Article Search

R E S E A R CH A R T I C L E

Tracer-specific reference tissues selection improves detection of 18F-FDG, 18F-florbetapir, and 18F-flortaucipir PET SUVR changes in Alzheimer's disease

Yanxiao Li1,2 | Yee Ling Ng1 | Manish D. Paranjpe3 | Qi Ge1 | Fengyun Gu1,4 |

Panlong Li5 | Shaozhen Yan6 | Jie Lu6 | Xiuying Wang2 | Yun Zhou1 |

for the Alzheimer’s Disease Neuroimaging Initiative

1Central Research Institute, United Imaging

Healthcare Group Co., Ltd, Shanghai, China

2School of Computer Science, The University

of Sydney, Sydney, New South Wales,

Australia

3Harvard-MIT Health Sciences and

Technology Program, Harvard Medical School,

Boston, Massachusetts, USA

4Department of Statistics, University College

Cork, Cork, Ireland

5School of Electrical and Information

Engineering, Zhengzhou University of Light

Industry, Zhengzhou, Henan, China

6Department of Radiology and Nuclear

Medicine, Xuanwu Hospital, Capital Medical

University, Beijing, China

Correspondence

Yun Zhou, United Imaging Healthcare Group

Co. Ltd, 2258 Chengbei Road, Jiading District,

Shanghai 201807, China.

Email: [email protected]

Xiuying Wang, School of Computer Science,

The University of Sydney, City Road,

Camperdown/Darlington NSW 2006,

Australia.

Email: [email protected]

Abstract

This study sought to identify a reference tissue-based quantification approach for

improving the statistical power in detecting changes in brain glucose metabolism,

amyloid, and tau deposition in Alzheimer’s disease studies. A total of 794, 906, and

903 scans were included for 18F-FDG, 18F-florbetapir, and 18F-flortaucipir, respec-

tively. Positron emission tomography (PET) and T1-weighted images of participants

were collected from the Alzheimer’s disease Neuroimaging Initiative database,

followed by partial volume correction. The standardized uptake value ratios (SUVRs)

calculated from the cerebellum gray matter, centrum semiovale, and pons were eval-

uated at both region of interest (ROI) and voxelwise levels. The statistical power of

reference tissues in detecting longitudinal SUVR changes was assessed via paired

t-test. In cross-sectional analysis, the impact of reference tissue-based SUVR differ-

ences between cognitively normal and cognitively impaired groups was evaluated by

effect sizes Cohen’s d and two sample t-test adjusted by age, sex, and education

levels. The average ROI t values of pons were 86.62 and 38.40% higher than that of

centrum semiovale and cerebellum gray matter in detecting glucose metabolism

decreases, while the centrum semiovale reference tissue-based SUVR provided

higher t values for the detection of amyloid and tau deposition increases. The three

reference tissues generated comparable d images for 18F-FDG, 18F-florbetapir, and 18F-flortaucipir and comparable t maps for 18F-florbetapir and 18F-flortaucipir, but

pons-based t map showed superior performance in 18F-FDG. In conclusion, the

tracer-specific reference tissue improved the detection of 18F-FDG, 18F-florbetapir,

and 18F-flortaucipir PET SUVR changes, which helps the early diagnosis, monitoring

of disease progression, and therapeutic response in Alzheimer’s disease.

Abbreviations: ACR, annual change rate; AD, Alzheimer’s disease; ADNI, Alzheimer’s Disease Neuroimaging Initiative; ATN, amyloid, tau, and neurodegeneration; Aβ, amyloid-β; CDR, clinical

dementia rating; CI, cognitively impaired; CN, cognitively normal; GM, gray matter; MCI, mild cognitively impaired; MMSE, Mini-Mental Status Examination; MNI, Montreal Neurologic Institute;

PVC, partial volume correction; ROI, region of interest; SUVR, standardized uptake value ratio.

Received: 15 September 2021 Revised: 17 December 2021 Accepted: 30 December 2021

DOI: 10.1002/hbm.25774

This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any

medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

© 2022 United Imaging Healthcare. Human Brain Mapping published by Wiley Periodicals LLC.

Hum Brain Mapp. 2022;43:2121–2133. wileyonlinelibrary.com/journal/hbm 2121

10970193, 2022, 7, D ow

nloaded from https://onlinelibrary.w

iley.com /doi/10.1002/hbm

.25774, W iley O

nline L ibrary on [26/11/2022]. See the T

erm s and C

onditions (https://onlinelibrary.w iley.com

/term s-and-conditions) on W

iley O nline L

ibrary for rules of use; O A

articles are governed by the applicable C reative C

om m

ons L icense

K E YWORD S 18F-FDG, 18F-florbetapir, 18F-flortaucipir, Alzheimer's disease, reference tissue

1 | INTRODUCTION

Alzheimer’s disease (AD) is a progressive neurodegenerative disease

associated with memory deficits and cognitive impairments, brain

deposition of amyloid-β (Aβ) peptide plaques, neurofibrillary tangles

composed of hyperphosphorylated tau (ptau) protein, and glucose

hypometabolism (DeTure & Dickson, 2019; Serrano-Pozo, Frosch,

Masliah, & Hyman, 2011; Uddin et al., 2018). The standardized assess-

ment of pathological processes underlying AD can be accomplished

by biomarker-evidenced amyloid, tau, and neurodegeneration (ATN)

framework (Jack Jr et al., 2018). Positron emission tomography (PET)

using radiolabeled ligands including 18F-FDG, 18F-florbetapir, and 18F-flortaucipir has been widely used to assess neurodegeneration,

deposition of Aβ fibrils, and tau for diagnosis and monitoring progres-

sion of AD. Standardized uptake value ratio (SUVR) relative to a refer-

ence tissue is commonly used for ATN PET quantification.

Specifically, 18F-FDG SUVR can be used to estimate the metabolic

glucose uptake rate ratio (Y. Wu et al., 2012; Y. G. Wu, 2008), while

the 18F-florbetapir and 18F-flortaucipir SUVRs can be used to approxi-

mate the tracer distribution volume ratio (DVR) of binding to Aβ and

tau, respectively (Wong et al., 2010; Zhou et al., 2021; Zhou, Endres,

Braši�c, Huang, & Wong, 2003; Zhou, Sojkova, Resnick, &

Wong, 2012).

Various reference tissues-based SUVRs have been used in previ-

ous AD studies, leading to different statistical power in PET assess-

ments (Chen et al., 2015; Zhou et al., 2021). The pons reference

region previously demonstrated the best preservation of glucose

metabolism in AD and therefore was deemed as a reliable reference

tissue for brain 18F-FDG PET normalization (Minoshima, Frey, Fos-

ter, & Kuhl, 1995). Reference tissues including whole brain (Nugent

et al., 2020), cerebellum gray matter (GM; Förster et al., 2012;

Ossenkoppele et al., 2012), and pons (Alexander, Chen, Pietrini,

Rapoport, & Reiman, 2002; Ortner et al., 2019; Schmidt et al., 2008)

have been used to calculate 18F-FDG PET SUVR in longitudinal AD

studies. The effect of reference tissues including cerebellum GM,

pons, and whole brain on 18F-FDG PET SUVR have been also evalu-

ated in cross-sectional (Minoshima et al., 1995; Yakushev et al., 2008)

and longitudinal AD studies (Nugent et al., 2020; Verger, Doyen, Cam-

pion, & Guedj, 2021). Similarly, different reference tissues including

cerebellar GM, centrum semiovale, pons, and corpus callosum have

been used to quantify 18F-florbetapir and 11C-PIB amyloid PET

(Blautzik et al., 2017; Chen et al., 2015; Chiao et al., 2019; Heeman

et al., 2020; Shokouhi et al., 2016; Su et al., 2015; Wang et al., 2021;

Xie et al., 2020) and 18F-flortaucipir tau PET (Baker et al., 2017; Cho

et al., 2020; Devous Sr. et al., 2018; Southekal et al., 2018; Zhao, Liu,

Ha, Zhou, & Alzheimer’s Disease Neuroimaging Initiative, 2019). The

amyloid PET SUVRs calculated from different reference tissues were

compared and evaluated by correlation analysis of SUVR versus cog-

nitive assessment (Chen et al., 2015), test–retest analysis (Blautzik

et al., 2017), and effect size for evaluation of the treatment response

(Chiao et al., 2019). Our previous research has demonstrated that spa-

tially constrained kinetic model with dual reference tissues comprising

of cerebellum GM and centrum semiovale significantly improves

quantification of relative perfusion and tau binding (Zhou et al., 2021).

In previous longitudinal 18F-FDG and 18F-florbetapir PET studies, dif-

ferent reference tissues based SUVRs were compared, but the com-

parisons were limited to the region of interest (ROI) levels. Also, these

previous studies focused 18F-FDG PET in normal aging (Nugent

et al., 2020; Verger et al., 2021) or amyloid treatment effects in mild

cognitive impairment (MCI) and AD participants (Chen et al., 2015;

Chiao et al., 2019). Moreover, for tau PET studies, to the best of our

knowledge, there has been no evaluation for multiple reference tis-

sues in longitudinal studies.

