB. In
this component pair, increased frontal and decreased PCC / precuneus
PiB binding was correlated with decreased FDG uptake in the frontal,
occipital and temporal regions ( Fig. 2 ). This component pair showed a
partial correlation of 0.75, with an FDR-corrected significance level of
p < 10 −6 . The adjusted R 2 value of the fitted model for this component
pair was 0.56 with p < 10 −8 . The FDG component in this pair was the
same component that showed, on its own, a trend correlation with
the AD-memory phenotype ( p = 0.062, Fig. 1D ). The PiB component
was not correlated with a specific phenotype (as was true for all PiB
components). This combined PiB-FDG component did not correlate
with a specific group.
In this study we applied pICA to FDG-PET and PiB-PET data in
an attempt to better understand the relationships between glucose
metabolism, amyloid aggregation and clinical phenotype in AD. We
found that memory, language, and visuospatial-predominant clini-
cal variants of AD were associated with independent components of
glucose metabolism but not with specific patterns of β-amyloid depo-
sition. FDG and PiB jointly improved the classification of one variant
from others, though the added effect of joint FDG-PiB versus FDG
alone was relatively small. Multivariate analyses further revealed an
inverse relationship between A β deposition and glucose metabolism
in the frontal cortex and PCC / precuneus, providing insight into the
biological interplay between these two biomarkers in key regions of
4.1. Replicating previous univariate efforts using a multivariate
Using pICA we replicated previous findings from univariate anal-
yses demonstrating that the clinical phenotype in AD is strongly
linked to anatomic patterns of glucose hypometabolism but not
to the spatial distribution of amyloid deposition ( Lehmann et al.,
R. Laforce Jr et al. / NeuroImage: Clinical 4 (2014) 508–516
AD-memory ( n = 27)
AD-language ( n = 10)
AD-visuospatial ( n = 9)
CVLT-SF total learning ( / 36)
CVLT-SF 10-min recall ( / 9)
Modified Rey 10-min recall
( / 17)
Boston naming test ( / 15)
Syntax comprehension ( / 5)
Letter fluency (D words)
Category fluency (animals)
Sentence repetition ( / 5)
Repetition and working
Digit span forward ( / 9)
Digit span backward ( / 8)
Modified trails B correct
lines / min
Stroop interference no. correct
Modified Rey copy ( / 17)
VOSP number location ( / 10)
Affect naming ( / 16)
Face matching ( / 12)
Arithmetics, written ( / 5)
Abbreviations: California Verbal Learning Test (CVLT); Comprehensive Affect Testing System (CATS); Visual Object and Space Perception (VOSP). Missing data: modified Rey copy:
1 AD-visuospatial; Boston naming: 2 AD-memory; syntax comprehension: 2 AD-memory, 1 AD-visuospatial; letter fluency: 1 AD-memory; digit span forward: 20 AD-memory,
4 AD-language, 5 AD-visuospatial; digit span backward: 1 AD-memory; modified trails: 7 AD-memory, 1 AD-language, 2 AD-visuospatial; Stroop: 6 AD-memory, 2 AD-language,
2 AD-visuospatial; VOSP number location: 3 AD-memory, 1 AD-language, 3 AD-visuospatial; arithmetic: 1 AD-visuospatial; face matching: 10 AD-memory, 3 AD-language, 3
AD-visuospatial; affect naming: 10 AD-memory, 3 AD-language, 4 AD-visuospatial.
∗p < 0.05.
2013a ). Specifically, three independent glucose metabolism compo-
nents were associated with specific clinical variants with high pre-
dictor accuracy. First, a left inferior frontal and temporoparietal hy-
pometabolism component was associated with AD-language. This is
congruent with previous studies demonstrating asymmetric left tem-
poroparietal atrophy and hypometabolism in AD-language ( Gorno-
Tempini et al., 2004 ; Rabinovici et al., 2008 ). Two AD-visuospatial
related components were found, one involving bilateral occipito-
parieto-temporal hypometabolism and right PCC / lateral parietal hy-
pometabolism. Again, this is in agreement with previous literature
showing focal patterns of neurodegeneration with bilateral occipito-
parieto-temporal atrophy and hypometabolism in AD-visuospatial
( Lehmann et al., 2011 ; Migliaccio et al., 2009 ; Rosenbloom et al., 2011 ;
Whitwell et al., 2007 ). The AD-memory variant was not significantly
associated with a specific FDG component. This may be due to inclu-
sion of relatively young AD patients in this group —patients with early
age-of-onset AD show relatively diffuse cognitive deficits, including
involvement of language and visuospatial domains that overlaps with
the more focal AD-language and AD-visuospatial groups ( Lehmann et
al., 2012 ; Migliaccio et al., 2009 ). However, at a trend level we found
an association with a primarily frontal component, consistent with
previous studies demonstrating that frontal involvement may distin-
guish this variant of AD from others ( Lehmann et al. , 2013a and b ;
Migliaccio, 2009 ).
