Longitudinal brain metabolic changes from amnestic
mild cognitive impairment to Alzheimer’s disease.
Marine Fouquet, B´ eatrice Desgranges, Brigitte Landeau, Edouard Duchesnay,
Florence M´ ezenge, Vincent De La Sayette, Fausto Viader, Jean-Claude Baron,
Francis Eustache, Ga¨ el Ch´ etelat
To cite this version:
Marine Fouquet, B´ eatrice Desgranges, Brigitte Landeau, Edouard Duchesnay, Florence
M´ ezenge, et al.. Longitudinal brain metabolic changes from amnestic mild cognitive impair-
ment to Alzheimer’s disease.. Brain, Oxford University Press (OUP): Policy B - Oxford Open
Option B, 2009, 132 (Pt 8), pp.2058-67. <10.1093/brain/awp132>. <inserm-00494098>
HAL Id: inserm-00494098
Submitted on 22 Jun 2010
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LONGITUDINAL BRAIN METABOLIC CHANGES FROM AMNESTIC MILD
COGNITIVE IMPAIRMENT TO ALZHEIMER’S DISEASE
Marine Fouquet,1 Béatrice Desgranges,1 Brigitte Landeau, 1 Edouard Duchesnay, 2
Florence Mézenge,1 Vincent de la Sayette, 1,3 Fausto Viader, 1,3 Jean-Claude Baron, 4
Francis Eustache, 1 Gaël Chételat, 1
1 Inserm - EPHE - Université de Caen/Basse-Normandie, Unité U923, GIP Cyceron,
CHU Côte de Nacre, Caen, France
2 CEA, Laboratoire de Neuroimagerie Assistée par Ordinateur, NeuroSpin, Gif-sur-
3 Département de Neurologie, CHU Côte de Nacre, Caen, France
4 Department of clinical Neurosciences, University of Cambridge, UK
Gaël Chételat, Inserm - EPHE - Université de Caen Basse Normandie, Unité U923,
Laboratoire de Neuropsychologie, GIP Cyceron, Bd H Becquerel, 14074 Caen
cedex, France; Tel: +33 (0)2 31 47 01 07; Fax: +33 (0)2 31 47 02 75; E-mail:
Running title: Metabolic changes from aMCI to AD
Number of words in the body of the manuscript: 3672
A sensitive marker for monitoring progression of early Alzheimer‟s Disease (AD)
would help to develop and test new therapeutic strategies. The present study aimed
at investigating brain metabolism changes over time, as potential monitoring marker,
in patients with amnestic Mild Cognitive Impairment (aMCI), according to their clinical
outcome (converters or non-converters), and in relation to their cognitive decline.
Seventeen aMCI patients underwent MRI and 18FDG-PET scans both at inclusion
and 18 months later. Baseline and follow-up PET data were corrected for partial
volume effects and spatially normalized using MRI data, scaled to the vermis and
compared using SPM2. „PET-PAC‟ maps reflecting metabolic percent annual
changes were created for correlation analyses with cognitive decline. In the whole
sample, the greatest metabolic decrease concerned the posterior cingulate-
precuneus area. Converters had significantly greater metabolic decrease than non-
converters in two ventro-medial prefrontal areas, the subgenual (BA25) and anterior
cingulate (BA24/32). PET-PAC in BA25 and BA24/32 combined allowed complete
between-group discrimination. BA25 PET-PAC significantly correlated with both
cognitive decline and PET-PAC in the hippocampal region and temporal pole, while
BA24/32 PET-PAC correlated with posterior cingulate PET-PAC. Finally, the
metabolic change in BA8/9/10 was inversely related to that in BA25 and showed
relative increase with cognitive decline, suggesting that compensatory processes
may occur in this dorso-medial prefrontal region. The observed ventro-medial
prefrontal disruption is likely to reflect disconnection from the hippocampus, both
indirectly through the cingulum bundle and posterior cingulate cortex for BA24/32,
and directly through the uncinate fasciculus for BA25. Altogether, our findings
emphasize the potential of 18FDG-PET for monitoring early AD progression.
amnestic Mild Cognitive Impairment, 18FDG-PET monitoring, ventro-medial prefrontal
cortex, longitudinal study
To develop and test new therapeutic strategies for Alzheimer‟s Disease (AD),
sensitive markers for monitoring disease progression are urgently needed especially
at its earliest stages, when neuropathological damage is still confined.
