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Sackl M, Tinauer C, Urschler M, Enzinger C, Stollberger R, Ropele S. Fully Automated Hippocampus Segmentation using T2-informed Deep Convolutional Neural Networks. Neuroimage 2024; 298:120767. [PMID: 39103064 DOI: 10.1016/j.neuroimage.2024.120767] [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: 12/05/2023] [Revised: 07/26/2024] [Accepted: 07/31/2024] [Indexed: 08/07/2024] Open
Abstract
Hippocampal atrophy (tissue loss) has become a fundamental outcome parameter in clinical trials on Alzheimer's disease. To accurately estimate hippocampus volume and track its volume loss, a robust and reliable segmentation is essential. Manual hippocampus segmentation is considered the gold standard but is extensive, time-consuming, and prone to rater bias. Therefore, it is often replaced by automated programs like FreeSurfer, one of the most commonly used tools in clinical research. Recently, deep learning-based methods have also been successfully applied to hippocampus segmentation. The basis of all approaches are clinically used T1-weighted whole-brain MR images with approximately 1 mm isotropic resolution. However, such T1 images show low contrast-to-noise ratios (CNRs), particularly for many hippocampal substructures, limiting delineation reliability. To overcome these limitations, high-resolution T2-weighted scans are suggested for better visualization and delineation, as they show higher CNRs and usually allow for higher resolutions. Unfortunately, such time-consuming T2-weighted sequences are not feasible in a clinical routine. We propose an automated hippocampus segmentation pipeline leveraging deep learning with T2-weighted MR images for enhanced hippocampus segmentation of clinical T1-weighted images based on a series of 3D convolutional neural networks and a specifically acquired multi-contrast dataset. This dataset consists of corresponding pairs of T1- and high-resolution T2-weighted images, with the T2 images only used to create more accurate manual ground truth annotations and to train the segmentation network. The T2-based ground truth labels were also used to evaluate all experiments by comparing the masks visually and by various quantitative measures. We compared our approach with four established state-of-the-art hippocampus segmentation algorithms (FreeSurfer, ASHS, HippoDeep, HippMapp3r) and demonstrated a superior segmentation performance. Moreover, we found that the automated segmentation of T1-weighted images benefits from the T2-based ground truth data. In conclusion, this work showed the beneficial use of high-resolution, T2-based ground truth data for training an automated, deep learning-based hippocampus segmentation and provides the basis for a reliable estimation of hippocampal atrophy in clinical studies.
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Affiliation(s)
- Maximilian Sackl
- Department of Neurology, Medical University of Graz, Austria; BioTechMed-Graz, Austria
| | | | - Martin Urschler
- Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Austria; BioTechMed-Graz, Austria
| | | | - Rudolf Stollberger
- Institute of Biomedical Imaging, Graz University of Technology, Austria; BioTechMed-Graz, Austria
| | - Stefan Ropele
- Department of Neurology, Medical University of Graz, Austria; BioTechMed-Graz, Austria.
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2
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Seyedmirzaei H, Salmannezhad A, Ashayeri H, Shushtari A, Farazinia B, Heidari MM, Momayezi A, Shaki Baher S. Growth-Associated Protein 43 and Tensor-Based Morphometry Indices in Mild Cognitive Impairment. Neuroinformatics 2024; 22:239-250. [PMID: 38630411 DOI: 10.1007/s12021-024-09663-9] [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] [Accepted: 04/08/2024] [Indexed: 08/17/2024]
Abstract
Growth-associated protein 43 (GAP-43) is found in the axonal terminal of neurons in the limbic system, which is affected in people with Alzheimer's disease (AD). We assumed GAP-43 may contribute to AD progression and serve as a biomarker. So, in a two-year follow-up study, we assessed GAP-43 changes and whether they are correlated with tensor-based morphometry (TBM) findings in patients with mild cognitive impairment (MCI). We included MCI and cognitively normal (CN) people with available baseline and follow-up cerebrospinal fluid (CSF) GAP-43 and TBM findings from the ADNI database. We assessed the difference between the two groups and correlations in each group at each time point. CSF GAP-43 and TBM measures were similar in the two study groups in all time points, except for the accelerated anatomical region of interest (ROI) of CN subjects that were significantly greater than those of MCI. The only significant correlations with GAP-43 observed were those inverse correlations with accelerated and non-accelerated anatomical ROI in MCI subjects at baseline. Plus, all TBM metrics decreased significantly in all study groups during the follow-up in contrast to CSF GAP-43 levels. Our study revealed significant associations between CSF GAP-43 levels and TBM indices among people of the AD spectrum.