In contrast with the syndrome-specific FDG components, PiB bind-
ing was similar across clinical syndromes. The fact that none of the
seven estimated amyloid deposition components were significant
predictors of clinical conditions is congruent with most studies, which
have reported overlapping patterns of amyloid accumulation in dis-
tinct variants of AD ( Lehmann et al., 2013a ). Although single case
reports and small series initially reported atypical binding patterns in
AD-language and AD-visuospatial ( Ng et al., 2007 ), larger series have
found a diffuse pattern indistinguishable from typical AD and dissoci-
ated from their focal structural and metabolic signatures ( de Souza et
al., 2011 ; Lehmann et al., 2013a ; Leyton et al., 2011 ; Rabinovici et al.,
2008 ; Rosenbloom et al., 2011 ). Other studies comparing PiB binding
in early and late age-of-onset AD found that differences in cogni-
tive profiles could not be explained by the distribution or burden of
PiB, which was identical in the groups ( Rabinovici et al., 2010 ). Al-
together, our multivariate pICA strategy replicated previous findings
using mass-univariate voxel-wise group comparisons.
4.2. Linking metabolic patterns to specific networks of degeneration in
There is accumulating evidence that neurodegeneration occurs in
specific networks in the brain ( Seeley et al., 2009 ; Zhou et al., 2012 ).
A recent PET study ( Lehmann et al., 2013a ) showed that patterns of
glucose hypometabolism in early-onset AD (EOAD), AD-language, and
AD-visuospatial matched the network templates of executive-control,
language, and visual networks, respectively. Notably, the FDG com-
ponent linked by pICA in our study to AD-language bears a striking
resemblance to the language network as identified by task-based or
task-free fMRI ( Fig. 1A ; Shirer et al., 2012 ; Smith et al., 2009 ). The FDG
components linked with AD-visuospatial closely resemble a high-
order visual network ( Fig. 1B ) and a right hemisphere posterior de-
fault mode network (DMN; Fig. 1C ). These findings support a recently
proposed model postulating that the emergence of heterogeneous AD
phenotypes is related to the involvement of specific functional net-
works that converge in the DMN ( Lehmann et al., 2013b ). This model
integrates the hypothesis that aggregation of amyloid-beta may be
driven by total flow of neuronal activity (yielding diffuse and sym-
metric patterns of PiB binding throughout ‘cortical hubs ’ ), whereas
R. Laforce Jr et al. / NeuroImage: Clinical 4 (2014) 508–516
Fig. 1. A) AD-language associated component included left inferior frontal and temporoparietal hypometabolism, B) AD-visuospatial related component 1 involved bilateral
occipito-parieto-temporal hypometabolism, C) AD-visuospatial related component 2 involved right PCC / precuneus and right lateral parietal hypometabolism, and D) AD-memory
associated component included predominantly bilateral inferior frontal, but also cuneus and inferior temporal, and right inferior parietal hypometabolism. Images are presented
on a study-specific template in radiological convention. Supplementary Figures S1–S4 illustrate the associations with clinical groups.
the aggregation of tau may be driven by transneuronal spread, gener-
ating patterns of neurodegeneration that coincide with specific func-
tional networks and ultimately lead to specific clinical phenotypes
( de Calignon et al., 2012 ; Seeley et al., 2009 ; Zhou et al., 2012 ).
4.3. Spatially distributed relationship between amyloid deposition and
An additional goal of this study was to explore spatially dis-
parate relationships between patterns of A β deposition and glucose
metabolism across a heterogenous AD population. We found a sig-
nificant pICA component pair, in which increased frontal and de-
creased PCC / precuneus β-amyloid deposition was correlated with
decreased glucose metabolism in frontal, occipital and lateral tem-
poral regions. The patterns of PiB and FDG showed partial overlap
in medial prefrontal cortex, but were otherwise spatially disparate.
In the context of current models of the AD pathophysiologic cascade
( Jack et al., 2013 ), these findings suggest that A βmay exert both local
and remote effects on brain metabolism, the latter potentially due
to deafferentation of remote areas ( Bourgeat et al., 2010 ). Traditional
univariate approaches have similarly demonstrated both local and
remote correlations between PiB and FDG ( Cohen et al., 2009 ; Edison
et al., 2007 ; Engler et al., 2006 ). Our results are further congruent
with a recent report in mild cognitive impairment, which showed via
pICA that increased amyloid- β burden in the left precuneus / cuneus
and medial-temporal regions was associated with increased brain
atrophy rates in the left medial-temporal and parietal regions, while
increased amyloid- βburden in bilateral precuneus / cuneus and pari-
etal regions was associated with increased brain atrophy rates in the
right medial temporal regions ( Tosun et al., 2011 ).