The selection of an appropriate reference tissue is reliant on mul-

tivariable factors and imperative aspects such as the studied popula-

tion, study sample size, PET acquisition protocol, and the type of

radiopharmaceutical used. The objective of this study is to improve

statistical power for detecting 18F-FDG, 18F-florbetapir, and 18F-flortaucipir PET SUVR changes in AD by selecting appropriate

reference tissues. Using the AD Neuroimaging Initiative (ADNI), we

performed longitudinal analysis on individuals with disease progres-

sion regardless of their disease stage, whether from cognitively nor-

mal (CN) to AD, MCI to AD, or CN to MCI. The impact of the

reference tissue selection on discriminating between CN and cogni-

tively impaired (CI) were also evaluated. This study is the most com-

prehensive comparative analysis of different reference tissues

measured with multiple radiotracers to monitor the progression of

AD. Our study may improve clinical staging diagnosis through quanti-

tative PET which has important implications for biomarker-guided

precision medicine.

2 | MATERIALS AND METHODS

2.1 | Participants

All 18F-FDG, 18F-florbetapir, and 18F-flortaucipir PET and structural

MRI data in this study were obtained from the AD Neuroimaging Ini-

tiative (ADNI) dataset (https://adni.loni.usc.edu). Informed written

consent was obtained from all participants at each site. In total, we

downloaded 794 18F-FDG-PET scans, 906 18F-florbetapir-PET scans,

and 903 18F-flortaucipir-PET scans, which encompass 420, 434, and

2122 LI ET AL.

10970193, 2022, 7, D ow

nloaded from https://onlinelibrary.w

iley.com /doi/10.1002/hbm

.25774, W iley O

nline L ibrary on [26/11/2022]. See the T

erm s and C

onditions (https://onlinelibrary.w iley.com

/term s-and-conditions) on W

iley O nline L

ibrary for rules of use; O A

articles are governed by the applicable C reative C

om m

ons L icense

666 participants, respectively. Demographics and clinical assessments

including the Mini-Mental Status Examination (MMSE), Clinical

Dementia Rating (CDR), and clinical diagnostic status of CN, MCI, and

AD participants were also obtained.

2.2 | PET data acquisition and image preprocessing

Raw T1-weighted structural MRI and preprocessed 18F-FDG, 18F-

florbetapir, and 18F-flortaucipir PET images of each subject were

downloaded from the ADNI database. The downloaded PET images

were aligned, averaged, reoriented, and interpolated into a standard

160 � 160 � 96 voxel image grid and smoothed with an 8 mm in full

width at half maximum (FWHM) 3D Gaussian filter by the ADNI con-

sortium with 1.5 mm cubic voxels. Further details of PET and

T1-weighted MR acquisition protocols can be found at http://adni.

loni.usc.edu/methods/pet-analysis-method/pet-analysis/ and http://

adni.loni.usc.edu/methods/documents/mri-protocols/, respectively.

The PET and MRI data were further processed by partial volume

correction (PVC) and spatial normalization, both using Statistical Para-

metric Mapping (SPM12, Wellcome Department of Imaging Neurosci-

ence, Institute of Neurology, London, UK) in the MATLAB R2020b (The

MathWorks Inc., Natick, MA) environment, as reported in our earlier

studies (Paranjpe et al., 2019; Yan et al., 2020, 2021). PVC was per-

formed to minimize the possibility of underestimation in PET images,

especially for small brain regions such as amygdala and striatum. The

reblurred Van Cittert iteration method was applied for PVC in individual

PET images, where a 3D Gaussian Kernel of 8 mm FWHM was used as

the spatial smoothing function with step length α of 1.5 (Tohka &

Reilhac, 2008). All PET images were then coregistered to the individ-

ual’s own structural MRI images, which were normalized to the stan-

dard Montreal Neurologic Institute (MNI) space using an MRI template

(image volume: 121 � 145 � 121, voxel size: 1.5 � 1.5 � 1.5 mm in x,

y, z). The median (interquartile range) of time intervals between PET

and MRI are 28(49) days, 30(48) days, and 51(129.5) days for 18F-FDG, 18F-florbetapir, and 18F-flortaucipir, respectively. The transformation

parameters determined by MRI spatial normalization were then applied

to the coregistered PET images for PET spatial normalization. SUVR

images were calculated relative to the cerebellum GM (SUVRCereb_GM),

centrum semiovale (SUVRCS), and pons (SUVRPons) reference tissues.

The ROI SUVR values were calculated by applying ROIs on the SUVR

images in the standard MNI space for minimizing variance related to

the variability of ROI volume and shape in native space (Gottesman

et al., 2017; Liu et al., 2019; Paranjpe et al., 2019; Tudorascu

et al., 2018; Yan et al., 2021). A total of 18 ROIs including three refer-

ence tissues (cerebellum GM, centrum semiovale, and pons) and an

additional 15 ROIs including the orbital frontal, prefrontal, superior

frontal, medial temporal, inferior temporal, lateral temporal, parietal,

posterior precuneus, anterior cingulate, posterior cingulate, occipital,

entorhinal cortex, amygdala, hippocampus, and parahippocampal gyrus

regions were manually delineated on the MRI template using the

PMOD software program (PMOD 4.002, PMOD Technologies Ltd.,

Zürich, Switzerland) in standard MNI space. These ROI templates were

previously developed in the Johns Hopkins Department of Radiology

and have been validated in our former studies (Liu et al., 2019; Paranjpe

et al., 2019; Yan et al., 2020, 2021; Zhou et al., 2021).

2.3 | Longitudinal SUVR PET analysis for cognitively declined participants

The effects of different reference tissues were evaluated on the sensi-

tivity of SUVR measurements for the detection of cognitively declined

populations. The baseline and last scans were defined as the subject’s first and last scan in the downloaded data. Participants at the last scan

who had an increased CDR score (Morris, 1993) or evidence of clinical

disease progression (CN to MCI, MCI to AD, or CN to AD) were

included in the longitudinal studies. This population inclusion criterion

was consistent across 18F-FDG, 18F-florbetapir, and 18F-flortaucipir

studies. Based on the baseline and last scan SUVR values of each sub-

ject, paired statistical t values were calculated at both ROI- and voxel-

levels for each reference tissue. The annual change rates for 18F-FDG, 18F-florbetapir, and 18F-flortaucipir uptake were further calculated as

follows:

Annual Change Rate ACRxð Þ ¼ SUVRlast scan�SUVRbaselineð Þ= SUVRbaseline �Tð Þ, ð1Þ

where x represents the reference tissue (cerebellum GM, centrum

semiovale, or pons), SUVRlast scan and SUVRbaseline are the SUVR values

of the ROI at the last scan and baseline, T is the time interval from

baseline to the last scan in years.

2.4 | Cross-sectional SUVR PET analysis for CN and CI participants

To comprehensively assess the performance of the cerebellum GM,

centrum semiovale, and pons reference tissues in discriminating

between CN or CI individuals, ROI- and voxelwise-based cross-

sectional statistical analyses were performed. All baseline scans

were included for cross-sectional analysis and participants were

classified as either CN (CDR = 0) or CI (CDR ≥ 0.5; Zhou

et al., 2021). To investigate the sensitivity of the PET SUVR mea-

surement in discriminating CN from CI, effects sizes were approxi-

mated using Cohen’s d (Chand et al., 2020; Cohen, 1988; Lopresti

et al., 2005; Sullivan & Feinn, 2012; Zhou et al., 2021) of CN and CI

groups as follows:

d¼ mean SUVRx at group1ð Þ�mean SUVRx at group2ð Þð Þ�SDpooled,

ð2Þ where SDpooled represents the standard deviation of SUVR in pooled

population, x represents either cerebellum GM, centrum semiovale, or

pons reference tissues. Since the 18F-FDG SUVR decreased while the 18F-florbetapir and 18F-flortaucipir SUVR increased with disease pro-

gression, we set group 1 to be CN and group 2 to be CI for 18F-FDG,

LI ET AL. 2123

10970193, 2022, 7, D ow

nloaded from https://onlinelibrary.w

iley.com /doi/10.1002/hbm

.25774, W iley O

nline L ibrary on [26/11/2022]. See the T

erm s and C

onditions (https://onlinelibrary.w iley.com

/term s-and-conditions) on W

iley O nline L

ibrary for rules of use; O A

articles are governed by the applicable C reative C

om m

ons L icense

and group1 to be CI and group2 to be CN for 18F-florbetapir and 18F-flortaucipir.

For correcting the influence of covariates, the two-sample inde-

pendent t test adjusted by age, sex, and education levels were per-

formed at voxelwise level using SPM12. For ROI-based analysis, the

generalized linear model was used to assess the group difference in

SUVR for each ROI by adjusting for covariates, the Bonferroni-

corrected p-value < .05 was defined as significant.

3 | RESULTS

3.1 | Study cohort characteristics

Study cohort characteristics for participants in the longitudinal ana-

lyses are summarized in Table 1. A total of 53, 55, and 20 participants

were included with a mean time interval between the baseline and last

scan of 63.42 ± 27.15 months, 57.05 ± 18.75 months, and 19.88

± 8.03 months for 18F-FDG, 18F-florbetapir, and 18F-flortaucipir,

respectively. For each tracer, there were substantial differences

between the baseline and last scan in age, education level, and CDR.

There was no significant difference in the baseline and last scan for

the MMSE of the participants in 18F-flortaucipir. Comparison across

tracers demonstrated that MMSE values at the baseline showed dif-

ferences between 18F-FDG and 18F-flortaucipir groups and between 18F-florbetapir and 18F-flortaucipir groups. Other groups showed no

significant difference in age and CDR in all three tracers.

Study cohort characteristics for participants in the cross-sectional

study are listed in Table 2. There were no significant differences

between CN and CI groups in terms of their age and education level,

but considerable differences were observed for the MMSE and CDR

scores for 18F-FDG and 18F-florbetapir PET studies. Significant differ-

ences were detected in age, education level, MMSE, and CDR scores

in 18F-flortaucipir PET study.

