Distinguishing between dementia with Lewy bodies and Alzheimer's disease using metabolic patterns

Journal Pre-proof Distinguishing between Dementia with Lewy Bodies and Alzheimer’s Disease using Metabolic Patterns Byoung Seok Ye, MD, PhD, Sangwon Lee, PhD, Hansoo Yoo, MD, Seok Jong Chung, MD, Yang Hyun Lee, MD, Yonghoon Choi, Phil Hyu Lee, MD, PhD, Young H. Sohn, MD, PhD, Mijin Yun, MD, PhD PII:

S0197-4580(19)30384-7

DOI:

https://doi.org/10.1016/j.neurobiolaging.2019.10.020

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To appear in:

Neurobiology of Aging

Received Date: 9 April 2019 Revised Date:

25 October 2019

Accepted Date: 29 October 2019

Please cite this article as: Ye, B.S., Lee, S., Yoo, H., Chung, S.J., Lee, Y.H., Choi, Y., Lee, P.H., Sohn, Y.H, Yun, M., Distinguishing between Dementia with Lewy Bodies and Alzheimer’s Disease using Metabolic Patterns, Neurobiology of Aging (2019), doi: https://doi.org/10.1016/ j.neurobiolaging.2019.10.020. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Inc.

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Distinguishing between Dementia with Lewy Bodies and Alzheimer’s Disease using Metabolic Patterns

Byoung Seok Ye, MD, PhD1, Sangwon Lee, PhD2, Hansoo Yoo, MD1, Seok Jong Chung, MD1, Yang Hyun Lee, MD1, Yonghoon Choi2, Phil Hyu Lee, MD, PhD1, Young H Sohn, MD, PhD1, Mijin Yun, MD, PhD2

1

Department of Neurology, Yonsei University College of Medicine, Seoul, Korea

2

Department of Nuclear Medicine, Yonsei University College of Medicine, Seoul, Korea

Corresponding author: Mijin Yun, MD, PhD. Corresponding author’s address: Department of Nuclear Medicine, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 120-752, South Korea. Corresponding author’s phone and fax: Tel.: +82-2-2228-2350. Fax: +82-2-312-0578 Corresponding author’s e-mail address: [email protected]

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Abstract

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Alzheimer’s disease (AD) and dementia with Lewy bodies (DLB) are the two most common

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causes of dementia. We compared the regional metabolism on 18F-fluorodeoxyglucose (FDG)

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positron emission tomography (PET) among 21 control subjects and cognitively impaired

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patients due to DLB (N = 63) and AD (N = 38). All participants underwent 18F-Florbetaben

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(FBB) PET, and all DLB patients had abnormality on dopamine transporter PET. Both the

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FBB-positive DLB (N = 38) and FBB-negative DLB (N = 25) groups had increased

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metabolism in the bilateral central cerebellum, posterior putamen and somatomotor cortices

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compared to the control and AD groups. Compared to the control group, the DLB and AD

10

groups commonly exhibited hypometabolism in the bilateral lateral temporal, temporo-

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parietal junction, posterior cingulate, and precuneus cortices. Both DLB groups had

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additional hypometabolism in the bilateral thalami and dorsolateral prefrontal cortices, while

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the AD group did in the bilateral entorhinal cortices and hippocampi. Our results suggest that

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hypermetabolism in the somatomotor cortex, posterior putamen, or central cerebellum could

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be a useful imaging biomarker for detecting DLB patients, while entorhinal/hippocampal

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hypometabolism could be a specific biomarker for AD.

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Key words: Dementia with Lewy bodies, Alzheimer’s disease, fluorodeoxyglucose positron

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emission tomography, β-amyloid, metabolism

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1. Introduction

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Dementia with Lewy bodies (DLB) is the second most common cause of degenerative

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dementia after Alzheimer’s disease (AD) (Hansen et al., 1990; Perry et al., 1990). Core

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clinical features for the diagnosis of DLB include cognitive fluctuation, visual hallucinations,

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parkinsonism, and rapid eye movement sleep behavior disorder (McKeith et al., 2017).

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However, extremely low sensitivity of clinical DLB diagnosis in cognitively impaired

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patients, especially in patients with concomitant AD pathology, causes a huge discrepancy

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between the clinical and pathological prevalence of DLB (Nelson et al., 2010).