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Affiliation(s)
- Homa Seyedmirzaei
- Sports Medicine Research Center, Neuroscience Institute, Tehran University of Medical Sciences, Tehran, Iran
| | | | - Hamidreza Ashayeri
- Student Research Committee, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Ali Shushtari
- Faculty of Medicine , Mazandaran University of Medical Sciences, Sari, Iran.
| | - Bita Farazinia
- Faculty of Economics and Management, Shiraz Branch, Islamic Azad University, Shiraz, Iran
| | - Mohammad Mahdi Heidari
- Student Research Committee, Faculty of Medicine, Kashan University of Medical Sciences, Kashan, Iran
| | - Amirali Momayezi
- School of Chemical engineering, Iran University of Science and Technology, Tehran, Iran
| | - Sara Shaki Baher
- Faculty of Medicine, Tehran Branch, Islamic Azad University, Tehran, Iran
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3
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Zamani J, Jafadideh AT. Predicting the Conversion from Mild Cognitive Impairment to Alzheimer's Disease Using Graph Frequency Bands and Functional Connectivity-Based Features. RESEARCH SQUARE 2024:rs.3.rs-4549428. [PMID: 38947050 PMCID: PMC11213162 DOI: 10.21203/rs.3.rs-4549428/v1] [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
Accurate prediction of the progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD) is crucial for disease management. Machine learning techniques have demonstrated success in classifying AD and MCI cases, particularly with the use of resting-state functional magnetic resonance imaging (rs-fMRI) data.This study utilized three years of rs-fMRI data from the ADNI, involving 142 patients with stable MCI (sMCI) and 136 with progressive MCI (pMCI). Graph signal processing was applied to filter rs-fMRI data into low, middle, and high frequency bands. Connectivity-based features were derived from both filtered and unfiltered data, resulting in a comprehensive set of 100 features, including global graph metrics, minimum spanning tree (MST) metrics, triadic interaction metrics, hub tendency metrics, and the number of links. Feature selection was enhanced using particle swarm optimization (PSO) and simulated annealing (SA). A support vector machine (SVM) with a radial basis function (RBF) kernel and a 10-fold cross-validation setup were employed for classification. The proposed approach demonstrated superior performance, achieving optimal accuracy with minimal feature utilization. When PSO selected five features, SVM exhibited accuracy, specificity, and sensitivity rates of 77%, 70%, and 83%, respectively. The identified features were as follows: (Mean of clustering coefficient, Mean of strength)/Radius/(Mean Eccentricity, and Modularity) from low/middle/high frequency bands of graph. The study highlights the efficacy of the proposed framework in identifying individuals at risk of AD development using a parsimonious feature set. This approach holds promise for advancing the precision of MCI to AD progression prediction, aiding in early diagnosis and intervention strategies.
