Validation of the cingulate island sign with optimized ratios for discriminating dementia with Lewy bodies from Alzheimer’s disease using brain perfusion SPECT
Validation of the cingulate island sign with optimized ratios for discriminating dementia with Lewy bodies from Alzheimer's disease using brain perfusion SPECT
Etsuko Imabayashi 0 1 2 3
Tsutomu Soma 0 1 2 3
Daichi Sone 0 1 2 3
Tadashi Tsukamoto 0 1 2 3
Yukio Kimura 0 1 2 3
Noriko Sato 0 1 2 3
Miho Murata 0 1 2 3
Hiroshi Matsuda 0 1 2 3
0 Department of Radiology, National Center of Neurology and Psychiatry , 4-1-1 Ogawahigashi, Kodaira, Tokyo 187-8551 , Japan
1 QMS Group, Quality Assurance Department, FUJIFILM RI Pharma Co., Ltd. , 14-1 Kyobashi 2-Chome, Chuo-Ku, Tokyo 104-0031 , Japan
2 Integrative Brain Imaging Center, National Center of Neurology and Psychiatry , 4-1-1 Ogawahigashi, Kodaira, Tokyo 187-8551 , Japan
3 Department of Neurology, National Center of Neurology and Psychiatry , 4-1-1 Ogawahigashi, Kodaira, Tokyo 187-8551 , Japan
Objective Dementia with Lewy bodies (DLB) is often cited as the second most common dementia after Alzheimer's disease (AD). It is clinically important to distinguish DLB from AD because specific side effects of antipsychotic drugs are limited to DLB. The relative preservation of cingulate glucose metabolism in the posterior cingulate gyri versus that in the precuni, known as the cingulate island sign (CIS), in patients with DLB compared with AD is supposed to be highly specific for diagnosing DLB. In a previous study, using brain perfusion SPECT, the largest value (0.873) for the area under the receiver operating characteristic (ROC) curve (AUC) for differentiating DLB from AD was obtained with the ratio of the posterior cingulate gyri from an early Alzheimer's disease-specific hypoperfusion volume of interest (VOI) versus the medial occipital lobe. Two purposes of this study are as follows: one is optimization of VOI setting for calculating CIS values and the other is to evaluate their accuracy and simultaneously to retest the method described in our previous paper. Methods We conducted a retest of this SPECT method with another cohort of 13 patients with DLB and 13 patients with AD. Furthermore, we optimized VOIs using contrast images obtained from group comparisons of DLB and normal controls; the same 18 patients with DLB and 18 normal controls examined in our previous study. We obtained DLB-specific VOIs from areas where brain perfusion was significantly decreased in DLB. As the numerators of these ratios, early Alzheimer's disease-specific VOIs were used after subtracting DLB-specific VOIs. The DLB-specific VOIs were used as the denominator. Results In retest, the obtained AUC was 0.858 and the accuracy, sensitivity, and specificity were 84.6, 84.6, and 84.6%, respectively. The ROC curve analysis with these optimized VOIs yielded a higher AUC of 0.882; and the accuracy, sensitivity, and specificity of these new CIS ratios were 84.6, 92.3, and 76.9%, respectively, with a threshold value of 0.281. Conclusion Optimized CISs using brain perfusion SPECT are clinically useful for differentiating DLB from AD.
Brain perfusion SPECT; Alzheimer's disease
& Etsuko Imabayashi
Dementia with Lewy bodies (DLB) is often cited as the
second most common dementia after Alzheimer’s disease
(AD). It is clinically important to distinguish DLB from
AD because specific side effects of antipsychotic drugs are
limited to DLB. The relative preservation of glucose
metabolism in the posterior cingulate gyri versus that in the
precuni, the cingulate island sign (CIS) , was reported in
1997 by Imamura et al.  to be higher in patients with
DLB than those with AD, and is highly specific for
diagnosing DLB. Lim et al.  reported that the sensitivities for
using CIS to diagnose DLB ranged from 62 to 86% and
only 43 to 50% for hypometabolism in the medial occipital
lobe. Recently, Graff-Radford et al.  reported the
pathologic association of CIS with autopsy findings. These
early CIS studies were limited to 18F-fluorodeoxyglucose
positron emission tomography (18F-FDG-PET) imaging.
O’Brien et al.  showed that, even with FDG, the
differentiation accuracy was 72.4%, and furthermore that the
CIS was not observed in SPECT images in their study.