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Metabolic changes on 18F-fluorodeoxyglucose (FDG) positron emission tomography

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(PET) is a useful biomarker for differential diagnosis of dementia. Hypometabolism in the

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occipital cortex and temporoparietal association cortices (Minoshima et al., 2001), and

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relative sparing of the posterior cingulate cortex (cingulate island sign) (Graff-Radford et al.,

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2014) have been suggested as characteristic metabolic patterns of DLB. However, previous

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studies have shown that AD and DLB share common metabolic patterns of temporoparietal

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hypometabolism (Minoshima et al., 2001), and that concurrent AD tangle pathology modifies

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the cingulate island sign in DLB patients (Graff-Radford et al., 2014; Iizuka et al., 2017).

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Therefore, it is difficult to differentiate DLB from AD or detect DLB patients with

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concomitant AD based on current FDG-PET biomarkers.

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In addition, there are several biomarkers supporting the diagnosis of DLB. Low

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dopamine transporter uptake in the basal ganglia on dopamine transporter imaging, cardiac

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postganglionic sympathetic denervation on iodine-123-metaiodobenzylguanidine myocardial

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scintigraphy, and polysomnography-confirmed rapid eye movement sleep behavior disorder

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are biomarkers indicative of DLB (McKeith et al., 2017). Dopamine transporter imaging

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could differentiate clinically diagnosed probable DLB from AD (McKeith et al., 2007).

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However, while its specificity is high (90.4%), the sensitivity of dopamine transporter

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imaging is relatively low (77.7%), and its diagnostic validity has not been evaluated in

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autopsy-confirmed DLB patients. Amyloid PET is currently used for the diagnosis of AD.

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However, as about half of DLB patients also exhibit significant β-amyloid deposition (Irwin

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et al., 2017), amyloid PET is not an optimal tool for the differential diagnosis of AD and

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DLB.

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Patients with Parkinson’s disease (PD), commonly caused by pathologic cerebral

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accumulation of α-synuclein, have hypermetabolism in the basal ganglia, thalamus, motor

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cortex and central cerebellum on FDG PET (Eckert et al., 2007), and their cognitive

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dysfunction correlates with hypermetabolism in the cerebellum (Eidelberg, 2009), paracentral

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cortex and putamen (Wu et al., 2018). Considering that PD dementia and DLB share a

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common pathology and clinical findings and that the distinction between them is only made

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clinically, we hypothesized that PD-related hypermetabolic patterns could exist in DLB

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patients. In this study, we recruited DLB patients whose dopaminergic depletion was

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confirmed using dopamine transporter imaging and compared their metabolic patterns with

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control subjects and AD patients whose cerebral Aβ status was evaluated using 18F-

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Florbetaben (FBB) PET.

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2. Methods

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2.1. Participants

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Six-three consecutive patients clinically diagnosed with cognitive impairment due to DLB

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were recruited between January 2015 and March 2019 from a dementia outpatient clinic

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and/or a movement disorders outpatient clinic at Severance Hospital, Yonsei University

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Health System. All DLB patients fulfilled the 2017 revised criteria for probable DLB

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(McKeith et al., 2017), and were confirmed to have dopaminergic depletion on 18F-N-

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fluoropropyl-2b-carbomethoxy-3b-(4-iodophenyl)nortropane (FP-CIT) PET. To ascertain

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early brain changes in DLB patients, we included mild cognitive impairment (MCI) patients

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(Petersen et al., 1999) who met all of the diagnostic criteria for probable DLB, except for the

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presence of dementia. Patients with secondary causes of cognitive deficits or parkinsonism,

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as confirmed by laboratory tests and brain structural changes, including territorial cerebral

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infarction, brain tumors, and normal pressure hydrocephalus, were excluded. Clinical features

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of Lewy body (LB) disease including parkinsonism, REM sleep behavior disorder (RBD),

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visual hallucinations, and cognitive fluctuation were evaluated using semi-structured

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questionnaires by caregivers. The severity of parkinsonism was assessed according to the

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Movement Disorder Society Unified Parkinson’s Disease Rating Scale (UPDRS) motor score

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and was regarded as moderate if the score was higher than 16.

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During the same study period, consecutive AD patients and those with MCI due to

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AD were also recruited. All AD patients met the criteria for probable AD dementia with high

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levels of biomarker evidence (McKhann et al., 2011), and all MCI patients met the criteria

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for high likelihood of MCI due to AD (Albert et al., 2011) from the National Institute on

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Aging-Alzheimer’s Association workgroups guidelines for AD. All of these patients were

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identified to have significant cerebral β-amyloid deposition on FBB PET as described below.