Positron Emission Tomography with 2-[18F]-Fluoro-2-Deoxy-D-Glucose
(18FDG-PET) is exquisitely sensitive to early AD-related brain changes. Significant
hypometabolism can be detected in patients with amnestic Mild Cognitive Impairment
(aMCI) that best represents the pre-dementia stage of AD Petersen, 2005. The
earliest metabolic changes involve the precuneus - posterior cingulate cortex (PCC)
and temporo-parietal areas (Salmon et al., 1994; Minoshima et al., 1997; Drzezga et
al., 2003; Nestor et al., 2003a; Chételat et al., 2003b; Nestor et al., 2003b; Nestor et
al., 2004; Mosconi, 2005; Ishii et al., 2005; Kawachi et al., 2006). By contrast, the
frontal cortex appears involved at the dementia stage (Minoshima et al., 1997;
Desgranges et al., 1998; Alexander et al., 2002; Herholz et al., 2002), suggesting
that metabolic changes should be detectable from aMCI to clinically probable AD.
Nonetheless, little is known about the accuracy of 18FDG-PET to monitor the
progression of early AD and only one previous longitudinal 18FDG-PET study in aMCI
patients has been published sofar (Drzezga et al., 2003). This study highlighted
significantly greater metabolic decreases in the right middle frontal gyrus in those
patients who converted to clinically probable AD as compared to non-converters over
a one-year follow-up period. However, whilst clinically meaningful, the dichotomous
approach used in this study (i.e. comparing rapid converters to non-converters) is
limited by the fact that non-converters do include patients who will later progress to
AD. A comprehensive approach should also consider all aMCI patients, whether they
rapidly convert or not, assessing in a whole sample analysis brain metabolic changes
in relation to cognitive decline over time (Chételat et al., 2005a). Indeed, these two
approaches would appear as complementary as the former would allow identifying
specific changes occurring in aMCI patients while they convert to clinically probable
AD while the latter would inform on changes characterizing rapid cognitive decline
whatever the clinical outcome at the end of an arbitrary-defined follow-up period.
Our main objective with the present study was therefore to further investigate
the brain pattern of metabolic changes over the course of early AD using these two
complementary approaches, i.e. comparing converters to non-converters in a
standard fashion, but also across the whole sample in relation to cognitive decline,
implementing methodology specially designed for this purpose. Furthermore, thanks
to supplementary analyses, we aimed at assessing their discriminant accuracy for
monitoring the progression to early AD and investigating the mechanisms underlying
these metabolic changes.
The present sample partly overlaps with those of our previous publications
using baseline PET data (Chételat et al., 2003a; Chételat et al., 2003b; Chételat et
al., 2005a; Mevel et al., 2007) or longitudinal magnetic resonance imaging (MRI) data
(Chételat et al., 2005b; Chételat et al., 2008), although only those patients with both
baseline and follow-up MRI and PET data were included in this study. Briefly, the 17
aMCI patients included here were all recruited through a memory clinic, and all
complained of memory impairment. They were right-handed, aged over 55 years and
had at least 7 years of education (see Table 1 for demographic and clinical
characteristics). They underwent medical, neurological, neuropsychological, and
neuroradiological examinations, and were selected according to current criteria of
aMCI, i.e. isolated episodic memory deficits (<1.5 SD of the normal mean for age and
education), normal performances in other areas of cognition and in global cognition
(assessed with MMSE and Mattis scales), and NINCDS-ADRDA criteria for probable
AD (McKhann et al., 1984) not met (see Chételat et al., 2005a for details). According
to the Declaration of Helsinki, each patient gave written informed consent to
participate in the study, which was approved by the regional ethics committee.