Intriguingly, the PiB component in this pair consisted of increased
medial frontal and decreased PCC / precuneus binding. It is important
not to misinterpret this finding as evidence of low amyloid in PCC /
precuneus — rather it must be interpreted as a dynamic relationship
between regional levels of amyloid accumulation (high in the medial
frontal cortex, low in the PCC / precuneus) and brain metabolism. This
raises the possibility that variations in amyloid aggregation within key
regions of the DMN may modulate the pattern of neurodegeneration
in AD. Notably, hypometabolism in the prefrontal and occipital cortex
typically occurs in advanced clinical stages of AD ( Kim et al., 2005 ),
whereas medial prefrontal amyloid aggregation may be an early event
in the AD cascade ( Sepulcre et al., 2013 ), further underscoring the
relative resilience of the prefrontal cortex to AD pathology ( Furst et
al., 2012 ). While the reliability and significance of this observation
will require further (and ideally longitudinal) study, our observation
underscores the complexity of the relationship between amyloid and
metabolism, which appears to vary by brain region and disease state
( Cohen et al., 2009 ; La Joie et al., 2012 ). Future studies with larger
sample sizes should also attempt to explore whether joint spatial
relationships between PiB and FDG correlate with specific clinical
features or neuropsychological profiles.
R. Laforce Jr et al. / NeuroImage: Clinical 4 (2014) 508–516
Fig. 2. Component pair depicting an association between hypometabolism and amyloid deposition. The spatial extent of this component pair showed that increased frontal
and decreased PCC / precuneus β-amyloid deposition correlated with decreased glucose metabolism in the frontal, occipital and temporal regions. Images are presented on a
study-specific template in radiological convention. Abbreviations. Amyloid beta (A β); Alzheimer’s disease (AD); default mode network (DMN); posterior cingulate cortex (PCC).
4.4. Role of multivariate analyses
Use of multivariate techniques in the analysis of imaging data
is gaining traction on the field. Multivariate approaches evaluate
correlation / covariance of data across brain regions rather than pro-
ceeding on a voxel-by-voxel basis. They may be particularly helpful
when integrating multiple imaging modalities that capture different
elements of disease pathophysiology ( Tosun et al., 2011 ). Unlike uni-
variate approaches, which treat each voxel or region as a spatially
independent unit, multivariate analyses can explicitly examine the
inter-relationship among these units and allow for better inference
of the biological interconnectivity among brain regions ( Devanand
et al., 2006 ; Eidelberg et al., 1991 ; Moeller and Eidelberg, 1997 ; see
O’Toole et al., 2007 for a review). Thus, results can be more easily
interpreted as a signature of neural networks, which may be a key
element to understanding the heterogeneity and pathophysiology of
neurodegenerative disease ( Lehmann et al. , 2013a and b ; Seeley et
al., 2009 ; Sepulcre et al., 2013 ; Zhang et al., 2012 ). Such approaches
also provide greater statistical power when compared with univari-
ate techniques, which are forced to employ very stringent, and often
overly conservative, corrections for voxel-wise multiple comparisons.
These strengths can be leveraged to improve diagnostic classification,
as seen in our joint classification analysis, where the combination of
PiB and FDG improved the discrimination of clinical variants com-
pared to FDG alone ( Habeck et al., 2008 ).
This study has several limitations. While our patients met the NIA–
AA criteria for high-likelihood AD, pathological confirmation of the
diagnosis was not available. Our sample size was too small to explore
relationships between individual components and specific cognitive
tests. We could not include a structural imaging component because
MRIs were performed on four different scanners with three differ-
ent magnetic field strengths. We did not correct PET data for partial
volume loss, because (1) we were interested in FDG as a marker of
neurodegeneration (rather than glucose metabolism per se), espe-
cially in lieu of MRI data, such that it was advantageous to include the
effects of atrophy and (2) atrophy correction of PiB data can introduce
significant bias, and the utility and optimal methods are controver-
sial ( Thomas et al., 2011 ). In the past we have found identical results
with and without atrophy correction examining similar relationships
with univariate methods ( Lehmann et al., 2013a ; Rabinovici et al.,
2010 ; Rosenbloom et al., 2011 ). pICA assumes that measurements in
each image voxel are independent and that the overall noise is iden-
tically distributed assumptions which may not be fully met by PET
data. PiB is a relatively novel tracer and may have ceiling effects or
undescribed binding interactions that may limit data interpretation.
