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Schiavolin S, Camarda G, Mazzucchelli A, Mariniello A, Marinoni G, Storti B, Canavero I, Bersano A, Leonardi M. Cognitive and psychological characteristics in patients with Cerebral Amyloid Angiopathy: a literature review. Neurol Sci 2024; 45:3031-3049. [PMID: 38388894 DOI: 10.1007/s10072-024-07399-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 02/08/2024] [Indexed: 02/24/2024]
Abstract
AIM To review the current data on cognitive and psychological characteristics of patients with CAA and on the instruments used for their evaluation. METHODS A systematic search was performed in Embase, Scopus and PubMed with terms related to "cerebral amyloid angiopathy", "neuropsychological measures" and "patient-reported outcome measures" from January 2001 to December 2021. RESULTS Out of 2851 records, 18 articles were selected. The cognitive evaluation was present in all of which, while the psychological one only in five articles. The MMSE (Mini Mental State Examination), TMT (Trail Making Test), fluency test, verbal learning test, digit span, digit symbol and Rey figure tests were the most used cognitive tests, while executive function, memory, processing speed, visuospatial function, attention and language were the most frequent impaired cognitive functions. Depression was the most considered psychological factor usually measured with BDI (Beck Depression Inventory) and GDS (Geriatric Depression Scale). CONCLUSIONS The results of this study might be used in clinical practice as a guide to choose cognitive and psychological instruments and integrate them in the clinical evaluation. The results might also be used in the research field for studies investigating the impact of cognitive and psychological variables on the disease course and for consensus studies aimed at define a standardized evaluation of these aspects.
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Affiliation(s)
- Silvia Schiavolin
- SC Neurologia, Salute Pubblica E Disabilità, Fondazione IRCCS Istituto Neurologico Carlo Besta, Via Celoria, 11, 20133, Milan, Italy
| | - Giorgia Camarda
- SC Neurologia, Salute Pubblica E Disabilità, Fondazione IRCCS Istituto Neurologico Carlo Besta, Via Celoria, 11, 20133, Milan, Italy.
| | - Alessia Mazzucchelli
- SC Neurologia, Salute Pubblica E Disabilità, Fondazione IRCCS Istituto Neurologico Carlo Besta, Via Celoria, 11, 20133, Milan, Italy
| | - Arianna Mariniello
- SC Neurologia, Salute Pubblica E Disabilità, Fondazione IRCCS Istituto Neurologico Carlo Besta, Via Celoria, 11, 20133, Milan, Italy
| | - Giulia Marinoni
- SC Malattie Cerebrovascolari, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Benedetta Storti
- SC Malattie Cerebrovascolari, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Isabella Canavero
- SC Malattie Cerebrovascolari, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Anna Bersano
- SC Malattie Cerebrovascolari, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Matilde Leonardi
- SC Neurologia, Salute Pubblica E Disabilità, Fondazione IRCCS Istituto Neurologico Carlo Besta, Via Celoria, 11, 20133, Milan, Italy
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Lee S, Kim SE, Jang H, Kim JP, Sohn G, Park YH, Ham H, Gu Y, Park CJ, Kim HJ, Na DL, Kim K, Seo SW. Distinct effects of blood pressure parameters on Alzheimer's and vascular markers in 1,952 Asian individuals without dementia. Alzheimers Res Ther 2024; 16:125. [PMID: 38863019 PMCID: PMC11167921 DOI: 10.1186/s13195-024-01483-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2023] [Accepted: 05/15/2024] [Indexed: 06/13/2024]
Abstract
BACKGROUND Risk factors for cardiovascular disease, including elevated blood pressure, are known to increase risk of Alzheimer's disease. There has been increasing awareness of the relationship between long-term blood pressure (BP) patterns and their effects on the brain. We aimed to investigate the association of repeated BP measurements with Alzheimer's and vascular disease markers. METHODS We recruited 1,952 participants without dementia between August 2015 and February 2022. During serial clinic visits, we assessed both systolic BP (SBP) and diastolic BP (DBP), and visit-to-visit BP variability (BPV) was quantified from repeated measurements. In order to investigate the relationship of mean SBP (or DBP) with Alzheimer's and vascular markers and cognition, we performed multiple linear and logistic regression analyses after controlling for potential confounders (Model 1). Next, we investigated the relationship of with variation of SBP (or DBP) with the aforementioned variables by adding it into Model 1 (Model 2). In addition, mediation analyses were conducted to determine mediation effects of Alzheimer's and vascular makers on the relationship between BP parameters and cognitive impairment. RESULTS High Aβ uptake was associated with greater mean SBP (β = 1.049, 95% confidence interval 1.016-1.083). High vascular burden was positively associated with mean SBP (odds ratio = 1.293, 95% CI 1.015-1.647) and mean DBP (1.390, 1.098-1.757). High tau uptake was related to greater systolic BPV (0.094, 0.001-0.187) and diastolic BPV (0.096, 0.007-0.184). High Aβ uptake partially mediated the relationship between mean SBP and the Mini-Mental State Examination (MMSE) scores. Hippocampal atrophy mediated the relationship between diastolic BPV and MMSE scores. CONCLUSIONS Each BP parameter affects Alzheimer's and vascular disease markers differently, which in turn leads to cognitive impairment. Therefore, it is necessary to appropriately control specific BP parameters to prevent the development of dementia. Furthermore, a better understanding of pathways from specific BP parameters to cognitive impairments might enable us to select the managements targeting the specific BP parameters to prevent dementia effectively.
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Affiliation(s)
- Sungjoo Lee
- Department of Digital Health, Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Si Eun Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
- Department of Neurology, Inje University College of Medicine, Haeundae Paik Hospital, Busan, 48108, Republic of Korea
| | - Hyemin Jang
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
- Department of Neurology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, 03080, Republic of Korea
| | - Jun Pyo Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, 06351, Republic of Korea
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, 06351, Republic of Korea
| | - Gyeongmo Sohn
- Department of Neurology, Inje University College of Medicine, Haeundae Paik Hospital, Busan, 48108, Republic of Korea
| | - Yu Hyun Park
- Neuroscience Center, Samsung Medical Center, Seoul, 06351, Republic of Korea
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, 06351, Republic of Korea
| | - Hongki Ham
- Neuroscience Center, Samsung Medical Center, Seoul, 06351, Republic of Korea
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, 06351, Republic of Korea
| | - Yuna Gu
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, 06351, Republic of Korea
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, 06351, Republic of Korea
| | - Chae Jung Park
- Research Institute, National Cancer Center, Goyang, 10408, Republic of Korea
| | - Hee Jin Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, 06351, Republic of Korea
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, 06351, Republic of Korea
| | - Duk L Na
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, 06351, Republic of Korea
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, 06351, Republic of Korea
| | - Kyunga Kim
- Department of Digital Health, Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.
- Biomedical Statistics Center, Research Institute for Future Medicine, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.
- Department of Data Convergence & Future Medicine, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.
| | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.
- Neuroscience Center, Samsung Medical Center, Seoul, 06351, Republic of Korea.
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, 06351, Republic of Korea.
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, 06351, Republic of Korea.
- Center for Clinical Epidemiology, Samsung Medical Center, Seoul, 06351, Republic of Korea.
- Clinical Research Design and Evaluation, SAIHST, Sungkyunkwan University, Seoul, 06351, Republic of Korea.
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Jung W, Kim SE, Kim JP, Jang H, Park CJ, Kim HJ, Na DL, Seo SW, Suk HI. Deep learning model for individualized trajectory prediction of clinical outcomes in mild cognitive impairment. Front Aging Neurosci 2024; 16:1356745. [PMID: 38813529 PMCID: PMC11135285 DOI: 10.3389/fnagi.2024.1356745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Accepted: 04/18/2024] [Indexed: 05/31/2024] Open
Abstract
Objectives Accurately predicting when patients with mild cognitive impairment (MCI) will progress to dementia is a formidable challenge. This work aims to develop a predictive deep learning model to accurately predict future cognitive decline and magnetic resonance imaging (MRI) marker changes over time at the individual level for patients with MCI. Methods We recruited 657 amnestic patients with MCI from the Samsung Medical Center who underwent cognitive tests, brain MRI scans, and amyloid-β (Aβ) positron emission tomography (PET) scans. We devised a novel deep learning architecture by leveraging an attention mechanism in a recurrent neural network. We trained a predictive model by inputting age, gender, education, apolipoprotein E genotype, neuropsychological test scores, and brain MRI and amyloid PET features. Cognitive outcomes and MRI features of an MCI subject were predicted using the proposed network. Results The proposed predictive model demonstrated good prediction performance (AUC = 0.814 ± 0.035) in five-fold cross-validation, along with reliable prediction in cognitive decline and MRI markers over time. Faster cognitive decline and brain atrophy in larger regions were forecasted in patients with Aβ (+) than with Aβ (-). Conclusion The proposed method provides effective and accurate means for predicting the progression of individuals within a specific period. This model could assist clinicians in identifying subjects at a higher risk of rapid cognitive decline by predicting future cognitive decline and MRI marker changes over time for patients with MCI. Future studies should validate and refine the proposed predictive model further to improve clinical decision-making.
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Affiliation(s)
- Wonsik Jung
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea
| | - Si Eun Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Department of Neurology, Inje University College of Medicine, Haeundae Paik Hospital, Busan, Republic of Korea
| | - Jun Pyo Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Neuroscience Center, Seoul, Republic of Korea
- Alzheimer’s Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Hyemin Jang
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Department of Neurology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Chae Jung Park
- National Cancer Center Research Institute, Goyang, Republic of Korea
| | - Hee Jin Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Neuroscience Center, Seoul, Republic of Korea
- Alzheimer’s Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Duk L. Na
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Neuroscience Center, Seoul, Republic of Korea
- Alzheimer’s Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Neuroscience Center, Seoul, Republic of Korea
- Alzheimer’s Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
- Center for Clinical Epidemiology, Samsung Medical Center, Seoul, Republic of Korea
- Clinical Research Design and Evaluation, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
| | - Heung-Il Suk
- Department of Artificial Intelligence, Korea University, Seoul, Republic of Korea
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Lombardi G, Berti V, Ginestroni A, Nacmias B, Sorbi S. The Association Between Positive Amyloid-PET and Cognitive Decline Is Not Always Supportive of Alzheimer's Disease: Suggestions from a Case Report. J Alzheimers Dis Rep 2024; 8:281-288. [PMID: 38405347 PMCID: PMC10894606 DOI: 10.3233/adr-230183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Accepted: 01/17/2024] [Indexed: 02/27/2024] Open
Abstract
Amyloid-β deposition is the pathological hallmark of both cerebral amyloid angiopathy and Alzheimer's disease dementia, clinical conditions that can share cognitive decline and positive Amyloid-PET scan. A case is reported involving an 82-year-old Italian female who presented initially a memory deficit, later transient focal neurologic episodes, and finally two symptomatic lobar intracerebral hemorrhages. In light of these events, MRI and PET imaging findings, acquired before cerebral hemorrhages, are reconsidered and discussed, highlighting the utility of Amyloid-PET in supporting an in vivo diagnosis of cerebral amyloid angiopathy.
