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Divya R, Shantha Selva Kumari R. Multi-instance learning attention model for amyloid quantification of brain sub regions in longitudinal cognitive decline. Brain Res 2024; 1842:149103. [PMID: 38955250 DOI: 10.1016/j.brainres.2024.149103] [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: 11/09/2023] [Revised: 05/21/2024] [Accepted: 06/26/2024] [Indexed: 07/04/2024]
Abstract
Amyloid PET scans help in identifying the beta-amyloid deposition in different brain regions. The purpose of this study is to develop a deep learning model that can automate the task of finding amyloid deposition in different regions of the brain only by using PET scan and without the corresponding MRI scan. 2647 18F-Florbetapir PET scans are collected from Alzheimer's Disease Neuroimaging Initiative (ADNI) from multiple centres taken over a period. A deep learning model based on multi-instance learning and attention is proposed which is trained and validated using 80% of the scans and the remaining 20% of the scans are used for testing the model. The performance of the model is validated using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The proposed model is further tested upon an external dataset consisting of 1413 18F-Florbetapir PET scans from the Anti-Amyloid Treatment in Asymptomatic Alzheimer's (A4) study. The proposed model achieves MAE of 0.0243 and RMSE of 0.0320 for summary Standardized Uptake Value Ratio (SUVR) based on composite reference region for ADNI test set. When tested on the A4-study dataset, the proposed model achieves MAE of 0.038 and RMSE of 0.0495 for summary SUVR based on the composite region. The results show that the proposed model provides less MAE and RMSE when compared with existing models. A graphical user interface is developed based on the proposed model where the predictions are made by selecting the files of 18F-Florbetapir PET scans.
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Affiliation(s)
- R Divya
- Department of Electronics and Communication Engineering, Mepco Schlenk Engineering College, Sivakasi 626 005, Tamil Nadu, India.
| | - R Shantha Selva Kumari
- Department of Electronics and Communication Engineering, Mepco Schlenk Engineering College, Sivakasi 626 005, Tamil Nadu, India.
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Yang B, Earnest T, Kumar S, Kothapalli D, Benzinger T, Gordon B, Sotiras A. Evaluation of ComBat harmonization for reducing across-tracer biases in regional amyloid PET analyses. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.06.14.24308952. [PMID: 38947044 PMCID: PMC11213066 DOI: 10.1101/2024.06.14.24308952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
Background Differences in amyloid positron emission tomography (PET) radiotracer pharmacokinetics and binding properties lead to discrepancies in amyloid-β uptake estimates. Harmonization of tracer-specific biases is crucial for optimal performance of downstream tasks. Here, we investigated the efficacy of ComBat, a data-driven harmonization model, for reducing tracer-specific biases in regional amyloid PET measurements from [18F]-florbetapir (FBP) and [11C]-Pittsburgh Compound-B (PiB). Methods One-hundred-thirteen head-to-head FBP-PiB scan pairs, scanned from the same subject within ninety days, were selected from the Open Access Series of Imaging Studies 3 (OASIS-3) dataset. The Centiloid scale, ComBat with no covariates, ComBat with biological covariates, and GAM-ComBat with biological covariates were used to harmonize both global and regional amyloid standardized uptake value ratios (SUVR). Intraclass correlation coefficient (ICC) and mean standardized absolute error (MsAE) were computed to measure the absolute agreement between tracers. Additionally, longitudinal amyloid SUVRs from an anti-amyloid drug trial were simulated using linear mixed effects modeling. Differences in rates-of-change between simulated treatment and placebo groups were tested, and change in statistical power/Type-I error after harmonization was quantified. Results In the head-to-head tracer comparison, the best ICC and MsAE were achieved after harmonizing with ComBat with no covariates for the global summary SUVR. ComBat with no covariates also performed the best in harmonizing regional SUVRs. In the clinical trial simulation, harmonization with both Centiloid and ComBat increased statistical power of detecting true rate-of-change differences between groups and decreased false discovery rate in the absence of a treatment effect. The greatest benefit of harmonization was observed when groups exhibited differing FPB-to-PiB proportions. Conclusions ComBat outperformed the Centiloid scale in harmonizing both global and regional amyloid estimates. Additionally, ComBat improved the detection of rate-of-change differences between clinical trial groups. Our findings suggest that ComBat is a viable alternative to Centiloid for harmonizing regional amyloid PET analyses.