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Affiliation(s)
- Jafar Zamani
- Department of Psychiatry and Behavioral Sciences, Stanford University, California, USA
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4
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Fu Z, Batta I, Wu L, Abrol A, Agcaoglu O, Salman MS, Du Y, Iraji A, Shultz S, Sui J, Calhoun VD. Searching Reproducible Brain Features using NeuroMark: Templates for Different Age Populations and Imaging Modalities. Neuroimage 2024; 292:120617. [PMID: 38636639 DOI: 10.1016/j.neuroimage.2024.120617] [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: 01/08/2024] [Revised: 04/03/2024] [Accepted: 04/15/2024] [Indexed: 04/20/2024] Open
Abstract
A primary challenge to the data-driven analysis is the balance between poor generalizability of population-based research and characterizing more subject-, study- and population-specific variability. We previously introduced a fully automated spatially constrained independent component analysis (ICA) framework called NeuroMark and its functional MRI (fMRI) template. NeuroMark has been successfully applied in numerous studies, identifying brain markers reproducible across datasets and disorders. The first NeuroMark template was constructed based on young adult cohorts. We recently expanded on this initiative by creating a standardized normative multi-spatial-scale functional template using over 100,000 subjects, aiming to improve generalizability and comparability across studies involving diverse cohorts. While a unified template across the lifespan is desirable, a comprehensive investigation of the similarities and differences between components from different age populations might help systematically transform our understanding of the human brain by revealing the most well-replicated and variable network features throughout the lifespan. In this work, we introduced two significant expansions of NeuroMark templates first by generating replicable fMRI templates for infants, adolescents, and aging cohorts, and second by incorporating structural MRI (sMRI) and diffusion MRI (dMRI) modalities. Specifically, we built spatiotemporal fMRI templates based on 6,000 resting-state scans from four datasets. This is the first attempt to create robust ICA templates covering dynamic brain development across the lifespan. For the sMRI and dMRI data, we used two large publicly available datasets including more than 30,000 scans to build reliable templates. We employed a spatial similarity analysis to identify replicable templates and investigate the degree to which unique and similar patterns are reflective in different age populations. Our results suggest remarkably high similarity of the resulting adapted components, even across extreme age differences. With the new templates, the NeuroMark framework allows us to perform age-specific adaptations and to capture features adaptable to each modality, therefore facilitating biomarker identification across brain disorders. In sum, the present work demonstrates the generalizability of NeuroMark templates and suggests the potential of new templates to boost accuracy in mental health research and advance our understanding of lifespan and cross-modal alterations.
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Affiliation(s)
- Zening Fu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States.
| | - Ishaan Batta
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States
| | - Lei Wu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States
| | - Anees Abrol
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States
| | - Oktay Agcaoglu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States
| | - Mustafa S Salman
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States
| | - Yuhui Du
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States
| | - Armin Iraji
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States
| | - Sarah Shultz
- Department of Pediatrics, Emory University School of Medicine, Atlanta, Georgia, United States
| | - Jing Sui
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States
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5
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Hahn A, Reed MB, Vraka C, Godbersen GM, Klug S, Komorowski A, Falb P, Nics L, Traub-Weidinger T, Hacker M, Lanzenberger R. High-temporal resolution functional PET/MRI reveals coupling between human metabolic and hemodynamic brain response. Eur J Nucl Med Mol Imaging 2024; 51:1310-1322. [PMID: 38052927 PMCID: PMC11399190 DOI: 10.1007/s00259-023-06542-4] [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: 08/25/2023] [Accepted: 11/22/2023] [Indexed: 12/07/2023]
Abstract
PURPOSE Positron emission tomography (PET) provides precise molecular information on physiological processes, but its low temporal resolution is a major obstacle. Consequently, we characterized the metabolic response of the human brain to working memory performance using an optimized functional PET (fPET) framework at a temporal resolution of 3 s. METHODS Thirty-five healthy volunteers underwent fPET with [18F]FDG bolus plus constant infusion, 19 of those at a hybrid PET/MRI scanner. During the scan, an n-back working memory paradigm was completed. fPET data were reconstructed to 3 s temporal resolution and processed with a novel sliding window filter to increase signal to noise ratio. BOLD fMRI signals were acquired at 2 s. RESULTS Consistent with simulated kinetic modeling, we observed a constant increase in the [18F]FDG signal during task execution, followed by a rapid return to baseline after stimulation ceased. These task-specific changes were robustly observed in brain regions involved in working memory processing. The simultaneous acquisition of BOLD fMRI revealed that the temporal coupling between hemodynamic and metabolic signals in the primary motor cortex was related to individual behavioral performance during working memory. Furthermore, task-induced BOLD deactivations in the posteromedial default mode network were accompanied by distinct temporal patterns in glucose metabolism, which were dependent on the metabolic demands of the corresponding task-positive networks. CONCLUSIONS In sum, the proposed approach enables the advancement from parallel to truly synchronized investigation of metabolic and hemodynamic responses during cognitive processing. This allows to capture unique information in the temporal domain, which is not accessible to conventional PET imaging.
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Affiliation(s)
- Andreas Hahn
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria.
- Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Vienna, Austria.
| | - Murray B Reed
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
- Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Vienna, Austria
| | - Chrysoula Vraka
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria
| | - Godber M Godbersen
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
- Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Vienna, Austria
| | - Sebastian Klug
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
- Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Vienna, Austria
| | - Arkadiusz Komorowski
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
- Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Vienna, Austria
| | - Pia Falb
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
- Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Vienna, Austria
| | - Lukas Nics
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria
| | - Tatjana Traub-Weidinger
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria
| | - Marcus Hacker
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria
| | - Rupert Lanzenberger
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria.
- Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Vienna, Austria.
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6
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Fislage M, Winzeck S, Woodrow R, Lammers‐Lietz F, Stamatakis EA, Correia MM, Preller J, Feinkohl I, Hendrikse J, Pischon T, Spies CD, Slooter AJC, Winterer G, Menon DK, Zacharias N. Structural disconnectivity in postoperative delirium: A perioperative two-center cohort study in older patients. Alzheimers Dement 2024; 20:2861-2872. [PMID: 38451782 PMCID: PMC11032567 DOI: 10.1002/alz.13749] [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: 09/01/2023] [Revised: 11/26/2023] [Accepted: 01/21/2024] [Indexed: 03/09/2024]
Abstract
BACKGROUND Structural disconnectivity was found to precede dementia. Global white matter abnormalities might also be associated with postoperative delirium (POD). METHODS We recruited older patients (≥65 years) without dementia that were scheduled for major surgery. Diffusion kurtosis imaging metrics were obtained preoperatively, after 3 and 12 months postoperatively. We calculated fractional anisotropy (FA), mean diffusivity (MD), mean kurtosis (MK), and free water (FW). A structured and validated delirium assessment was performed twice daily. RESULTS Of 325 patients, 53 patients developed POD (16.3%). Preoperative global MD (standardized beta 0.27 [95% confidence interval [CI] 0.21-0.32] p < 0.001) was higher in patients with POD. Preoperative global MK (-0.07 [95% CI -0.11 to (-0.04)] p < 0.001) and FA (0.07 [95% CI -0.10 to (-0.04)] p < 0.001) were lower. When correcting for baseline diffusion, postoperative MD was lower after 3 months (0.05 [95% CI -0.08 to (-0.03)] p < 0.001; n = 183) and higher after 12 months (0.28 [95% CI 0.20-0.35] p < 0.001; n = 45) among patients with POD. DISCUSSION Preoperative structural disconnectivity was associated with POD. POD might lead to white matter depletion 3 and 12 months after surgery.
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Affiliation(s)
- Marinus Fislage
- Department of Anesthesiology and Intensive Care MedicineCharité – Universitätsmedizin Berlincorporate member of Freie Universität Berlin and Humboldt‐Universität zu BerlinBerlinGermany
- Department of NeurologyNational Taiwan University HospitalTaipei CityTaiwan
| | - Stefan Winzeck
- Department of ComputingImperial College LondonBioMedIA GroupLondonUK
- University Division of Anaesthesia, Department of MedicineUniversity of Cambridge, Addenbrooke's HospitalCambridgeUK
| | - Rebecca Woodrow
- University Division of Anaesthesia, Department of MedicineUniversity of Cambridge, Addenbrooke's HospitalCambridgeUK
- Department of Clinical NeurosciencesUniversity of Cambridge; Addenbrooke's HospitalCambridgeUK
| | - Florian Lammers‐Lietz
- Department of Anesthesiology and Intensive Care MedicineCharité – Universitätsmedizin Berlincorporate member of Freie Universität Berlin and Humboldt‐Universität zu BerlinBerlinGermany
| | - Emmanuel A. Stamatakis
- University Division of Anaesthesia, Department of MedicineUniversity of Cambridge, Addenbrooke's HospitalCambridgeUK