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Affiliation(s)
- Gemma Lombardi
- Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Florence, Italy
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, Florence, Italy
| | - Valentina Berti
- Department of Experimental and Clinical Biomedical Sciences “Mario Serio”, Nuclear Medicine Unit, University of Florence, Florence, Italy
| | | | - Benedetta Nacmias
- Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Florence, Italy
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, Florence, Italy
| | - Sandro Sorbi
- Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Florence, Italy
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, Florence, Italy
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5
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Deng L, Lin Y, Lin Y, Huang W. Infratentorial superficial siderosis: report of six cases and review of the literature. Front Neurosci 2024; 18:1373358. [PMID: 38435058 PMCID: PMC10904549 DOI: 10.3389/fnins.2024.1373358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Accepted: 02/05/2024] [Indexed: 03/05/2024] Open
Abstract
Objectives To investigate the etiology, clinical manifestations, imaging features, and treatment of patients with infratentorial superficial siderosis (iSS), enhance clinicians' comprehension of this rare disease, and conduct oral deferiprone intervention and subsequent monitoring. Methods Six patients diagnosed with iSS based on magnetic resonance imaging (MRI) and susceptibility weighted imaging (SWI) were enrolled from 2021 to 2023 at the First Affiliated Hospital of Fujian Medical University. Their clinical datas were summarized, and the etiology and imaging characteristics were analyzed. Follow-up was conducted through telephone or outpatient visits. Results Among the 6 patients, there were 3 males and 3 females. The onset age ranged from 35 to 71 years, with an average onset age of 53 years. The clinical symptoms mainly included acoustic disturbances (6/6), gait imbalance (6/6), dysolfactory (6/6), cognitive impairment (2/6), epilepsy (2/6), and pyramidal tract sign (2/6). Evidence of superficial siderosis was observed on MRI across the cortex, brainstem, cerebellum, and spinal cord in all patients. T2-space sequence MRI revealed two instances of dural tear. During the follow-up period ranging from 1 month to 3 years, three patients who received oral deferiprone treatment showed improvement, whereas the remaining three patients who declined deferiprone treatment demonstrated progression. Conclusion The primary clinical manifestations of iSS include bilateral sensorineural hearing disturbances, progressive cerebellar ataxia, and spinal cord lesions. The key diagnostic criteria involve the presence of linear hypointensity on T2-WI in the surface region of the nervous system. Dural tear caused by various factors is considered to be the most common cause of iSS, and its treatment mainly involves surgical intervention for hemorrhagic primary diseases as well as pharmacotherapy with deferiprone.
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Affiliation(s)
- Lixia Deng
- Department of Neurology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
- Department of Neurology, The Third Hospital of Xiamen, Xiamen, Fujian, China
- Fujian Institute of Neurology, The First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, China
| | - Yi Lin
- Department of Neurology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
- Fujian Institute of Neurology, The First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, China
- Department of Neurology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, China
| | - Yu Lin
- Department of Neurology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
- Fujian Institute of Neurology, The First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, China
- Department of Neurology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, China
| | - Weibin Huang
- Department of Neurology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
- Fujian Institute of Neurology, The First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, China
- Department of Neurology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, China
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Wheeler KV, Irimia A, Braskie MN. Using Neuroimaging to Study Cerebral Amyloid Angiopathy and Its Relationship to Alzheimer's Disease. J Alzheimers Dis 2024; 97:1479-1502. [PMID: 38306032 DOI: 10.3233/jad-230553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2024]
Abstract
Cerebral amyloid angiopathy (CAA) is characterized by amyloid-β aggregation in the media and adventitia of the leptomeningeal and cortical blood vessels. CAA is one of the strongest vascular contributors to Alzheimer's disease (AD). It frequently co-occurs in AD patients, but the relationship between CAA and AD is incompletely understood. CAA may drive AD risk through damage to the neurovascular unit and accelerate parenchymal amyloid and tau deposition. Conversely, early AD may also drive CAA through cerebrovascular remodeling that impairs blood vessels from clearing amyloid-β. Sole reliance on autopsy examination to study CAA limits researchers' ability to investigate CAA's natural disease course and the effect of CAA on cognitive decline. Neuroimaging allows for in vivo assessment of brain function and structure and can be leveraged to investigate CAA staging and explore its associations with AD. In this review, we will discuss neuroimaging modalities that can be used to investigate markers associated with CAA that may impact AD vulnerability including hemorrhages and microbleeds, blood-brain barrier permeability disruption, reduced cerebral blood flow, amyloid and tau accumulation, white matter tract disruption, reduced cerebrovascular reactivity, and lowered brain glucose metabolism. We present possible areas for research inquiry to advance biomarker discovery and improve diagnostics.
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Affiliation(s)
- Koral V Wheeler
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina Del Rey, CA, USA
| | - Andrei Irimia
- Ethel Percy Andrus Gerontology Center, USC Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
- Department of Biomedical Engineering, Corwin D. Denney Research Center, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA
| | - Meredith N Braskie
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina Del Rey, CA, USA
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Chun MY, Jang H, Kim SJ, Park YH, Yun J, Lockhart SN, Weiner M, De Carli C, Moon SH, Choi JY, Nam KR, Byun BH, Lim SM, Kim JP, Choe YS, Kim YJ, Na DL, Kim HJ, Seo SW. Emerging role of vascular burden in AT(N) classification in individuals with Alzheimer's and concomitant cerebrovascular burdens. J Neurol Neurosurg Psychiatry 2023; 95:44-51. [PMID: 37558399 PMCID: PMC10803958 DOI: 10.1136/jnnp-2023-331603] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 07/22/2023] [Indexed: 08/11/2023]
Abstract
OBJECTIVES Alzheimer's disease (AD) is characterised by amyloid-beta accumulation (A), tau aggregation (T) and neurodegeneration (N). Vascular (V) burden has been found concomitantly with AD pathology and has synergistic effects on cognitive decline with AD biomarkers. We determined whether cognitive trajectories of AT(N) categories differed according to vascular (V) burden. METHODS We prospectively recruited 205 participants and classified them into groups based on the AT(N) system using neuroimaging markers. Abnormal V markers were identified based on the presence of severe white matter hyperintensities. RESULTS In A+ category, compared with the frequency of Alzheimer's pathological change category (A+T-), the frequency of AD category (A+T+) was significantly lower in V+ group (31.8%) than in V- group (64.4%) (p=0.004). Each AT(N) biomarker was predictive of cognitive decline in the V+ group as well as in the V- group (p<0.001). Additionally, the V+ group showed more severe cognitive trajectories than the V- group in the non-Alzheimer's pathological changes (A-T+, A-N+; p=0.002) and Alzheimer's pathological changes (p<0.001) categories. CONCLUSION The distribution and longitudinal outcomes of AT(N) system differed according to vascular burdens, suggesting the importance of incorporating a V biomarker into the AT(N) system.
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Affiliation(s)
- Min Young Chun
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Gangnam-gu, Seoul, South Korea
- Department of Neurology, Yonsei University College of Medicine, Seoul, South Korea
- Department of Neurology, Yongin Severance Hospital, Yonsei University Health System, Yongin, South Korea
| | - Hyemin Jang
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Gangnam-gu, Seoul, South Korea
- Neuroscience Center, Samsung Medical Center, Seoul, South Korea
- Samsung Alzheimer's Convergence Research Center, Samsung Medical Center, Seoul, South Korea
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, South Korea
| | - Soo-Jong Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Gangnam-gu, Seoul, South Korea
- Neuroscience Center, Samsung Medical Center, Seoul, South Korea
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, South Korea
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, South Korea
| | - Yu Hyun Park
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Gangnam-gu, Seoul, South Korea
- Neuroscience Center, Samsung Medical Center, Seoul, South Korea
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, South Korea
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, South Korea
| | - Jihwan Yun
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Gangnam-gu, Seoul, South Korea
| | - Samuel N Lockhart
- Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | - Michael Weiner
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA
| | - Charles De Carli
- Department of Neurology, University of California-Davis, Davis, California, USA
| | - Seung Hwan Moon
- Departmentof Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Jae Yong Choi
- Division of Applied RI, Korea Institute of Radiological and Medical Sciences, Seoul, South Korea
| | - Kyung Rok Nam
- Division of Applied RI, Korea Institute of Radiological and Medical Sciences, Seoul, South Korea
| | - Byung-Hyun Byun
- Department of Nuclear Medicine, Korea Cancer Center Hospital, Korea Institute of Radiological and Medical Sciences, Seoul, South Korea
| | - Sang-Moo Lim
- Department of Nuclear Medicine, Korea Cancer Center Hospital, Korea Institute of Radiological and Medical Sciences, Seoul, South Korea
| | - Jun Pyo Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Gangnam-gu, Seoul, South Korea
- Neuroscience Center, Samsung Medical Center, Seoul, South Korea
- Samsung Alzheimer's Convergence Research Center, Samsung Medical Center, Seoul, South Korea
- Center for Neuroimaging, Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Yeong Sim Choe
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Gangnam-gu, Seoul, South Korea
| | - Young Ju Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Gangnam-gu, Seoul, South Korea
- Neuroscience Center, Samsung Medical Center, Seoul, South Korea
| | - Duk L Na
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Gangnam-gu, Seoul, South Korea
- Neuroscience Center, Samsung Medical Center, Seoul, South Korea
- Samsung Alzheimer's Convergence Research Center, Samsung Medical Center, Seoul, South Korea
- Cell and Gene Therapy Institute (CGTI), Research Institute for Future Medicine, Samsung Medical Center, Seoul, South Korea
| | - Hee Jin Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Gangnam-gu, Seoul, South Korea
- Neuroscience Center, Samsung Medical Center, Seoul, South Korea
- Samsung Alzheimer's Convergence Research Center, Samsung Medical Center, Seoul, South Korea
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, South Korea
| | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Gangnam-gu, Seoul, South Korea
- Neuroscience Center, Samsung Medical Center, Seoul, South Korea
- Samsung Alzheimer's Convergence Research Center, Samsung Medical Center, Seoul, South Korea
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, South Korea
- Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, South Korea
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8
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Chun MY, Jang H, Kim HJ, Kim JP, Gallacher J, Allué JA, Sarasa L, Castillo S, Pascual-Lucas M, Na DL, Seo SW. Contribution of clinical information to the predictive performance of plasma β-amyloid levels for amyloid positron emission tomography positivity. Front Aging Neurosci 2023; 15:1126799. [PMID: 36998318 PMCID: PMC10044013 DOI: 10.3389/fnagi.2023.1126799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Accepted: 02/24/2023] [Indexed: 03/15/2023] Open
Abstract
BackgroundEarly detection of β-amyloid (Aβ) accumulation, a major biomarker for Alzheimer’s disease (AD), has become important. As fluid biomarkers, the accuracy of cerebrospinal fluid (CSF) Aβ for predicting Aβ deposition on positron emission tomography (PET) has been extensively studied, and the development of plasma Aβ is beginning to receive increased attention recently. In the present study, we aimed to determine whether APOE genotypes, age, and cognitive status increase the predictive performance of plasma Aβ and CSF Aβ levels for Aβ PET positivity.MethodsWe recruited 488 participants who underwent both plasma Aβ and Aβ PET studies (Cohort 1) and 217 participants who underwent both cerebrospinal fluid (CSF) Aβ and Aβ PET studies (Cohort 2). Plasma and CSF samples were analyzed using ABtest-MS, an antibody-free liquid chromatography-differential mobility spectrometry-triple quadrupole mass spectrometry method and INNOTEST enzyme-linked immunosorbent assay kits, respectively. To evaluate the predictive performance of plasma Aβ and CSF Aβ, respectively, logistic regression and receiver operating characteristic analyses were performed.ResultsWhen predicting Aβ PET status, both plasma Aβ42/40 ratio and CSF Aβ42 showed high accuracy (plasma Aβ area under the curve (AUC) 0.814; CSF Aβ AUC 0.848). In the plasma Aβ models, the AUC values were higher than plasma Aβ alone model, when the models were combined with either cognitive stage (p < 0.001) or APOE genotype (p = 0.011). On the other hand, there was no difference between the CSF Aβ models, when these variables were added.ConclusionPlasma Aβ might be a useful predictor of Aβ deposition on PET status as much as CSF Aβ, particularly when considered with clinical information such as APOE genotype and cognitive stage.