Furthermore, PiB binds to fibrillar forms of A β but not to the more
toxic soluble A β aggregates. Finally, as discussed above, our cross-
sectional design limits inferences about cause / effect and temporal
relationships between amyloid aggregation and brain metabolism —
R. Laforce et al. / NeuroImage: Clinical 4 (2014) 508–516
further, longitudinal studies will be needed to further clarify these
Multivariate analysis of PET data from a clinically heterogeneous
population of patients with probable AD showed that clinical pheno-
type correlated with independent components of glucose metabolism
but not with patterns of β-amyloid deposition. These findings are
strikingly similar to those derived from univariate paradigms and
provide additional support for involvement of specific functional net-
works in clinical variants of AD via A β-independent mechanisms.
pICA revealed that β-amyloid deposition and glucose metabolism
show both local and spatially disparate relationships across the brain,
highlighting the complexity of the relationship between molecular
pathology and neurodegeneration in AD. Further clarifying the re-
lations between these processes is of utmost importance given the
effort to develop treatments targeting specific events in the AD patho-
Conflicts of interest
RL has nothing to disclose. GDR received consulting fees from Eli
Lilly and GE Healthcare and receives grant support from Avid Radio-
pharmaceuticals, a wholly-owned subsidiary of Eli Lilly and Company
that is developing amyloid PET tracers for commercial purposes. WJJ
has consulted for Synarc and for Hoffman LaRoche.
We would like to thank all patients and their families who partic-
ipated in this study. We are also grateful to Dr Hwamee Oh for her
advice on multivariate statistical issues. This work was supported by
a Fellowship grant from Fonds de Recherche en Sant ´ e du Qu ´ ebec (to
RL), National Institute on Aging grants K23-AG031861 (to GDR), P01-
AG1972403 and P50-AG023501 (to BLM), R01-AG027859 (to WJJ);
Alzheimer’s Association grant NIRG-07-59422 (to GDR); Alzheimer’s
Research UK grant ART-TRFUS2011-2 (to ML); John Douglas French
Alzheimer’s Foundation grant (to GDR); State of California Depart-
ment of Health Services Alzheimer’s Disease Research Center of Cal-
ifornia grant 04-33516 (to BLM); and Hellman Family Foundation
grant (to GDR).
Supplementary material associated with this article can be found,
in the online version, at doi:10.1016 / j.nicl.2014.03.005 .
Bourgeat, P., Ch ´ etelat, G., Villemagne, V.L., Fripp, J., Raniga, P., Pike, K., et al. 2010.
Beta-amyloid burden in the temporal neocortex is related to hippocampal atrophy
in elderly subjects without dementia. Neurology 74 (2), 121–7. http://dx.doi.org/
10.1212/WNL.0b013e3181c918b5 , 20065247 .
Brickman, A.M., Habeck, C., Zarahn, E., Flynn, J., Stern, Y., 2007. Structural MRI covari-
ance patterns associated with normal aging and neuropsychological functioning.
Neurobiology of Aging 28 (2), 284–95. http://dx.doi.org/10.1016/j.neurobiolaging.
2005.12.016 , 16469419 .
Calhoun, V.D., Adali, T., Kiehl, K.A., Astur, R., Pekar, J.J., Pearlson, G.D., 2006. A method
for multitask fMRI data fusion applied to schizophrenia. Human Brain Mapping 27
(7), 598–610. http://dx.doi.org/10.1002/hbm.20204 , 16342150 .
Cohen, A.D., Price, J.C., Weissfeld, L.A., James, J., Rosario, B.L., Bi, W., et al. 2009. Basal
cerebral metabolism may modulate the cognitive effects of Abeta in mild cognitive
impairment: an example of brain reserve. Journal of Neuroscience: the Official
Journal of the Society for Neuroscience 29 (47), 14770–8. http://dx.doi.org/10.
1523/JNEUROSCI.3669-09.2009 , 19940172 .
de Calignon, A., Polydoro, M., Su ´ arez-Calvet, M., William, C., Adamowicz, D.H., Kopeik-
ina, K.J., et al. 2012. Propagation of tau pathology in a model of early Alzheimer’s
disease. Neuron 73 (4), 685–97. http://dx.doi.org/10.1016/j.neuron.2011.11.033 ,
de Souza, L.C., Corlier, F., Habert, M.O., Uspenskaya, O., Maroy, R., Lamari, F., et al. 2011.
Similar amyloid-beta burden in posterior cortical atrophy and Alzheimer’s disease.