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As previous studies reported frequent LB pathologies in AD patients (Chung et al., 2015;

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Ditter and Mirra, 1987; Lemstra et al., 2017; Leverenz and Sumi, 1986), we did not include

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AD patients with significant parkinsonism (Unified Parkinson Disease Rating Scale > 15) on

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neurologic examinations and clinical features including RBD and visual hallucination

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suggesting concomitant LB pathology. Resultingly, 22 patients with MCI due to AD and 16

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with AD dementia were recruited.

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Control subjects were recruited through an on-going independent study (Institutional

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Review Board No. 4-2015-0551) using poster advertisement for normal older adults visiting

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Yonsei University Medical Center. Twenty-one healthy control subjects had no previous

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history of neurologic or psychiatric illnesses, showed normal cognitive function on all

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neuropsychological tests, and exhibited normal findings on the neurologic examination,

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structural MRI, FDG PET, and FBB PET. This study was approved by the Institutional

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Review Board of Severance Hospital, and written informed consent was obtained from all

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participants.

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2.2. Neuropsychological assessment

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All participants underwent a standardized neuropsychological battery called the Seoul

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Neuropsychological Screening Battery (SNSB), which is described in our previous study

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(Lee et al., 2018). Briefly, this battery included the backward digit span test for attention;

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Korean version of the Boston Naming Test (K-BNT) for language function; the immediate

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recall, delayed recall and recognition items of Seoul Verbal Learning Test (SVLT) and Rey-

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Osterrieth Complex Figure Test (RCFT) for memory function; RCFT copy for visuospatial

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function; and phonemic and semantic Controlled Oral Word Association Test (COWAT) and

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the Stroop color reading for frontal/executive function. Age- and education-matched norms

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were available for all scorable tests of SNSB, and standardized z scores were used in the

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current study.

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2.3. MRI acquisition

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MRI was performed using a Philips 3.0-Tesla MRI scanner (Philips Achieva; Philips Medical

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System, Best, The Netherlands) with previously described protocols (Lee et al., 2018).

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2.4. PET imaging

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FP-CIT PET, FDG PET and FBB PET acquisitions were performed using Discovery 600

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(General Electric Healthcare, Milwaukee, MI, USA). Imaging and reconstruction protocols

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for FP-CIT PET and FBB PET are described in our previous study (Lee et al., 2018). Based

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on visual ratings by an expert reader (MJ Yun), brain β-amyloid plaque load (BAPL) score

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(Barthel et al., 2011) and FP-CIT PET abnormalities (Oh et al., 2012) were assessed.

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Participants with BAPL scores of 2 and 3 were regarded to have significant β-amyloid

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deposition, while those with a BAPL score of 1 were not. FDG PET scans were acquired

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according to the following protocol. Approximately 4.1 MBq per body weight (kg) of 18F-

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FDG was intravenously administered to the patients. After the 60 min of uptake period, PET

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images were acquired for 15 min. All FDG-PET images were acquired with a Discovery 600

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(GE Medical Systems, Milwaukee, WI) PET-computed tomography (CT) scanner. The spiral

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CT scan for attenuation correction was performed with a 0.8 s rotation time, 60 mA, 120 kVp,

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3.75 mm section thickness, 0.625 mm collimation, and 9.375 mm table feed per rotation.

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FDG-PET images were reconstructed using the ordered subset expectation maximization

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(OSEM) algorithm with 4 iterations and 32 subsets. To compare the FDG metabolism

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between the patient group and control, FDG PET images were normalized to the FDG-PET

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dementia-specific template, which was created by averaging a number of 100 FDG-PET

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images (50 control subjects and 50 patients) that were smoothed with an 8-mm full width half

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maximum Gaussian filter and normalized to the Montreal Neurological Institute reference

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space (Della Rosa et al., 2014; Perani et al., 2014). The spatially normalized and smoothed

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PET images underwent an intensity normalization to the cerebellar cortex to standardize the

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magnitude of all voxel values.

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All DLB patients underwent FP-CIT PET, FDG PET and FBB PET. From the 63

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DLB patients, 38 of them had significant β-amyloid deposition (FBB+ DLB), and the rest of

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the 25 DLB patients did not (FBB- DLB). All AD patients underwent FBB PET and

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exhibited significant β-amyloid deposition.

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2.5. Statistical analysis

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Analyses of demographic variables and cognitive scores were performed using the Statistical

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Package for the Social Sciences version 23.0 (SPSS Inc., Chicago, IL, USA). Statistical

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analyses for FDG PET were performed using the MATLAB (The MathWorks, Inc, Natick,

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MA)-based software called statistical parametric mapping (SPM, Wellcome Trust Centre for

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Neuroimaging, London, UK) (Kiebel et al., 2007). General linear models were used to

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compare standardized neuropsychological test scores and regional FDG metabolism between

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FBB+ DLB, FBB- DLB, AD, and control groups after controlling for age, sex, and education.