3.2 | Effect of reference tissue selection on sensitivity to detect longitudinal PET SUVR changes in AD

3.2.1 | 18F-FDG PET

The statistic t maps based on the 18F-FDG SUVR images for three ref-

erence tissues are illustrated in Figure 1. It was evident that the t-

values calculated from SUVR images were in the order of t(SUVRPons)

> t(SUVRCereb_GM) > t(SUVRCS) in the frontal, temporal and parietal

regions. ROI-based analysis showed consistent results as demon-

strated in Figure 2. The t(SUVRPons) showed the greatest sensitivity in

the orbital frontal, prefrontal, superior frontal, lateral temporal,

inferior temporal, posterior precuneus, anterior cingulate, posterior

cingulate, caudate, entorhinal cortex, amygdala, hippocampus, and

parahippocampal gyrus, 86.62 ± 47.63% higher than the t(SUVRCS)

and 38.40 ± 29.13% higher than the t(SUVRCereb_GM). In contrast, T A B L E 1

C ha

ra ct er is ti cs

o f lo ng

it ud

in al st ud

y co

ho rt M ea

n ± SD

p va

lu e

1 8 F- FD

G 1 8 F- fl o rb et ap

ir 1 8 F- fl o rt au

ci pi r

1 8 F- FD

G ve

rs us

1 8 F- fl o rb et ap

ir

1 8 F- FD

G ve

rs us

1 8 F -f lo rt au

ci pi r

1 8 F- fl o rb et ap

ir ve

rs us

1 8 F -f lo rt au

ci pi r

P ar ti ci pa

nt (C N /M

C I/ A D )

2 9 /2

2 /2

2 5 /2

9 /1

9 /8

/3

Sc an

s 1 0 6

1 1 0

4 0

Se x (M

/F )

2 4 /2

9 2 5 /3

0 9 /1

1

E du

ca ti o n (y ea

rs )

1 5 .4 0 ± 2 .6 6

1 5 .4 0 ± 2 .6 5

1 5 .5 0 ± 2 .5 6

A ge

(y ea

rs )

B as el in e

7 4 .5 3 ± 5 .6 5

7 3 .9 8 ± 6 .9 0

7 7 .1 7 ± 7 .8 9

.6 5

.1 2

.0 9

La st

sc an

7 9 .8 2 ± 5 .9 4 †

7 8 .7 3 ± 7 .2 6 †

7 8 .8 3 ± 7 .9 8 †

.4 0

.5 6

.9 6

M M SE

B as el in e

2 8 .1 3 ± 1 .8 3

2 7 .8 9 ± 1 .7 4

2 6 .4 5 ± 3 .3 9

.4 8

.0 1 **

.0 2 *

La st

sc an

2 5 .0 8 ± 4 .5 1 †

2 4 .6 5 ± 5 .2 5 †

2 5 .2 5 ± 4 .6 3

.6 6

.1 5

.6 6

C D R

B as el in e

0 .2 1 ± 0 .2 5

0 .2 6 ± 0 .2 5

0 .3 5 ± 0 .3 7

.2 5

.0 6

.2 5

La st

sc an

0 .6 7 ± 0 .4 8 †

0 .7 8 ± 0 .5 1 †

0 .7 0 ± 0 .5 2 †

.2 4

0 .8 2

0 .5 4

N ot e: Si gn

if ic an

t di ff er en

ce s be

tw ee

n th e ba

se lin

e an

d la st

sc an

va lu es

us in g tw

o -s am

pl e t- te st

ar e de

no te d as

†. Si gn

if ic an

t di ff er en

ce s be

tw ee

n th e

1 8 F -F D G an

d 1 8 F -f lo rb et ap

ir gr o up

,1 8 F -F D G

an d

1 8 F -f lo rt au

ci pi r gr o up

,1 8 F -f lo rb et ap

ir ,a nd

1 8 F -f lo rt au

ci pi r gr o up

ar e de

no te d by

*, ** ,a nd

** * w he

n p < .0 5 ,. 0 1 ,a nd

0 .0 0 1 ,r es pe

ct iv el y.

2124 LI ET AL.

10970193, 2022, 7, D ow

nloaded from https://onlinelibrary.w

iley.com /doi/10.1002/hbm

.25774, W iley O

nline L ibrary on [26/11/2022]. See the T

erm s and C

onditions (https://onlinelibrary.w iley.com

/term s-and-conditions) on W

iley O nline L

ibrary for rules of use; O A

articles are governed by the applicable C reative C

om m

ons L icense

SUVRCS had the lowest sensitivity and failed to detect SUVR

decrease in the orbital frontal, prefrontal, and amygdala (t < 1.67).

Compared with the SUVRCS, the SUVRCereb_GM detected changes in

the orbital frontal and prefrontal. The ACRs of ROIs for each refer-

ence tissue are listed in Table 3. The pons demonstrated the largest

ACR across all listed brain regions, with an average of 73.8 ± 42.4%

and 92.2 ± 61.7% higher than the ACRCereb_GM and ACRCS, respec-

tively. Regardless of which reference tissue was used, the caudate,

anterior cingulate, lateral temporal, parahippocampal gyrus, poste-

rior cingulate, parietal, and entorhinal cortex showed the greatest

longitudinal annual change rates of 18F-FDG uptake with each hav-

ing at least a 1% reduction (Table 3).

3.2.2 | 18F-florbetapir PET

In contrast to the 18F-FDG PET analysis where the SUVRPons demon-

strated the greatest sensitivity, the SUVRCS in 18F-florbetapir was

superior in detecting longitudinal amyloid depositions (Figure 3). As

demonstrated in Figure 4, significant longitudinal differences in the

orbital frontal, prefrontal, superior frontal, lateral temporal, inferior

temporal, parietal, posterior precuneus, occipital, anterior cingulate,

and posterior cingulate were detected using the centrum semiovale

reference tissue. The t(SUVRCS) was 27.12 ± 12.31% and 52.12

± 23.78% higher than the t(SUVRCereb_GM) and t(SUVRPons), respec-

tively, which was consistent with the t map at the voxel-level. The

cerebellum GM and pons demonstrated similar sensitivities in moni-

toring all regions, however, the use of these reference tissues was

unable to detect changes in the anterior cingulate. The greatest amy-

loid ACR for disease-progressed participants was observed in the pos-

terior cingulate (2.2%) when centrum semiovale was used as

reference tissue (Table 3). The average of ACRs in all the studied brain

regions was 1.2% using a cerebellum GM reference tissue

(ACRCereb_GM), 1.7% using a centrum semiovale reference tissue

(ACRCS), and 1.6% using the pons reference (ACRPons) tissue (Table 3).

ACRCS was 10.2 ± 1.8% and 44.3 ± 10.1% greater than the

ACRCereb_GM and ACRPons, respectively, highlighting the superiority of

the centrum semiovale in detecting longitudinal amyloid changes.

3.2.3 | 18F-flortaucipir PET

The ROI and voxelwise results for 18F-flortaucipir are displayed in Fig-

ures 5 and 6, respectively. Centrum semiovale reference tissue could

only identify longitudinal changes in known AD-sensitive regions includ-

ing the superior frontal, lateral temporal, and inferior temporal. The t

(SUVRCS) ranged from 2.02 to 2.34, which was 8.46 and 15.42 times

higher than the t(SUVRCereb_GM) and t(SUVRPons), respectively (Figure 6).

The ACRs of these significant brain regions using three reference tissues

are also listed in Table 3. The ACRs of the superior frontal, lateral tempo-

ral, and inferior temporal demonstrated an average of 3.2% when using

the centrum semiovale as the reference tissue, exceeding ACRCereb_GM

(0.5%) and ACRPons (0.8%) by 5.5 and 3.2 times, respectively.T A B L E 2

C ha

ra ct er is ti cs

o f cr o ss -s ec ti o na

ls tu dy

co ho

rt

C ha

ra ct er is ti cs

1 8 F- FD

G 1 8 F- fl o rb et ap

ir 1 8 F -f lo rt au

ci pi r

C N

C I

(M C I/ A D )

p va

lu e

C N

C I

(M C I/ A D )

p va

lu e

C N

C I

(M C I/ A D )

p va

lu e

P ar ti ci pa

nt s (n )

1 6 8

2 5 2 (2 2 0 /3

2 )

2 5 7

1 7 7 (1 6 2 /1

5 )

3 9 5

2 7 1 (2 0 5 /6

6 )

Se x (M

/F )

7 4 /9

4 1 5 1 /1

0 1

1 0 4 /1

5 3

1 0 6 /7

1 1 0 4 /1

5 3

1 6 2 /1

0 9

A ge

(y ea

rs )

7 2 .7 9 ± 6 .1 4

7 1 .7 6 ± 7 .8 9

.1 6

7 2 .2 8 ± 6 .4 1

7 1 .7 7 ± 7 .7 1

.4 5

7 3 .3 1 ± 7 .0 8

7 5 .7 8 ± 8 .3 7

< .0 0 0 1 ** *

E du

ca ti o n (y ea

rs )

1 6 .5 7 ± 2 .6 1

1 6 .2 4 ± 2 .6 6

.2 1

1 6 .6 3 ± 2 .5 2

1 6 .3 3 ± 2 .6 6

.2 3

1 6 .8 4 ± 2 .3 2

1 6 .0 8 ± 2 .6 8

.0 0 0 1 **

M M SE

sc o re

2 9 .1 3 ± 1 .1 4

2 7 .4 3 ± 2 .5 8

< .0 0 0 1 ** *

2 9 .1 3 ± 1 .0 6

2 7 .5 3 ± 2 .7 6

< .0 0 0 1 ** *

2 9 .0 8 ± 1 .1 8

2 6 .4 8 ± 3 .7 0

< .0 0 0 1 ** *

C D R

0 .0 0 ± 0 .0 4

0 .5 2 ± 0 .1 4

< .0 0 0 1 ** *

0 .0 0 ± 0 .0 4

0 .5 2 ± 0 .1 3

< .0 0 0 1 ** *

0 .0 2 ± 0 .1 2

0 .5 6 ± 0 .3 5

< .0 0 0 1 ** *

N ot e: Si gn

if ic an

t di ff er en

ce s be

tw ee

n C N

an d C Ig

ro up

s ar e de

no te d by

*, ** ,a nd

** * in di ca ti ng

p < .0 5 ,. 0 1 ,a nd

.0 0 1 ,r es pe

ct iv el y.