Using brain perfusion SPECT to differentiate DLB from
AD , we previously showed that the largest value for the
area under the receiver operating characteristic (ROC)
curve (AUC) was 0.873; the accuracy, sensitivity, and
specificity were 85.7, 88.9, and 82.4%, respectively,
obtained for the ratio of the posterior cingulate gyri within
early Alzheimer’s disease-specific hypoperfusion volumes
of interest (VOI) in a group comparison between patients
with AD and cognitively normal subjects versus the medial
occipital lobe. The AUC calculated using the medial
occipital VOIs was only 0.614, while the accuracy,
sensitivity, and specificity were 68.6, 55.6, and 82.4%,
respectively. Because AD and DLB are known to have
pathological overlaps and it is difficult to distinguish these
two diseases using 18F-FDG-PET or brain perfusion
SPECT , it was logical to use these early AD-specific
VOIs to discriminate AD from DLB .
Therefore, in the current study to optimize these VOIs
and to verify the clinical usefulness of CIS in different
groups of patients [i.e., group A with DLB from our
previous study, group B with DLB from the present study and
group C with AD from the present study (Table 1)], we
first compared a group of patients with DLB (group A in
Table 1) to normal controls to obtain specific VOIs. We
then applied these optimized VOIs to new groups (group B
and C in Table 1) of patients with DLB and AD to evaluate
Table 1 Demographic data
* every normal subject’s MMSE score was 26 or more
AD Alzheimer’s disease, DLB dementia with Lewy bodies, SPECT single-photon emission computed tomography, MMSE Mini Mental State
Examination, [123I]MIBG [123I]metaiodobenzylguanidine
their accuracy using ROC curve analysis and to determine
the thresholding CIS values to discriminate DLB from AD.
Accordingly, two purposes of this study are as follows:
one is optimization of VOI setting for calculating CIS
values by setting statistically significant hypoperfusion
areas as VOIs for both denominators and numerators and
the other is to evaluate their accuracy and simultaneously
to retest the method described in our previous paper .
Materials and methods
This single-center, retrospective study was conducted in
accordance with the tenets of the Declaration of Helsinki,
and the use of previously obtained images with public
notification was approved by the ethical committee of our
institute. Thirty-one patients with DLB (M:F 16:15, age
74.9 ± 6.7 years), 13 11C-PiB-positive patients with AD
(M:F 3:10, age 72.5 ± 8.6 years), and 18 cognitively
normal subjects (M:F 10:8, age 73.9 ± 6.9 years) were
studied. The 31 patients with DLB consisted of two groups:
18 subjects with DLB (group A) (M:F 10:8, age
73.9 ± 6.8 years) were the same patients described in our
previous paper , and 13 other patients (group B) (M:F
7:6, age 76.6 ± 6.5 years). The 18 normal subjects were
semi-randomly selected to be age- and gender-matched to
the subjects with DLB in group A from a previous study
performed in our institute . The patients with DLB in
group B were chosen from those who underwent [123I]
metaiodobenzylguanidine (MIBG) myocardial scintigraphy
and who fulfilled the criteria of probable DLB proposed in
the third consortium on DLB international workshop 
from chart screenings. The 13 AD subjects were selected
from another of our studies after providing informed
consent . These previous studies were approved by the
Ethics Committee for Clinical Research in our institute and
informed, written consent was obtained from all subjects.
Brain perfusion SPECT
All subjects were asked to remain in a comfortable, supine
position with their eyes closed in dark, quiet surroundings.
An intravenous injection of 740 MBq [99mTc] ethyl
cysteinate dimer (ECD; Fujifilm RI Pharma, Tokyo, Japan)
was administered. Ten minutes later, a SPECT scan was
obtained using a 2-head gamma camera and 6-slice CT
system (Symbia T6; Siemens, Erlangen, Germany)
equipped with low-energy, high-resolution, and parallel-hole
collimators. Ninety views were obtained continuously
throughout 360 of rotation (4 /step, 128 9 128 matrix,
zoom 1.45). The voxel size was 3.3 9 3.3 9 3.3 mm. To
reconstruct the SPECT image, a combination of Fourier
rebinning followed by ordered subset
expectation–maximization (iteration number 8 and subset 10) and a 7-mm
full width at half maximum Gaussian filter were used. To
reconstruct the image by fitting it to a normal data base,
Chang’s method  was used for attenuation correction.
To demarcate the areas where specific hypoperfusion is
observed in DLB patients, a group comparison between the
18 DLB subjects in group A and the 18 normal subjects
was conducted using statistical parametric mapping (SPM)
12 (http://www.fil.ion.ucl.ac.uk/spm/), which implements
the general linear model; statistically significant
hypoperfusion areas were then extracted from this comparison.