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Affiliation(s)
- Min Young Chun
- Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Department of Neurology, Yonsei University College of Medicine, Seoul, Republic of Korea
- Department of Neurology, Yongin Severance Hospital, Yonsei University Health System, Yongin, Republic of Korea
| | - Hyemin Jang
- Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea
- *Correspondence: Hyemin Jang, ; Sang Won Seo,
| | - Hee Jin Kim
- Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
- Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
| | - Jun Pyo Kim
- Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Center for Neuroimaging, Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, United States
| | - John Gallacher
- Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford, United Kingdom
| | | | | | | | | | - Duk L. Na
- Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Sang Won Seo
- Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
- Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
- Department of Clinical Research Design and Evaluation, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
- *Correspondence: Hyemin Jang, ; Sang Won Seo,
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9
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Kim YJ, Kim SE, Hahn A, Jang H, Kim JP, Kim HJ, Na DL, Chin J, Seo SW. Classification and prediction of cognitive trajectories of cognitively unimpaired individuals. Front Aging Neurosci 2023; 15:1122927. [PMID: 36993907 PMCID: PMC10040799 DOI: 10.3389/fnagi.2023.1122927] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Accepted: 02/27/2023] [Indexed: 03/14/2023] Open
Abstract
Objectives Efforts to prevent Alzheimer's disease (AD) would benefit from identifying cognitively unimpaired (CU) individuals who are liable to progress to cognitive impairment. Therefore, we aimed to develop a model to predict cognitive decline among CU individuals in two independent cohorts. Methods A total of 407 CU individuals from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and 285 CU individuals from the Samsung Medical Center (SMC) were recruited in this study. We assessed cognitive outcomes by using neuropsychological composite scores in the ADNI and SMC cohorts. We performed latent growth mixture modeling and developed the predictive model. Results Growth mixture modeling identified 13.8 and 13.0% of CU individuals in the ADNI and SMC cohorts, respectively, as the "declining group." In the ADNI cohort, multivariable logistic regression modeling showed that increased amyloid-β (Aβ) uptake (β [SE]: 4.852 [0.862], p < 0.001), low baseline cognitive composite scores (β [SE]: -0.274 [0.070], p < 0.001), and reduced hippocampal volume (β [SE]: -0.952 [0.302], p = 0.002) were predictive of cognitive decline. In the SMC cohort, increased Aβ uptake (β [SE]: 2.007 [0.549], p < 0.001) and low baseline cognitive composite scores (β [SE]: -4.464 [0.758], p < 0.001) predicted cognitive decline. Finally, predictive models of cognitive decline showed good to excellent discrimination and calibration capabilities (C-statistic = 0.85 for the ADNI model and 0.94 for the SMC model). Conclusion Our study provides novel insights into the cognitive trajectories of CU individuals. Furthermore, the predictive model can facilitate the classification of CU individuals in future primary prevention trials.
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Affiliation(s)
- Young Ju Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Si Eun Kim
- Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea
| | - Alice Hahn
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
| | - Hyemin Jang
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Jun Pyo Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
- Center for Neuroimaging, Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Hee Jin Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Duk L. Na
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
- Institute of Stem Cell and Regenerative Medicine, Seoul, Republic of Korea
- Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Juhee Chin
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
- Institute of Stem Cell and Regenerative Medicine, Seoul, Republic of Korea
- Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Republic of Korea
- Center for Clinical Epidemiology, Samsung Medical Center, Seoul, Republic of Korea
- Department of Health Sciences and Technology, Seoul, Republic of Korea
- Clinical Research Design and Evaluation, Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Seoul, Republic of Korea
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10
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Jang H, Chun MY, Kim HJ, Na DL, Seo SW. The effects of imaging markers on clinical trajectory in cerebral amyloid angiopathy: a longitudinal study in a memory clinic. Alzheimers Res Ther 2023; 15:14. [PMID: 36635759 PMCID: PMC9835259 DOI: 10.1186/s13195-023-01161-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Accepted: 01/02/2023] [Indexed: 01/14/2023]
Abstract
BACKGROUND We investigated the relevance of various imaging markers for the clinical trajectory of cerebral amyloid angiopathy (CAA) patients in a memory clinic. METHODS A total of 226 patients with probable CAA were included in this study with a mean follow-up period of 3.5 ± 2.7 years. Although all had more than one follow-up visit, 173 underwent follow-up Mini-Mental Status Examination (MMSE) and Clinical Dementia Rating Sum of Boxes (CDR-SB) ranging from 2 to 15 time points. Among 226, 122 patients underwent amyloid-β (Aβ) PET imaging. The prevalence of intracerebral hemorrhage (ICH) and its imaging predictors was investigated. The effects of CAA imaging markers and Aβ PET positivity on longitudinal cognition based on the MMSE and CDR-SB were evaluated using mixed effects models. RESULTS During the follow-up, 10 (4.4%) patients developed ICH: cortical superficial siderosis (cSS; hazard ratio [HR], 6.45) and previous lobar ICH (HR, 4.9), but lobar cerebral microbleeds (CMBs) were not predictors of ICH development. The presence of CMIs (p = 0.045) and Aβ positivity (p = 0.002) were associated with worse MMSE trajectory in CAA patients. Regarding CDR-SB trajectory, only Aβ positivity was marginally associated with worse longitudinal change (p = 0.050). CONCLUSION The results of the present study indicated that various imaging markers in CAA patients have different clinical relevance and predictive values for further clinical courses.
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Affiliation(s)
- Hyemin Jang
- grid.414964.a0000 0001 0640 5613Samsung Alzheimer’s Convergence Research Center, Samsung Medical Center, Seoul, South Korea ,grid.264381.a0000 0001 2181 989XDepartments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351 South Korea ,grid.414964.a0000 0001 0640 5613Neuroscience Center, Samsung Medical Center, Seoul, South Korea ,grid.264381.a0000 0001 2181 989XDepartment of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, South Korea
| | - Min Young Chun
- grid.264381.a0000 0001 2181 989XDepartments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351 South Korea ,grid.414964.a0000 0001 0640 5613Neuroscience Center, Samsung Medical Center, Seoul, South Korea
| | - Hee Jin Kim
- grid.264381.a0000 0001 2181 989XDepartments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351 South Korea ,grid.414964.a0000 0001 0640 5613Neuroscience Center, Samsung Medical Center, Seoul, South Korea
| | - Duk L. Na
- grid.264381.a0000 0001 2181 989XDepartments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351 South Korea ,Happymind Clinic, Seoul, South Korea
| | - Sang Won Seo
- grid.414964.a0000 0001 0640 5613Samsung Alzheimer’s Convergence Research Center, Samsung Medical Center, Seoul, South Korea ,grid.264381.a0000 0001 2181 989XDepartments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351 South Korea ,grid.414964.a0000 0001 0640 5613Neuroscience Center, Samsung Medical Center, Seoul, South Korea ,grid.264381.a0000 0001 2181 989XDepartment of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, South Korea
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11
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Kim SJ, Ham H, Park YH, Choe YS, Kim YJ, Jang H, Na DL, Kim HJ, Moon SH, Seo SW. Development and clinical validation of CT-based regional modified Centiloid method for amyloid PET. Alzheimers Res Ther 2022; 14:157. [PMID: 36266688 PMCID: PMC9585745 DOI: 10.1186/s13195-022-01099-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 10/12/2022] [Indexed: 11/06/2022]
Abstract
Background The standard Centiloid (CL) method was proposed to harmonize and quantify global 18F-labeled amyloid beta (Aβ) PET ligands using MRI as an anatomical reference. However, there is need for harmonizing and quantifying regional Aβ uptakes between ligands using CT as an anatomical reference. In the present study, we developed and validated a CT-based regional direct comparison of 18F-florbetaben (FBB) and 18F-flutemetamol (FMM) Centiloid (rdcCL). Methods For development of MRI-based or CT-based rdcCLs, the cohort consisted of 63 subjects (20 young controls (YC) and 18 old controls (OC), and 25 participants with Alzheimer’s disease dementia (ADD)). We performed a direct comparison of the FMM-FBB rdcCL method using MRI and CT images to define a common target region and the six regional VOIs of frontal, temporal, parietal, posterior cingulate, occipital, and striatal regions. Global and regional rdcCL scales were compared between MRI-based and CT-based methods. For clinical validation, the cohort consisted of 2245 subjects (627 CN, 933 MCI, and 685 ADD). Results Both MRI-based and CT-based rdcCL scales showed that FMM and FBB were highly correlated with each other, globally and regionally (R2 = 0.96~0.99). Both FMM and FBB showed that CT-based rdcCL scales were highly correlated with MRI-based rdcCL scales (R2 = 0.97~0.99). Regarding the absolute difference of rdcCLs between FMM and FBB, the CT-based method was not different from the MRI-based method, globally or regionally (p value = 0.07~0.95). In our clinical validation study, the global negative group showed that the regional positive subgroup had worse neuropsychological performance than the regional negative subgroup (p < 0.05). The global positive group also showed that the striatal positive subgroup had worse neuropsychological performance than the striatal negative subgroup (p < 0.05). Conclusions Our findings suggest that it is feasible to convert regional FMM or FBB rdcSUVR values into rdcCL scales without additional MRI scans. This allows a more easily accessible method for researchers that can be applicable to a variety of different conditions. Supplementary Information The online version contains supplementary material available at 10.1186/s13195-022-01099-0.
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Affiliation(s)
- Soo-Jong Kim
- grid.264381.a0000 0001 2181 989XDepartment of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351 Republic of Korea ,grid.414964.a0000 0001 0640 5613Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea ,grid.264381.a0000 0001 2181 989XDepartment of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea ,grid.264381.a0000 0001 2181 989XDepartment of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, Republic of Korea
| | - Hongki Ham
- grid.264381.a0000 0001 2181 989XDepartment of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351 Republic of Korea ,grid.414964.a0000 0001 0640 5613Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea ,grid.264381.a0000 0001 2181 989XDepartment of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, Republic of Korea ,grid.264381.a0000 0001 2181 989XDepartment of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
| | - Yu Hyun Park
- grid.264381.a0000 0001 2181 989XDepartment of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351 Republic of Korea ,grid.414964.a0000 0001 0640 5613Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea ,grid.264381.a0000 0001 2181 989XDepartment of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea ,grid.264381.a0000 0001 2181 989XDepartment of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, Republic of Korea
| | - Yeong Sim Choe
- grid.264381.a0000 0001 2181 989XDepartment of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351 Republic of Korea ,grid.414964.a0000 0001 0640 5613Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea ,grid.264381.a0000 0001 2181 989XDepartment of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea ,grid.264381.a0000 0001 2181 989XDepartment of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, Republic of Korea
| | - Young Ju Kim
- grid.264381.a0000 0001 2181 989XDepartment of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351 Republic of Korea ,grid.414964.a0000 0001 0640 5613Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Hyemin Jang
- grid.264381.a0000 0001 2181 989XDepartment of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351 Republic of Korea ,grid.414964.a0000 0001 0640 5613Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Duk L. Na
- grid.264381.a0000 0001 2181 989XDepartment of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351 Republic of Korea ,grid.414964.a0000 0001 0640 5613Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea ,grid.264381.a0000 0001 2181 989XDepartment of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea ,grid.414964.a0000 0001 0640 5613Stem Cell and Regenerative Medicine Institute, Samsung Medical Center, Seoul, Republic of Korea
| | - Hee Jin Kim
- grid.264381.a0000 0001 2181 989XDepartment of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351 Republic of Korea ,grid.414964.a0000 0001 0640 5613Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Seung Hwan Moon
- grid.264381.a0000 0001 2181 989XDepartment of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Sang Won Seo
- grid.264381.a0000 0001 2181 989XDepartment of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351 Republic of Korea ,grid.264381.a0000 0001 2181 989XDepartment of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea ,grid.264381.a0000 0001 2181 989XDepartment of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, Republic of Korea ,grid.264381.a0000 0001 2181 989XDepartment of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea ,grid.414964.a0000 0001 0640 5613Alzheimer’s Disease Convergence Research Center, Samsung Medical Center, Seoul, Republic of Korea
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Kim SE, Kim HJ, Jang H, Weiner MW, DeCarli C, Na DL, Seo SW. Interaction between Alzheimer's Disease and Cerebral Small Vessel Disease: A Review Focused on Neuroimaging Markers. Int J Mol Sci 2022; 23:10490. [PMID: 36142419 PMCID: PMC9499680 DOI: 10.3390/ijms231810490] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 09/07/2022] [Accepted: 09/07/2022] [Indexed: 11/26/2022] Open
Abstract
Alzheimer's disease (AD) is characterized by the presence of β-amyloid (Aβ) and tau, and subcortical vascular cognitive impairment (SVCI) is characterized by cerebral small vessel disease (CSVD). They are the most common causes of cognitive impairment in the elderly population. Concurrent CSVD burden is more commonly observed in AD-type dementia than in other neurodegenerative diseases. Recent developments in Aβ and tau positron emission tomography (PET) have enabled the investigation of the relationship between AD biomarkers and CSVD in vivo. In this review, we focus on the interaction between AD and CSVD markers and the clinical effects of these two markers based on molecular imaging studies. First, we cover the frequency of AD imaging markers, including Aβ and tau, in patients with SVCI. Second, we discuss the relationship between AD and CSVD markers and the potential distinct pathobiology of AD markers in SVCI compared to AD-type dementia. Next, we discuss the clinical effects of AD and CSVD markers in SVCI, and hemorrhagic markers in cerebral amyloid angiopathy. Finally, this review provides both the current challenges and future perspectives for SVCI.