- Department of Clinical NeurosciencesUniversity of Cambridge; Addenbrooke's HospitalCambridgeUK
| | - Marta M. Correia
- MRC Cognition and Brain Sciences Unit, University of CambridgeCambridgeUK
| | - Jacobus Preller
- Addenbrooke's Cambridge University Hospitals NHS Foundation TrustCambridgeUK
| | - Insa Feinkohl
- Faculty of Health/School of MedicineWitten/Herdecke UniversityWittenGermany
- Max‐Delbrueck‐Center for Molecular Medicine in the Helmholtz Association (MDC), Molecular Epidemiology Research GroupBerlinGermany
| | - Jeroen Hendrikse
- Department of RadiologyUniversity Medical Center UtrechtUtrechtThe Netherlands
| | - Tobias Pischon
- Max‐Delbrueck‐Center for Molecular Medicine in the Helmholtz Association (MDC), Molecular Epidemiology Research GroupBerlinGermany
- Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt‐Universität zu BerlinBerlinGermany
- Max‐Delbrueck‐Center for Molecular Medicine in the Helmholtz Association (MDC), Biobank Technology PlatformBerlinGermany
- Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Core Facility BiobankBerlinGermany
| | - Claudia D. Spies
- Department of Anesthesiology and Intensive Care MedicineCharité – Universitätsmedizin Berlincorporate member of Freie Universität Berlin and Humboldt‐Universität zu BerlinBerlinGermany
| | - Arjen J. C. Slooter
- Departments of Psychiatry and Intensive Care Medicine, and UMC Utrecht Brain CenterUniversity Medical Center Utrecht, Utrecht UniversityUtrechtThe Netherlands
- Department of NeurologyUZ Brussel and Vrije Universiteit BrusselBrusselsBelgium
| | - Georg Winterer
- Department of Anesthesiology and Intensive Care MedicineCharité – Universitätsmedizin Berlincorporate member of Freie Universität Berlin and Humboldt‐Universität zu BerlinBerlinGermany
- Pharmaimage Biomarker Solutions GmbHBerlinGermany
| | - David K. Menon
- University Division of Anaesthesia, Department of MedicineUniversity of Cambridge, Addenbrooke's HospitalCambridgeUK
| | - Norman Zacharias
- Department of Anesthesiology and Intensive Care MedicineCharité – Universitätsmedizin Berlincorporate member of Freie Universität Berlin and Humboldt‐Universität zu BerlinBerlinGermany
- Pharmaimage Biomarker Solutions GmbHBerlinGermany
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Mi J, Liu C, Chen H, Qian Y, Zhu J, Zhang Y, Liang Y, Wang L, Ta D. Light on Alzheimer's disease: from basic insights to preclinical studies. Front Aging Neurosci 2024; 16:1363458. [PMID: 38566826 PMCID: PMC10986738 DOI: 10.3389/fnagi.2024.1363458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Accepted: 03/04/2024] [Indexed: 04/04/2024] Open
Abstract
Alzheimer's disease (AD), referring to a gradual deterioration in cognitive function, including memory loss and impaired thinking skills, has emerged as a substantial worldwide challenge with profound social and economic implications. As the prevalence of AD continues to rise and the population ages, there is an imperative demand for innovative imaging techniques to help improve our understanding of these complex conditions. Photoacoustic (PA) imaging forms a hybrid imaging modality by integrating the high-contrast of optical imaging and deep-penetration of ultrasound imaging. PA imaging enables the visualization and characterization of tissue structures and multifunctional information at high resolution and, has demonstrated promising preliminary results in the study and diagnosis of AD. This review endeavors to offer a thorough overview of the current applications and potential of PA imaging on AD diagnosis and treatment. Firstly, the structural, functional, molecular parameter changes associated with AD-related brain imaging captured by PA imaging will be summarized, shaping the diagnostic standpoint of this review. Then, the therapeutic methods aimed at AD is discussed further. Lastly, the potential solutions and clinical applications to expand the extent of PA imaging into deeper AD scenarios is proposed. While certain aspects might not be fully covered, this mini-review provides valuable insights into AD diagnosis and treatment through the utilization of innovative tissue photothermal effects. We hope that it will spark further exploration in this field, fostering improved and earlier theranostics for AD.