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False discovery rate (FDR) corrections were performed on multiple statistical tests applied to

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13 neuropsychological tests on neuropsychological analyses and multiple voxels on FDG-

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PET analyses with statistical significance set at a corrected q-value less than 0.05.

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3. Results

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3.1. Demographic and clinical characteristics

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The demographic and clinical characteristics of the participants are presented in Table 1. The

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FBB- DLB group was older than the control group and the AD group. The proportions of

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male patients were higher both in the FBB- DLB and FBB+ DLB groups than in the AD

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group. There were no significant differences in terms of education and cardiovascular risk

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factors among the FBB- DLB, FBB+ DLB, AD, and control groups. The three disease groups

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did not differ in disease duration, the proportion of demented patients, and K-MMSE score.

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However, the FBB- DLB and FBB+ DLB groups had higher Clinical Dementia Rating Sum

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of Boxes (CDR-SOB) than the AD group. Since AD patients who showed clinical features of

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DLB were excluded from our AD group, both DLB groups had higher proportions of patients

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with parkinsonism, cognitive fluctuation, visual hallucination, and RBD on semi-structured

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questionnaires by caregivers, compared to AD group. The severity of parkinsonism measured

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by the UPDRS motor score and that of hyposmia measured by the cross-cultural smell

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identification test were more severe in the two DLB groups compared to the AD group.

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3.2. Neuropsychological performances

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Compared to the control group, the FBB- DLB, FBB+ DLB, and AD groups had lower

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cognitive scores on all neuropsychological tests (Table 2). Compared to the AD group, the

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FBB+ DLB group had lower scores in COWAT phonemic and Stroop color reading test and

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a higher score in SVLT delayed recall. Compared to the AD group, the FBB- DLB group had

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lower scores in RCFT copy, COWAT phonemic, and Stroop color reading test and a higher

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score in SVLT delayed recall. There was no significant difference in other

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neuropsychological tests between the disease groups.

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3.3. Group-wise comparisons of regional FDG metabolism

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Compared to the control group, the FBB+ DLB and FBB- DLB groups commonly exhibited

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increased metabolism in the bilateral central cerebellum, posterior putamen, and

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somatomotor cortices, in addition to the bilateral medial temporal cortices (Figure 1A-B).

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The AD group did not have increased metabolism, compared to the control group (Figure 1C).

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Compared to the control group, the FBB+ DLB, FBB- DLB, and AD groups commonly

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exhibited decreased metabolism in the bilateral lateral temporal, temporo-parietal junction,

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posterior cingulate, and precuneus cortices (Figure 1A-C). The FBB+ DLB group exhibited

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an additional hypometabolism in the bilateral dorsolateral prefrontal and occipital cortices, in

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addition to the bilateral thalami and caudate nuclei; the FBB- DLB group did so in the

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bilateral thalami and dorsolateral prefrontal cortices; and the AD group did in the bilateral

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entorhinal cortices and hippocampi. When the FBB+ DLB and AD groups were directly

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compared, the FBB+ DLB group exhibited higher metabolism in the bilateral central

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cerebellum, posterior putamen, and somatomotor cortices, in addition to the bilateral

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entorhinal cortices, amygdala, and hippocampi, than the AD group (Figure 1D), while the

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FBB+ DLB group had lower metabolism in the bilateral caudate, thalami, and bilateral lateral

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temporal, temporo-parietal, precuneus, dorsolateral prefrontal, and occipital cortices. When

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the FBB- DLB and AD groups were directly compared, the FBB- DLB group had higher

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metabolism in the same brain regions where the FBB+ DLB group did (Figure 1E), while the

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FBB- DLB group had lower metabolism in the bilateral caudate, bilateral dorsolateral

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prefrontal, and right parietal cortices. There was no region where the two DLB groups had

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significantly different metabolism (Figure 1F). We summarized the SPM findings in the

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Supplementary Table 1.