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Affiliation(s)
- Si Eun Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea
- Neuroscience Center, Samsung Medical Center, Seoul 06351, Korea
- Samsung Alzheimer Research Center, Samsung Medical Center, Seoul 06351, Korea
- Department of Neurology, Inje University College of Medicine, Haeundae Paik Hospital, Busan 48108, Korea
| | - Hee Jin Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea
- Neuroscience Center, Samsung Medical Center, Seoul 06351, Korea
- Samsung Alzheimer Research Center, Samsung Medical Center, Seoul 06351, Korea
| | - Hyemin Jang
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea
- Neuroscience Center, Samsung Medical Center, Seoul 06351, Korea
- Samsung Alzheimer Research Center, Samsung Medical Center, Seoul 06351, Korea
| | - Michael W. Weiner
- Center for Imaging of Neurodegenerative Diseases, University of California, San Francisco, CA 94121, USA
| | - Charles DeCarli
- Department of Neurology and Center for Neuroscience, University of California, Davis, CA 95616, USA
| | - Duk L. Na
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea
- Neuroscience Center, Samsung Medical Center, Seoul 06351, Korea
- Samsung Alzheimer Research Center, Samsung Medical Center, Seoul 06351, Korea
- Department of Health Sciences and Technology, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Seoul 06355, Korea
- Stem Cell and Regenerative Medicine Institute, Samsung Medical Center, Seoul 06351, Korea
| | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea
- Neuroscience Center, Samsung Medical Center, Seoul 06351, Korea
- Samsung Alzheimer Research Center, Samsung Medical Center, Seoul 06351, Korea
- Department of Clinical Research Design and Evaluation, SAIHST, Sungkyunkwan University, Seoul 06355, Korea
- Center for Clinical Epidemiology, Samsung Medical Center, Seoul 06351, Korea
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13
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Jang JW, Kim J, Park SW, Kasani PH, Kim Y, Kim S, Kim SJ, Na DL, Moon SH, Seo SW, Seong JK. Machine learning-based automatic estimation of cortical atrophy using brain computed tomography images. Sci Rep 2022; 12:14740. [PMID: 36042322 PMCID: PMC9427760 DOI: 10.1038/s41598-022-18696-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Accepted: 08/17/2022] [Indexed: 11/09/2022] Open
Abstract
Cortical atrophy is measured clinically according to established visual rating scales based on magnetic resonance imaging (MRI). Although brain MRI is the primary imaging marker for neurodegeneration, computed tomography (CT) is also widely used for the early detection and diagnosis of dementia. However, they are seldom investigated. Therefore, we developed a machine learning algorithm for the automatic estimation of cortical atrophy on brain CT. Brain CT images (259 Alzheimer’s dementia and 55 cognitively normal subjects) were visually rated by three neurologists and used for training. We constructed an algorithm by combining the convolutional neural network and regularized logistic regression (RLR). Model performance was then compared with that of neurologists, and feature importance was measured. RLR provided fast and reliable automatic estimations of frontal atrophy (75.2% accuracy, 93.6% sensitivity, 67.2% specificity, and 0.87 area under the curve [AUC]), posterior atrophy (79.6% accuracy, 87.2% sensitivity, 75.9% specificity, and 0.88 AUC), right medial temporal atrophy (81.2% accuracy, 84.7% sensitivity, 79.6% specificity, and 0.88 AUC), and left medial temporal atrophy (77.7% accuracy, 91.1% sensitivity, 72.3% specificity, and 0.90 AUC). We concluded that RLR-based automatic estimation of brain CT provided a comprehensive rating of atrophy that can potentially support physicians in real clinical settings.
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Affiliation(s)
- Jae-Won Jang
- Department of Neurology, School of Medicine, Kangwon National University, Chuncheon, Republic of Korea.,Department of Neurology, Kangwon National University Hospital, Chuncheon, Republic of Korea.,Department of Medical Bigdata Convergence, Kangwon National University, Chuncheon, Republic of Korea
| | - Jeonghun Kim
- Department of Biomedical Engineering, Korea University, Seoul, Republic of Korea
| | - Sang-Won Park
- Department of Neurology, Kangwon National University Hospital, Chuncheon, Republic of Korea.,Department of Medical Bigdata Convergence, Kangwon National University, Chuncheon, Republic of Korea
| | - Payam Hosseinzadeh Kasani
- Department of Neurology, Kangwon National University Hospital, Chuncheon, Republic of Korea.,Department of Medical Bigdata Convergence, Kangwon National University, Chuncheon, Republic of Korea
| | - Yeshin Kim
- Department of Neurology, School of Medicine, Kangwon National University, Chuncheon, Republic of Korea.,Department of Neurology, Kangwon National University Hospital, Chuncheon, Republic of Korea
| | - Seongheon Kim
- Department of Neurology, School of Medicine, Kangwon National University, Chuncheon, Republic of Korea.,Department of Neurology, Kangwon National University Hospital, Chuncheon, Republic of Korea
| | - Soo-Jong Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Duk L Na
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Seung Hwan Moon
- Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
| | - Joon-Kyung Seong
- Department of Biomedical Engineering, Korea University, Seoul, Republic of Korea.
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Dondi F, Bertoli M, Lucchini S, Cerudelli E, Albano D, Bertagna F. PET imaging for the evaluation of cerebral amyloid angiopathy: a systematic review. Clin Transl Imaging 2022. [DOI: 10.1007/s40336-022-00511-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
Abstract
Purpose
In the last years, the role of PET imaging in the assessment of cerebral amyloid angiopathy (CAA) is emerging. In this setting, some tracers have proven their utility for the evaluation of the disease (mainly 11C-Pittsburgh compound B [11C-PIB]), however, the value of other radiotracers has to be clarified. The aim of this systematic review is, therefore, to assess the role of PET imaging in the evaluation of CAA.
Methods
A wide literature search of the PubMed/MEDLINE, Scopus, Embase, Web of Science and Cochrane library databases was made to find relevant published articles about the diagnostic performance of PET imaging for the evaluation of CAA. Quality assessment including the risk of bias and applicability concerns was carried out using QUADAS-2 evaluation.
Results
The comprehensive computer literature search revealed 651 articles. On reviewing the titles and abstracts, 622 articles were excluded because the reported data were not within the field of interest. Twenty-nine studies were included in the review. In general, PET imaging with amyloid tracers revealed its value for the assessment of CAA, for its differential diagnosis and a correlation with some clinico-pathological features. With less evidence, a role for 18F-fluorodeoxiglucose (18F-FDG) and tau tracers is starting to emerge.
Conclusion
PET imaging demonstrated its utility for the assessment of CAA. In particular, amiloid tracers revealed higher retention in CAA patients, correlation with cerebral bleed, the ability to differentiate between CAA and other related conditions (such as Alzheimer's disease) and a correlation with some cerebrospinal fluid biomarkers.
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Kim J, Jung SH, Choe YS, Kim S, Kim B, Kim HR, Son SJ, Hong CH, Na DL, Kim HJ, Cho SJ, Won HH, Seo SW. Ethnic differences in the frequency of β-amyloid deposition in cognitively normal individuals. Neurobiol Aging 2022; 114:27-37. [DOI: 10.1016/j.neurobiolaging.2022.03.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2021] [Revised: 02/24/2022] [Accepted: 03/01/2022] [Indexed: 10/18/2022]
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16
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Finze A, Wahl H, Janowitz D, Buerger K, Linn J, Rominger A, Stöcklein S, Bartenstein P, Wollenweber FA, Catak C, Brendel M. Regional Associations of Cortical Superficial Siderosis and β-Amyloid-Positron-Emission-Tomography Positivity in Patients With Cerebral Amyloid Angiopathy. Front Aging Neurosci 2022; 13:786143. [PMID: 35185518 PMCID: PMC8851392 DOI: 10.3389/fnagi.2021.786143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 12/20/2021] [Indexed: 11/20/2022] Open
Abstract
Objective This is a cross-sectional study to evaluate whether β-amyloid-(Aβ)-PET positivity and cortical superficial siderosis (cSS) in patients with cerebral amyloid angiopathy (CAA) are regionally colocalized. Methods Ten patients with probable or possible CAA (73.3 ± 10.9 years, 40% women) underwent MRI examination with a gradient-echo-T2*-weighted-imaging sequence to detect cSS and 18F-florbetaben PET examination to detect fibrillar Aβ. In all cortical regions of the Hammers Atlas, cSS positivity (MRI: ITK-SNAP segmentation) and Aβ-PET positivity (PET: ≥ mean value + 2 standard deviations of 14 healthy controls) were defined. Regional agreement of cSS- and Aβ-PET positivity was evaluated. Aβ-PET quantification was compared between cSS-positive and corresponding contralateral cSS-negative atlas regions. Furthermore, the Aβ-PET quantification of cSS-positive regions was evaluated in voxels close to cSS and in direct cSS voxels. Results cSS- and Aβ-PET positivity did not indicate similarity of their regional patterns, despite a minor association between the frequency of Aβ-positive patients and the frequency of cSS-positive patients within individual regions (rs = 0.277, p = 0.032). However, this association was driven by temporal regions lacking cSS- and Aβ-PET positivity. When analyzing all composite brain regions, Aβ-PET values in regions close to cSS were significantly higher than in regions directly affected with cSS (p < 0.0001). However, Aβ-PET values in regions close to cSS were not different when compared to corresponding contralateral cSS-negative regions (p = 0.603). Conclusion In this cross-sectional study, cSS and Aβ-PET positivity did not show regional association in patients with CAA and deserve further exploitation in longitudinal designs. In clinical routine, a specific cross-sectional evaluation of Aβ-PET in cSS-positive regions is probably not useful for visual reading of Aβ-PETs in patients with CAA.