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Affiliation(s)
- Braden Yang
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA 63110
| | - Tom Earnest
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA 63110
| | - Sayantan Kumar
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA 63110
| | - Deydeep Kothapalli
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA 63110
| | - Tammie Benzinger
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA 63110
| | - Brian Gordon
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA 63110
| | - Aristeidis Sotiras
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA 63110
- Institute for Informatics, Data Science and Biostatistics, Washington University School of Medicine in St. Louis, St. Louis, MO, USA 63110
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3
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Li J, Chen D, Liu H, Xi Y, Luo H, Wei Y, Liu J, Liang H, Zhang Q. Identifying potential genetic epistasis implicated in Alzheimer's disease via detection of SNP-SNP interaction on quantitative trait CSF Aβ 42. Neurobiol Aging 2024; 134:84-93. [PMID: 38039940 DOI: 10.1016/j.neurobiolaging.2023.10.003] [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: 06/22/2023] [Revised: 10/06/2023] [Accepted: 10/10/2023] [Indexed: 12/03/2023]
Abstract
Although genome-wide association studies have identified multiple Alzheimer's disease (AD)-associated loci by selecting the main effects of individual single-nucleotide polymorphisms (SNPs), the interpretation of genetic variance in AD is limited. Based on the linear regression method, we performed genome-wide SNP-SNP interaction on cerebrospinal fluid Aβ42 to identify potential genetic epistasis implicated in AD, with age, gender, and diagnosis as covariates. A GPU-based method was used to address the computational challenges posed by the analysis of epistasis. We found 368 SNP pairs to be statistically significant, and highly significant SNP-SNP interactions were identified between the marginal main effects of SNP pairs, which explained a relatively high variance at the Aβ42 level. Our results replicated 100 previously reported AD-related genes and 5 gene-gene interaction pairs of the protein-protein interaction network. Our bioinformatics analyses provided preliminary evidence that the 5-overlapping gene-gene interaction pairs play critical roles in inducing synaptic loss and dysfunction, thereby leading to memory decline and cognitive impairment in AD-affected brains.
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Affiliation(s)
- Jin Li
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, China
| | - Dandan Chen
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, China; School of Automation Engineering, Northeast Electric Power University, Jilin, China
| | - Hongwei Liu
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, China
| | - Yang Xi
- School of Computer Science, Northeast Electric Power University, Jilin, China
| | - Haoran Luo
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, China
| | - Yiming Wei
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, China
| | - Junfeng Liu
- School of Computer Science, Northeast Electric Power University, Jilin, China
| | - Hong Liang
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, China.
| | - Qiushi Zhang
- School of Computer Science, Northeast Electric Power University, Jilin, 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] [Grants] [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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5
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Veitch DP, Weiner MW, Miller M, Aisen PS, Ashford MA, Beckett LA, Green RC, Harvey D, Jack CR, Jagust W, Landau SM, Morris JC, Nho KT, Nosheny R, Okonkwo O, Perrin RJ, Petersen RC, Rivera Mindt M, Saykin A, Shaw LM, Toga AW, Tosun D. The Alzheimer's Disease Neuroimaging Initiative in the era of Alzheimer's disease treatment: A review of ADNI studies from 2021 to 2022. Alzheimers Dement 2024; 20:652-694. [PMID: 37698424 PMCID: PMC10841343 DOI: 10.1002/alz.13449] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 07/27/2023] [Accepted: 08/01/2023] [Indexed: 09/13/2023]
Abstract
The Alzheimer's Disease Neuroimaging Initiative (ADNI) aims to improve Alzheimer's disease (AD) clinical trials. Since 2006, ADNI has shared clinical, neuroimaging, and cognitive data, and biofluid samples. We used conventional search methods to identify 1459 publications from 2021 to 2022 using ADNI data/samples and reviewed 291 impactful studies. This review details how ADNI studies improved disease progression understanding and clinical trial efficiency. Advances in subject selection, detection of treatment effects, harmonization, and modeling improved clinical trials and plasma biomarkers like phosphorylated tau showed promise for clinical use. Biomarkers of amyloid beta, tau, neurodegeneration, inflammation, and others were prognostic with individualized prediction algorithms available online. Studies supported the amyloid cascade, emphasized the importance of neuroinflammation, and detailed widespread heterogeneity in disease, linked to genetic and vascular risk, co-pathologies, sex, and resilience. Biological subtypes were consistently observed. Generalizability of ADNI results is limited by lack of cohort diversity, an issue ADNI-4 aims to address by enrolling a diverse cohort.