Brain: a Journal of Neurology 134 (7), 2036–43. http://dx.doi.org/10.1093/brain/
awr130 , 21705422 .
Devanand, D.P., Habeck, C.G., Tabert, M.H., Scarmeas, N., Pelton, G.H., Moeller, J.R.,
et al. 2006. PET network abnormalities and cognitive decline in patients with
mild cognitive impairment. Neuropsychopharmacology: Official Publication of the
American College of Neuropsychopharmacology 31 (6), 1327–34, 16292330 .
Dubois, B., Feldman, H.H., Jacova, C., Dekosky, S.T., Barberger-Gateau, P., Cummings,
J., et al. 2007. Research criteria for the diagnosis of Alzheimer’s disease: revising
the NINCDS–ADRDA criteria. Lancet Neurology 6 (8), 734–46. http://dx.doi.org/10.
1016/S1474- 4422(07)70178- 3 , 17616482 .
Edison, P., Archer, H.A., Hinz, R., Hammers, A., Pavese, N., Tai, Y.F., et al. 2007. Amyloid,
hypometabolism, and cognition in Alzheimer disease: an [
PET study. Neurology 68 (7), 501–8. http://dx.doi.org/10.1212/01.wnl.0000244749.
20056.d4 , 17065593 .
Eidelberg, D., Dhawan, V., Moeller, J.R., Sidtis, J.J., Ginos, J.Z., Strother, S.C., et al. 1991.
The metabolic landscape of corticobasal ganglionic degeneration: regional asym-
metries studied with positron emission tomography. Journal of Neurology, Neuro-
surgery, and Psychiatry 54 (10), 856–62. http://dx.doi.org/10.1136/jnnp.54.10.856 ,
Engler, H., Forsberg, A., Almkvist, O., Blomquist, G., Larsson, E., Savitcheva, I., et al. 2006.
Two-year follow-up of amyloid deposition in patients with Alzheimer’s disease.
Brain: a Journal of Neurology 129 (11), 2856–66. http://dx.doi.org/10.1093/brain/
awl178 , 16854944 .
Folstein,M.F., Folstein,S.E., McHugh,P.R., 1975.“Mini-MentalState”. Apracticalmethod
for grading the cognitive state of patients for the clinician. Journal of Psychi-
atric Research 12 (3), 189–98. http://dx.doi.org/10.1016/0022- 3956(75)90026- 6 ,
Furst, A.J., Rabinovici, G.D., Rostomian, A.H., Steed, T., Alkalay, A., Racine, C., et al.
2012. Cognition, glucose metabolism and amyloid burden in Alzheimer’s disease.
Neurobiology of Aging 33 (2), 215–25. http://dx.doi.org/10.1016/j.neurobiolaging.
2010.03.011 , 20417582 .
Gorno-Tempini, M.L., Dronkers, N.F., Rankin, K.P., Ogar, J.M., Phengrasamy, L., Rosen,
H.J., et al. 2004. Cognition and anatomy in three variants of primary progressive
aphasia. Annals of Neurology 55 (3), 335–46. http://dx.doi.org/10.1002/ana.10825 ,
Gorno-Tempini, M.L., Hillis, A.E., Weintraub, S., Kertesz, A., Mendez, M., Cappa,
S.F., et al. 2011. Classification of primary progressive aphasia and its variants.
Neurology 76 (11), 1006–14. http://dx.doi.org/10.1212/WNL.0b013e31821103e6 ,
Habeck, C., Foster, N.L., Perneczky, R., Kurz, A., Alexopoulos, P., Koeppe, R.A., et al.
2008. Multivariate and univariateneuroimaging biomarkers of Alzheimer’s disease.
NeuroImage 40 (4), 1503–15. http://dx.doi.org/10.1016/j.neuroimage.2008.01.056 ,
Jack, C.R. Jr., Knopman, D.S., Jagust, W.J., Petersen, R.C., Weiner, M.W., Aisen, P.S., et al.
2013. Tracking pathophysiological processes in Alzheimer’s disease: an updated
hypothetical model of dynamic biomarkers. Lancet Neurology 12 (2), 207–16. http:
//dx.doi.org/10.1016/S1474- 4422(12)70291- 0 , 23332364 .
Kim,E.J.,Cho, S.S.,Jeong, Y.,Park, K.C., Kang,S.J., Kang,E., etal. 2005. Glucosemetabolism
in earlyonset versuslate onsetAlzheimer’s disease:an SPManalysis of120 patients.
Brain: a Journal of Neurology 128 (8), 1790–801. http://dx.doi.org/10.1093/brain/
awh539 , 15888536 .