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Affiliation(s)
- Anika Finze
- Department of Nuclear Medicine, University Hospital of Munich, LMU Munich, Munich, Germany
| | - Hannes Wahl
- Department of Neuroradiology, University Hospital of Dresden, Carl Gustav Carus University Dresden, Dresden, Germany
| | - Daniel Janowitz
- Institute for Stroke and Dementia Research, University Hospital of Munich, LMU Munich, Munich, Germany
| | - Katharina Buerger
- Institute for Stroke and Dementia Research, University Hospital of Munich, LMU Munich, Munich, Germany
| | - Jennifer Linn
- Department of Neuroradiology, University Hospital of Dresden, Carl Gustav Carus University Dresden, Dresden, Germany
| | - Axel Rominger
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Sophia Stöcklein
- Department of Radiology, University Hospital of Munich, LMU Munich, Munich, Germany
| | - Peter Bartenstein
- Department of Nuclear Medicine, University Hospital of Munich, LMU Munich, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Frank Arne Wollenweber
- Institute for Stroke and Dementia Research, University Hospital of Munich, LMU Munich, Munich, Germany
| | - Cihan Catak
- Institute for Stroke and Dementia Research, University Hospital of Munich, LMU Munich, Munich, Germany
| | - Matthias Brendel
- Department of Nuclear Medicine, University Hospital of Munich, LMU Munich, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- *Correspondence: Matthias Brendel,
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Jo S, Cheong EN, Kim N, Oh JS, Shim WH, Kim HJ, Lee SJ, Lee Y, Oh M, Kim JS, Kim BJ, Roh JH, Kim SJ, Lee JH. Role of White Matter Abnormalities in the Relationship Between Microbleed Burden and Cognitive Impairment in Cerebral Amyloid Angiopathy. J Alzheimers Dis 2022; 86:667-678. [DOI: 10.3233/jad-215094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Background: Cerebral amyloid angiopathy (CAA) often presents as cognitive impairment, but the mechanism of cognitive decline is unclear. Recent studies showed that number of microbleeds were associated with cognitive decline. Objective: We aimed to investigate how microbleeds contribute to cognitive impairment in association with white matter tract abnormalities or cortical thickness in CAA. Methods: This retrospective comparative study involved patients with probable CAA according to the Boston criteria (Aβ + CAA) and patients with Alzheimer’s disease (Aβ + AD), all of whom showed severe amyloid deposition on amyloid PET. Using mediation analysis, we investigated how FA or cortical thickness mediates the correlation between the number of lobar microbleeds and cognition. Results: We analyzed 30 patients with Aβ + CAA (age 72.2±7.6, female 53.3%) and 30 patients with Aβ + AD (age 71.5±7.6, female 53.3%). The two groups showed similar degrees of cortical amyloid deposition in AD-related regions. The Aβ + CAA group had significantly lower FA values in the clusters of the posterior area than did the Aβ + AD group (family-wise error-corrected p < 0.05). The correlation between the number of lobar microbleeds and visuospatial function was indirectly mediated by white matter tract abnormality of right posterior thalamic radiation (PTR) and tapetum, while lobar microbleeds and language function was indirectly mediated by the abnormality of left PTR and sagittal stratum. Cortical thickness did not mediate the association between lobar microbleeds and cognition. Conclusion: This result supports the hypothesis that microbleeds burden leads to white matter tract damage and subsequent cognitive decline in CAA.
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Affiliation(s)
- Sungyang Jo
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - E-Nae Cheong
- Department of Medical Science and Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Nayoung Kim
- Department of Clinical Epidemiology and Biostatistics, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Jungsu S. Oh
- Department of Nuclear Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Woo Hyun Shim
- Department of Medical Science and Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Hyung-Ji Kim
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Sun Ju Lee
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Yoojin Lee
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Minyoung Oh
- Department of Nuclear Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Jae Seung Kim
- Department of Nuclear Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Bum Joon Kim
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Jee Hoon Roh
- Department of Physiology, Neuroscience Research Institute, Korea University College of Medicine, Seoul, Republic of Korea
| | - Sang Joon Kim
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Jae-Hong Lee
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
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18
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Michiels L, Dobbels L, Demeestere J, Demaerel P, Van Laere K, Lemmens R. Simplified Edinburgh and modified Boston criteria in relation to amyloid PET for lobar intracerebral hemorrhage. NEUROIMAGE: CLINICAL 2022; 35:103107. [PMID: 35853346 PMCID: PMC9421490 DOI: 10.1016/j.nicl.2022.103107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 06/28/2022] [Accepted: 07/10/2022] [Indexed: 11/26/2022] Open
Abstract
Amyloid PET was positive in 63% of patients with lobar ICH. Simplified Edinburgh criteria and amyloid PET have similar accuracy vs Boston criteria. Simplified Edinburgh and Boston criteria have similar accuracy vs amyloid PET. Amyloid PET could assist in diagnosing CAA.
Background Histopathological evidence of cerebral vascular amyloid β accumulation is the gold standard to diagnose cerebral amyloid angiopathy (CAA). Neuroimaging findings obtained with CT and MRI can suggest the presence of CAA when histopathology is lacking. We explored the role of amyloid PET in patients with lobar intracerebral hemorrhage (ICH) as this may provide molecular evidence for CAA as well. Methods In this retrospective, monocenter analysis, we included consecutive patients with non-traumatic lobar ICH who had undergone amyloid PET. We categorized patients according to amyloid PET status and compared demographics and neuroimaging findings. We calculated sensitivity and specificity of the simplified Edinburgh criteria and amyloid PET with probable modified Boston criteria as reference standard, as well as sensitivity and specificity of the simplified Edinburgh and modified Boston criteria with amyloid PET status as molecular marker for presence or absence of CAA. Results We included 38 patients of whom 24 (63%) were amyloid PET positive. Amyloid PET positive patients were older at presentation (p = 0.004). We observed no difference in prevalence of subarachnoid hemorrhages, fingerlike projections or microbleeds between both groups, but cortical superficial siderosis (p = 0.003) was more frequent in the amyloid PET positive group. In 5 out of 38 patients (13%), the modified Boston criteria were not fulfilled due to young age or concomitant vitamin K antagonist use with INR > 3.0. With the modified Boston criteria as reference standard, there was no difference in sensitivity nor specificity between the simplified Edinburgh criteria and amyloid PET status. With amyloid PET status as reference standard, there was also no difference in sensitivity nor specificity between the simplified Edinburgh and modified Boston criteria. Conclusions Amyloid PET was positive in 63% of lobar ICH patients. Under certain circumstances, patients might not be diagnosed with probable CAA according to the modified Boston criteria and in these cases, amyloid PET may be useful. Accuracy to predict CAA based on amyloid PET status did not differ between the simplified Edinburgh and modified Boston criteria.
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19
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Chang Y, Liu J, Wang L, Li X, Wang Z, Lin M, Jin W, Zhu M, Xu B. Diagnostic Utility of Integrated 11C-Pittsburgh Compound B Positron Emission Tomography/Magnetic Resonance for Cerebral Amyloid Angiopathy: A Pilot Study. Front Aging Neurosci 2021; 13:721780. [PMID: 34899265 PMCID: PMC8660657 DOI: 10.3389/fnagi.2021.721780] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Accepted: 10/25/2021] [Indexed: 11/13/2022] Open
Abstract
Objective: We aimed to compare amyloid deposition at the lobar cerebral microbleed (CMB) sites of cerebral amyloid angiopathy (CAA), Alzheimer’s disease (AD), and cognitively normal healthy controls (NC) and to propose a novel diagnostic method for differentiating CAA patients from AD patients with integrated 11C-Pittsburgh compound B (PIB) positron emission tomography (PET)/magnetic resonance (MR) and assess its diagnostic value. Methods: Nine CAA, 15 AD patients, and 15 NC subjects were enrolled in this study. Each subject underwent an 11C-PIB brain PET/MR examination. Susceptibility weighted imaging was assessed to detect CMB locations, and standardized uptake value ratios (SUVRs) were measured at these sites. Cortical PIB distributions were quantitatively evaluated. Patients with CAA, AD, and NC subjects were compared with global and regional cortical SUVRs at CMB cites. The diagnostic accuracy of MRI, PIB-PET, and PET/MR in differentiating CAA and AD was evaluated. Results: Lobar CMBs were detected in all the CAA patients, eight of the 15 AD patients (53.3%), and four of the 15 NC subjects (26.7%), respectively. The PIB deposition at CMB sites was significantly higher in CAA patients compared with AD patients and NC subjects in terms of SUVR (1.72 ± 0.10 vs. 1.42 ± 0.16 and 1.17 ± 0.08; p < 0.0001). The PIB deposition was associated with CMB locations and was greatest in the occipital and temporal regions of CAA patients. The global cortical PIB deposition was significantly higher in CAA than in NC subjects (1.66 ± 0.06 vs. 1.21 ± 0.06; p < 0.0001) and significantly lower than in AD patients (1.66 ± 0.06 vs. 1.86 ± 0.17; p < 0.0001). In contrast, the occipital/global PIB uptake ratio was significantly increased in CAA (occipital/global ratio, 1.05 ± 0.02) relative to AD patients (1.05 ± 0.02 vs. 0.99 ± 0.04; p < 0.001). PET/MR had a higher accuracy (sensitivity, 88.9%; specificity, 93.3%) than separate PET and MR. Conclusion: Our results indicate that the CMBs occur preferentially at loci with concentrated amyloid. By combining lobar CMBs with regional cortical amyloid deposition, the proposed workflow can further improve CAA diagnostic accuracy compared to each method alone. These findings improve our knowledge regarding the pathogenesis of CMBs and highlight the potential utility of PIB-PET/MR as a non-invasive tool for distinguishing CAA and AD patients.
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Affiliation(s)
- Yan Chang
- Department of Nuclear Medicine, The First Medical Centre, Chinese PLA General Hospital, Beijing, China
| | - Jiajin Liu
- Department of Nuclear Medicine, The First Medical Centre, Chinese PLA General Hospital, Beijing, China
| | - Liang Wang
- PET/CT, Jixi Ji Mine Hospital, Jixi, China
| | - Xin Li
- Department of Interventional Radiology, The First Medical Centre, Chinese PLA General Hospital, Beijing, China
| | - Zhenjun Wang
- Department of Radiology, The First Medical Centre, Chinese PLA General Hospital, Beijing, China
| | - Mu Lin
- MR Collaboration, Diagnostic Imaging, Siemens Healthcare Ltd., Shanghai, China
| | - Wei Jin
- Department of Pathology, The First Medical Centre, Chinese PLA General Hospital, Beijing, China
| | - Mingwei Zhu
- Department of Neurology Medicine, The Second Medical Centre, Chinese PLA General Hospital, Beijing, China
| | - Baixuan Xu
- Department of Nuclear Medicine, The First Medical Centre, Chinese PLA General Hospital, Beijing, China
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20
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Kang SH, Woo SY, Kim S, Kim JP, Jang H, Koh SB, Na DL, Kim HJ, Seo SW. Independent effects of amyloid and vascular markers on long-term functional outcomes: An 8-year longitudinal study of subcortical vascular cognitive impairment. Eur J Neurol 2021; 29:413-421. [PMID: 34716964 DOI: 10.1111/ene.15159] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Revised: 09/11/2021] [Accepted: 09/18/2021] [Indexed: 11/30/2022]
Abstract
BACKGROUND AND PURPOSE Subcortical vascular cognitive impairment (SVCI) is characterized by the presence of cerebral small vessel disease (CSVD) markers. Some SVCI patients also show Alzheimer's disease and cerebral amyloid angiopathy markers. However, the effects of these imaging markers on long-term clinical outcomes have not yet been established. The present study, therefore, aimed to determine how these imaging markers influence functional disability and/or mortality. METHODS We recruited 194 participants with SVCI from the memory clinic and followed them up. All participants underwent brain magnetic resonance imaging at baseline, and 177 (91.2%) participants underwent beta-amyloid (Aβ) positron emission tomography. We examined the occurrence of ischemic or hemorrhagic strokes. We also evaluated functional disability and mortality using the modified Rankin scale. To determine the effects of imaging markers on functional disability or mortality, we used Fine and Gray competing regression or Cox regression analysis. RESULTS During a 8.6-year follow-up period, 46 of 194 patients (23.7%) experienced a stroke, 110 patients (56.7%) developed functional disabilities and 75 (38.6%) died. Aβ positivity (subdistribution hazard ratio [SHR] = 2.73), greater white matter hyperintensity (WMH) volume (SHR = 3.11) and ≥3 microbleeds (SHR = 2.29) at baseline were independent predictors of functional disability regardless of the occurrence of stroke. Greater WMH volume (hazard ratio = 2.07) was an independent predictor of mortality. CONCLUSIONS Our findings suggest that diverse imaging markers may predict long-term functional disability and mortality in patients with SVCI, which in turn may provide clinicians with a more insightful understanding of the long-term outcomes of SVCI.