Proportional scaling was used to achieve global
normalization of voxel values among the images. We studied the
differences in gray matter perfusion between these two
groups using t statistics. The resulting sets of t values
constituted statistical parametric maps: SPM (t) that were
transformed to the unit normal distribution (SPM[Z]).
Group analysis of gray matter accumulation between the
DLB and normal controls was performed using a spatial
extent threshold of 1000 for contiguous voxels. Main
effects used whole-brain analyses with a threshold at a
voxel level of family-wise error correction (FWE) of
p \ 0.01 or p \ 0.05. These contrast maps were saved as
‘‘DLB_specific_VOI_1’’ for p \ 0.01 (Fig. 1a) and
‘‘DLB_specific_VOI_2’’ for p \ 0.05 (Fig. 1b) and
‘‘tDLB_specific_VOI_1’’ and ‘‘tDLB_specific_VOI_2’’
were defined as total positive Z score values within these
Z score maps of the obtained SPECT images of the 13
patients with DLB in group B and 13 normal controls were
converted using the easy Z score imaging system (eZIS)
analysis (Fujifilm RI Pharma Co., Ltd., Tokyo, Japan)
software . It included spatial normalization parameters
in SPM2 and a [99mTc] ECD brain template in the same
space as the Montreal Neurological Institute (MNI)
standard brain template . Normal databases were included
in the eZIS and inter-institutional differences were
corrected using previously scanned phantom data .
After the inter-institutional correction, specially
normalized [99mTc]ECD SPECT images from each patient
were compared with normal images from the database;
ECD60-69yDB (the database of 60–69-year-olds),
ECD70y-DB (the database of over 70-year-olds), using
voxel-by-voxel Z score analysis after pixel normalization
to the global mean values [Z score = [(control mean) –
(individual value)]/(control SD)] as previously reported by
Minoshima et al. .
Volumes of interest
The eZIS software included a set of AD-specific
hypoperfusion VOIs identified in patients with AD following a
group comparison with cognitively healthy individuals
. The contrast maps obtained from the group
comparison described above (DLB_specific_VOI_1 and
DLB_specific_VOI_2) were then subtracted from the
ADspecific hypoperfusion areas originally included in the
eZIS software (Fig. 1c, d). We named these VOIs
‘‘AD_DLB_num_1’’ and ‘‘AD_DLB_num_2’’ and these
CIS ratio numerator ‘‘tAD_DLB_num_1’’ and
‘‘tAD_DLB_num_2’’ were defined as total positive Z score
values within these VOIs, respectively, that is, the
AD_DLB_num_1 is equal to the AD-specific VOI minus
the DLB_specific_VOI_1, and AD_DLB_num_2 is equal
to the AD-specific VOI minus DLB_specific_VOI_2. We
also investigated VOIs described in our previous paper as
‘‘PCG_AD_VOIs’’ and ‘‘tPCG_AD_VOIs’’ was defined as
total positive Z score values within these VOIs, which were
overlap areas of significant perfusion reduction identified
in patients with AD following a group comparison with
cognitively healthy individuals  and also included in
the eZIS software and posterior cingulate gyrus in
automated anatomical labeling (AAL). Furthermore, we
investigated the posterior cingulate VOI included in the
AAL atlas. This was named ‘‘AAL_PCG’’ and’’
tAAL_PCG’’ was defined as total positive Z score values
within these VOIs. To determine the CIS ratios of the
above, as denominator we used DLB_specific_VOI_1 and
DLB_specific_VOI_2, medial occipital VOI in AAL
(AAL_medOccipital), and precuneus and cuneus VOIs in
AAL (AAL_PreC&C) and ‘‘tAAL_medOccipital’’ and
‘‘tAAL_PreC&C’’ were defined as total positive Z score
values within these VOIs, respectively. This
AAL_PreC&C VOI was used to calculate the original CIS for the
FDG-PET analysis [1, 3]. We then calculated a total
Fig. 1 a, b Group analysis of
gray matter accumulation
between the DLB and normal
controls was performed using a
spatial extent threshold of 1000
for contiguous voxels. Main
effects used whole-brain
analyses with a threshold at a
voxel level of family-wise error
(FWE) of p \ 0.01 or p \ 0.05.