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Affiliation(s)
- Jie Mi
- Yiwu Research Institute, Fudan University, Yiwu, China
| | - Chao Liu
- Yiwu Research Institute, Fudan University, Yiwu, China
- Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China
- Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention, Shanghai, China
| | - Honglei Chen
- Yiwu Research Institute, Fudan University, Yiwu, China
| | - Yan Qian
- Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China
- Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention, Shanghai, China
| | - Jingyi Zhu
- Department of Biomedical Engineering, City University of Hong Kong, Kowloon, Hong Kong SAR, China
| | - Yachao Zhang
- Medical Ultrasound Department, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
| | - Yizhi Liang
- Guangdong Provincial Key Laboratory of Optical Fiber Sensing and Communications, Institute of Photonics Technology, Jinan University, Guangzhou, China
| | - Lidai Wang
- Department of Biomedical Engineering, City University of Hong Kong, Kowloon, Hong Kong SAR, China
| | - Dean Ta
- Yiwu Research Institute, Fudan University, Yiwu, China
- Department of Electronic Engineering, Fudan University, Shanghai, China
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8
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Li W, Zhang M, Huang R, Hu J, Wang L, Ye G, Meng H, Lin X, Liu J, Li B, Zhang Y, Li Y. Topographic metabolism-function relationships in Alzheimer's disease: A simultaneous PET/MRI study. Hum Brain Mapp 2024; 45:e26604. [PMID: 38339890 DOI: 10.1002/hbm.26604] [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: 09/24/2023] [Revised: 12/20/2023] [Accepted: 01/10/2024] [Indexed: 02/12/2024] Open
Abstract
Disruptions of neural metabolism and function occur in parallel during Alzheimer's disease (AD). While many studies have shown diverse metabolic-functional relationships in specific brain regions, much less is known about how large-scale network-level functional activity is associated with the topology of metabolism in AD. In this study, we took the advantages of simultaneous PET/MRI and multivariate analyses to investigate the associations between AD-related stereotypical spatial patterns (topographies) of glucose metabolism, measured by fluorodeoxyglucose PET, and functional connectivity, measured by resting-state functional MRI. A total of 101 participants, including 37 patients with AD, 25 patients with mild cognitive impairment (MCI), and 39 cognitively normal controls, underwent PET/MRI scans and cognitive assessments. Three pairs of distinct but optimally correlated metabolic and functional topographies were identified, encompassing large-scale networks including the default-mode, executive and control, salience, attention, and subcortical networks. Importantly, the metabolic-functional associations were not only limited to one-to-one-corresponding regions, but also occur in remote and non-overlapping regions. Furthermore, both glucose metabolism and functional connectivity, as well as their linkages, exhibited various degrees of disruptions in patients with MCI and AD, and were correlated with cognitive decline. In conclusion, our results support distributed and heterogeneous topographic associations between metabolism and function, which are jeopardized by AD. Findings of this study may deepen our understanding of the pathological mechanism of AD through the perspectives of both local energy efficiency and long-term interactions between synaptic disruption and functional disconnection contributing to the clinical symptomatology in AD.
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Affiliation(s)
- Wenli Li
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Miao Zhang
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ruodong Huang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Jialin Hu
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Lijun Wang
- Department of Neurovascular Center, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Guanyu Ye
- Department of Neurology & Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hongping Meng
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaozhu Lin
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jun Liu
- Department of Neurology & Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Biao Li
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Collaborative Innovation Center for Molecular Imaging of Precision Medicine, Ruijin Center, Shanghai, China
| | - Yaoyu Zhang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Yao Li
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
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9
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Angelidou IA, Stocker H, Beyreuther K, Teichmann B. Validation of the "Perceptions Regarding pRE-Symptomatic Alzheimer's Disease Screening" (PRE-ADS) Questionnaire in the German Population: Attitudes, Motivations, and Barriers to Pre-Symptomatic Dementia Screening. J Alzheimers Dis 2024; 97:309-325. [PMID: 38189757 PMCID: PMC10789340 DOI: 10.3233/jad-230961] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/17/2023] [Indexed: 01/09/2024]
Abstract
BACKGROUND Attitudes, motivations, and barriers to pre-symptomatic screening for Alzheimer's disease (AD) in the general population are unclear, and validated measurement tools are lacking. OBJECTIVE Translation and validation of the German version of the "Perceptions regarding pRE-symptomatic Alzheimer's Disease Screening" (PRE-ADS) questionnaire. METHODS A convenience sample (N = 256) was recruited via an online platform. Validation of the PRE-ADS-D consisted of assessments of reliability, structural validity using Principal Component Analysis (PCA) and Exploratory Factor Analysis (EFA) and construct validity using known-group tests. A subscale "Acceptability of Screening", with 5 PRE-ADS-D items, was extracted to measure acceptance of screening in clinical practice. The STROBE checklist was used for reporting. RESULTS EFA revealed a three-factor model for the PRE-ADS-D. Acceptable to good internal consistency was found for the 25-item scale (α= 0.78), as well as for the three factors "Concerns about Screening" (α= 0.85), "Intention to be Screened" (α= 0.87), and "Preventive Health Behaviors" (α= 0.81). Construct validity was confirmed for both the 25-item PRE-ADS-D and the "Acceptability of Screening" scale (α= 0.91). Overall, 51.2% of the participants showed a preference for screening. Non-parametric tests were conducted to further explore group differences of the sample. CONCLUSIONS The PRE-ADS-D is a reliable and valid tool to measure attitudes, motives, and barriers regarding pre-symptomatic dementia screening in the German-speaking general population. Additionally, the subscale "Acceptability of Screening" demonstrated good construct validity and reliability, suggesting its promising potential as a practical tool in clinical practice.