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Affiliation(s)
- Dallas P. Veitch
- Department of Veterans Affairs Medical CenterNorthern California Institute for Research and Education (NCIRE)San FranciscoCaliforniaUSA
- Department of Veterans Affairs Medical CenterCenter for Imaging of Neurodegenerative DiseasesSan FranciscoCaliforniaUSA
| | - Michael W. Weiner
- Department of Veterans Affairs Medical CenterCenter for Imaging of Neurodegenerative DiseasesSan FranciscoCaliforniaUSA
- Department of Radiology and Biomedical ImagingUniversity of CaliforniaSan FranciscoCaliforniaUSA
- Department of MedicineUniversity of CaliforniaSan FranciscoCaliforniaUSA
- Department of Psychiatry and Behavioral SciencesUniversity of CaliforniaSan FranciscoCaliforniaUSA
- Department of NeurologyUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Melanie Miller
- Department of Veterans Affairs Medical CenterNorthern California Institute for Research and Education (NCIRE)San FranciscoCaliforniaUSA
- Department of Veterans Affairs Medical CenterCenter for Imaging of Neurodegenerative DiseasesSan FranciscoCaliforniaUSA
| | - Paul S. Aisen
- Alzheimer's Therapeutic Research InstituteUniversity of Southern CaliforniaSan DiegoCaliforniaUSA
| | - Miriam A. Ashford
- Department of Veterans Affairs Medical CenterNorthern California Institute for Research and Education (NCIRE)San FranciscoCaliforniaUSA
| | - Laurel A. Beckett
- Division of BiostatisticsDepartment of Public Health SciencesUniversity of CaliforniaDavisCaliforniaUSA
| | - Robert C. Green
- Division of GeneticsDepartment of MedicineBrigham and Women's HospitalBroad Institute Ariadne Labs and Harvard Medical SchoolBostonMassachusettsUSA
| | - Danielle Harvey
- Division of BiostatisticsDepartment of Public Health SciencesUniversity of CaliforniaDavisCaliforniaUSA
| | | | - William Jagust
- Helen Wills Neuroscience InstituteUniversity of California BerkeleyBerkeleyCaliforniaUSA
| | - Susan M. Landau
- Helen Wills Neuroscience InstituteUniversity of California BerkeleyBerkeleyCaliforniaUSA
| | - John C. Morris
- Knight Alzheimer's Disease Research CenterWashington University School of MedicineSaint LouisMissouriUSA
- Department of NeurologyWashington University School of MedicineSaint LouisMissouriUSA
- Department of Pathology and ImmunologyWashington University School of MedicineSaint LouisMissouriUSA
| | - Kwangsik T. Nho
- Department of Radiology and Imaging Sciences and the Indiana Alzheimer's Disease Research CenterIndiana University School of MedicineIndianapolisIndianaUSA
- Center for Computational Biology and BioinformaticsIndiana University School of MedicineIndianapolisIndianaUSA
| | - Rachel Nosheny
- Department of Veterans Affairs Medical CenterCenter for Imaging of Neurodegenerative DiseasesSan FranciscoCaliforniaUSA
- Department of Psychiatry and Behavioral SciencesUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Ozioma Okonkwo
- Wisconsin Alzheimer's Disease Research Center and Department of MedicineUniversity of Wisconsin School of Medicine and Public HealthMadisonWisconsinUSA
| | - Richard J. Perrin
- Knight Alzheimer's Disease Research CenterWashington University School of MedicineSaint LouisMissouriUSA
- Department of NeurologyWashington University School of MedicineSaint LouisMissouriUSA
- Department of Pathology and ImmunologyWashington University School of MedicineSaint LouisMissouriUSA
| | | | - Monica Rivera Mindt
- Department of PsychologyLatin American and Latino Studies InstituteAfrican and African American StudiesFordham UniversityNew YorkNew YorkUSA
- Department of NeurologyIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Andrew Saykin
- Department of Radiology and Imaging Sciences and the Indiana Alzheimer's Disease Research CenterIndiana University School of MedicineIndianapolisIndianaUSA
- Department of Medical and Molecular GeneticsIndiana University School of MedicineIndianapolisIndianaUSA
| | - Leslie M. Shaw
- Department of Pathology and Laboratory Medicine and the PENN Alzheimer's Disease Research CenterCenter for Neurodegenerative ResearchPerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Arthur W. Toga
- Laboratory of Neuro ImagingInstitute of Neuroimaging and InformaticsKeck School of Medicine of University of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Duygu Tosun
- Department of Veterans Affairs Medical CenterCenter for Imaging of Neurodegenerative DiseasesSan FranciscoCaliforniaUSA
- Department of Radiology and Biomedical ImagingUniversity of CaliforniaSan FranciscoCaliforniaUSA
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Bhattarai P, Taha A, Soni B, Thakuri DS, Ritter E, Chand GB. Predicting cognitive dysfunction and regional hubs using Braak staging amyloid-beta biomarkers and machine learning. Brain Inform 2023; 10:33. [PMID: 38043122 PMCID: PMC10694120 DOI: 10.1186/s40708-023-00213-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Accepted: 11/21/2023] [Indexed: 12/05/2023] Open
Abstract
Mild cognitive impairment (MCI) is a transitional stage between normal aging and early Alzheimer's disease (AD). The presence of extracellular amyloid-beta (Aβ) in Braak regions suggests a connection with cognitive dysfunction in MCI/AD. Investigating the multivariate predictive relationships between regional Aβ biomarkers and cognitive function can aid in the early detection and prevention of AD. We introduced machine learning approaches to estimate cognitive dysfunction from regional Aβ biomarkers and identify the Aβ-related dominant brain regions involved with cognitive impairment. We employed Aβ biomarkers and cognitive measurements from the same individuals to train support vector regression (SVR) and artificial neural network (ANN) models and predict cognitive performance solely based on Aβ biomarkers on the test set. To identify Aβ-related dominant brain regions involved in cognitive prediction, we built the local interpretable model-agnostic explanations (LIME) model. We found elevated Aβ in MCI compared to controls and a stronger correlation between Aβ and cognition, particularly in Braak stages III-IV and V-VII (p < 0.05) biomarkers. Both SVR and ANN, especially ANN, showed strong predictive relationships between regional Aβ biomarkers and cognitive impairment (p < 0.05). LIME integrated with ANN showed that the parahippocampal gyrus, inferior temporal gyrus, and hippocampus were the most decisive Braak regions for predicting cognitive decline. Consistent with previous findings, this new approach suggests relationships between Aβ biomarkers and cognitive impairment. The proposed analytical framework can estimate cognitive impairment from Braak staging Aβ biomarkers and delineate the dominant brain regions collectively involved in AD pathophysiology.