Kramer, J.H., Jurik, J., Sha, S.J., Rankin, K.P., Rosen, H.J., Johnson, J.K., et al. 2003.
Distinctive neuropsychological patterns in frontotemporal dementia, semantic
dementia, and Alzheimer disease. Cognitive and Behavioral Neurology: Official
Journal of the Society for Behavioral and Cognitive Neurology 16 (4), 211–18.
http://dx.doi.org/10.1097/00146965- 200312000- 00002 , 14665820 .
La, Joie R., Perrotin, A., Barr ´ e, L., Hommet, C., M ´ ezenge, F., Ibazizene, M., et al. 2012.
Region-specific hierarchy between atrophy, hypometabolism, and β-amyloid (A β)
load in Alzheimer’s disease dementia. Journal of Neuroscience: the Official Jour-
nal of the Society for Neuroscience 32 (46), 16265–73. http://dx.doi.org/10.1523/
JNEUROSCI.2170-12.2012 , 23152610 .
Lehmann, M., Crutch, S.J., Ridgway, G.R., Ridha, B.H., Barnes, J., Warrington, E.K., et al.
2011. Cortical thickness and voxel-based morphometry in posterior cortical at-
rophy and typical. Alzheimer’S Disease. Neurobiology of Aging 32 (8), 1466–76.
Lehmann, M., Barnes, J., Ridgway, G.R., Ryan, N.S., Warrington, E.K., Crutch, S.J.,
et al. 2012. Global gray matter changes in posterior cortical atrophy: a serial
imaging study. Alzheimer’s & Dementia: the Journal of the Alzheimer’s Associa-
tion 8, 502–12. http://dx.doi.org/10.1016/j.jalz.2011.09.22510.1016/j.jalz.2012.05 ,
Lehmann, M., Ghosh, P.M., Madison, C., Laforce, R. Jr., Corbetta-Rastelli, C., Weiner,
M.W., et al. 2013. Diverging patterns of amyloid deposition and hypometabolism
in clinical variants of probable Alzheimer’s disease. Brain: a Journal of Neurology
136 (3), 844–58. http://dx.doi.org/10.1093/brain/aws327 , 23358601 .
Lehmann, M., Madison, C.M., Ghosh, P.M., Seeley, W.W., Mormino, E., Greicius, M.D.,
et al. 2013. Intrinsic connectivity networks in healthy subjects explain clinical
variability in Alzheimer’s disease. Proceedings of the National Academy of Sciences
of the United States of America 110 (28), 11606–11. http://dx.doi.org/10.1073/
pnas.1221536110 , 23798398 .
Leyton, C.E., Villemagne, V.L., Savage, S., Pike, K.E., Ballard, K.J., Piguet, O., et al. 2011.
Subtypes of progressive aphasia: application of the international consensus criteria
and validation using beta-amyloid imaging. Brain: a Journal of Neurology 134 (10),
3030–43. http://dx.doi.org/10.1093/brain/awr216 , 21908392 .
11 C]PIB and [
516 Download full-text
R. Laforce Jr et al. / NeuroImage: Clinical 4 (2014) 508–516
Li, Y., Rinne, J.O., Mosconi, L., Pirraglia, E., Rusinek, H., DeSanti, S., et al. 2008. Re-
gional analysis of FDG and PIB-PET images in normal aging, mild cognitive impair-
ment, and Alzheimer’s disease. European Journal of Nuclear Medicine and Molec-
ular imaging 35 (12), 2169–81. http://dx.doi.org/10.1007/s00259- 008- 0833- y ,
Liu, J., Pearlson, G., Windemuth, A., Ruano, G., Perrone-Bizzozero, N.I., Calhoun, V.,
2009. Combining fMRI and SNP data to investigate connections between brain
function and genetics using parallel ICA. Human Brain Mapping 30 (1), 241–55.
http://dx.doi.org/10.1002/hbm.20508 , 18072279 .
Logan, J., Fowler, J.S., Volkow, N.D., Wang, G.J., Ding, Y.S., Alexoff, D.L., 1996. Distribution
volume ratios without blood sampling from graphical analysis of PET data. Journal
of Cerebral Blood Flow and Metabolism: Official Journal of the International Society
of Cerebral Blood Flow and Metabolism 16 (5), 834–40, 8784228 .
McKhann, G.M., Knopman, D.S., Chertkow, H., Hyman, B.T., Jack, C.R. Jr., Kawas, C.H.,
et al. 2011. The diagnosis of dementia due to Alzheimer’s disease: recommenda-
tions from the National Institute on Aging-Alzheimer’s Association workgroups on
diagnostic guidelines for Alzheimer’s disease. Alzheimer’s & Dementia: the Journal
of the Alzheimer’s Association 7 (3), 263–9. http://dx.doi.org/10.1016/j.jalz.2011.