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3.4. Sensitivity analyses

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As there was a significant difference in CDR-SOB and as the AD group tended to have a

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lower proportion of demented patients (Table 1), sensitivity analyses were performed among

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the three disease groups 1) using CDR-SOB as an additional covariate, 2) performing

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analysis confined to MCI patients, and 3) performing analyses confined to demented patients

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and controlling for CDR-SOB. The patterns of the results did not change in the sensitivity

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analysis 1 (Supplementary Figures 1). The sensitivity analysis 2 confined to MCI patients

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showed that the FBB+ DLB MCI group had a significant metabolic difference compared to

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the AD-MCI group, while the FBB-DLB MCI group did not (Supplementary Figure 2). The

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sensitivity analysis 3 confined to demented patients showed that the FBB- DLB dementia

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group had a significant metabolic difference, compared to the AD dementia group, while the

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FBB+ DLB dementia group did not (Supplementary Figure 3). However, the patterns of

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differences were similar on T value maps (Supplementary Figure 2 and 3).

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We also performed the identical sensitivity analyses for neuropsychological test

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scores. Throughout the three sensitivity analyses, the FBB+ DLB group had lower scores in

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the Stroop color reading test and the FBB- DLB group had lower scores in the RCFT copy

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and Stroop color reading test, compared to the AD group (Supplementary Tables 2-4).

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4. Discussion

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In this study, we evaluated the patterns of FDG metabolic changes in patients with DLB

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whose diagnoses were supported by dopamine transporter imaging, and compared them with

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those observed in AD. The major findings of this study are as follows. First, both the FBB+

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DLB and FBB- DLB groups showed increased metabolism in the bilateral central cerebellum,

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posterior putamen, somatomotor, and medial temporal cortices, compared to the control and

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AD groups, whereas the AD group did not. Second, compared to the control group, the FBB+

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DLB, FBB- DLB, and AD groups commonly exhibited a decreased metabolism in the

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bilateral lateral temporal, temporo-parietal, posterior cingulate, and precuneus cortices. Third,

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both the FBB+ DLB and FBB- DLB groups exhibited additional hypometabolism in the

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bilateral thalami and dorsolateral prefrontal cortices, compared to the control group; the

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FBB+ DLB group had additional hypometabolism in the bilateral caudate nuclei and occipital

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cortices, compared to the control and AD groups; whereas the AD group had an additional

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hypometabolism in the bilateral entorhinal cortices and hippocampi compared to the control

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and DLB groups. Taken together, our findings suggest that hypermetabolic or relatively

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preserved metabolic patterns involving the somatomotor cortex, posterior putamen, and

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central cerebellum could be a useful imaging biomarker suggesting DLB, and there are AD-

241

specific hypometabolic patterns involving entorhinal cortex and hippocampus; DLB-specific

242

hypometabolic patterns involving the thalamus, caudate nucleus, and occipital cortex; and

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common hypometabolic patterns for AD and DLB involving the bilateral temporo-parietal,

244

posterior cingulate, and precuneus cortices.

245

In line with our hypothesis, the DLB group had an increased metabolism in the

246

bilateral central cerebellum, posterior putamen, and somatomotor cortices compared to the

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control and AD groups. This result is consistent with the previously reported PD-related

248

spatial covariance pattern (PDRP) (Eckert et al., 2007). Cerebellar hypermetabolism in our

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DLB group is partly consistent with the previously reported PD-related cognitive pattern

250

(PDCP) in non-demented PD patients (Huang et al., 2007). A recent study reported

251

significant correlation between cognitive dysfunction and hypermetabolism in the posterior

252

cerebellar vermis (Blum et al., 2018), and another study reported a significant negative

253

correlation between FDG uptake in the putamen and precentral gyrus and cognitive

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dysfunction in non-demented and demented PD patients (Wu et al., 2018). Further, relative

255

preservation of metabolism in the sensorimotor cortices (Brown et al., 2014; Ishii et al., 1998)

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and basal ganglia (Ishii et al., 1998) has been reported in DLB patients. Moreover, a previous

257

study showed that the metabolic increases in these brain regions are correlated with the

258

degree of nigrostriatal dopaminergic depletion (Holtbernd et al., 2015). Metabolic increase in

259

these brain regions could be interpreted as a compensatory mechanism. However, it also

260

could be interpreted as a pathophysiological change (Mirdamadi, 2016). Previous studies

261

showed that basal ganglia and cerebellar circuits are closely connected to form an integrated

262

network (Bostan and Strick, 2018), and that an increased cerebellar activity could potentiate

263

cortico-striatal synaptic pathways (Chen et al., 2014). As cerebellar cortex (especially the

264

vermis) is connected to the subthalamic nucleus via di-synaptic subcortical pathway (Bostan

265

and Strick, 2018), hyperactivity of the subthalamic nucleus in PD patients (Jahanshahi et al.,