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Affiliation(s)
- Puskar Bhattarai
- Department of Radiology, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Ahmed Taha
- Department of Radiology, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Bhavin Soni
- Department of Radiology, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Deepa S Thakuri
- Department of Radiology, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
- University of Missouri School of Medicine, Columbia, MO, USA
| | - Erin Ritter
- Department of Radiology, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
- Department of Biomedical Engineering, Washington University McKelvey School of Engineering, St. Louis, MO, USA
| | - Ganesh B Chand
- Department of Radiology, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA.
- Imaging Core, Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA.
- Institute of Clinical and Translational Sciences, Washington University School of Medicine, St. Louis, MO, USA.
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA.
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Wang D, Xu K, Dang M, Sang F, Chen K, Zhang Z, Li X. Multi-domain cognition dysfunction accompanies frontoparietal and temporal amyloid accumulation in the elderly. Cereb Cortex 2023; 33:11329-11338. [PMID: 37859548 DOI: 10.1093/cercor/bhad369] [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: 08/15/2023] [Revised: 09/18/2023] [Accepted: 09/19/2023] [Indexed: 10/21/2023] Open
Abstract
It is helpful to understand the pathology of Alzheimer's disease by exploring the relationship between amyloid-β accumulation and cognition. The study explored the relationship between regional amyloid-β accumulation and multiple cognitions and study their application value in the Alzheimer's disease diagnosis. 135 participants completed 18F-florbetapir Positron Emission Tomography (PET), structural MRI, and a cognitive battery. Partial correlation was used to examine the relationship between global and regional amyloid-β accumulation and cognitions. Then, a support vector machine was applied to determine whether cognition-related accumulation regions can adequately distinguish the cognitively normal controls (76 participants) and mild cognitive impairment (30 participants) groups or mild cognitive impairment and Alzheimer's disease (29 participants) groups. The result showed that amyloid-β accumulation regions were mainly located in the frontoparietal cortex, calcarine fissure, and surrounding cortex and temporal pole regions. Episodic memory-related regions included the frontoparietal cortices; executive function-related regions included the frontoparietal, temporal, and occipital cortices; and processing speed-related regions included the frontal and occipital cortices. Support vector machine analysis showed that only episodic memory-related amyloid-β accumulation regions had better classification performance during the progression of Alzheimer's disease. Assessing regional changes in amyloid, particularly in frontoparietal regions, can aid in the early detection of amyloid-related decline in cognitive function.