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Affiliation(s)
- Sung Hoon Kang
- Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.,Neuroscience Center, Samsung Medical Center, Seoul, South Korea.,Department of Neurology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, South Korea
| | - Sook-Young Woo
- Statistics and Data Center, Samsung Medical Center, Seoul, South Korea
| | - Seonwoo Kim
- Statistics and Data Center, Samsung Medical Center, Seoul, South Korea
| | - Jun Pyo Kim
- Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.,Neuroscience Center, Samsung Medical Center, Seoul, South Korea
| | - Hyemin Jang
- Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.,Neuroscience Center, Samsung Medical Center, Seoul, South Korea
| | - Seong-Beom Koh
- Department of Neurology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, South Korea
| | - Duk L Na
- Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.,Neuroscience Center, Samsung Medical Center, Seoul, South Korea
| | - Hee Jin Kim
- Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.,Neuroscience Center, Samsung Medical Center, Seoul, South Korea
| | - Sang Won Seo
- Departments of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.,Neuroscience Center, Samsung Medical Center, Seoul, South Korea.,Department of Clinical Research Design and Evaluation, SAIHST, Sungkyunkwan University, Seoul, South Korea.,Samsung Alzheimer Research Center and Center for Clinical Epidemiology Medical Center, Seoul, South Korea.,Department of Intelligent Precision Healthcare Convergence, SAIHST, Sungkyunkwan University, Suwon, South Korea
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21
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Jang H, Kim JS, Lee HJ, Kim CH, Na DL, Kim HJ, Allué JA, Sarasa L, Castillo S, Pesini P, Gallacher J, Seo SW. Performance of the plasma Aβ42/Aβ40 ratio, measured with a novel HPLC-MS/MS method, as a biomarker of amyloid PET status in a DPUK-KOREAN cohort. ALZHEIMERS RESEARCH & THERAPY 2021; 13:179. [PMID: 34686209 PMCID: PMC8540152 DOI: 10.1186/s13195-021-00911-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Accepted: 10/02/2021] [Indexed: 12/20/2022]
Abstract
Background We assessed the feasibility of plasma Aβ42/Aβ40 determined using a novel liquid chromatography-mass spectrometry method (LC-MS) as a useful biomarker of PET status in a Korean cohort from the DPUK Study. Methods A total of 580 participants belonging to six groups, Alzheimer’s disease dementia (ADD, n = 134), amnestic mild cognitive impairment (aMCI, n = 212), old controls (OC, n = 149), young controls (YC, n = 15), subcortical vascular cognitive impairment (SVCI, n = 58), and cerebral amyloid angiopathy (CAA, n = 12), were included in this study. Plasma Aβ40 and Aβ42 were quantitated using a new antibody-free, LC-MS, which drastically reduced the sample preparation time and cost. We performed receiver operating characteristic (ROC) analysis to develop the cutoff of Aβ42/Aβ40 and investigated its performance predicting centiloid-based PET positivity (PET+). Results Plasma Aβ42/Aβ40 were lower for PET+ individuals in ADD, aMCI, OC, and SVCI (p < 0.001), but not in CAA (p = 0.133). In the group of YC, OC, aMCI, and ADD groups, plasma Aβ42/Aβ40 predicted PET+ with an area under the ROC curve (AUC) of 0.814 at a cutoff of 0.2576. When adding age, APOE4, and diagnosis, the AUC significantly improved to 0.912. Conclusion Plasma Aβ42/Aβ40, as measured by this novel LC-MS method, showed good discriminating performance based on PET positivity. Supplementary Information The online version contains supplementary material available at 10.1186/s13195-021-00911-7.
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Affiliation(s)
- Hyemin Jang
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.,Neuroscience Center, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.,Alzheimer's Disease Convergence Research Center, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Ji Sun Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.,Neuroscience Center, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Hye Joo Lee
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.,Alzheimer's Disease Convergence Research Center, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Chi-Hun Kim
- Department of Neurology, School of Medicine, Kyungpook National University, Kyungpook National University Chilgok Hospital, Daegu, South Korea.,Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, UK
| | - Duk L Na
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.,Neuroscience Center, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.,Alzheimer's Disease Convergence Research Center, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.,Stem Cell & Regenerative Medicine Institute, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.,Department of Health Sciences and Technology, Seoul, Republic of Korea
| | - Hee Jin Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.,Neuroscience Center, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.,Alzheimer's Disease Convergence Research Center, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | | | - Leticia Sarasa
- Araclon Biotech-Grifols, Vía Hispanidad, 21, 50009, Zaragoza, Spain
| | - Sergio Castillo
- Araclon Biotech-Grifols, Vía Hispanidad, 21, 50009, Zaragoza, Spain
| | - Pedro Pesini
- Araclon Biotech-Grifols, Vía Hispanidad, 21, 50009, Zaragoza, Spain
| | - John Gallacher
- Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, UK
| | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea. .,Neuroscience Center, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea. .,Alzheimer's Disease Convergence Research Center, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea. .,Department of Clinical Research Design & Evaluation, SAIHST, Sungkyunkwan University, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea. .,Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
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22
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Wang M, Lv J, Huang X, Wisniewski T, Zhang W. High-fat diet-induced atherosclerosis promotes neurodegeneration in the triple transgenic (3 × Tg) mouse model of Alzheimer's disease associated with chronic platelet activation. ALZHEIMERS RESEARCH & THERAPY 2021; 13:144. [PMID: 34454596 PMCID: PMC8403418 DOI: 10.1186/s13195-021-00890-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Accepted: 08/18/2021] [Indexed: 01/14/2023]
Abstract
Background Epidemiological studies link vascular disease risk factors such as atherosclerosis, hypertension, and diabetes mellitus with Alzheimer’s disease (AD). Whether there are direct links between these conditions to β-amyloid (Aβ) aggregation and tau pathology is uncertain. Methods To investigate the possible link between atherosclerosis and AD pathology, we subjected triple transgenic (3 × Tg) AD mice to a high-fat diet (HFD) at 3 months of age, which corresponds to early adulthood in humans. Results After 9 months of treatment, HFD-treated 3 × Tg mice exhibited worse memory deficits accompanied by blood hypercoagulation, thrombocytosis, and chronic platelet activation. Procoagulant platelets from HFD-treated 3 × Tg mice actively induced the conversion of soluble Aβ40 into fibrillar Aβ aggregates, associated with increased expression of integrin αIIbβ3 and clusterin. At 9 months and older, platelet-associated fibrillar Aβ aggregates were observed to obstruct the cerebral blood vessels in HFD-treated 3 × Tg mice. HFD-treated 3 × Tg mice exhibited a greater cerebral amyloid angiopathy (CAA) burden and increased cerebral vascular permeability, as well as more extensive neuroinflammation, tau hyperphosphorylation, and neuron loss. Disaggregation of preexisting platelet micro-clots with humanized GPIIIa49-66 scFv Ab (A11) significantly reduced platelet-associated fibrillar Aβ aggregates in vitro and improved vascular permeability in vivo. Conclusions These findings suggest that a major contribution of atherosclerosis to AD pathology is via its effects on blood coagulation and the formation of platelet-mediated Aβ aggregates that compromise cerebral blood flow and therefore neuronal function. This leads to cognitive decline. Supplementary Information The online version contains supplementary material available at 10.1186/s13195-021-00890-9.
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Affiliation(s)
- Min Wang
- Key Laboratory of Brain Functional Genomics (Ministry of Education and Shanghai), School of Life Sciences, East China Normal University, 3663 North Zhongshan Road, Shanghai, 200062, China
| | - Junyan Lv
- Key Laboratory of Brain Functional Genomics (Ministry of Education and Shanghai), School of Life Sciences, East China Normal University, 3663 North Zhongshan Road, Shanghai, 200062, China
| | - Xiaoshan Huang
- Key Laboratory of Brain Functional Genomics (Ministry of Education and Shanghai), School of Life Sciences, East China Normal University, 3663 North Zhongshan Road, Shanghai, 200062, China
| | - Thomas Wisniewski
- Center for Cognitive Neurology and Departments of Neurology, Pathology and Psychiatry, New York University School of Medicine, Science Building, Rm1017, 435 East 30th Street, New York, NY, 10016, USA.
| | - Wei Zhang
- Key Laboratory of Brain Functional Genomics (Ministry of Education and Shanghai), School of Life Sciences, East China Normal University, 3663 North Zhongshan Road, Shanghai, 200062, China.
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23
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Su Y, Fu J, Zhang Y, Xu J, Dong Q, Cheng X. Visuospatial dysfunction is associated with posterior distribution of white matter damage in non-demented cerebral amyloid angiopathy. Eur J Neurol 2021; 28:3113-3120. [PMID: 34157199 DOI: 10.1111/ene.14993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 06/16/2021] [Accepted: 06/17/2021] [Indexed: 11/29/2022]
Abstract
BACKGROUND AND PURPOSE Cerebral amyloid angiopathy (CAA) is a well-recognized contributor to cognitive decline in the elderly. The posterior cortical predilection of CAA pathology would cause visuospatial dysfunction, which is still underexplored. We aimed to investigate whether the visuospatial dysfunction in CAA is associated with the posterior distribution of small vessel disease (SVD) imaging markers. METHODS We recruited 60 non-demented CAA cases from a Chinese prospective cohort and 30 cases with non-CAA SVD as controls. We used the Visual Object and Space Perception (VOSP) battery to evaluate visuospatial abilities, and multivariable regression models to assess their associations with SVD imaging markers. RESULTS There was visuospatial dysfunction, especially visual object perception impairment, in CAA compared to controls (Z-score of VOSP: -0.11 ± 0.66 vs. 0.22 ± 0.54, p = 0.023). The VOSP score in CAA was independently related to the fronto-occipital gradient of white matter hyperintensity volumes (coefficient = 0.03, 95% confidence interval [CI] = 0.003-0.05, p = 0.030) and mean fractional anisotropy values on diffusion tensor imaging (coefficient = 4.72, 95% CI = 0.97-8.48, p = 0.015), but not the severity of global SVD imaging markers or the gradient of lobar cerebral microbleeds with adjustments for age and global cognition score. CONCLUSIONS This finding suggests that the damage of posterior white matter rather than global disease severity may be a major contributor to visuospatial dysfunction in CAA.
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Affiliation(s)
- Ya Su
- Department of Neurology, National Center for Neurological Disorders, National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Jiayu Fu
- Department of Neurology, National Center for Neurological Disorders, National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Yanrong Zhang
- Department of Nursing, Huashan Hospital, Fudan University, Shanghai, China
| | - Jiajie Xu
- Department of Neurology, National Center for Neurological Disorders, National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Qiang Dong
- Department of Neurology, National Center for Neurological Disorders, National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Xin Cheng
- Department of Neurology, National Center for Neurological Disorders, National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
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24
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Cho SH, Choe YS, Kim YJ, Kim HJ, Jang H, Kim Y, Kim SE, Kim SJ, Kim JP, Jung YH, Kim BC, Lockhart SN, Farrar G, Na DL, Moon SH, Seo SW. Head-to-Head Comparison of 18F-Florbetaben and 18F-Flutemetamol in the Cortical and Striatal Regions. J Alzheimers Dis 2021; 76:281-290. [PMID: 32474468 DOI: 10.3233/jad-200079] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND 18F-florbetaben (FBB) and 18F-flutemetamol (FMM) amyloid PET have been developed and approved for clinical use. It is important to understand the distinct features of these ligands to compare and correctly interpret the results of different amyloid PET studies. OBJECTIVE We performed a head-to-head comparison of FBB and FMM to compare with regard to imaging characteristics, including dynamic range of retention, and differences in quantitative measurements between the two ligands in cortical, striatal, and white matter (WM) regions. METHODS Paired FBB and FMM PET images were acquired in 107 participants. Correlations of FBB and FMM amyloid deposition in the cortex, striatum, and WM were investigated and compared in different reference regions (cerebellar gray matter (CG), whole cerebellum (WC), WC with brainstem (WC + B), and pons). RESULTS The cortical SUVR (R2 = 0.97) and striatal SUVR (R2 = 0.95) demonstrated an excellent linear correlation between FBB and FMM using a WC as reference region. There was no difference in the cortical SUVR ratio between the two ligands (p = 0.90), but the striatal SUVR ratio was higher in FMM than in FBB (p < 0.001). Also, the effect size of differences in striatal SUVR seemed to be higher with FMM (2.61) than with FBB (2.34). These trends were similarly observed according to four different reference regions (CG, WC, WC + B, and pons). CONCLUSION Our findings suggest that FMM might be better than FBB to detect amyloid burden in the striatum, although both ligands are comparable for imaging AD pathology in vivo.