LI ET AL. 2125

10970193, 2022, 7, D ow

nloaded from https://onlinelibrary.w

iley.com /doi/10.1002/hbm

.25774, W iley O

nline L ibrary on [26/11/2022]. See the T

erm s and C

onditions (https://onlinelibrary.w iley.com

/term s-and-conditions) on W

iley O nline L

ibrary for rules of use; O A

articles are governed by the applicable C reative C

om m

ons L icense

3.3 | Effect of reference tissue selection on sensitivity to detect PET SUVR differences between CN and CI groups in cross-sectional study cohort

The Cohen’s d maps (Figure 7) generated from the cerebellum GM,

centrum semiovale, and pons reference tissue-based SUVR images

were visually comparable. The average d-values over 15 ROIs for the

cerebellum GM, centrum semiovale and pons in 18F-FDG were d

(SUVRCereb_GM) = 0.33 ± 0.89, d(SUVRCS) = 0.30 ± 0.15 and d

(SUVRPons) = 0.38 ± 0.35; in 18F-florbetapir were d

(SUVRCereb_GM) = 0.44 ± 0.16, d(SUVRCS) = 0.46 ± 0.14, and d

(SUVRPons) = 0.49 ± 0.13; in 18F-flortaucipir were d

(SUVRCereb_GM) = 0.60 ± 0.20, d(SUVRCS) = 0.56 ± 0.23, and d

(SUVRPons) = 0.59 ± 0.21. The covariates adjusted t maps (Figure 8)

generated from reference tissue-based SUVR images were compara-

ble in 18F-florbetapir and 18F-flortaucipir, but pons had increased per-

formance in 18F-FDG. The average t values from ROIs for the

cerebellum GM, centrum semiovale, and pons in 18F-FDG were t

(SUVRCereb_GM) = 3.52 ± 1.09, t(SUVRCS) = 3.37 ± 0.96, and t

(SUVRPons) = 4.76 ± 0.91; in 18F-florbetapir were t

(SUVRCereb_GM) = 4.54 ± 1.67, t(SUVRCS) = 4.75 ± 1.43, and t

(SUVRPons) = 5.51 ± 1.20; in 18F-flortaucipir were t

(SUVRCereb_GM) = 8.39 ± 2.58, t(SUVRCS) = 7.51 ± 2.90, and t

(SUVRPons) = 7.96 ± 2.67.

4 | DISCUSSION

In this study, we evaluated the effect of reference tissue selection on

the statistical power to detect longitudinal and cross-sectional SUVR

changes in 18F-FDG, 18F-florbetapir, and 18F-flortaucipir PET studies

of AD. Specifically, the results from 18F-FDG were consistent with

previous reference tissue selection studies in which the pons showed

superiority in both longitudinal and cross-sectional analyses, especially

in elucidating longitudinal SUVR changes (Minoshima et al., 1995;

Nugent et al., 2020; Verger et al., 2021). After the correction of

covariates (age, sex, and education level) effects in cross-sectional

analysis, the pons demonstrated better performance in distinguishing

F IGURE 2 Statistical t values of ROI-based longitudinal 18F-FDG SUVR changes in subjects with cognitive decline. Statistical p values indicate *p < .05, **p < .001, ***p < .0001. ACC, anterior cingulate; Amy, amygdala; Caud, Caudate; EC, entorhinal cortex; Hip, hippocampus; InfT, inferior temporal; LatT, lateral temporal; MT, medial temporal; OrbF, orbital frontal; Par, parietal; PCC, posterior cingulate; PHip, parahippocampal gyrus; PPrC, posterior precuneus; PreF, prefrontal; SupF, superior frontal

F IGURE 1 Paired statistical t map of longitudinal (mean follow-up period: 63.42 ± 27.15 months, n = 53) 18F-FDG SUVR changes in participants with cognitive decline. The SUVR was calculated for reference tissue cerebellum GM, centrum semiovale, and pons, respectively

2126 LI ET AL.

10970193, 2022, 7, D ow

nloaded from https://onlinelibrary.w

iley.com /doi/10.1002/hbm

.25774, W iley O

nline L ibrary on [26/11/2022]. See the T

erm s and C

onditions (https://onlinelibrary.w iley.com

/term s-and-conditions) on W

iley O nline L

ibrary for rules of use; O A

articles are governed by the applicable C reative C

om m

ons L icense

CN and CI groups, particularly in the orbital frontal, prefrontal, and

inferior temporal (Table S1). Minoshima et al. (1995) evaluated pons

and other reference tissues in AD by observing the preservation of

glucose metabolism. They concluded that pons was a reliable and

appropriate reference tissue for data normalization to distinguish

between CN and CI groups. In the investigation of the metabolic

changes of normal aging, pons was also identified as the most appro-

priate area for brain intensity normalization due to its minimal corre-

lation with age and greater significant longitudinal cluster volumes

compared to other normalizations (Verger et al., 2021). Similarly, in

the context of healthy aging, pons was verified as the optimal refer-

ence tissue by examining changes in brain glucose uptake when com-

paring with the whole brain based on the posterior cingulate and the

precuneus regions (Nugent et al., 2020). Although these previous

studies have identified pons as the most appropriate reference tis-

sue, these studies did not conduct voxelwise analysis and consid-

ered only a limited number of ROIs. More importantly, the

longitudinal population these studies included were either normal

aging or patients with only follow-up scans, regardless of the sub-

jects' disease progression and clinical severity. In our study, we

confirmed the use of pons in 18F-FDG for the population with dis-

ease progression according to their clinical diagnosis. The annual

reduction rates in 18F-FDG uptake under normal aging in the ante-

rior cingulate cortex and posterior cingulate cortex/precuneus

were previously reported as 0.6 and 0.6% (Ishibashi et al., 2018).

By using pons, our result illustrated that the ACRs of the subjects

with clinical declines in the anterior cingulate and posterior cingu-

late were 1.7 and 1.6%, as more than twice as that of the normal

participants reported (Ishibashi et al., 2018).

For 18F-florbetapir amyloid PET, our results suggest that the cen-

trum semiovale is the ideal reference tissue to detect 18F-florbetapir

longitudinal changes in AD for monitoring brain amyloid accumulation.

The centrum semiovale showed the greatest statistical power to

detect brain regions with highly significant t-values, as well as the

highest ACR (ACRCS = 1.7%) compared to the cerebellum GM and

pons. The cerebral white matter has been previously validated as the

reference tissue with less variability and great statistical power to

detect longitudinal increases in Aβ depositions (Chen et al., 2015).

Interestingly, the application of subcortical white matter and cerebellar

white matter alone or in combination produced an enhanced result

when assessed by effect size (Cohen’s d; Chiao et al., 2019). More-

over, in a test–retest 18F-florbetapir longitudinal SUVR study, the

brainstem had the highest stability and correlation between PET and

concurrent cerebrospinal fluid Aβ1–42 levels (Shokouhi et al., 2016). It

is well-recognized that partial volume effects exist in brainstem- or

pons-based SUVR measurements (Chen et al., 2015; Su et al., 2019).