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Affiliation(s)
- Dandan Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, No. 19, Xinjiekouwai St, Haidian District, Beijing, 100875, P.R. China
- Beijing Aging Brain Rejuvenation Initiative (BABRI) Center, Beijing Normal University, No. 19, Xinjiekouwai St, Haidian District, Beijing, 100875, P.R. China
| | - Kai Xu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, No. 19, Xinjiekouwai St, Haidian District, Beijing, 100875, P.R. China
- Beijing Aging Brain Rejuvenation Initiative (BABRI) Center, Beijing Normal University, No. 19, Xinjiekouwai St, Haidian District, Beijing, 100875, P.R. China
- School of Artificial Intelligence, Beijing Normal University, No. 19, Xinjiekouwai St, Haidian District, Beijing, 100875, P.R. China
| | - Mingxi Dang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, No. 19, Xinjiekouwai St, Haidian District, Beijing, 100875, P.R. China
- Beijing Aging Brain Rejuvenation Initiative (BABRI) Center, Beijing Normal University, No. 19, Xinjiekouwai St, Haidian District, Beijing, 100875, P.R. China
| | - Feng Sang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, No. 19, Xinjiekouwai St, Haidian District, Beijing, 100875, P.R. China
- Beijing Aging Brain Rejuvenation Initiative (BABRI) Center, Beijing Normal University, No. 19, Xinjiekouwai St, Haidian District, Beijing, 100875, P.R. China
| | - Kewei Chen
- Beijing Aging Brain Rejuvenation Initiative (BABRI) Center, Beijing Normal University, No. 19, Xinjiekouwai St, Haidian District, Beijing, 100875, P.R. China
- Banner Alzheimer's Institute, Phoenix, AZ 85006, United States
| | - Zhanjun Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, No. 19, Xinjiekouwai St, Haidian District, Beijing, 100875, P.R. China
- Beijing Aging Brain Rejuvenation Initiative (BABRI) Center, Beijing Normal University, No. 19, Xinjiekouwai St, Haidian District, Beijing, 100875, P.R. China
| | - Xin Li
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, No. 19, Xinjiekouwai St, Haidian District, Beijing, 100875, P.R. China
- Beijing Aging Brain Rejuvenation Initiative (BABRI) Center, Beijing Normal University, No. 19, Xinjiekouwai St, Haidian District, Beijing, 100875, P.R. China
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Ali DG, Bahrani AA, El Khouli RH, Gold BT, Jiang Y, Zachariou V, Wilcock DM, Jicha GA. White matter hyperintensities influence distal cortical β-amyloid accumulation in default mode network pathways. Brain Behav 2023; 13:e3209. [PMID: 37534614 PMCID: PMC10570488 DOI: 10.1002/brb3.3209] [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: 02/16/2023] [Revised: 07/19/2023] [Accepted: 07/22/2023] [Indexed: 08/04/2023] Open
Abstract
BACKGROUND AND PURPOSE Cerebral small vessel disease (SVD) has been suggested to contribute to the pathogenesis of Alzheimer's disease (AD). Yet, the role of SVD in potentially contributing to AD pathology is unclear. The main objective of this study was to test the hypothesis that WMHs influence amyloid β (Aβ) levels within connected default mode network (DMN) tracts and cortical regions in cognitively unimpaired older adults. METHODS Regional standard uptake value ratios (SUVr) from Aβ-PET and white matter hyperintensity (WMH) volumes from three-dimensional magnetic resonance imaging FLAIR images were analyzed across a sample of 72 clinically unimpaired (mini-mental state examination ≥26), older adults (mean age 74.96 and standard deviation 8.13) from the Alzheimer's Disease Neuroimaging Initiative (ADNI3). The association of WMH volumes in major fiber tracts projecting from cortical DMN regions and Aβ-PET SUVr in the connected cortical DMN regions was analyzed using linear regression models adjusted for age, sex, ApoE, and total brain volumes. RESULTS The regression analyses demonstrate that increased WMH volumes in the superior longitudinal fasciculus were associated with increased regional SUVr in the inferior parietal lobule (p = .011). CONCLUSION The findings suggest that the relation between Aβ in parietal cortex is associated with SVD in downstream white matter (WM) pathways in preclinical AD. The biological relationships and interplay between Aβ and WM microstructure alterations that precede overt WMH development across the continuum of AD progression warrant further study.
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Affiliation(s)
- Doaa G. Ali
- Sanders‐Brown Center on Aging, College of MedicineUniversity of KentuckyLexingtonKentuckyUSA
- Department of Behavioral Science, College of MedicineUniversity of KentuckyLexingtonKentuckyUSA
| | - Ahmed A. Bahrani
- Sanders‐Brown Center on Aging, College of MedicineUniversity of KentuckyLexingtonKentuckyUSA
- Department of Neurology, College of MedicineUniversity of KentuckyLexingtonKentuckyUSA
| | - Riham H. El Khouli
- Department of Radiology, College of MedicineUniversity of KentuckyLexingtonKentuckyUSA
| | - Brian T. Gold
- Sanders‐Brown Center on Aging, College of MedicineUniversity of KentuckyLexingtonKentuckyUSA
- Department of Neuroscience, College of MedicineUniversity of KentuckyLexingtonKentuckyUSA
| | - Yang Jiang
- Sanders‐Brown Center on Aging, College of MedicineUniversity of KentuckyLexingtonKentuckyUSA
- Department of Behavioral Science, College of MedicineUniversity of KentuckyLexingtonKentuckyUSA
| | - Valentinos Zachariou
- Department of Neuroscience, College of MedicineUniversity of KentuckyLexingtonKentuckyUSA
| | - Donna M. Wilcock