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Affiliation(s)
- Soo Hyun Cho
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.,Department of Neurology, Chonnam National University Medical School, Chonnam National University Hospital, Gwangju, Korea
| | - Yeong Sim Choe
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.,Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Young Ju Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Hee Jin Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Hyemin Jang
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Yeshin Kim
- Department of Neurology, Kangwon National University Hospital, Kangwon National University College of Medicine, Chuncheon, Korea
| | - Si Eun Kim
- Departments of Neurology, Inje University College of Medicine, Haeundae Paik Hospital, Busan, Korea
| | - Seung Joo Kim
- Department of Neurology, Gyeongsang National University School of Medicine and Gyeongsang National University Changwon Hospital, Changwon, Korea
| | - Jun Pyo Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Young Hee Jung
- Department of Neurology, Myoungji Hospital, Hanyang University, Goyangsi, Korea
| | - Byeong C Kim
- Department of Neurology, Chonnam National University Medical School, Chonnam National University Hospital, Gwangju, Korea
| | - Samuel N Lockhart
- Internal Medicine - Gerontology and Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Gill Farrar
- Pharmaceutical Diagnostics, GE Healthcare, Chalfont St Giles, UK
| | - Duk L Na
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.,Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea.,Stem Cell & Regenerative Medicine Institute, Samsung Medical Center, Seoul, Korea
| | - Seung Hwan Moon
- Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea.,Department of Clinical Research Design & Evaluation, SAIHST, Sungkyunkwan University, Seoul, Korea.,Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Korea.,Center for Clinical Epidemiology, Samsung Medical Center, Seoul, Korea
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25
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Oldan JD, Jewells VL, Pieper B, Wong TZ. Complete Evaluation of Dementia: PET and MRI Correlation and Diagnosis for the Neuroradiologist. AJNR Am J Neuroradiol 2021; 42:998-1007. [PMID: 33926896 DOI: 10.3174/ajnr.a7079] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2020] [Accepted: 11/14/2020] [Indexed: 12/12/2022]
Abstract
This article will familiarize neuroradiologists with the pathophysiology, clinical findings, and standard MR imaging and PET imaging features of multiple forms of dementia as well as new emerging techniques. Cases were compiled from multiple institutions with the goal of improved diagnostic accuracy and improved patient care as well as information about biomarkers on the horizon. Dementia topics addressed include the following: Alzheimer disease, frontotemporal dementia, cerebral amyloid angiopathy, Lewy body dementia, Parkinson disease and Parkinson disease variants, amyotrophic lateral sclerosis, multisystem atrophy, Huntington disease vascular dementia, and Creutzfeldt-Jakob disease.
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Affiliation(s)
- J D Oldan
- From the Department of Radiology (J.D.O., V.L.J), University of North Carolina, Chapel Hill, North Carolina
| | - V L Jewells
- From the Department of Radiology (J.D.O., V.L.J), University of North Carolina, Chapel Hill, North Carolina
| | - B Pieper
- Department of Radiology (B.P.), Richard L. Roudebush VA Medical Center, Indianapolis, Indiana
| | - T Z Wong
- Department of Radiology (T.Z.W.), Duke University Hospital, Durham, North Carolina
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26
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Jung YH, Jang H, Park SB, Choe YS, Park Y, Kang SH, Lee JM, Kim JS, Kim J, Kim JP, Kim HJ, Na DL, Seo SW. Strictly Lobar Microbleeds Reflect Amyloid Angiopathy Regardless of Cerebral and Cerebellar Compartments. Stroke 2020; 51:3600-3607. [PMID: 33198580 DOI: 10.1161/strokeaha.119.028487] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
BACKGROUND AND PURPOSE We aimed to determine whether lobar cerebellar microbleeds or concomitant lobar cerebellar and deep microbleeds, in the presence of lobar cerebral microbleeds, attribute to underlying advanced cerebral amyloid angiopathy pathology or hypertensive arteriopathy. METHODS We categorized 71 patients with suspected cerebral amyloid angiopathy markers (regardless of the presence of deep and cerebellar microbleeds) into 4 groups according to microbleed distribution: L (strictly lobar cerebral, n=33), L/LCbll (strictly lobar cerebral and strictly lobar cerebellar microbleeds, n=13), L/Cbll/D (lobar, cerebellar, and deep microbleeds, n=17), and L/D (lobar and deep, n=8). We additionally categorized patients with cerebellar microbleeds into 2 groups according to dentate nucleus involvement: strictly lobar cerebellar (n=16) and dentate (n=14). We then compared clinical characteristics, Aβ (amyloid-β) positivity on PET (positron emission tomography), magnetic resonance imaging cerebral amyloid angiopathy markers, and cerebral small vessel disease burden among groups. RESULTS The frequency of Aβ positivity was higher in the L and L/LCbll groups (81.8% and 84.6%) than in the L/Cbll/D and L/D groups (37.5% and 29.4%; P<0.001), while lacune numbers were lower in the L and L/LCbll groups (1.7±3.3 and 1.7±2.6) than in the L/Cbll/D and L/D groups (8.0±10.3 and 13.4±17.7, P=0.001). The L/LCbll group had more lobar cerebral microbleeds than the L group (93.2±121.8 versus 38.0±40.8, P=0.047). The lobar cerebellar group had a higher Aβ positivity (75% versus 28.6%, P=0.011) and lower lacune number (2.3±3.7 versus 8.6±1.2, P=0.041) than the dentate group. CONCLUSIONS Strictly lobar cerebral and cerebellar microbleeds are related to cerebral amyloid angiopathy, whereas any combination of concurrent lobar and deep microbleeds suggest hypertensive angiopathy regardless of cerebral or cerebellar compartments.
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Affiliation(s)
- Young Hee Jung
- Department of Neurology, Myongji Hospital, Hanyang University, Goyang, Korea (Y.H.J)
| | - Hyemin Jang
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea (H.J., S.B.P., S.H.K., J.M.L., J.S.K., J.K., J.P.K., H.J.K., D.L.N., S.W.S.).,Neuroscience Center, Samsung Medical Center, Samsung Alzheimer Research Center, Research Institute for Future Medicine, Samsung Medical Center, Seoul, Korea (H.J., S.B.P., S.H.K., J.M.L., J.S.K., J.K., J.P.K., H.J.K., D.L.N., S.W.S.)
| | - Seong Beom Park
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea (H.J., S.B.P., S.H.K., J.M.L., J.S.K., J.K., J.P.K., H.J.K., D.L.N., S.W.S.).,Neuroscience Center, Samsung Medical Center, Samsung Alzheimer Research Center, Research Institute for Future Medicine, Samsung Medical Center, Seoul, Korea (H.J., S.B.P., S.H.K., J.M.L., J.S.K., J.K., J.P.K., H.J.K., D.L.N., S.W.S.)
| | | | | | - Sung Hoon Kang
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea (H.J., S.B.P., S.H.K., J.M.L., J.S.K., J.K., J.P.K., H.J.K., D.L.N., S.W.S.).,Neuroscience Center, Samsung Medical Center, Samsung Alzheimer Research Center, Research Institute for Future Medicine, Samsung Medical Center, Seoul, Korea (H.J., S.B.P., S.H.K., J.M.L., J.S.K., J.K., J.P.K., H.J.K., D.L.N., S.W.S.)
| | - Jong Min Lee
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea (H.J., S.B.P., S.H.K., J.M.L., J.S.K., J.K., J.P.K., H.J.K., D.L.N., S.W.S.).,Neuroscience Center, Samsung Medical Center, Samsung Alzheimer Research Center, Research Institute for Future Medicine, Samsung Medical Center, Seoul, Korea (H.J., S.B.P., S.H.K., J.M.L., J.S.K., J.K., J.P.K., H.J.K., D.L.N., S.W.S.)
| | - Ji Sun Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea (H.J., S.B.P., S.H.K., J.M.L., J.S.K., J.K., J.P.K., H.J.K., D.L.N., S.W.S.).,Neuroscience Center, Samsung Medical Center, Samsung Alzheimer Research Center, Research Institute for Future Medicine, Samsung Medical Center, Seoul, Korea (H.J., S.B.P., S.H.K., J.M.L., J.S.K., J.K., J.P.K., H.J.K., D.L.N., S.W.S.)
| | - Jaeho Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea (H.J., S.B.P., S.H.K., J.M.L., J.S.K., J.K., J.P.K., H.J.K., D.L.N., S.W.S.).,Neuroscience Center, Samsung Medical Center, Samsung Alzheimer Research Center, Research Institute for Future Medicine, Samsung Medical Center, Seoul, Korea (H.J., S.B.P., S.H.K., J.M.L., J.S.K., J.K., J.P.K., H.J.K., D.L.N., S.W.S.)
| | - Jun Pyo Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea (H.J., S.B.P., S.H.K., J.M.L., J.S.K., J.K., J.P.K., H.J.K., D.L.N., S.W.S.).,Neuroscience Center, Samsung Medical Center, Samsung Alzheimer Research Center, Research Institute for Future Medicine, Samsung Medical Center, Seoul, Korea (H.J., S.B.P., S.H.K., J.M.L., J.S.K., J.K., J.P.K., H.J.K., D.L.N., S.W.S.)
| | - Hee Jin Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea (H.J., S.B.P., S.H.K., J.M.L., J.S.K., J.K., J.P.K., H.J.K., D.L.N., S.W.S.).,Neuroscience Center, Samsung Medical Center, Samsung Alzheimer Research Center, Research Institute for Future Medicine, Samsung Medical Center, Seoul, Korea (H.J., S.B.P., S.H.K., J.M.L., J.S.K., J.K., J.P.K., H.J.K., D.L.N., S.W.S.)
| | - Duk L Na
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea (H.J., S.B.P., S.H.K., J.M.L., J.S.K., J.K., J.P.K., H.J.K., D.L.N., S.W.S.).,Neuroscience Center, Samsung Medical Center, Samsung Alzheimer Research Center, Research Institute for Future Medicine, Samsung Medical Center, Seoul, Korea (H.J., S.B.P., S.H.K., J.M.L., J.S.K., J.K., J.P.K., H.J.K., D.L.N., S.W.S.).,Stem Cell & Regenerative Medicine Institute, Samsung Medical Center, Seoul, Korea (D.L.N.)
| | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea (H.J., S.B.P., S.H.K., J.M.L., J.S.K., J.K., J.P.K., H.J.K., D.L.N., S.W.S.).,Neuroscience Center, Samsung Medical Center, Samsung Alzheimer Research Center, Research Institute for Future Medicine, Samsung Medical Center, Seoul, Korea (H.J., S.B.P., S.H.K., J.M.L., J.S.K., J.K., J.P.K., H.J.K., D.L.N., S.W.S.).,Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, Korea (S.W.S.).,Department of Health Science and Technology, SAIHST, Sungkyunkwan University, Seoul, Korea (S.W.S.)