In our cross-sectional analysis including effect size estimates and two-

independent sample t-test, cerebellum GM, centrum semiovale, and

pons all demonstrated comparable statistical power in distinguishing

SUVR differences between CN and CI groups. Cerebellum GM was

ultimately recommended as the ideal reference tissue in 18F-

florbetapir PET studies especially in studies involving relative cerebel-

lar perfusion measurement (Bilgel et al., 2020; Hsiao et al., 2013).T A B L E 3

C o m pa

ri so n o f th e an

nu al ch

an ge

ra te s (%

)o f 1 8 F -F D G ,1

8 F -f lo rb et ap

ir ,a nd

1 8 F -f lo rt au

ci pi r SU

V R

O rb F

P re F

Su pF

La tT

In fT

M T

P ar

P P rC

A C C

P C C

C au

d E C

A m y

H ip

P H iP

O cc

1 8 F -F D G

C er eb

G M

�0 .4

± 2 .8

�0 .3

± 2 .6

�0 .5

± 2 .6

�1 .2

± 2 .6

�0 .8

± 2 .4

�0 .6

± 2 .4

�1 .1

± 2 .6

�0 .8

± 2 .7

�1 .2

± 2 .9

�1 .1

± 2 .6

�1 .5

± 3 .0

�1 .0

± 3 .2

�0 .3

± 2 .9

�0 .7

± 2 .4

�1 .1

± 3 .0

C S

�0 .3

± 3 .3

�0 .3

± 3 .5

�0 .5

± 3 .3

�1 .1

± 3 .3

�0 .8

± 2 .9

�0 .6

± 2 .8

�1 .0

± 3 .0

�0 .8

± 3 .3

�1 .2

± 2 .6

�1 .0

± 3 .2

�1 .4

± 3 .4

�1 .0

± 2 .9

�0 .3

± 2 .9

�0 .7

± 2 .6

�1 .1

± 2 .4

P o ns

�0 .9

± 2 .2

�0 .8

± 2 .2

�1 .0

± 2 .4

�1 .7

± 2 .5

�1 .3

± 2 .2

�1 .1

± 2 .1

�1 .5

± 2 .5

�1 .3

± 2 .7

�1 .7

± 1 .7

�1 .6

± 2 .3

�2 .0

± 2 .2

�1 .5

± 3 .0

�0 .8

± 2 .5

�1 .2

± 2 .3

�1 .6

± 2 .4

1 8 F – fl o rb et ap

ir

C er eb

G M

1 .3

± 2 .7

1 .4

± 2 .4

1 .3

± 2 .5

1 .0

± 2 .0

0 .8

± 2 .2

1 .2

± 2 .2

1 .3

± 2 .1

1 .0

± 3 .0

1 .8

± 2 .5

1 .1

± 2 .3

C S

1 .8

± 2 .9

1 .9

± 2 .4

1 .8

± 2 .3

1 .5

± 2 .2

1 .3

± 2 .1

1 .7

± 2 .1

1 .8

± 2 .1

1 .6

± 3 .6

2 .4

± 2 .9

1 .5

± 1 .9

P o ns

1 .6

± 2 .7

1 .7

± 2 .4

1 .6

± 2 .4

1 .4

± 2 .2

1 .2

± 2 .2

1 .5

± 2 .3

1 .6

± 2 .2

1 .4

± 3 .6

2 .2

± 2 .9

1 .4

± 2 .2

1 8 F – fl o rt au

ci pi r

C er eb

G M

0 .5

± 3 .1

0 .5

± 2 .7

0 .5

± 3 .1

C S

3 .3

± 6 .4

3 .2

± 5 .9

3 .2

± 5 .7

P o ns

0 .8

± 5 .7

0 .8

± 4 .8

0 .7

± 4 .9

N ot e: T he

va lu es

de m o ns tr at e A C R s o f SU

V R s in

1 8 F -F D G ,1

8 F -f lo rb et ap

ir an

d 1 8 F -f lo rt au

ci pi r w he

n us in g th e ce re be

llu m

G M ,c en

tr um

se m io va le ,a nd

po ns

as re fe re nc

e ti ss ue

s.

A bb

re vi at io ns :A

C C ,a nt er io r ci ng

ul at e;

A m y, am

yg da

la ;C

au d,

C au

da te ;E

C ,e nt o rh in al co

rt ex

;H ip ,h

ip po

ca m pu

s; In fT ,i nf er io r te m po

ra l; La tT ,l at er al te m po

ra l; M T ,m

ed ia lt em

po ra l; O cc ,O

cc ip it al ;O

rb F ,o

rb it al fr o nt al ;P

ar ,p

ar ie ta l; P C C ,p

o st er io r ci n gu

la te ;P

H ip ,p

ar ah

ip p o ca m p al gy

ru s; P P rC

,p o st er io r p re cu

n eu

s;

P re F ,p

re fr o nt al ;S

up F ,s up

er io r fr o nt al .

LI ET AL. 2127

10970193, 2022, 7, D ow

nloaded from https://onlinelibrary.w

iley.com /doi/10.1002/hbm

.25774, W iley O

nline L ibrary on [26/11/2022]. See the T

erm s and C

onditions (https://onlinelibrary.w iley.com

/term s-and-conditions) on W

iley O nline L

ibrary for rules of use; O A

articles are governed by the applicable C reative C

om m

ons L icense

Our study also revealed the centrum semiovale as the optimal ref-

erence tissue for 18F-flortaucipir tau PET studies. Superior frontal, lat-

eral temporal, and inferior temporal were the only brain regions that

were detected as significant using the centrum semiovale reference in

tau PET. Considering the shorter time intervals between tau scans, all

three reference tissues were indicated as the sensitive regions in tau

PET imaging which strongly correlated with neurodegeneration (Zhou

et al., 2021). To the best of our knowledge, this is the first study that

evaluated reference tissue effects longitudinally for tau PET. For

cross-sectional analysis, the presented d maps and covariates adjusted

t maps showed that the cerebellum GM, centrum semiovale, and pons

revealed similar performance in 18F-flortaucipir, but the cerebellum

GM was still the recommended reference tissue due to previous cere-

bral blood flow studies (Rubinski et al., 2021; Zhou et al., 2021). Our

previous research has demonstrated that the centrum semiovale ref-

erence tissue-based DVR and SUVR at the late phase, as well as the

cerebellum GM reference tissue-based R1 and SUVR at the early

phase, demonstrated higher Cohen’s d effect size to detect tau depo-

sition with improved quantification power for dynamic 18F-flortaucipir

PET quantifications (Zhou et al., 2021). Notice that the cerebellum

GM was frequently used as reference tissue in full dynamic 18F-

flortaucipir PET studies (Baker et al., 2017; Barret et al., 2017; Devous

Sr. et al., 2018; Golla et al., 2017). The average ACR of tau obtained in

this study when centrum semiovale as reference tissue was

ACRCS = 3.22%, which was close to the previous annual 18F-

flortaucipir change of 4% collected from the inferior temporal region

when cerebral white matter was used as the reference tissue

(Hanseeuw et al., 2019).

To date, the cerebellum GM reference tissue, a region without

relevant specific binding, has been suggested for the quantitation of

amyloid and tau burden (Baker et al., 2017; Barret et al., 2017;

Joachim, Morris, & Selkoe, 1989; Price et al., 2005; Yamaguchi, Hirai,

Morimatsu, Shoji, & Nakazato, 1989), as well as for the quantification

of the tracer perfusion (Bilgel et al., 2020; Weiner et al., 2013; Zhou

et al., 2007, 2021). For example, cerebellum GM was the suggested

reference tissue to estimate relative transport rate R1 for dynamic 18F-flortaucipir PET studies (Zhou et al., 2021). Based on the cerebel-

lum GM reference, the R1 images derived from 11C-PIB dynamic PET

have been used to assess the cerebral blood flow decreases in AD

studies (Bilgel et al., 2020). Previous studies have also proposed and

F IGURE 3 Paired statistical t map of longitudinal (mean follow-up period: 57.05 ± 18.75 months, n = 55) 18F-florbetapir SUVR changes in participants with cognitive decline. The SUVR was calculated for reference tissue cerebellum GM, centrum semiovale, and pons, respectively

F IGURE 4 Statistical t values of ROI-based longitudinal 18F-florbetapir SUVR changes in subjects with cognitive decline. Statistical p values indicate *p < .05, **p < .001, ***p < .0001. ACC, anterior cingulate; InfT, inferior temporal; LatT, lateral temporal; Occ, occipital; OrbF, orbital frontal; Par, parietal; PCC, posterior cingulate; PPrC, posterior precuneus; PreF, prefrontal; SupF, superior frontal

2128 LI ET AL.

10970193, 2022, 7, D ow

nloaded from https://onlinelibrary.w

iley.com /doi/10.1002/hbm

.25774, W iley O

nline L ibrary on [26/11/2022]. See the T

erm s and C

onditions (https://onlinelibrary.w iley.com

/term s-and-conditions) on W

iley O nline L

ibrary for rules of use; O A

articles are governed by the applicable C reative C

om m

ons L icense

validated the benefits of using a white matter reference in longitudinal

amyloid PET studies (Brendel et al., 2015; Chen et al., 2015). The

cerebral white voxels were usually collected in the corpus callosum

and centrum semiovale, rather than the cerebellum white matter

which is close to gray matter or ventricles due to the likelihood of

being confounded by the partial volume effect. Although the whole

cerebellum is also used for cross-sectional analyses of florbetapir PET

(Doraiswamy et al., 2012; Fleisher et al., 2013; Landau et al., 2013), a

reference region including subcortical white matter is suggested for

the measurement of the SUVR changes in longitudinal florbetapir PET

study (Landau et al., 2015), which is consistent with our longitudinal

study. We have also implemented the longitudinal analysis and

covariates corrected cross-sectional analysis using the whole cerebel-

lum (a combination of gray matter and white matter) reference tissue

and compared the results. In the longitudinal analysis, the sensitivity of

the whole cerebellum reference tissue to detect 18F-FDG PET SUVR

changes is lower than that of cerebellum GM and is close to that of

white matter (centrum semiovale; Figure S1). In 18F-florbetapir and 18F-

flortaucipir PET studies, the whole cerebellum still has comparable sen-

sitivity to detect SUVR changes with centrum semiovale but has

remarkable higher detection power as compared with cerebellum GM

reference tissue (Figures S2 and S3). The covariates adjusted t-test

showed that the whole cerebellum reference tissue based SUVR has

the least sensitivity to discriminate CN and CI groups in both 18F-FDG

(Figure S4) and 18F-florbetapir (Figure S4) when compared to the cere-

bellum GM, centrum semiovale and pons based SUVRs, specifically for

the frontal and hippocampus (Table S1) in 18F-FDG and occipital and

parahippocampal gyrus in 18F-florbetapir (Table S2). Whereas in 18F-

flortaucipir, the cerebellum GM, centrum semiovale, pons, and whole

cerebellum demonstrate similar sensitivities in distinguishing CN and CI

groups (Table S3 and Figure S4).