Using the same neuropsychological battery as used at inclusion, all aMCI
patients were evaluated every 6 months over an 18-month follow-up period to assess
whether they met NINCDS-ADRDA criteria of probable AD or not; at the end of the
follow-up period, patients were classified as converters or non-converters,
respectively. Patients were declared as converters if they had impaired performances
(more than 1.5 SD below the normal means according to age and education when
available) in at least one of general intellectual function scales as well as in at least
two areas of cognition including memory, leading to impaired daily activities as
judged by the clinicians from the consultation interviews. Moreover, as an index of
cognitive decline, a Mattis - Percent Annual Change (Mattis-PAC) was obtained for
each patient. This index was calculated by first modelling a simple linear regression
from Mattis scores collected at each neuropsychological evaluation (y=ax+b; where
y=Mattis score and x=time from first evaluation). Then, estimated a and b values
were used to calculate percent change in Mattis scores over 12 months using the
formula: [(12a/b)*100] (Chételat et al., 2005a).
Neuroimaging data acquisition
Within a few days interval at most from inclusion and 18 months later, each
patient underwent MRI and 18FDG-PET scans on the same scanners and using the
same acquisition parameters. MRI consisted of a set of 128 adjacent axial cuts
parallel to the anterior-posterior commissure (AC-PC) line and with slice thickness
1.5 mm and pixel size 1x1 mm, using the SPGR (spin gradient recalled) sequence
(TR=10.3 ms; TE=2.1 ms; FOV=24x18 cm; matrix=256x192). PET data were
collected using the ECAT Exact HR+ device with isotropic resolution of 4.2x4.2x4.6
mm (FOV=158 mm). A catheter was introduced in a vein of the arm to inject the
radiotracer. Following 68Ga transmission scans, three to five mCi of 18FDG were
injected as a bolus at time 0, and a 10 min PET data acquisition started at 50 min
post-injection period. Sixty-three planes were acquired with septa out (volume
acquisition), using a voxel size of 2.2x2.2x2.43 mm (see Chételat et al., 2005a for
PET data processing and analysis
To implement the two complementary analyses described in Introduction,
metabolic changes were evaluated, first, directly from baseline and follow-up PET
data and comparing changes in converters and non-converters. Second, maps
reflecting metabolic percent annual changes, called “PET-PAC” maps in what follows,
were generated for each patients and used to assess relationships with cognitive
decline as well as for supplementary analyses. The following sections will
successively briefly describe the common and specific processing steps for these
analyses. Further details and illustration of these processing steps are provided as
Common processing steps
A first coregistration was performed to place baseline and follow-up MRI and
PET data of each patient in the same space. Second, all PET data were voxel-wise
corrected for partial volume effects (PVE) using the patient contemporary MRI and
the “modified Muller-Gardner” method (Quarantelli et al., 2004). Third, PET data were
scaled using a metabolically preserved brain region, namely the cerebellar vermis
(Mevel et al., 2007), to control for inter- and intra-individual global variations in PET
Optimal spatial normalization parameters, to be used in the subsequent
specific procedures, were estimated from the spatial normalization of MRI data onto a
customized aMCI template using optimal Voxel-Based Morphometry (Good et al.,
2001) as previously used in our laboratory (Chételat et al., 2005b). Note that a single
set of normalization parameters was estimated for each patient so as to normalize
baseline and follow-up PET data using the same parameters to avoid bias due to
differential spatial normalization.
PET data processing for analyses comparing baseline and follow-up PET
The optimal normalization parameters were applied to baseline and follow-up
coregistered, PVE-corrected and scaled PET data resulting from the common
processing steps. Spatially normalized PET data were subsequently smoothed using
a Gaussian kernel of 10mm, and entered into the following statistical analyses.