These contrast maps
DLB_specific_VOI_1 (a) for
p \ 0.01, DLB_specific_VOI_2
(b) for p \ 0.05.
c AD_DLB_num_1, which is
equal to AD-specific VOI in
eZIS software minus
d AD_DLB_num_2, which is
equal to AD-specific VOI in
eZIS software minus
Fig. 2 Box plots of CIS ratios
with optimized VOIs and AAL
VOIs. a tAD_DLB_num_1/
b CISRo: tAD_DLB_num_2/
c CISRaal: tAAL_PCG/
d CISRmix: tAD_DLB_num_1/
tAAL_PreC&C; f CIS:
The boxes indicate the upper
and lower quartiles with the
median, and the whiskers show
the minimum, maximum, or
1.5 9 interquartile ranges. The
red lines on a–e indicate the
thresholds shown in Table 3
positive Z score in each VOI and divided the numerator by
Receiver operating characteristic (ROC) curve
The AUC of the ROC curve was obtained by thresholding
with each of these values for all VOIs. Finally, the AUCs
were statistically compared  (Table 2). The AUC value
of C0.87 demonstrated moderate discriminatory power
 and, therefore, optimal thresholds were determined
that supplied the maximum number of true positive and
true negative subjects. Accuracy, sensitivity, and
specificity resulting from that threshold were then calculated
The patient characteristics are summarized in Table 1. No
significant differences were observed for age or gender. All
the heart to mediastinum ratios in the [123I] MIBGs scanned
3 h after injection in patients with DLB were below 2.0.
As shown in Table 2, there were no significant
differences among any of the analysed AUCs. Combinations of
these that resulted in AUCs C0.87 are listed in Table 3. A
higher accuracy of 84.6% was obtained with three VOI
combinations for numerator and denominator when the
ratio sum of all the positive Z scores within those VOIs was
used as thresholds. The first of these utilized the optimized
VOIs: tAD_DLB_num_2/tDLB_specific_VOI_1, which
was termed as ‘‘CISRo’’ (CIS ratio optimized); the second
the mixed VOIs: tAD_DLB_num_1/tAAL_PreC&C,
which was termed as ‘‘CISRmix’’ (CIS ratio optimized and
with AAL); and the third the AAL VOIs: tAAL_PCG/
tAAL_medOccipital, which was termed as ‘‘CISRaal’’
(CIS ratio with AAL). The accuracy, sensitivity, and
specificity of the CISRo for differentiating patients with
DLB from those with AD were 84.6, 92.3, and 76.9%,
respectively, with a threshold value less than 0.281; for the
CISmix, they were 84.6, 84.6, and 84.6%, respectively,
with a threshold value less than 0.348; and for the CISRaal,
they were 84.6, 76.9, and 92.3%, respectively, with a
threshold value less than 0.0308. Box plots of these CIS
ratios with optimized VOIs and AAL VOIs were in Fig. 2.
The CIS, the relative preservation demonstrated in
18FFDG-PET of glucose metabolism in the posterior cingulate
gyri versus that in the precuni, in patients with DLB
compared with those with AD is considered to be highly
Table 3 Accuracy, sensitivity, and specificity of area under the receiver operating characteristic (ROC) curve (AUC) demonstrating moderate
discrimination of DLB from AD
specific for diagnosing DLB . In our previous study 
of brain perfusion SPECT using [99mTc] ECD, we also
observed a differential CIS ratio when the method was
optimized using the posterior cingulate area of an early
AD-specific VOI included in eZIS software as numerator
and the anatomical medial occipital area as denominator
for this ratio. Because brain perfusion SPECT is relatively
more economical and widely available worldwide than
PET, the clinical application of CIS to differentiate patients
with DLB from AD would be more effective using brain
perfusion SPECT. In the current study, not only did we
determine the threshold value for clinical use, but we also
tried to optimize the CIS in brain perfusion SPECT and at
the same time to retest it in another patient cohort. As a
result, we obtained a larger AUC than that of our previous
Previously, we used the posterior cingulate area within
the early AD-specific VOI as numerator, and did not take
into account a DLB-specific VOI. When we used the same
numerator in the current retest study that previously
produced the largest AUC, we received an AUC of 0.858.
Although this was smaller than the previous AUC value of
0.873, it still fell within the 95% confidence interval. Also
the accuracy (84.6 versus 85.7%) and sensitivity (84.6
versus 88.9%) of the retest were slightly lower than those
of the previous study; however, also all of these values are
over 84% and thus appear to be reliable enough to evaluate
the differentiation of DLB from AD.