In conclusion, the reference tissue pons-based 18F-FDG SUVR

and centrum semiovale-based 18F-florbetapir and 18F-flortaucipir

SUVR significantly improved the detection power of longitudinal PET

changes in subjects with cognitive decline. For our cross-sectional

analyses, the pons demonstrated a better performance in dis-

tinguishing CN and CI groups in 18F-FDG. For amyloid and tau PET,

the cerebellum GM, centrum semiovale and pons revealed a compara-

ble statistical power to distinguish between CN and CI, but the cere-

bellum GM was suggested as the ideal reference tissue for

quantification of relative cerebral blood flow, as well as amyloid and

tau depositions using 18F-florbetapir and 18F-flortaucipir. The

suggested tracer-specific reference tissues for SUVR calculation pro-

vide a basis for clinical quantitative ATN PET normalization and stan-

dardization. Our study supports an improved quantitative PET

approach for early diagnosis, monitoring of disease progression, and

therapeutic response in AD studies.

F IGURE 5 Paired statistical tmap of longitudinal (mean follow-up period: 19.88 ± 8.03 months, n = 20) 18F-flortaucipir SUVR changes in participants with cognitive decline. The SUVR was calculated for reference tissue cerebellum GM, centrum semiovale, and pons, respectively

F IGURE 6 Statistical t values of ROI-based longitudinal 18F-flortaucipir SUVR changes in subjects with cognitive decline. Statistical p values indicate *p < .05, **p < .001, ***p < .0001

LI ET AL. 2129

10970193, 2022, 7, D ow

nloaded from https://onlinelibrary.w

iley.com /doi/10.1002/hbm

.25774, W iley O

nline L ibrary on [26/11/2022]. See the T

erm s and C

onditions (https://onlinelibrary.w iley.com

/term s-and-conditions) on W

iley O nline L

ibrary for rules of use; O A

articles are governed by the applicable C reative C

om m

ons L icense

ACKNOWLEDGMENTS

Data collection and sharing for this project was funded by the

Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Insti-

tutes of Health Grant U01 AG024904) and DOD ADNI (Department

of Defense award number W81XWH-12-2-0012). ADNI is funded by

the National Institute on Aging, the National Institute of Biomedical

Imaging and Bioengineering, and through generous contributions from

the following: AbbVie, Alzheimer's Association; Alzheimer's Drug Discov-

ery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers

Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuti-

cals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd

and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare;

IXICO Ltd.;Janssen Alzheimer Immunotherapy Research & Development,

LLC.; Johnson & Johnson Pharmaceutical Research & Development

LLC.; Lumosity; Lundbeck; Merck & Co., Inc.;Meso Scale Diagnostics,

LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharma-

ceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda

Pharmaceutical Company; and Transition Therapeutics. The Canadian

Institutes of Health Research is providing funds to support ADNI clinical

sites in Canada. Private sector contributions are facilitated by the Foun-

dation for the National Institutes of Health (www.fnih.org). The grantee

organization is the Northern California Institute for Research and Educa-

tion, and the study is coordinated by the Alzheimer's Therapeutic

Research Institute at the University of Southern California. ADNI data

are disseminated by the Laboratory for Neuro Imaging at the University

of Southern California.

CONFLICT OF INTEREST

The authors have declared no conflicts of interest for this article.

DATA AVAILABILITY STATEMENT

All 18F-FDG, 18F-florbetapir, and 18F-flortaucipir PET and structural

MRI data in this study were obtained from the AD Neuroimaging Ini-

tiative (ADNI) dataset (https://adni.loni.usc.edu).

F IGURE 8 Statistical t images for SUVR differences between cognitively normal (CN) and impaired (CI) groups in 18F-FDG (a), 18F-florbetapir (b), and 18F-flortaucipir (c) PET studies when using cerebellum GM, centrum semiovale, and pons as reference tissue. The SUVR was calculated for reference tissue cerebellum GM, centrum semiovale, and pons, respectively

F IGURE 7 Statistical Cohen’s d images for SUVR differences between cognitively normal (CN) and impaired (CI) groups in 18F-FDG (a), 18F-florbetapir (b), and 18F-flortaucipir (c) PET studies when using cerebellum GM, centrum semiovale, and pons as reference tissue. The SUVR was calculated for reference tissue cerebellum GM, centrum semiovale, and pons, respectively

2130 LI ET AL.

10970193, 2022, 7, D ow

nloaded from https://onlinelibrary.w

iley.com /doi/10.1002/hbm

.25774, W iley O

nline L ibrary on [26/11/2022]. See the T

erm s and C

onditions (https://onlinelibrary.w iley.com

/term s-and-conditions) on W

iley O nline L

ibrary for rules of use; O A

articles are governed by the applicable C reative C

om m

ons L icense

ORCID

Jie Lu https://orcid.org/0000-0003-0425-3921

Xiuying Wang https://orcid.org/0000-0001-7160-5929

Yun Zhou https://orcid.org/0000-0001-9135-336X

REFERENCES

Alexander, G. E., Chen, K., Pietrini, P., Rapoport, S. I., & Reiman, E. M.

(2002). Longitudinal PET evaluation of cerebral metabolic decline in

dementia: A potential outcome measure in Alzheimer’s disease treat-

ment studies. The American Journal of Psychiatry, 159(5), 738–745. https://doi.org/10.1176/appi.ajp.159.5.738

Baker, S. L., Lockhart, S. N., Price, J. C., He, M., Huesman, R. H.,

Schonhaut, D., & Jagust, W. J. (2017). Reference tissue-based kinetic

evaluation of 18F-AV-1451 for tau imaging. Journal of Nuclear Medi-

cine, 58(2), 332–338. Barret, O., Alagille, D., Sanabria, S., Comley, R. A., Weimer, R. M.,

Borroni, E., & Morley, T. (2017). Kinetic modeling of the tau PET tracer

18F-AV-1451 in human healthy volunteers and Alzheimer disease

subjects. Journal of Nuclear Medicine, 58(7), 1124–1131. Bilgel, M., Beason-Held, L., An, Y., Zhou, Y., Wong, D. F., & Resnick, S. M.

(2020). Longitudinal evaluation of surrogates of regional cerebral

blood flow computed from dynamic amyloid PET imaging. Journal of

Cerebral Blood Flow and Metabolism, 40(2), 288–297. https://doi.org/ 10.1177/0271678X19830537

Blautzik, J., Brendel, M., Sauerbeck, J., Kotz, S., Scheiwein, F.,

Bartenstein, P., & Alzheimer’s Disease Neuroimaging Initiative. (2017).

Reference region selection and the association between the rate of

amyloid accumulation over time and the baseline amyloid burden.

European Journal of Nuclear Medicine and Molecular Imaging, 44(8),

1364–1374. https://doi.org/10.1007/s00259-017-3666-8 Brendel, M., Högenauer, M., Delker, A., Sauerbeck, J., Bartenstein, P.,

Seibyl, J., … Alzheimer’s Disease Neuroimaging Initiative. (2015).

Improved longitudinal [18F]-AV45 amyloid PET by white matter refer-

ence and VOI-based partial volume effect correction. NeuroImage,

108, 450–459. Chand, G. B., Dwyer, D. B., Erus, G., Sotiras, A., Varol, E.,

Srinivasan, D., & Dazzan, P. (2020). Two distinct neuroanatomical

subtypes of schizophrenia revealed using machine learning. Brain,

143(3), 1027–1038. Chen, K., Roontiva, A., Thiyyagura, P., Lee, W., Liu, X., Ayutyanont, N., &

Alzheimer’s Disease Neuroimaging Initiative. (2015). Improved power

for characterizing longitudinal amyloid-beta PET changes and evaluat-

ing amyloid-modifying treatments with a cerebral white matter refer-

ence region. Journal of Nuclear Medicine, 56(4), 560–566. https://doi. org/10.2967/jnumed.114.149732

Chiao, P., Bedell, B. J., Avants, B., Zijdenbos, A. P., Grand’Maison, M.,

O’Neill, P., & Koeppe, R. (2019). Impact of reference and target region

selection on amyloid PET SUV ratios in the phase 1b PRIME study of

Aducanumab. Journal of Nuclear Medicine, 60(1), 100–106. https://doi. org/10.2967/jnumed.118.209130

Cho, H., Baek, M. S., Lee, H. S., Lee, J. H., Ryu, Y. H., & Lyoo, C. H. (2020).

Principal components of tau positron emission tomography and longi-

tudinal tau accumulation in Alzheimer’s disease. Alzheimer's Research &

Therapy, 12(1), 114. https://doi.org/10.1186/s13195-020-00685-4

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd

ed.). Hillsdale, NJ: Lawrence Erlbaum Associates Inc.