266

2015) could be a potential biological source for these overall pathophysiological metabolic

267

changes. Hyperactivity of the subthalamic nucleus could be related to the increased cerebellar

Ye 13 268

activity, which in turn could be related to the potentiation of cortico-striatal pathways. Future

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studies investigating the effects of metabolic increase in these brain regions on motor and

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cognitive performance are warranted to confirm this hypothesis. Considering the

271

hypometabolism observed in the frontal and parietal association cortices in DLB patients

272

(Ishii et al., 1998), the relative and contrasted hypermetabolic pattern in the somatomotor

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cortex could be useful in accurately detecting DLB patients using a visual FDG PET

274

interpretation (Brown et al., 2014).

275

Our second major finding is that FBB+ DLB, FBB- DLB and AD groups commonly

276

exhibit decreased metabolism in the bilateral lateral temporal, temporo-parietal, posterior

277

cingulate, and precuneus cortices, which suggests that these hypometabolic patterns could be

278

common imaging biomarkers for AD and DLB. The absence of a metabolic increase in the

279

posterior cingulate cortex is not consistent with the cingulate island sign in DLB (Graff-

280

Radford et al., 2014). However, relative hypermetabolism in the posterior cingulate cortex

281

was observed in the FBB+ DLB and FBB- DLB groups, compared to the AD group, although

282

statistical significance was not reached (Supplementary Figure 4 and Figure 1). Considering

283

that the posterior cingulate cortex is anatomically and functionally connected with medial

284

temporal lobe structures, cingulate island sign could be related with relative hypermetabolism

285

in the medial temporal lobe structures, including the hippocampus and amygdala, in DLB

286

patients (Figure 1). In view of our results, we suggest that the cingulate island sign could be

287

an imaging biomarker differentiating DLB from AD, but may be of little use in detecting

288

DLB. This point of view is consistent with a recent study showing that the cingulate island

289

sign is negatively associated with medial temporal lobe atrophy (Iizuka and Kameyama, 2016)

290

and temporal changes in DLB (Iizuka et al., 2017).

291 292

Our third major finding is that both FBB+ DLB and FBB- DLB groups exhibited an additional hypometabolism in the bilateral thalami and dorsolateral prefrontal cortices,

Ye 14 293

compared to the control group, while only the FBB+ DLB group exhibited hypometabolism

294

in the bilateral caudate nuclei and occipital cortices, compared to the control and AD groups.

295

Occipital hypometabolism is a well-known imaging biomarker for DLB (Ishii et al., 1998).

296

Although we cannot emphasize the absence of occipital hypometabolism in the FBB- DLB

297

group due to smaller sample size of the FBB- DLB group (N = 25), compared to the FBB+

298

DLB group (N = 38), occipital hypometabolism was less prominent than parietal

299

hypometabolism in both DLB groups (Figure 1 and Supplementary Figure 1).

300

The AD group had an additional hypometabolism in the bilateral entorhinal cortices

301

and hippocampi compared to the control and DLB groups. In contrast to the hypometabolism

302

in the lateral temporal, temporo-parietal, posterior cingulate, and precuneus cortices, which

303

were simultaneously observed in AD and DLB groups, the hypometabolism in the entorhinal

304

cortices and hippocampi was observed only in AD patients. Considering that entorhinal

305

hypometabolism was observed in AD patients (Mosconi et al., 2004), and successfully

306

predicted cognitive decline in elderly healthy subjects (de Leon et al., 2001; Ewers et al.,

307

2014), entorhinal hypometabolism could be an AD-specific metabolic biomarker that could

308

be used for the differential diagnosis of AD from DLB.

309

Compared to the AD group, the FBB+ DLB and FBB- DLB groups commonly

310

showed lower scores in COWAT phonemic and Stroop color reading tests, as well as higher

311

scores in SVLT delayed recall. The FBB- DLB group had lower scores than the AD group in

312

RCFT copy. These results are line with previous studies showing that DLB is characterized

313

by deficits in visuospatial and attention function, while AD is characterized by deficits in

314

memory function (Ferman et al., 2006; Guidi et al., 2006; Kraybill et al., 2005). Also, our

315

finding is consistent with a previous study showing that deficits in semantic fluency and

316

phonemic fluency tests are similar in DLB, while those in phonemic fluency test are

317

relatively preserved in AD (Lambon Ralph et al., 2001). However, the sensitivity analyses of

Ye 15 318

disease severity in the three disease groups (Supplementary Tables 2-4) consistently showed

319

that deficits in Stroop color reading test could be a neuropsychological hallmark

320

differentiating DLB from AD.