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27
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Cho SH, Choe YS, Kim YJ, Lee B, Kim HJ, Jang H, Kim JP, Jung YH, Kim SJ, Kim BC, Farrar G, Na DL, Moon SH, Seo SW. Concordance in detecting amyloid positivity between 18F-florbetaben and 18F-flutemetamol amyloid PET using quantitative and qualitative assessments. Sci Rep 2020; 10:19576. [PMID: 33177593 PMCID: PMC7658982 DOI: 10.1038/s41598-020-76102-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Accepted: 10/20/2020] [Indexed: 01/19/2023] Open
Abstract
We aimed to quantitatively and qualitatively assess whether there is a discrepancy in detecting amyloid beta (Aβ) positivity between 18F-florbetaben (FBB) and 18F-flutemetamol (FMM) positron emission tomography (PET). We obtained paired FBB and FMM PET images from 107 participants. Three experts visually quantified the Aβ deposition as positive or negative. Quantitative assessment was performed using global cortical standardized uptake value ratio (SUVR) with the whole cerebellum as the reference region. Inter-rater agreement was excellent for FBB and FMM. The concordance rates between FBB and FMM were 94.4% (101/107) for visual assessment and 98.1% (105/107) for SUVR cut-off categorization. Both FBB and FMM showed high agreement rates between visual assessment and SUVR positive or negative categorization (93.5% in FBB and 91.2% in FMM). When the two ligands were compared based on SUVR cut-off categorization as standard of truth, although not statistically significant, the false-positive rate was higher in FMM (9.1%) than in FBB (1.8%) (p = 0.13). Our findings suggested that both FBB and FMM had excellent agreement when used to quantitatively and qualitatively evaluate Aβ deposits, thus, combining amyloid PET data associated with the use of different ligands from multi-centers is feasible.
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Affiliation(s)
- Soo Hyun Cho
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.,Department of Neurology, Chonnam National University Medical School, Chonnam National University Hospital, Gwangju, Korea
| | - Yeong Sim Choe
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.,Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Young Ju Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Byungju Lee
- Department of Neurology, Yuseong Geriatric Rehabilitation Hospital, Pohang, Korea
| | - Hee Jin Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Hyemin Jang
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Jun Pyo Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Young Hee Jung
- Department of Neurology, Myoungji Hospital, Hanyang University, Goyangsi, Korea
| | - Soo-Jong Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.,Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea
| | - Byeong C Kim
- Department of Neurology, Chonnam National University Medical School, Chonnam National University Hospital, Gwangju, Korea
| | - Gill Farrar
- Pharmaceutical Diagnostics, GE Healthcare, Chalfont St Giles, UK
| | - Duk L Na
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.,Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Korea.,Neuroscience Center, Samsung Medical Center, Seoul, Korea.,Stem Cell and Regenerative Medicine Institute, Samsung Medical Center, Seoul, Korea
| | - Seung Hwan Moon
- Department of Nuclear Medicine, Sungkyunkwan University School of Medicine, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.
| | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea. .,Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Korea. .,Neuroscience Center, Samsung Medical Center, Seoul, Korea. .,Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Korea. .,Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University School of Medicine, Suwon, Korea.
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28
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Jung YH, Lee H, Kim HJ, Na DL, Han HJ, Jang H, Seo SW. Prediction of amyloid β PET positivity using machine learning in patients with suspected cerebral amyloid angiopathy markers. Sci Rep 2020; 10:18806. [PMID: 33139780 PMCID: PMC7608617 DOI: 10.1038/s41598-020-75664-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Accepted: 10/13/2020] [Indexed: 12/31/2022] Open
Abstract
Amyloid-β(Aβ) PET positivity in patients with suspected cerebral amyloid angiopathy (CAA) MRI markers is predictive of a worse cognitive trajectory, and it provides insights into the underlying vascular pathology (CAA vs. hypertensive angiopathy) to facilitate prognostic prediction and appropriate treatment decisions. In this study, we applied two interpretable machine learning algorithms, gradient boosting machine (GBM) and random forest (RF), to predict Aβ PET positivity in patients with CAA MRI markers. In the GBM algorithm, the number of lobar cerebral microbleeds (CMBs), deep CMBs, lacunes, CMBs in dentate nuclei, and age were ranked as the most influential to predict Aβ positivity. In the RF algorithm, the absence of diabetes was additionally chosen. Cut-off values of the above variables predictive of Aβ positivity were as follows: (1) the number of lobar CMBs > 16.4(GBM)/14.3(RF), (2) no deep CMBs(GBM/RF), (3) the number of lacunes > 7.4(GBM/RF), (4) age > 74.3(GBM)/64(RF), (5) no CMBs in dentate nucleus(GBM/RF). The classification performances based on the area under the receiver operating characteristic curve were 0.83 in GBM and 0.80 in RF. Our study demonstrates the utility of interpretable machine learning in the clinical setting by quantifying the relative importance and cutoff values of predictive variables for Aβ positivity in patients with suspected CAA markers.
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Affiliation(s)
- Young Hee Jung
- Department of Neurology, College of Medicine, Myoungji Hospital, Hanyang University, Goyang, Republic of Korea
- Department of Neurology, Sungkyunkwan University of School of Medicine, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Hyejoo Lee
- Department of Neurology, Sungkyunkwan University of School of Medicine, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
- Samsung Alzheimer Research Center, Research Institute for Future Medicine, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Hee Jin Kim
- Department of Neurology, Sungkyunkwan University of School of Medicine, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
- Samsung Alzheimer Research Center, Research Institute for Future Medicine, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Duk L Na
- Department of Neurology, Sungkyunkwan University of School of Medicine, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea
- Samsung Alzheimer Research Center, Research Institute for Future Medicine, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
- Department of Health Science and Technology, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
- Stem Cell and Regenerative Medicine Institute, Samsung Medical Center, Seoul, Republic of Korea
| | - Hyun Jeong Han
- Department of Neurology, College of Medicine, Myoungji Hospital, Hanyang University, Goyang, Republic of Korea
| | - Hyemin Jang
- Department of Neurology, Sungkyunkwan University of School of Medicine, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea.
- Samsung Alzheimer Research Center, Research Institute for Future Medicine, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.
| | - Sang Won Seo
- Department of Neurology, Sungkyunkwan University of School of Medicine, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.
- Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea.
- Samsung Alzheimer Research Center, Research Institute for Future Medicine, Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, Republic of Korea.
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29
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Brendel M, Catak C, Beyer L, Linn J, Wahl H, Janowitz D, Rominger A, Patt M, Barthel H, Sabri O, Bartenstein P, Wollenweber FA. Colocalization of Tau but Not β-Amyloid with Cortical Superficial Siderosis in a Case with Probable CAA. Case Rep Neurol 2020; 12:232-237. [PMID: 32774280 PMCID: PMC7383156 DOI: 10.1159/000506765] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Accepted: 01/26/2020] [Indexed: 12/11/2022] Open
Abstract
Cortical superficial siderosis (cSS) is a common feature in patients with cerebral amyloid angiopathy (CAA). The correlation between β-amyloid and/or tau pathology and the occurrence of cSS is unclear. We report on an 80-year-old male patient who was diagnosed with probable CAA according to modified Boston criteria and underwent longitudinal magnetic resonance imaging, amyloid positron emission tomography (PET), and additional tau PET imaging. Amyloid deposition presented predominantly in the contralateral hemisphere not affected by cSS. In contrast, tau deposition was predominantly overlapping with brain regions affected by cSS. Amyloid deposition was not different in the vicinity of cSS whereas tau depositions were elevated in the vicinity of CSS-affected regions compared to non-cSS-affected brain regions. This case of probable CAA suggests that cSS may be associated with a locally elevated tau pathology but not with increased fibrillary amyloid deposition.
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Affiliation(s)
- Matthias Brendel
- Department of Nuclear Medicine, University Hospital of Munich, Ludwig-Maximilians-Universität (LMU), Munich, Germany
| | - Cihan Catak
- Institute for Stroke and Dementia Research, University Hospital of Munich, Ludwig-Maximilians-Universität (LMU), Munich, Germany
| | - Leonie Beyer
- Department of Nuclear Medicine, University Hospital of Munich, Ludwig-Maximilians-Universität (LMU), Munich, Germany
| | - Jennifer Linn
- Institut und Poliklinik für Neuroradiologie, Universitätsklinikum Carl Gustav Carus, Dresden, Germany
| | - Hannes Wahl
- Institut und Poliklinik für Neuroradiologie, Universitätsklinikum Carl Gustav Carus, Dresden, Germany
| | - Daniel Janowitz
- Institute for Stroke and Dementia Research, University Hospital of Munich, Ludwig-Maximilians-Universität (LMU), Munich, Germany
| | - Axel Rominger
- Department of Nuclear Medicine, University Hospital of Munich, Ludwig-Maximilians-Universität (LMU), Munich, Germany.,Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Marianne Patt
- Department of Nuclear Medicine, University Hospital of Leipzig, Leipzig, Germany
| | - Henryk Barthel
- Department of Nuclear Medicine, University Hospital of Leipzig, Leipzig, Germany
| | - Osama Sabri
- Department of Nuclear Medicine, University Hospital of Leipzig, Leipzig, Germany
| | - Peter Bartenstein
- Department of Nuclear Medicine, University Hospital of Munich, Ludwig-Maximilians-Universität (LMU), Munich, Germany
| | - Frank Arne Wollenweber
- Institute for Stroke and Dementia Research, University Hospital of Munich, Ludwig-Maximilians-Universität (LMU), Munich, Germany.,Department of Neurology, Helios Dr. Horst Schmidt hospital, Wiesbaden, Germany
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A new Centiloid method for 18F-florbetaben and 18F-flutemetamol PET without conversion to PiB. Eur J Nucl Med Mol Imaging 2019; 47:1938-1948. [DOI: 10.1007/s00259-019-04596-x] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2019] [Accepted: 11/04/2019] [Indexed: 10/25/2022]
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31
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Thal DR, Ronisz A, Tousseyn T, Rijal Upadhaya A, Balakrishnan K, Vandenberghe R, Vandenbulcke M, von Arnim CAF, Otto M, Beach TG, Lilja J, Heurling K, Chakrabarty A, Ismail A, Buckley C, Smith APL, Kumar S, Farrar G, Walter J. Different aspects of Alzheimer's disease-related amyloid β-peptide pathology and their relationship to amyloid positron emission tomography imaging and dementia. Acta Neuropathol Commun 2019; 7:178. [PMID: 31727169 PMCID: PMC6854805 DOI: 10.1186/s40478-019-0837-9] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2019] [Accepted: 10/28/2019] [Indexed: 11/16/2022] Open
Abstract
Alzheimer’s disease (AD)-related amyloid β-peptide (Aβ) pathology in the form of amyloid plaques and cerebral amyloid angiopathy (CAA) spreads in its topographical distribution, increases in quantity, and undergoes qualitative changes in its composition of modified Aβ species throughout the pathogenesis of AD. It is not clear which of these aspects of Aβ pathology contribute to AD progression and to what extent amyloid positron emission tomography (PET) reflects each of these aspects. To address these questions three cohorts of human autopsy cases (in total n = 271) were neuropathologically and biochemically examined for the topographical distribution of Aβ pathology (plaques and CAA), its quantity and its composition. These parameters were compared with neurofibrillary tangle (NFT) and neuritic plaque pathology, the degree of dementia and the results from [18F]flutemetamol amyloid PET imaging in cohort 3. All three aspects of Aβ pathology correlated with one another, the estimation of Aβ pathology by [18F]flutemetamol PET, AD-related NFT pathology, neuritic plaques, and with the degree of dementia. These results show that one aspect of Aβ pathology can be used to predict the other two, and correlates well with the development of dementia, advancing NFT and neuritic plaque pathology. Moreover, amyloid PET estimates all three aspects of Aβ pathology in-vivo. Accordingly, amyloid PET-based estimates for staging of amyloid pathology indicate the progression status of amyloid pathology in general and, in doing so, also of AD pathology. Only 7.75% of our cases deviated from this general association.
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