DeTure, M. A., & Dickson, D. W. (2019). The neuropathological diagnosis

of Alzheimer’s disease. Molecular Neurodegeneration, 14(1), 32.

https://doi.org/10.1186/s13024-019-0333-5

Devous, M. D., Sr., Joshi, A. D., Navitsky, M., Southekal, S., Pontecorvo, M. J.,

Shen, H., & Mintun, M. A. (2018). Test-retest reproducibility for the tau

PET imaging agent Flortaucipir F 18. Journal of Nuclear Medicine, 59(6),

937–943. https://doi.org/10.2967/jnumed.117.200691

Doraiswamy, P. M., Sperling, R. A., Coleman, R. E., Johnson, K. A.,

Reiman, E. M., Davis, M. D., & Fleisher, A. S. (2012). Amyloid-β assessed by florbetapir F 18 PET and 18-month cognitive decline: A

multicenter study. Neurology, 79(16), 1636–1644. Fleisher, A. S., Chen, K., Liu, X., Ayutyanont, N., Roontiva, A.,

Thiyyagura, P., & Sadowsky, C. H. (2013). Apolipoprotein E ε4 and age

effects on florbetapir positron emission tomography in healthy aging

and Alzheimer's disease. Neurobiology of Aging, 34(1), 1–12. Förster, S., Yousefi, B. H., Wester, H.-J., Klupp, E., Rominger, A.,

Förstl, H., & Drzezga, A. (2012). Quantitative longitudinal interrelation-

ships between brain metabolism and amyloid deposition during a

2-year follow-up in patients with early Alzheimer’s disease. European

Journal of Nuclear Medicine and Molecular Imaging, 39(12), 1927–1936. Golla, S. S. V., Timmers, T., Ossenkoppele, R., Groot, C., Verfaillie, S.,

Scheltens, P., & Yaqub, M. (2017). Quantification of tau load using

[(18)F]AV1451 PET. Molecular Imaging and Biology, 19(6), 963–971. https://doi.org/10.1007/s11307-017-1080-z

Gottesman, R. F., Schneider, A. L., Zhou, Y., Coresh, J., Green, E.,

Gupta, N., & Sharrett, A. R. (2017). Association between midlife vascu-

lar risk factors and estimated brain amyloid deposition. JAMA, 317(14),

1443–1450. Hanseeuw, B. J., Betensky, R. A., Jacobs, H. I. L., Schultz, A. P., Sepulcre, J.,

Becker, J. A., & Johnson, K. (2019). Association of Amyloid and tau

with COGNITION in preclinical Alzheimer disease: A longitudinal

study. JAMA Neurology, 76(8), 915–924. https://doi.org/10.1001/

jamaneurol.2019.1424

Heeman, F., Hendriks, J., Lopes Alves, I., Ossenkoppele, R., Tolboom, N.,

van Berckel, B. N. M., … AMYPAD Consortium. (2020). [(11)C]PIB

amyloid quantification: Effect of reference region selection. EJNMMI

Research, 10(1), 123. https://doi.org/10.1186/s13550-020-00714-1

Hsiao, I. T., Huang, C. C., Hsieh, C. J., Wey, S. P., Kung, M. P., Yen, T. C., &

Lin, K. J. (2013). Perfusion-like template and standardized

normalization-based brain image analysis using 18F-florbetapir (AV-

45/Amyvid) PET. European Journal of Nuclear Medicine and Molecular

Imaging, 40(6), 908–920. https://doi.org/10.1007/s00259-013-

2350-x

Ishibashi, K., Onishi, A., Fujiwara, Y., Oda, K., Ishiwata, K., & Ishii, K. (2018).

Longitudinal effects of aging on (18)F-FDG distribution in cognitively

normal elderly individuals. Scientific Reports, 8(1), 11557. https://doi.

org/10.1038/s41598-018-29937-y

Jack, C. R., Jr., Bennett, D. A., Blennow, K., Carrillo, M. C., Dunn, B.,

Haeberlein, S. B., & Karlawish, J. (2018). NIA-AA research framework:

Toward a biological definition of Alzheimer’s disease. Alzheimer's &

Dementia, 14(4), 535–562.

Joachim, C. L., Morris, J. H., & Selkoe, D. J. (1989). Diffuse senile plaques

occur commonly in the cerebellum in Alzheimer’s disease. The Ameri-

can Journal of Pathology, 135(2), 309.

Landau, S. M., Breault, C., Joshi, A. D., Pontecorvo, M., Mathis, C. A.,

Jagust, W. J., & Mintun, M. A. (2013). Amyloid-β imaging with Pitts-

burgh compound B and florbetapir: Comparing radiotracers and quan-

tification methods. Journal of Nuclear Medicine, 54(1), 70–77.

Landau, S. M., Fero, A., Baker, S. L., Koeppe, R., Mintun, M., Chen, K., &

Jagust, W. J. (2015). Measurement of longitudinal β-amyloid change

with 18F-florbetapir PET and standardized uptake value ratios. Journal

of Nuclear Medicine, 56(4), 567–574.

Liu, M., Paranjpe, M. D., Zhou, X., Duy, P. Q., Goyal, M. S.,

Benzinger, T. L. S., & Zhou, Y. (2019). Sex modulates the ApoE epsi-

lon4 effect on brain tau deposition measured by (18)F-AV-1451 PET

in individuals with mild cognitive impairment. Theranostics, 9(17),

4959–4970. https://doi.org/10.7150/thno.35366

Lopresti, B. J., Klunk, W. E., Mathis, C. A., Hoge, J. A., Ziolko, S. K., Lu, X., &

DeKosky, S. T. (2005). Simplified quantification of Pittsburgh com-

pound B amyloid imaging PET studies: A comparative analysis. Journal

of Nuclear Medicine, 46(12), 1959–1972.

LI ET AL. 2131

10970193, 2022, 7, D ow

nloaded from https://onlinelibrary.w

iley.com /doi/10.1002/hbm

.25774, W iley O

nline L ibrary on [26/11/2022]. See the T

erm s and C

onditions (https://onlinelibrary.w iley.com

/term s-and-conditions) on W

iley O nline L

ibrary for rules of use; O A

articles are governed by the applicable C reative C

om m

ons L icense

Minoshima, S., Frey, K. A., Foster, N. L., & Kuhl, D. E. (1995). Preserved

pontine glucose metabolism in Alzheimer disease: A reference region

for functional brain image (PET) analysis. Journal of Computer Assisted

Tomography, 19(4), 541–547. Morris, J. C. (1993). The clinical dementia rating (CDR): Current version

and scoring rules. Neurology, 43(11), 2412–2414. https://doi.org/10. 1212/wnl.43.11.2412-a

Nugent, S., Croteau, E., Potvin, O., Castellano, C. A., Dieumegarde, L.,

Cunnane, S. C., & Duchesne, S. (2020). Selection of the optimal inten-

sity normalization region for FDG-PET studies of normal aging and

Alzheimer’s disease. Scientific Reports, 10(1), 9261. https://doi.org/10. 1038/s41598-020-65957-3

Ortner, M., Drost, R., Hedderich, D., Goldhardt, O., Müller-Sarnowski, F.,

Diehl-Schmid, J., & Grimmer, T. (2019). Amyloid PET, FDG-PET or

MRI? – the power of different imaging biomarkers to detect progres-

sion of early Alzheimer’s disease. BMC Neurology, 19(1), 264. https://

doi.org/10.1186/s12883-019-1498-9

Ossenkoppele, R., Tolboom, N., Foster-Dingley, J. C., Adriaanse, S. F.,

Boellaard, R., Yaqub, M., & van Berckel, B. N. (2012). Longitudinal

imaging of Alzheimer pathology using [11C]PIB, [18F]FDDNP and

[18F]FDG PET. European Journal of Nuclear Medicine and Molecular

Imaging, 39(6), 990–1000. https://doi.org/10.1007/s00259-012-

2102-3

Paranjpe, M. D., Chen, X., Liu, M., Paranjpe, I., Leal, J. P., Wang, R., &

Zhou, Y. (2019). The effect of ApoE ε4 on longitudinal brain region-

specific glucose metabolism in patients with mild cognitive impair-

ment: A FDG-PET study. NeuroImage: Clinical, 22, 101795.

Price, J. C., Klunk, W. E., Lopresti, B. J., Lu, X., Hoge, J. A., Ziolko, S. K., &

Mathis, C. A. (2005). Kinetic modeling of amyloid binding in humans

using PET imaging and Pittsburgh compound-B. Journal of Cerebral

Blood Flow & Metabolism, 25(11), 1528–1547. Rubinski, A., Tosun, D., Franzmeier, N., Neitzel, J., Frontzkowski, L.,

Weiner, M., & Ewers, M. (2021). Lower cerebral perfusion is associ-

ated with tau-PET in the entorhinal cortex across the Alzheimer’s con- tinuum. Neurobiology of Aging, 102, 111–118. https://doi.org/10.

1016/j.neurobiolaging.2021.02.003

Schmidt, R., Ropele, S., Pendl, B., Ofner, P., Enzinger, C., Schmidt, H., &

Fazekas, F. (2008). Longitudinal multimodal imaging in mild to moder-

ate Alzheimer disease: A pilot study with memantine. Journal of Neurol-

ogy, Neurosurgery, and Psychiatry, 79(12), 1312–1317. https://doi.org/ 10.1136/jnnp.2007.141648

Serrano-Pozo, A., Frosch, M. P., Masliah, E., & Hyman, B. T. (2011). Neuro-

pathological alterations in Alzheimer disease. Cold Spring Harbor Per-

spectives in Medicine, 1(1), a006189. https://doi.org/10.1101/

cshperspect.a006189

Shokouhi, S., McKay, J. W., Baker, S. L., Kang, H., Brill, A. B.,

Gwirtsman, H. E., & Alzheimer’s Disease Neuroimaging Initiative.

(2016). Reference tissue normalization in longitudinal (18)F-florbetapir

positron emission tomography of late mild cognitive impairment.