03.005 , 21514250 .
Migliaccio, R., Agosta, F., Rascovsky, K., Karydas, A., Bonasera, S., Rabinovici, G.D.,
et al. 2009. Clinical syndromes associated with posterior atrophy: early age at
onset AD spectrum. Neurology 73 (19), 1571–8. http://dx.doi.org/10.1212/WNL.
0b013e3181c0d427 , 19901249 .
Moeller, J.R., Eidelberg, D., 1997. Divergent expression of regional metabolic topogra-
phies in Parkinson’s disease and normal ageing. Brain: a Journal of Neurology 120
(12), 2197–206. http://dx.doi.org/10.1093/brain/120.12.2197 , 9448575 .
Mormino, E.C., Brandel, M.G., Madison, C.M., Rabinovici, G.D., Marks, S., Baker, S.L.,
et al. 2012. Not quite PIB-positive, not quite PIB-negative: slight PIB elevations
in elderly normal control subjects are biologically relevant. Neuroimage 59 (2),
1152–60. http://dx.doi.org/10.1016/j.neuroimage.2011.07.098 , 21884802 .
Morris, J.C., 1993. The Clinical Dementia Rating (CDR): Current version and scor-
ing rules. Neurology 43 (11), 2412–14. http://dx.doi.org/10.1212/WNL.43.11.
2412-a10.1212/WNL.43.11.2412 , 8232972 .
Mueller, S.G., Laxer, K.D., Barakos, J., Cheong, I., Garcia, P., Weiner, M.W., 2009.
Widespread neocortical abnormalities in temporal lobe epilepsy with and with-
out mesial sclerosis. NeuroImage 46 (2), 353–9. http://dx.doi.org/10.1016/j.
neuroimage.2009.02.020 , 19249372 .
Ng, S.Y., Villemagne, V.L., Masters, C.L., Rowe, C.C., 2007. Evaluating atypical dementia
syndromes using positron emission tomography with carbon 11 labeled Pittsburgh
Compound B. Archives of Neurology 64 (8), 1140–4. http://dx.doi.org/10.1001/
archneur.64.8.1140 , 17698704 .
O’Toole, A.J., Jiang, F., Abdi, H., P ´ enard, N., Dunlop, J.P., Parent, M.A., 2007. Theoretical,
statistical, and practical perspectives on pattern-based classification approaches to
the analysis of functional neuroimaging data. Journal of Cognitive Neuroscience 19
(11), 1735–52. http://dx.doi.org/10.1162/jocn.2007.19.11.1735 , 17958478 .
Price, J.C., Klunk, W.E., Lopresti, B.J., Lu, X., Hoge, J.A., Ziolko, S.K., et al. 2005. Ki-
netic modeling of amyloid binding in humans using PET imaging and Pittsburgh
Compound-B. Journal of Cerebral Blood Flow & Metabolism 25 (11), 1528–47.
http://dx.doi.org/10.1038/sj.jcbfm.9600146 , 15944649 .
Rabinovici, G.D., Furst, A.J., Alkalay, A., Racine, C.A., O’Neil, J.P., Janabi, M., et al. 2010.
Increased metabolic vulnerability in early-onset Alzheimer’s disease is not re-
lated to amyloid burden. Brain 133 (2), 512–28. http://dx.doi.org/10.1093/brain/
Rabinovici, G.D., Furst, A.J., O’Neil, J.P., Racine, C.A., Mormino, E.C., Baker, S.L., et al.
2007. 11 C-PIB PET imaging in Alzheimer disease and frontotemporal lobar degener-
ation. Neurology 68 (15), 1205–12. http://dx.doi.org/10.1212/01.wnl.0000259035.
98480.ed , 17420404 .
Rabinovici, G.D., Jagust, W.J., Furst, A.J., Ogar, J.M., Racine, C.A., Mormino, E.C., et al.
2008. Abeta amyloid and glucose metabolism in three variants of primary progres-
sive aphasia. Annals of Neurology 64 (4), 388–401. http://dx.doi.org/10.1002/ana.
21451 , 18991338 .
Rabinovici, G.D., Rosen, H.J., Alkalay, A., Kornak, J., Furst, A.J., Agarwal, N., et al. 2011.
Amyloid vs FDG-PET in the differential diagnosis of AD and FTLD. Neurology 77
(23), 2034–42. http://dx.doi.org/10.1212/WNL.0b013e31823b9c5e , 22131541 .