- Sanders‐Brown Center on Aging, College of MedicineUniversity of KentuckyLexingtonKentuckyUSA
- Department of Physiology, College of MedicineUniversity of KentuckyLexingtonKentuckyUSA
| | - Gregory A. Jicha
- Sanders‐Brown Center on Aging, College of MedicineUniversity of KentuckyLexingtonKentuckyUSA
- Department of Behavioral Science, College of MedicineUniversity of KentuckyLexingtonKentuckyUSA
- Department of Neurology, College of MedicineUniversity of KentuckyLexingtonKentuckyUSA
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Jeong SH, Cha J, Jung JH, Yun M, Sohn YH, Chung SJ, Lee PH. Occipital Amyloid Deposition Is Associated with Rapid Cognitive Decline in the Alzheimer's Disease Continuum. J Alzheimers Dis 2023:JAD230187. [PMID: 37355901 DOI: 10.3233/jad-230187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/26/2023]
Abstract
BACKGROUND Clinical significance of additional occipital amyloid-β (Aβ) plaques in Alzheimer's disease (AD) remains unclear. OBJECTIVE In this study, we investigated the effect of regional Aβ deposition on cognition in patients on the AD continuum, especially in the occipital region. METHODS We retrospectively reviewed the medical record of 208 patients with AD across the cognitive continuum (non-dementia and dementia). Multivariable linear regression analyses were performed to determine the effect of regional Aβ deposition on cognitive function. A linear mixed model was used to assess the effect of regional deposition on longitudinal changes in Mini-Mental State Examination (MMSE) scores. Additionally, the patients were dichotomized according to the occipital-to-global Aβ deposition ratio (ratio ≤1, Aβ-OCC- group; ratio >1, Aβ-OCC+ group), and the same statistical analyses were applied for between-group comparisons. RESULTS Regional Aβ burden itself was not associated with baseline cognitive function. In terms of Aβ-OCC group effect, the Aβ-OCC+ group exhibited a poorer cognitive performance on language function compared to the Aβ-OCC- group. High Aβ retention in each region was associated with a rapid decline in MMSE scores, only in the dementia subgroup. Additionally, Aβ-OCC+ individuals exhibited a faster annual decline in MMSE scores than Aβ-OCC- individuals in the non-dementia subgroup (β= -0.77, standard error [SE] = 0.31, p = 0.013). CONCLUSION The present study demonstrated that additional occipital Aβ deposition was associated with poor baseline language function and rapid cognitive deterioration in patients on the AD continuum.
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Affiliation(s)
- Seong Ho Jeong
- Department of Neurology, Inje University Sanggye Paik Hospital, Seoul, South Korea
- Department of Neurology, Yonsei University College of Medicine, Seoul, South Korea
| | - Jungho Cha
- Nash Family Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jin Ho Jung
- Department of Neurology, Inje University Busan Paik Hospital, Busan, South Korea
| | - Mijin Yun
- Department of Nuclear Medicine, Yonsei University College of Medicine, Seoul, South Korea
| | - Young H Sohn
- Department of Neurology, Yonsei University College of Medicine, Seoul, South Korea
| | - Seok Jong Chung
- Department of Neurology, Yonsei University College of Medicine, Seoul, South Korea
- Department of Neurology, Yongin Severance Hospital, Yonsei University Health System, Yongin, South Korea
- YONSEI BEYOND LAB, Yongin, South Korea
| | - Phil Hyu Lee
- Department of Neurology, Yonsei University College of Medicine, Seoul, South Korea
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10
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Hoenig MC, Drzezga A. Clear-headed into old age: Resilience and resistance against brain aging-A PET imaging perspective. J Neurochem 2023; 164:325-345. [PMID: 35226362 DOI: 10.1111/jnc.15598] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 02/18/2022] [Accepted: 02/22/2022] [Indexed: 11/28/2022]
Abstract
With the advances in modern medicine and the adaptation towards healthier lifestyles, the average life expectancy has doubled since the 1930s, with individuals born in the millennium years now carrying an estimated life expectancy of around 100 years. And even though many individuals around the globe manage to age successfully, the prevalence of aging-associated neurodegenerative diseases such as sporadic Alzheimer's disease has never been as high as nowadays. The prevalence of Alzheimer's disease is anticipated to triple by 2050, increasing the societal and economic burden tremendously. Despite all efforts, there is still no available treatment defeating the accelerated aging process as seen in this disease. Yet, given the advances in neuroimaging techniques that are discussed in the current Review article, such as in positron emission tomography (PET) or magnetic resonance imaging (MRI), pivotal insights into the heterogenous effects of aging-associated processes and the contribution of distinct lifestyle and risk factors already have and are still being gathered. In particular, the concepts of resilience (i.e. coping with brain pathology) and resistance (i.e. avoiding brain pathology) have more recently been discussed as they relate to mechanisms that are associated with the prolongation and/or even stop of the progressive brain aging process. Better understanding of the underlying mechanisms of resilience and resistance may one day, hopefully, support the identification of defeating mechanism against accelerating aging.