Alzheimer's Research & Therapy, 8, 2. https://doi.org/10.1186/s13195-

016-0172-3

Southekal, S., Devous, M. D., Sr., Kennedy, I., Navitsky, M., Lu, M.,

Joshi, A. D., & Mintun, M. A. (2018). Flortaucipir F 18 quantitation

using parametric estimation of reference signal intensity. Journal of

Nuclear Medicine, 59(6), 944–951. https://doi.org/10.2967/jnumed.

117.200006

Su, Y., Blazey, T. M., Snyder, A. Z., Raichle, M. E., Marcus, D. S.,

Ances, B. M., & Benzinger, T. L. S. (2015). Partial volume correction in

quantitative amyloid imaging. NeuroImage, 107, 55–64. https://doi.

org/10.1016/j.neuroimage.2014.11.058

Su, Y., Flores, S., Wang, G., Hornbeck, R. C., Speidel, B., Joseph-

Mathurin, N., & Benzinger, T. L. S. (2019). Comparison of Pittsburgh

compound B and florbetapir in cross-sectional and longitudinal studies.

Alzheimers Dement (Amst), 11, 180–190. https://doi.org/10.1016/j.

dadm.2018.12.008

Sullivan, G. M., & Feinn, R. (2012). Using effect size—Or why the P

value is not enough. Journal of Graduate Medical Education, 4(3),

279–282. Tohka, J., & Reilhac, A. (2008). Deconvolution-based partial volume correc-

tion in Raclopride-PET and Monte Carlo comparison to MR-based

method. NeuroImage, 39(4), 1570–1584. https://doi.org/10.1016/j.

neuroimage.2007.10.038

Tudorascu, D. L., Minhas, D. S., Lao, P. J., Betthauser, T. J., Yu, Z.,

Laymon, C. M., & Handen, B. L. (2018). The use of Centiloids for apply-

ing [11C] PiB classification cutoffs across region-of-interest delinea-

tion methods. Alzheimer’s & Dementia: Diagnosis, Assessment & Disease

Monitoring, 10(1), 332–339. Uddin, M. S., Stachowiak, A., Mamun, A. A., Tzvetkov, N. T., Takeda, S.,

Atanasov, A. G., & Stankiewicz, A. M. (2018). Autophagy and

Alzheimer’s disease: From molecular mechanisms to therapeutic impli-

cations. Frontiers in Aging Neuroscience, 10, 4. https://doi.org/10.

3389/fnagi.2018.00004

Verger, A., Doyen, M., Campion, J. Y., & Guedj, E. (2021). The pons as ref-

erence region for intensity normalization in semi-quantitative analysis

of brain (18)FDG PET: Application to metabolic changes related to

ageing in conventional and digital control databases. EJNMMI Research,

11(1), 31. https://doi.org/10.1186/s13550-021-00771-0

Wang, M., Yan, Z., Zhang, H., Lu, J., Li, L., Yu, J., & Jiang, J. (2021). Paramet-

ric estimation of reference signal intensity in the quantification of

amyloid-beta deposition: An (18)F-AV-45 study. Quantitative Imaging

in Medicine and Surgery, 11(1), 249–263. https://doi.org/10.21037/ qims-20-110

Weiner, M. W., Veitch, D. P., Aisen, P. S., Beckett, L. A., Cairns, N. J.,

Green, R. C., & Liu, E. (2013). The Alzheimer’s Disease Neuroimaging

Initiative: A review of papers published since its inception.

Alzheimer's & Dementia, 9(5), e111–e194. Wong, D. F., Rosenberg, P. B., Zhou, Y., Kumar, A., Raymont, V.,

Ravert, H. T., & Pontecorvo, M. J. (2010). In vivo imaging of amyloid

deposition in Alzheimer disease using the radioligand 18F-AV-45

(florbetapir [corrected] F 18). Journal of Nuclear Medicine, 51(6), 913– 920. https://doi.org/10.2967/jnumed.109.069088

Wu, Y., Zhou, Y., Bao, S., Huang, S., Zhao, X., & Li, J. (2012). Using the rPatlak

plot and dynamic FDG-PET to generate parametric images of relative

local cerebral metabolic rate of glucose. Chinese Science Bulletin, 57(28– 29), 3811–3818. https://doi.org/10.1007/s11434-012-5401-y

Wu, Y. G. (2008). Noninvasive quantification of local cerebral metabolic

rate of glucose for clinical application using positron emission tomog-

raphy and 18F-fluoro-2-deoxy-D-glucose. Journal of Cerebral Blood

Flow and Metabolism, 28(2), 242–250. https://doi.org/10.1038/sj.

jcbfm.9600535

Xie, Y., Yang, Q., Liu, C., Zhang, Q., Jiang, J., & Han, Y. (2020). Exploring

the pattern associated with longitudinal changes of β-amyloid deposi-

tion during cognitively Normal healthy aging. Front Med (Lausanne), 7,

617173. https://doi.org/10.3389/fmed.2020.617173

Yakushev, I., Landvogt, C., Buchholz, H. G., Fellgiebel, A., Hammers, A.,

Scheurich, A., & Bartenstein, P. (2008). Choice of reference area in

studies of Alzheimer’s disease using positron emission tomography

with fluorodeoxyglucose-F18. Psychiatry Research, 164(2), 143–153. https://doi.org/10.1016/j.pscychresns.2007.11.004

Yamaguchi, H., Hirai, S., Morimatsu, M., Shoji, M., & Nakazato, Y. (1989).

Diffuse type of senile plaques in the cerebellum of Alzheimer-type

dementia demonstrated byβ protein immunostain. Acta Neu-

ropathologica, 77(3), 314–319. Yan, S., Zheng, C., Paranjpe, M. D., Li, J., Benzinger, T. L. S., Lu, J., &

Zhou, Y. (2020). Association of sex and APOE epsilon4 with brain tau

deposition and atrophy in older adults with Alzheimer’s disease.

Theranostics, 10(23), 10563–10572. https://doi.org/10.7150/thno.

48522

Yan, S., Zheng, C., Paranjpe, M. D., Li, Y., Li, W., Wang, X., & Alzheimer’s Disease Neuroimaging Initiative. (2021). Sex modifies APOE epsilon4

2132 LI ET AL.

10970193, 2022, 7, D ow

nloaded from https://onlinelibrary.w

iley.com /doi/10.1002/hbm

.25774, W iley O

nline L ibrary on [26/11/2022]. See the T

erm s and C

onditions (https://onlinelibrary.w iley.com

/term s-and-conditions) on W

iley O nline L

ibrary for rules of use; O A

articles are governed by the applicable C reative C

om m

ons L icense

dose effect on brain tau deposition in cognitively impaired individuals.

Brain, 144, 3201–3211. https://doi.org/10.1093/brain/awab160

Zhao, Q., Liu, M., Ha, L., Zhou, Y., & Alzheimer’s Disease Neuroimaging Ini-

tiative. (2019). Quantitative (18)F-AV1451 brain tau PET imaging in

cognitively normal older adults, mild cognitive impairment, and

Alzheimer’s disease patients. Frontiers in Neurology, 10, 486. https://

doi.org/10.3389/fneur.2019.00486

Zhou, Y., Endres, C. J., Braši�c, J. R., Huang, S.-C., & Wong, D. F. (2003). Lin-

ear regression with spatial constraint to generate parametric images of

ligand-receptor dynamic PET studies with a simplified reference tissue

model. NeuroImage, 18(4), 975–989. https://doi.org/10.1016/s1053- 8119(03)00017-x

Zhou, Y., Flores, S., Mansor, S., Hornbeck, R. C., Tu, Z., Perlmutter, J. S., &

Benzinger, T. L. S. (2021). Spatially constrained kinetic modeling with

dual reference tissues improves (18)F-flortaucipir PET in studies of

Alzheimer disease. European Journal of Nuclear Medicine and Molecular

Imaging, 48, 3172–3186. https://doi.org/10.1007/s00259-020-

05134-w

Zhou, Y., Resnick, S. M., Ye, W., Fan, H., Holt, D. P., Klunk, W. E., &

Wong, D. F. (2007). Using a reference tissue model with spatial con-

straint to quantify [11C] Pittsburgh compound B PET for early diagno-

sis of Alzheimer’s disease. NeuroImage, 36(2), 298–312.

Zhou, Y., Sojkova, J., Resnick, S. M., & Wong, D. F. (2012). Relative equilib-

rium plot improves graphical analysis and allows bias correction of

standardized uptake value ratio in quantitative 11C-PiB PET studies.

Journal of Nuclear Medicine, 53(4), 622–628. https://doi.org/10.2967/ jnumed.111.095927

SUPPORTING INFORMATION

Additional supporting information may be found in the online version

of the article at the publisher's website.

How to cite this article: Li, Y., Ng, Y. L., Paranjpe, M. D., Ge,

Q., Gu, F., Li, P., Yan, S., Lu, J., Wang, X., Zhou, Y., & for the

Alzheimer’s Disease Neuroimaging Initiative (2022).

Tracer-specific reference tissues selection improves detection

of 18F-FDG, 18F-florbetapir, and 18F-flortaucipir PET SUVR

changes in Alzheimer's disease. Human Brain Mapping, 43(7),

2121–2133. https://doi.org/10.1002/hbm.25774

LI ET AL. 2133

10970193, 2022, 7, D ow

nloaded from https://onlinelibrary.w

iley.com /doi/10.1002/hbm

.25774, W iley O

nline L ibrary on [26/11/2022]. See the T

erm s and C

onditions (https://onlinelibrary.w iley.com

/term s-and-conditions) on W

iley O nline L

ibrary for rules of use; O A

articles are governed by the applicable C reative C

om m

ons L icense