Rachakonda, S., Liu, J., Calhoun, V. (2008), Fusion ICA Toolbox (FIT)
Rosen, H.J., Gorno-Tempini, M.L., Goldman, W.P., Perry, R.J., Schuff, N., Weiner, M.,
et al. 2002. Patterns of brain atrophy in frontotemporal dementia and seman-
tic dementia. Neurology 58 (2), 198–208. http://dx.doi.org/10.1212/WNL.58.2.198 ,
Rosenbloom, M.H., Alkalay, A., Agarwal, N., Baker, S.L., O’Neil, J.P., Janabi, M., et al.
2011. Distinct clinical and metabolic deficits in PCA and AD are not related to
amyloid distribution. Neurology 76 (21), 1789–96. http://dx.doi.org/10.1212/WNL.
0b013e31821cccad , 21525424 .
Scheltens, P., Erkinjunti, T., Leys, D., Wahlund, L.O., Inzitari, D., del Ser, T., et al. 1998.
White matter changes on CT and MRI: an overview of visual rating scales. European
Task Force on Age-Related White Matter Changes. European Neurology 39 (2), 80–
9. http://dx.doi.org/10.1159/000007921 , 9520068 .
Seeley, W.W., Crawford, R.K., Zhou, J., Miller, B.L., Greicius, M.D., 2009. Neurodegen-
erative diseases target large-scale human brain networks. Neuron 62 (1), 42–52.
http://dx.doi.org/10.1016/j.neuron.2009.03.024 , 19376066 .
Sepulcre, J.,Sabuncu, M.R.,Becker, A.,Sperling, R.,Johnson,K.A., 2013. In vivo characteri-
zation of the early states of the amyloid-beta network. Brain: a Journal of Neurology
136 (7), 2239–52. http://dx.doi.org/10.1093/brain/awt146 , 23801740 .
Shirer, W.R., Ryali, S., Rykhlevskaia, E., Menon, V., Greicius, M.D., 2012. Decoding
subject-driven cognitive states with whole-brain connectivity patterns. Cerebral
Cortex (New York, N.Y.: 1991) 22 (1), 158–65. http://dx.doi.org/10.1093/cercor/
bhr099 , 21616982 .
Smith, S.M., Fox, P.T., Miller, K.L., Glahn, D.C., Fox, P.M., Mackay, C.E., et al. 2009.
Correspondence of the brain’s functional architecture during activation and rest.
Proceedings of the National Academy of Sciences of the United States of America
106, 13040–5. http://dx.doi.org/10.1073/pnas.0905267106 , 19620724 .
Tang-Wai, D.F., Graff-Radford, N.R., Boeve, B.F., Dickson, D.W., Parisi, J.E., Crook, R., et al.
2004. Clinical, genetic, and neuropathologic characteristics of posterior cortical at-
rophy. Neurology 63 (7), 1168–74. http://dx.doi.org/10.1212/01.WNL.0000140289.
18472.15 , 15477533 .
Thomas, B.A., Erlandsson, K., Modat, M., Thurfjell, L., Vandenberghe, R., Ourselin, S., et al.
2011. The importance of appropriate partial volume correction for PET quantifica-
tion in Alzheimer’s disease. European Journal of Nuclear Medicine and Molec-
ular imaging 38 (6), 1104–19. http://dx.doi.org/10.1007/s00259- 011- 1745- 9 ,
Tosun, D., Schuff, N., Mathis, C.A., Jagust, W., Weiner, M.W., 2011. Spatial patterns of
brain amyloid-beta burden and atrophy rate associations in mild cognitive impair-
ment. Brain: a Journal of Neurology 134 (4), 1077–88. http://dx.doi.org/10.1093/
brain/awr044 , 21429865 .
Whitwell, J.L., Jack, C.R. Jr., Kantarci, K., Weigand, S.D., Boeve, B.F., Knopman, D.S., et al.
2007. Imaging correlates of posterior cortical atrophy. Neurobiology of Aging 28 (7),
1051–61. http://dx.doi.org/10.1016/j.neurobiolaging.2006.05.026 , 16797786 .
Zhang, S., Li, C.S., 2012. Functional connectivity mapping of the human precuneus
by resting state fMRI. Neuroimage 59 (4), 3548–62. http://dx.doi.org/10.1016/j.
neuroimage.2011.11.023 , 22116037 .
Zhou, J., Gennatas, E.D., Kramer, J.H., Miller, B.L., Seeley, W.W., 2012. Predicting regional
neurodegeneration from the healthy brain functional connectome. Neuron 73 (6),
1216–27. http://dx.doi.org/10.1016/j.neuron.2012.03.004 , 22445348 .