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Affiliation(s)
- Merle C Hoenig
- Research Center Juelich, Institute for Neuroscience and Medicine II, Molecular Organization of the Brain, Juelich, Germany.,Department of Nuclear Medicine, Faculty of Medicine, University Hospital Cologne, Cologne, Germany
| | - Alexander Drzezga
- Research Center Juelich, Institute for Neuroscience and Medicine II, Molecular Organization of the Brain, Juelich, Germany.,Department of Nuclear Medicine, Faculty of Medicine, University Hospital Cologne, Cologne, Germany.,German Center for Neurodegenerative Diseases, Bonn/Cologne, Germany
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11
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Ciarmiello A, Giovannini E, Pastorino S, Ferrando O, Foppiano F, Mannironi A, Tartaglione A, Giovacchini G. Machine Learning Model to Predict Diagnosis of Mild Cognitive Impairment by Using Radiomic and Amyloid Brain PET. Clin Nucl Med 2023; 48:1-7. [PMID: 36240660 DOI: 10.1097/rlu.0000000000004433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
PURPOSE The study aimed to develop a deep learning model for predicting amnestic mild cognitive impairment (aMCI) diagnosis using radiomic features and amyloid brain PET. PATIENTS AND METHODS Subjects (n = 328) from the Alzheimer's Disease Neuroimaging Initiative database and the EudraCT 2015-001184-39 trial (159 males, 169 females), with a mean age of 72 ± 7.4 years, underwent PET/CT with 18 F-florbetaben. The study cohort consisted of normal controls (n = 149) and subjects with aMCI (n = 179). Thirteen gray-level run-length matrix radiomic features and amyloid loads were extracted from 27 cortical brain areas. The least absolute shrinkage and selection operator regression was used to select features with the highest predictive value. A feed-forward neural multilayer network was trained, validated, and tested on 70%, 15%, and 15% of the sample, respectively. Accuracy, precision, F1-score, and area under the curve were used to assess model performance. SUV performance in predicting the diagnosis of aMCI was also assessed and compared with that obtained from the machine learning model. RESULTS The machine learning model achieved an area under the receiver operating characteristic curve of 90% (95% confidence interval, 89.4-90.4) on the test set, with 80% and 78% for accuracy and F1-score, respectively. The deep learning model outperformed SUV performance (area under the curve, 71%; 95% confidence interval, 69.7-71.4; 57% accuracy, 48% F1-score). CONCLUSIONS Using radiomic and amyloid PET load, the machine learning model identified MCI subjects with 84% specificity at 81% sensitivity. These findings show that a deep learning algorithm based on radiomic data and amyloid load obtained from brain PET images improves the prediction of MCI diagnosis compared with SUV alone.
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12
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Elsworthy RJ, Hill EJ, Dunleavy C, Aldred S. The role of ADAM10 in astrocytes: Implications for Alzheimer’s disease. Front Aging Neurosci 2022; 14:1056507. [DOI: 10.3389/fnagi.2022.1056507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Accepted: 11/14/2022] [Indexed: 12/05/2022] Open
Abstract
Much of the early research into AD relies on a neuron-centric view of the brain, however, evidence of multiple altered cellular interactions between glial cells and the vasculature early in AD has been demonstrated. As such, alterations in astrocyte function are widely recognized a contributing factor in the pathogenesis of AD. The processes by which astrocytes may be involved in AD make them an interesting target for therapeutic intervention, but in order for this to be most effective, there is a need for the specific mechanisms involving astrocyte dysfunction to be investigated. “α disintegrin and metalloproteinase” 10 (ADAM10) is capable of proteolytic cleavage of the amyloid precursor protein which prevents amyloid-β generation. As such ADAM10 has been identified as an interesting enzyme in AD pathology. ADAM10 is also known to play a role in a significant number of cellular processes, most notable in notch signaling and in inflammatory processes. There is a growing research base for the involvement of ADAM10 in regulating astrocytic function, primarily from an immune perspective. This review aims to bring together available evidence for ADAM10 activity in astrocytes, and how this relates to AD pathology.
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13
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Park CJ, Seo Y, Choe YS, Jang H, Lee H, Kim JP. Predicting conversion of brain β-amyloid positivity in amyloid-negative individuals. Alzheimers Res Ther 2022; 14:129. [PMID: 36096822 PMCID: PMC9465850 DOI: 10.1186/s13195-022-01067-8] [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: 02/16/2022] [Accepted: 08/16/2022] [Indexed: 11/21/2022]
Abstract
Background Cortical deposition of β-amyloid (Aβ) plaque is one of the main hallmarks of Alzheimer’s disease (AD). While Aβ positivity has been the main concern so far, predicting whether Aβ (−) individuals will convert to Aβ (+) has become crucial in clinical and research aspects. In this study, we aimed to develop a classifier that predicts the conversion from Aβ (−) to Aβ (+) using artificial intelligence. Methods Data were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort regarding patients who were initially Aβ (−). We developed an artificial neural network-based classifier with baseline age, gender, APOE ε4 genotype, and global and regional standardized uptake value ratios (SUVRs) from positron emission tomography. Ten times repeated 10-fold cross-validation was performed for model measurement, and the feature importance was assessed. To validate the prediction model, we recruited subjects at the Samsung Medical Center (SMC). Results A total of 229 participants (53 converters) from the ADNI dataset and a total of 40 subjects (10 converters) from the SMC dataset were included. The average area under the receiver operating characteristic values of three developed models are as follows: Model 1 (age, gender, APOE ε4) of 0.674, Model 2 (age, gender, APOE ε4, global SUVR) of 0.814, and Model 3 (age, gender, APOE ε4, global and regional SUVR) of 0.841. External validation result showed an AUROC of 0.900. Conclusion We developed prediction models regarding Aβ positivity conversion. With the growing recognition of the need for earlier intervention in AD, the results of this study are expected to contribute to the screening of early treatment candidates. Supplementary Information The online version contains supplementary material available at 10.1186/s13195-022-01067-8.