321

Our study has the following strengths. First, all DLB patients were confirmed with

322

abnormal FP-CIT PET results, which could increase the specificity of the diagnosis (Thomas

323

et al., 2017). Second, AD patients were carefully screened by semi-structured questionnaires

324

and a neurologic examination to rule out the possibility of concomitant LB pathologies and

325

were confirmed with FBB PET. Third, all DLB patients underwent FBB PET and we could

326

evaluate the metabolic patterns related with DLB diagnosis independent of cerebral β-

327

amyloid deposition. However, there are several limitations to this study. First, we did not

328

pathologically confirm the diagnosis of our patients. Although we increased the specificity of

329

our study participants using PET biomarkers, as about 10% of autopsy-confirmed DLB

330

patients have normal dopamine transporter uptake (Walker et al., 2007), there is a possibility

331

that some of our AD patients had clinically silent Lewy body pathology, such that our results

332

should be interpreted cautiously. Second, as the DLB group had relatively higher metabolism

333

in the cerebellum and pons, compared to the control group and AD group, use of the

334

cerebellar cortex as a reference ROI could underestimate hypermetabolic patterns in DLB

335

patients. Even with these limitations, our results suggest that hypermetabolism in the

336

somatomotor cortex, posterior putamen, and central cerebellum is a useful imaging biomarker

337

for DLB patients. Further, hypometabolism in the temporo-parietal, posterior cingulate, and

338

precuneus cortices could be common metabolic patterns of AD and DLB, while

339

entorhinal/hippocampal hypometabolism could be a specific biomarker for AD.

340

Ye 16 341

Acknowledgements

342

Funding sources for study: This research was supported by the National Research Foundation

343

of Korea Grant funded by the Korean Government (NRF-2019R1I1A1A01059454 and NRF-

344

2018M3C7A1056898).

345 346

Financial Disclosures of all authors

347

Nothing to report.

348 349 350

Ye 17 351

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352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397

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Ye 21 512

Figure legends

513 514

Figure 1. Comparison of regional glucose metabolism between FBB+ DLB, FBB- DLB, AD

515

and control groups

516

Results are based on general linear models controlling for age, sex, and education. The FDR

517

method was used for multiple comparison correction across multiple voxels (A-F). The brain

518

images are displayed in neurological convention. Abbreviations: AD, Alzheimer’s disease;

519

DLB, dementia with Lewy bodies; FBB, 18F-Florbetaben; FDR, false discovery rate.

Ye 22 520

Table 1. Demographic and clinical characteristics of the study subjects Pa

Pb

70.8 (7.5)f

0.022

0.017

11 (44.0)f

27 (71.1)e, f

0.066

0.034

11.0 (5.3)

8.8 (5.9)

9.8 (4.8)

0.059

0.276

2.5 (1.4)

2.2 (1.1)

2.9 (1.9)

NA

0.163

NA

0.078

Control

FBB+ DLB

FBB- DLB

AD

N

21

38

25

38

Age, years

71.8 (5.3)

73.6 (7.9)

76.3 (6.2)c, f

Sex, female

13 (61.9)

17 (44.7)e

Education, years

12.8 (5.2)

Disease duration, years

NA

Disease stage Non-demented

NA

13 (34.2)

9 (36.0)

22 (57.9)

Demented

NA

25 (65.8)

16 (64.0)

16 (42.1)

Hypertension

6 (28.6)

17 (44.7)

14 (56.0)

16 (42.1)

0.315

0.535

Diabetes mellitus

3 (14.3)

10 (26.3)

10 (40.0)

8 (21.1)

0.206

0.251

Coronary heart disease

2 (9.5)

5 (13.2)

4 (16.0)

4 (10.5)

0.927

0.929

Hyperlipidemia

8 (38.1)

16 (42.1)

10 (40.0)

16 (42.1)

0.989

0.983

Parkinsonism

NA

32 (84.2)e

22 (88.0)f

4 (10.5)e, f

NA

< 0.001

Cognitive fluctuation

NA

23 (60.5)e

17 (68.0)f

3 (7.9)e, f

NA

< 0.001

Visual hallucination

NA

9 (23.7)e

8 (32.0)f

0e, f

NA

0.001

RBD

NA

21 (55.3)e

14 (56.0)f

3 (7.9)e, f

NA

< 0.001

CCSIT score

NA

4.6 (2.4)e

5.5 (2.6)f

7.1 (2.3)e, f

NA

< 0.001

UDPRS motor

NA

25.1 (14.3)e

26.6 (10.2)f

6.6 (5.9)e, f

NA

< 0.001

CDR-SOB

0

5.3 (3.6)c, e

4.5 (3.2)c, f

2.9 (2.1)c, e, f

< 0.001

0.003

K-MMSE

28.5 (1.4)