In the original CIS study , AAL_PCG VOI was used
as a numerator and AAL_PreC&C VOI as a denominator,
resulting in accuracy, sensitivity, and specificity of 78, 77,
and 80%, respectively. These ratios were also used as
thresholds, and ROC analysis was applied in the current
study, which resulted in accuracy, sensitivity, and
specificity of 76.9, 61.5, and 92.3%, respectively. These results
indicate that even though the resolution of SPECT is
generally inferior to those of PET, the accuracy, sensitivity,
and specificity obtained in the current study were
comparable to those obtained with PET. Furthermore, we also
effectively optimized the VOI parameters.
DLB is often cited as the second most common
dementia after AD. It is clinically important to distinguish
DLB from AD because specific side effects of
antipsychotic drugs are limited to DLB. In discriminating DLB
from AD clinically, dopamine transporter (DAT) imaging
 and [123I] MIBG  are useful because they detect
early disturbances of the nigrostriatal pathway or
peripheral sympathetic nervous system in patients with DLB.
With a combination of these two techniques, over 90%
sensitivity and specificity in discriminating DLB from AD
are reported . Nevertheless, brain perfusion SPECT is
more widely and commonly used for clinical screening of
patients with dementia. Furthermore, compared with
morphometric imaging, SPECT is a more sensitive modality
for functional imaging used to detect early stages of
neurodegenerative disease before shrinkage . SPECT also
reveals useful information for differentiating AD as well as
other dementias, including vascular dementia or
frontotemporal lobe degeneration .
In this study, in trying to optimize the use of the CIS in
SPECT, we subtracted DLB-specific VOIs derived from
comparisons between DLB and normal controls from
ADspecific VOIs. Furthermore, the CIS ratio was determined
by comparing these with specific hypoperfusion areas in
DLB instead of the entire anatomical occipital area. This
resulted in a higher AUC of 0.882 compared with our
previous study . With these optimized numerators and
denominators determining the CIS ratio, we obtained the
largest specificity of 92.3% for differentiating DLB from
AD. Hypothetically, if we used this procedure with highly
specific examinations such as [123I] MIBG  or DAT
imaging , an even higher accuracy might be achieved.
Moreover, a combination of these two with brain perfusion
SPECT might prove to be even more valuable, considering
the purpose of the screening, compared with using only
MIBG and DAT, which only detect degeneration of central
or peripheral monoamine pathways, while brain perfusion
SPECT measures whole brain activity and can differentiate
many brain disorders.
Contrary to practical clinical situations, amyloid PET
was used as a biomarker for diagnosing AD in this study.
As a subject of important future investigation, additional
clinical prospective study using our CIS procedure for
diagnosing AD without amyloid PET may reinforce the
effective diagnostic flowchart with combination of the CIS,
amyloid PET, MIBG and DAT imaging.
The CIS observed in 18F-FDG-PET was reportedly
indicative of a lower Braak neurofibrillary tangle stage in
patients with DLB . In our study, we compared the CIS
detected using brain perfusion SPECT in patients with
DLB and AD. While we cannot compare these two studies
directly, brain perfusion and metabolism are
physiologically coupled . The limitations of SPECT include lower
image resolution and a large partial volume effect. These
limitations, in conjunction with a patient’s pathological
status, result in decreased accuracy when attempting to
differentiate between DLB and AD. However, optimization
of the procedure using the CIS ratio described in this paper
seems to overcome these limitations and achieve a larger
accuracy compared with the existing procedure.
Furthermore, SPECT is widely accessible and more economical
To further the clinical usage of CIS to discriminate
between DLB from AD, we retested and optimized the
procedure, and thereby achieved a larger AUC of 0.882. In
so doing, the accuracy, sensitivity, and specificity were
84.6, 92.3, and 76.9%, respectively, with a threshold value
less than 0.281 for CISRo, 84.6, 84.6, and 84.6%,
respectively, with a threshold value less than 0.348 for CISRmix,
and 84.6, 76.9, and 92.3%, respectively, with a threshold
value less than 0.03 for CISRaal. Brain perfusion SPECT is
not only useful in screening for dementia and economically
and widely accessible, but also can facilitate the
differentiation of DLB from AD when these new CIS ratios are
Acknowledgements We would like to thank the Radiology
Department technical staff at National Center of Neurology and Psychiatry.
Compliance with ethical standards
Funding This work was supported by the Japan Foundation for
Neuroscience and Mental Health.
Open Access This article is distributed under the terms of the
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appropriate credit to the original author(s) and the source, provide a
link to the Creative Commons license, and indicate if changes were
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