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Affiliation(s)
| | - Hannah Stocker
- Network Aging Research, Heidelberg University, Heidelberg, Germany
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center, Heidelberg, Germany
| | | | - Birgit Teichmann
- Network Aging Research, Heidelberg University, Heidelberg, Germany
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10
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D'Amico F, Sofia L, Bauckneht M, Morbelli S. Amyloid PET Imaging: Standard Procedures and Semiquantification. Methods Mol Biol 2024; 2785:165-175. [PMID: 38427194 DOI: 10.1007/978-1-0716-3774-6_11] [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: 03/02/2024]
Abstract
Amyloid plaques are a neuropathologic hallmark of Alzheimer's disease (AD), which can be imaged through positron emission tomography (PET) technology using radiopharmaceuticals that selectively bind to the fibrillar aggregates of amyloid-β plaques (Amy-PET). Several radiotracers for amyloid PET have been validated (11C-Pittsburgh compound B and the 18F-labeled compounds such as 18F-florbetaben, 18F-florbetapir, and 18F-flutemetamol). Images can be interpreted by means of visual/qualitative, semiquantitative, and quantitative criteria. Here, we summarize the main differences between the available radiotracers for Amy-PET, the proposed interpretation criteria, and main proposed quantification methods.
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Affiliation(s)
- Francesca D'Amico
- Nuclear Medicine Unit, Department of Health Sciences, University of Genoa, Genoa, Italy
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Luca Sofia
- Nuclear Medicine Unit, Department of Health Sciences, University of Genoa, Genoa, Italy
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Matteo Bauckneht
- Nuclear Medicine Unit, Department of Health Sciences, University of Genoa, Genoa, Italy
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Silvia Morbelli
- Nuclear Medicine Unit, Department of Health Sciences, University of Genoa, Genoa, Italy.
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy.
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11
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Woo MS, Nilsson J, Therriault J, Rahmouni N, Brinkmalm A, Benedet AL, Ashton NJ, Macedo AC, Servaes S, Wang YT, Tissot C, Arias JF, Hosseini SA, Chamoun M, Lussier FZ, Karikari TK, Stevenson J, Mayer C, Ferrari-Souza JP, Kobayashi E, Massarweh G, Friese MA, Pascoal TA, Gauthier S, Zetterberg H, Blennow K, Rosa-Neto P. 14-3-3 [Formula: see text]-reported early synaptic injury in Alzheimer's disease is independently mediated by sTREM2. J Neuroinflammation 2023; 20:278. [PMID: 38001539 PMCID: PMC10675887 DOI: 10.1186/s12974-023-02962-z] [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: 10/15/2023] [Accepted: 11/17/2023] [Indexed: 11/26/2023] Open
Abstract
INTRODUCTION Synaptic loss is closely associated with tau aggregation and microglia activation in later stages of Alzheimer's disease (AD). However, synaptic damage happens early in AD at the very early stages of tau accumulation. It remains unclear whether microglia activation independently causes synaptic cleavage before tau aggregation appears. METHODS We investigated 104 participants across the AD continuum by measuring 14-3-3 zeta/delta ([Formula: see text]) as a cerebrospinal fluid biomarker for synaptic degradation, and fluid and imaging biomarkers of tau, amyloidosis, astrogliosis, neurodegeneration, and inflammation. We performed correlation analyses in cognitively unimpaired and impaired participants and used structural equation models to estimate the impact of microglia activation on synaptic injury in different disease stages. RESULTS 14-3-3 [Formula: see text] was increased in participants with amyloid pathology at the early stages of tau aggregation before hippocampal volume loss was detectable. 14-3-3 [Formula: see text] correlated with amyloidosis and tau load in all participants but only with biomarkers of neurodegeneration and memory deficits in cognitively unimpaired participants. This early synaptic damage was independently mediated by sTREM2. At later disease stages, tau and astrogliosis additionally mediated synaptic loss. CONCLUSIONS Our results advertise that sTREM2 is mediating synaptic injury at the early stages of tau accumulation, underlining the importance of microglia activation for AD disease propagation.