First, a 'Population main effect: 2 cond's, 1 scan/cond' (paired t-test) with 2
conditions (baseline and follow-up) was performed to assess the pattern of metabolic
evolution in all aMCI patients by comparing baseline to follow-up data (with a 1 -1
contrast). The resulting SPM-T map was projected onto the aMCI whole brain
Second, a 'Multi-group: conditions & covariates' (repeated measures ANOVA)
with 2 groups (converters and non-converters) and 2 conditions (baseline and follow-
up) was performed on the same data to assess the patterns of metabolic evolution in
converters and in non-converters separately, by comparing baseline to follow-up data
in each group (with a 1 -1 and a 0 0 1 -1 contrast, respectively). Resulting SPM-T
maps were projected onto the customized aMCI whole brain template. To highlight
areas of greater metabolic decrease in converters as compared to non-converters,
between-group comparison of baseline minus follow-up PET data was then
performed (through a 1 -1 -1 1 interaction contrast) onto the voxels exhibiting
significant metabolic decreases in converters (using the inclusive masking procedure
Clusters showing significant interaction in the above analysis were also used
to define Volumes Of Interest (VOIs) for subsequent application onto PET-PAC maps
PET data processing for analyses with PET-PAC maps
The baseline and follow-up coregistered, PVE-corrected and scaled PET data
resulting from the common processing steps were used to create individual PET-PAC
maps. These PET-PAC maps represent the voxel-wise calculation of percent
metabolic change over the 18-month follow-up period (i.e. the difference between
follow-up and baseline scaled PET value divided by baseline PET value X 100)
expressed in annual percent change (i.e. multiplied by 12/18). Note that this
calculation was performed only onto those voxels common to both baseline and
follow-up PET data, identified using a masking procedure. The optimal spatial
normalization parameters were then applied to these PET-PAC maps, which were
subsequently smoothed using a Gaussian kernel of 10mm.
A „Single subjects: covariates only‟ analysis was then conducted onto these 17
PET-PAC maps using Mattis-PAC as covariate to assess the relationship between
metabolism changes and global cognitive decline. Both positive and negative
correlations were assessed.
To perform VOI-based discriminant and correlation supplementary analyses,
mean PET-PAC values were extracted from each PET-PAC map in the VOIs defined
above, using the „binary ROIs analysis‟ option of the „fMRI-ROI analysis‟ SPM2
To assess the accuracy of the metabolic changes in the VOIs for monitoring
the progression to AD in converters, a discriminant analysis was performed.
Univariate analyses (T-test) of the mean PET-PAC value of each VOI independently
were computed, and a multivariate F-statistic based on MANOVA analysis was
performed on all VOIs values combined thanks to a Linear Discriminant.
Finally, to highlight the brain networks whose dysfunction or relative
preservation may be related to that of each VOI, correlation analyses were then
performed by entering the mean PET-PAC value of each VOI as covariate in a
„Single subjects: covariates only‟ voxel-based analysis with PET-PAC maps,
assessing both positive and negative correlations, respectively.
All data processing and voxel-based statistical analyses were performed using
SPM2 running on MATLAB 6.5. The threshold for significance was set to
p(uncorrected)<0.005, which is identical (Alexander et al., 2002) or more severe
(Drzezga et al., 2003) than previously used in longitudinal PET studies in AD and
judged to provide the best compromise, neither too permissive nor over-conservative
with risk of type 2 errors.
Baseline and follow-up clinical characteristics are presented in Table 1. Over
the 18 months follow-up period, 7 of the 17 aMCI patients converted to clinical
diagnosis of probable AD. Baseline MMSE scores and follow-up Mattis scores were
lower in converters than in non-converters. As already reported (Chételat et al.,
2005a), the Mattis scores significantly decreased over the follow-up period in
converters, but not in non-converters.
Comparison between baseline and follow-up PET
The patterns of metabolic changes from baseline to follow-up in the whole
aMCI sample, and in converters and non-converters separately, are illustrated in
Figure 1. In the whole aMCI sample, metabolic decreases were largely bilateral and
involved medially the PCC and frontal areas (Brodman areas BA 11 and 24/32), and
laterally the temporo-parietal cortex (with right predominance), insula and inferior
temporal cortex. Assessing converters and non-converters separately, effects were
similar but higher in the former and lower in the latter, a difference that was
particularly prominent in ventro-medial prefrontal regions (BA25 and 24/32). Also, the
PCC changes observed in the whole sample extended to the middle cingulate cortex
The repeated measures ANOVA comparing converters to non-converters
revealed areas of significantly greater metabolism decrease in converters than non-
converters, but not in the reverse contrast. These changes were located in two
distinct ventro-medial prefrontal regions: the left anterior cingulate cortex (BA24/32)
and the subgenual area (BA25; Figure 2A). As described above, these two clusters
were then made into VOIs for the correlation and discriminant analyses.
Positive correlation between PET-PAC maps and Mattis-PAC revealed a
single significant cluster located in the subgenual area (BA25; Figure 2B). The
reverse contrast (i.e. PET-PAC increases with Mattis-PAC decreases) disclosed a
single cluster located in the right dorso-medial prefrontal cortex (BA9/10; Figure 3A).