21.1 (5.4)c

20.8 (4.6)c

22.0 (3.8)c

< 0.001

0.537

Cardiovascular risk factors

Caregiver reported symptoms

521 522 523 524 525 526 527 528 529

Data are expressed as a mean (SD) or number (%). Key: AD, Alzheimer’s disease; CCSIT, cross-cultural smell identification test; CDR-SOB, clinical dementia rating scale sum of boxes; DLB, dementia with Lewy bodies; FBB, 18FFlorbetaben; K-MMSE, Korean version of the Mini-Mental State Examination; RBD, rapid eye movement sleep behavior disorder; UPDRS, unified Parkinson's disease rating scale a P values are results of analyses of variance or chi-square tests as appropriate comparing control, FBB+ DLB, FBB- DLB, and AD groups. b P values are results of independent t-tests or chi-square tests as appropriate comparing FBB+DLB, FBB- DLB, and AD groups.

Ye 23 530 531 532

Significant difference in comparisons with the control group,c between the FBB+ DLB and FBB- DLB groups,d between the FBB+ DLB and AD groups,e and between the FBB- DLB and AD groups.f

Ye 24 533

Table 2. Comparison of standardized neuropsychological test scores Neuropsychological test

534 535 536 537 538 539 540 541 542 543 544

Control

FBB+ DLB

FBB- DLB

AD

Digit span backward

0.87 (1.33)

-0.92 (1.18)a

-0.95 (1.54)a

-0.31 (1.04)a

K-BNT

0.36 (0.70)

-1.50 (1.54)a

-1.44 (2.25)a

-0.99 (1.25)a

RCFT copy

0.50 (0.55)

-1.99 (2.42)a

-2.93 (4.20)a, c

-0.99 (1.33)a, c

SVLT immediate recall

0.72 (0.80)

-1.71 (1.04)a

-1.34 (0.97)a

-1.20 (0.87)a

SVLT delayed recall

0.76 (0.84)

-1.73 (1.10)a, b

-1.61 (0.99)a, c

-2.28 (0.76)a, b, c

SVLT recognition score

0.54 (0.82)

-1.48 (1.18)a

-1.65 (1.91)a

-1.97 (1.47)a

RCFT immediate recall

0.21 (0.88)

-1.46 (0.73)a

-1.29 (0.98)a

-1.52 (0.79)a

RCFT delayed recall

0.21 (0.66)

-1.65 (0.83)a

-1.36 (1.12)a

-1.68 (0.81)a

RCFT recognition score

0.11 (0.91)

-1.53 (1.37)a

-0.98 (1.58)a

-1.39 (1.41)a

COWAT animal

0.31 (1.10)

-1.39 (0.98)a

-1.48 (1.08)a

-0.87 (1.02)a

COWAT supermarket

0.05 (0.83)

-1.39 (0.72)a

-1.35 (1.11)a

-1.05 (0.65)a

COWAT phonemic

0.32 (0.75)

-1.43 (0.92)a, b

-1.16 (1.30)a, c

-0.32 (0.92)a, b, c

Stroop test color reading

0.16 (0.94)

-2.23 (1.33)a, b

-2.26 (1.44)a, c

-0.96 (1.33)a, b, c

Data are expressed as mean (standard deviation). Key: K-BNT, Korean version of Boston Naming Test; RCFT, Rey-Osterrieth Complex Figure Test; SVLT, Seoul Verbal Learning Test; COWAT, Controlled Oral Word Association Test Data represent results of general linear models for standardized neuropsychological test scores comparing the control, FBB+ DLB, FBB- DLB, and AD groups after controlling for age, sex, and education. Significant difference in comparisons with the control groupa, between the FBB+ DLB group and the AD groupb, and between the FBB- DLB and the AD groupc after false discovery rate correction for multiple comparisons across 13 neuropsychological tests.

•

There is a DLB-specific hypermetabolic pattern compared to AD and controls

•

The pattern involves somatomotor cortex, posterior putamen, and central cerebellum

•

Metabolism in the parietal lobe is commonly decreased in DLB and AD

•

AD-specific hypometabolism involves the entorhinal cortex and hippocampus