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14
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Doering E, Hoenig MC, Bischof GN, Bohn KP, Ellingsen LM, van Eimeren T, Drzezga A. Introducing a gatekeeping system for amyloid status assessment in mild cognitive impairment. Eur J Nucl Med Mol Imaging 2022; 49:4478-4489. [PMID: 35831715 PMCID: PMC9605923 DOI: 10.1007/s00259-022-05879-6] [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: 01/12/2022] [Accepted: 06/14/2022] [Indexed: 11/19/2022]
Abstract
Background In patients with mild cognitive impairment (MCI), enhanced cerebral amyloid-β plaque burden is a high-risk factor to develop dementia with Alzheimer’s disease (AD). Not all patients have immediate access to the assessment of amyloid status (A-status) via gold standard methods. It may therefore be of interest to find suitable biomarkers to preselect patients benefitting most from additional workup of the A-status. In this study, we propose a machine learning–based gatekeeping system for the prediction of A-status on the grounds of pre-existing information on APOE-genotype 18F-FDG PET, age, and sex. Methods Three hundred and forty-two MCI patients were used to train different machine learning classifiers to predict A-status majority classes among APOE-ε4 non-carriers (APOE4-nc; majority class: amyloid negative (Aβ-)) and carriers (APOE4-c; majority class: amyloid positive (Aβ +)) from 18F-FDG-PET, age, and sex. Classifiers were tested on two different datasets. Finally, frequencies of progression to dementia were compared between gold standard and predicted A-status. Results Aβ- in APOE4-nc and Aβ + in APOE4-c were predicted with a precision of 87% and a recall of 79% and 51%, respectively. Predicted A-status and gold standard A-status were at least equally indicative of risk of progression to dementia. Conclusion We developed an algorithm allowing approximation of A-status in MCI with good reliability using APOE-genotype, 18F-FDG PET, age, and sex information. The algorithm could enable better estimation of individual risk for developing AD based on existing biomarker information, and support efficient selection of patients who would benefit most from further etiological clarification. Further potential utility in clinical routine and clinical trials is discussed. Supplementary Information The online version contains supplementary material available at 10.1007/s00259-022-05879-6.
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Affiliation(s)
- E Doering
- German Center for Neurodegenerative Diseases (DZNE), Bonn-Cologne, Germany. .,University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Nuclear Medicine, Multimodal Neuroimaging Group, Cologne, Germany.
| | - M C Hoenig
- German Center for Neurodegenerative Diseases (DZNE), Bonn-Cologne, Germany.,Institute for Neuroscience and Medicine II-Molecular Organization of the Brain, Research Center Juelich, Jülich, Germany
| | - G N Bischof
- German Center for Neurodegenerative Diseases (DZNE), Bonn-Cologne, Germany.,Institute for Neuroscience and Medicine II-Molecular Organization of the Brain, Research Center Juelich, Jülich, Germany
| | - K P Bohn
- Klinikum Dritter Orden, Department of Radiology and Nuclear Medicine, Munich, Germany
| | - L M Ellingsen
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA.,Department of Electrical and Computer Engineering, University of Iceland, Reykjavik, Iceland
| | - T van Eimeren
- German Center for Neurodegenerative Diseases (DZNE), Bonn-Cologne, Germany.,University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Neurology, Cologne, Germany
| | - A Drzezga
- German Center for Neurodegenerative Diseases (DZNE), Bonn-Cologne, Germany.,University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Nuclear Medicine, Multimodal Neuroimaging Group, Cologne, Germany.,Institute for Neuroscience and Medicine II-Molecular Organization of the Brain, Research Center Juelich, Jülich, Germany
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15
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Differential Effects of White Matter Hyperintensities and Regional Amyloid Deposition on Regional Cortical Thickness. Neurobiol Aging 2022; 115:12-19. [DOI: 10.1016/j.neurobiolaging.2022.03.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Revised: 03/12/2022] [Accepted: 03/17/2022] [Indexed: 11/22/2022]
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