While a partial overlap was observed between individual values of converters
and non-converters using the mean PET-PAC in BA24/32 (p=0.001; AUC=0.94) or in
BA25 (p=0.006; AUC=0.87) separately, the combination of the mean PET-PAC in
these two VOIs improved the between-group discrimination (p=0.0001; AUC=1;
Positive correlation between BA24/32 mean PET-PAC value and PET-PAC
maps highlighted surrounding medial prefrontal areas (BA24/32/11) as well as the
right PCC including the retrosplenial cortex (BA23/26/29; Figure 5). The reverse
contrast did not reveal any significant negative correlation.
Positive correlation between BA25 mean PET-PAC value and PET-PAC maps
revealed two clusters, the first encompassing surrounding prefrontal areas (BA25/24)
and right hippocampus and amygdala, and the second involving the left
parahippocampal cortex (BA20; Figure 5). The reverse contrast (negative
correlation) highlighted two close clusters in the right dorso-medial prefrontal cortex
(BA8 and BA9; Figure 3B).
In the present study, we used a method specifically designed for the
longitudinal assessment of PET changes, including voxel-based PVE correction and
optimal normalization of each pair of PET data with the same parameters, as well as
PET-PAC maps calculation restricted to common GM voxels. This method prevents
as far as possible any confounding effects of brain tissue atrophy or methodological
bias due to differential normalization and segmentation of baseline and follow-up
data. The effects highlighted here are thus thought to reflect genuine metabolic
changes taking place during the transition from aMCI to AD.
In the whole aMCI sample, we found progressive metabolic decreases over an
18-month follow-up period encompassing the temporo-parietal cortex and posterior
medial parietal areas, consistent with numerous previous studies underlining the
early involvement of these areas in AD (see Introduction). Our results also disclosed
significant changes in specific prefrontal areas, suggesting that prefrontal metabolic
alteration are in fact initiated early in the course of AD. Most notably, the metabolic
declines found to be significantly greater in converters relative to non-converters
specifically and uniquely pointed to two medial prefrontal areas, namely the anterior
cingulate cortex (BA24/32) and the subgenual area (BA25). A similar analysis also
pointed to prefrontal areas in Drzezga et al. study (2003), but involved lateral
prefrontal rather than medial regions. In that study, the medial prefrontal areas were
found to show similar decreases in converters and nonconverters which was
interpreted as reflecting a normal aging process. Our findings disagree with this
interpretation as the two groups did not differ in age or follow-up duration, and
furthermore the metabolic changes in both medial prefrontal areas were found not to
correlate with age (data not shown). In contradiction with Drzezga et al. (2003),
therefore, the present study argues in favour of AD-related pathological processes in
these two regions. In support of this contention, the same two medial prefrontal
regions have been previously reported to show specific perfusion decreases from the
entorhinal to the limbic neuropathologic Braak stages (Braak and Braak, 1991),
corresponding respectively to aMCI and early AD (Bradley et al., 2002).
For reasons detailed in Introduction, we also assessed metabolic changes in
relation to global cognitive decline across the whole aMCI sample. Positive
correlation between PET-PAC maps and Mattis-PAC highlighted a single ventro-
medial prefrontal area encompassing the same BA25 region as that found in the
between-group comparison, but surprisingly failed to highlight the BA24/32 cluster.
As previously proposed (Chételat et al., 2005a), patients expected to present with
probable AD criteria at the end of the follow-up period (converters) include both
patients with rapid cognitive decline, and patients with less rapid cognitive decline but
who started from lower baseline cognitive status. Our findings thus suggest that the
metabolic decrease in BA25 is specifically related to the slope of cognitive decline,
while that in BA24/32 may instead be related to baseline cognitive performance.
Consistent with this hypothesis, we found a significant positive correlation between
baseline MMSE performances and BA24/32 PET-PAC values (p=0.0006; data not
shown), while no significant correlation was found with BA25 PET-PAC values
(p=0.209; data not shown). Overall, these two regions thus appear to serve
complementary roles in expressing the metabolic decreases from aMCI to AD. This
was also supported by our multivariate analysis showing improved discrimination
between converters and non-converters when combining both BA25 and BA24/32 as
compared to either VOI separately. While the complete discrimination found here
would need to be validated from an independent and larger sample, our results
strongly support the use of 18FDG-PET to monitor early AD progression.
So as to better understand the mechanisms underlying these metabolic
changes, we also performed metabolic-metabolic correlations between PET-PAC in
each VOI and whole brain PET-PAC maps, allowing unravelling the whole brain
networks whose metabolic changes relate to those in each of the two prefrontal VOIs
(i.e. BA24/32 and BA25). Interestingly, these analyses highlighted two distinct
networks for BA24/32 and BA25, the former involving the PCC and the latter the
hippocampal region and temporal pole. These distinct relationships suggest that the
medial prefrontal metabolic decreases characterizing the progression from aMCI to
clinically probable AD may result from disconnection from limbic structures, i.e. from
the PCC for BA24/32 and from the hippocampus for BA25. This so-called diaschisis
hypothesis (Minoshima et al., 1997; Meguro et al., 2001; Bradley et al., 2002;
Chételat et al., 2003b; Nestor et al., 2004) is consistent with recent functional MRI
studies of functional connectivity showing, through a method similar to the correlation
approach used here, altered hippocampal functional connectivity with the PCC and
ventro-medial prefrontal cortex in early AD (Greicius et al., 2004; Wang et al., 2007;
Allen et al., 2007). As the uncinate fasciculus directly connects the hippocampus,
amygdala and temporal poles to the subgenual cortex (Kier et al., 2004;
Schmahmann et al., 2007), disruption of this WM tract may lead to the specific
relationships found here. Furthermore, alteration of this tract has been reported in AD
(Taoka et al., 2006; Yasmin et al., 2008), and direct hippocampal projection fibers to
BA25 were shown to mainly originate from the CA1 subfield (Zhong et al., 2006), i.e.
the hippocampal subregion most involved by atrophic processes from aMCI to
clinically probable AD (Chételat et al., 2008). The progressive metabolic decrease in
BA25 is thus thought to directly reflect its disconnection from the hippocampus. In
contrast, disruption of the rostral cingulum bundle relating the PCC to the frontal
cortex (Mufson and Pandya, 1984; Schmahmann et al., 2007; Mori et al., 2008) is
probably responsible for the metabolic decrease observed in BA24/32. The caudal
part of this tract, which connects the hippocampus to the PCC, is also altered early in
AD (Rose et al., 2000; Xie et al., 2005; Medina et al., 2006; Villain et al., 2008)
probably accounting for early PCC hypometabolism (Rose et al., 2000; Chételat et
al., 2003b; Nestor et al., 2004; Xie et al., 2005; Villain et al., 2008). Our findings
suggest that, as aMCI progress to AD, PCC alterations progressively lead to medial
prefrontal disruption through involvement of the rostral part of the cingulum bundle.
Overall, therefore, BA24/32 metabolic decreases may reflect indirect hippocampo-
frontal disconnection processes, as already mentioned elsewhere (Grady et al., 2001;
Bradley et al., 2002; Villain et al., 2008) probably mediated by the cingulum bundle
which is the major path for fronto-hippocampal connectivity (Kobayashi and Amaral,
Intriguingly, most structures highlighted in the present study, namely the
hippocampus, amygdala, PCC and medial prefrontal cortex, are key components of
the episodic memory network (Cabeza and Nyberg, 2000). The role of the uncinate
fasciculus and cingulum bundle in memory processes has also been highlighted
(Levine et al., 1998; Gaffan and Wilson, 2008; Sepulcre et al., 2008), more
specifically for autobiographical memory related to emotional events (Markowitsch et
al., 2003). In addition, dysfunction in ventro-medial prefrontal areas has been related
to depressive symptoms in healthy subjects (Steele et al., 2007) and apathy in AD
(Marshall et al., 2007). Taken together, disruption of the brain networks leading to
progressive decrease in ventro-medial prefrontal metabolism may underlie the
worsening of memory impairments as well as the emergence of mood disorders
reported as aMCI progresses to clinical AD (Assal and Cummings, 2002).
Finally negative correlations between PET-PAC maps and Mattis-PAC as well
as BA25 PET-PAC, both highlighted a single and identical dorso-medial prefrontal
region encroaching BA8/9/10. This suggests that, as the disease progresses and
BA25 metabolism decreases, BA8/9/10 metabolism relatively increases, potentially
reflecting functional compensatory mechanism, as proposed in previous studies for
the same dorso-medial prefrontal areas (Grady et al., 2001; Grady et al., 2003; Remy
et al., 2005; Wang et al., 2007). The striking difference between metabolic changes
taking place in ventro-medial and dorso-medial prefrontal regions, both known to be
connected to the hippocampus (Schmahmann et al., 2007) but showing either
relative metabolic decreases or increases respectively, would merit further
In sum, our findings highlight the specific metabolic changes associated with
progression from aMCI to clinical AD, showing metabolic decrease in ventro-medial
prefrontal BA24/32 and BA25 paralleled by relative increases in dorso-medial
BA8/9/10. Prefrontal metabolic disruptions are likely to reflect disconnection from the
hippocampus, both indirectly through the posterior cingulate cortex via cingulum
bundle breakdown for BA24/32, and directly through uncinate fasciculus disruption
for BA25. Metabolic decreases in these two areas combined specifically
characterized rapid progression to AD, suggesting the potential of 18FDG-PET to
monitor early AD progression and to test the effects of new therapies.
This work was supported by the Institut National de la Santé Et de la
Recherche Médicale U320 and U923, Ministère de la Santé (Programme Hospitalier
de Reherche Clinique, Principal Investigator: J-C Baron), Ministère de l‟éducation
nationale, Association France-Alzheimer, Institut de Recherches Internationales
Servier and Conseil Régional de Basse-Normandie.
We are indebted to Ms. C. Lalevée, Ms. A. Pélerin, D. Hannequin, B. Dupuy,
M.H. Noël, M.C. Onfroy, D. Luet, O. Tirel, and L. Barré for their help in this study.
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Table 1: Demographic and clinical data of aMCI patients at baseline (t0) and at
follow-up (t18). ** significant difference between converters and non-converters
p<0.01, ¤ significant difference between t0 and t18 p<0.05, ¤¤ p<0.01, ¤¤¤ p<0.001.
Figure 1: Patterns of brain metabolic changes over 18 months in the whole aMCI
sample (A), as well as in non-converters (B) and in converters (C) separately, as
illustrated by projection of the SPM-T maps onto a 3D representation of the aMCI
customized whole brain template.
Figure 2: Brain areas showing significantly greater metabolic decreases in
converters compared to non-converters (A-left), used as VOIs represented onto 3D
views of the aMCI whole brain template in further analyses (A-right), and significant
positive correlation between metabolic decrease (PET-PAC maps) and global
cognitive decline (Mattis-PAC – B) as illustrated by SPM-2 „Glass brain‟
representation and projection of the SPM-T maps (thresholded at p<0.005; k>50
voxels) onto sagittal sections of the aMCI whole brain template. Peak MNI
coordinates (xyz), size in voxels (k), and T and P values are indicated for each
significant cluster, and correlation plot and R2 values are also provided.
Figure 3: Brain areas showing significant negative correlation between PET-PAC
maps and Mattis-PAC (A) or BA25 PET-PAC value (B) as illustrated by SPM-2
„Glass brain‟ representations and projection of the SPM-T maps (thresholded at
p<0.005; k>100 voxels) onto sagittal section of the aMCI whole brain template. Peak
MNI coordinates (xyz), size in voxels (k), and T and P values are indicated for each
significant cluster and the corresponding plots and R2 values are also provided.
29 Download full-text
Figure 4: Illustration of the discriminant accuracy of the mean PET-PAC values in
BA24/32 and BA25 separately, and of both values combined (2D representation) to
separate converters from nonconverters.
Figure 5: VOI-based correlation analysis. Brain areas showing significant positive
correlation between PET-PAC maps and PET-PAC values in BA25 (left), and
BA24/32 (right), as illustrated in SPM-2 „Glass brain‟ representations and projection
of the SPM-T maps (thresholded at p<0.005; k>100 voxels) onto sagittal sections of
the aMCI whole brain template. Peak and sub-peak MNI coordinates (xyz), size in
voxels (k), and T and P values are indicated for each significant cluster and the
corresponding plots and R2 values are also provided. Hcp: hippocampus.