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Affiliation(s)
- Marcel S Woo
- Institute of Neuroimmunology and Multiple Sclerosis, University Medical Center Hamburg Eppendorf, Falkenried 94, 20251 Hamburg, Germany
| | - Johanna Nilsson
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, 40530 Gothenburg, Sweden
| | - Joseph Therriault
- Translational Neuroimaging Laboratory, McGill Research Centre for Studies in Aging, 6875 La Salle Blvd, FBC Room 3149, Montreal, QC H4H 1R3 Canada
- Department of Neurology and Neurosurgery, Faculty of Medicine, McGill University, Montreal, QC H4H 1R3 Canada
| | - Nesrine Rahmouni
- Translational Neuroimaging Laboratory, McGill Research Centre for Studies in Aging, 6875 La Salle Blvd, FBC Room 3149, Montreal, QC H4H 1R3 Canada
| | - Ann Brinkmalm
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, 40530 Gothenburg, Sweden
| | - Andrea L Benedet
- Department of Psychiatry and Neurochemistry, The Sahlgrenska Academy at the University of Gothenburg, 40530 Mölndal, Sweden
| | - Nicholas J Ashton
- Department of Psychiatry and Neurochemistry, The Sahlgrenska Academy at the University of Gothenburg, 40530 Mölndal, Sweden
- Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, 40530 Gothenburg, Sweden
| | - Arthur C Macedo
- Translational Neuroimaging Laboratory, McGill Research Centre for Studies in Aging, 6875 La Salle Blvd, FBC Room 3149, Montreal, QC H4H 1R3 Canada
- Department of Neurology and Neurosurgery, Faculty of Medicine, McGill University, Montreal, QC H4H 1R3 Canada
| | - Stijn Servaes
- Translational Neuroimaging Laboratory, McGill Research Centre for Studies in Aging, 6875 La Salle Blvd, FBC Room 3149, Montreal, QC H4H 1R3 Canada
| | - Yi-Ting Wang
- Translational Neuroimaging Laboratory, McGill Research Centre for Studies in Aging, 6875 La Salle Blvd, FBC Room 3149, Montreal, QC H4H 1R3 Canada
- Department of Neurology and Neurosurgery, Faculty of Medicine, McGill University, Montreal, QC H4H 1R3 Canada
| | - Cécile Tissot
- Translational Neuroimaging Laboratory, McGill Research Centre for Studies in Aging, 6875 La Salle Blvd, FBC Room 3149, Montreal, QC H4H 1R3 Canada
| | - Jaime Fernandez Arias
- Translational Neuroimaging Laboratory, McGill Research Centre for Studies in Aging, 6875 La Salle Blvd, FBC Room 3149, Montreal, QC H4H 1R3 Canada
| | - Seyyed Ali Hosseini
- Translational Neuroimaging Laboratory, McGill Research Centre for Studies in Aging, 6875 La Salle Blvd, FBC Room 3149, Montreal, QC H4H 1R3 Canada
| | - Mira Chamoun
- Translational Neuroimaging Laboratory, McGill Research Centre for Studies in Aging, 6875 La Salle Blvd, FBC Room 3149, Montreal, QC H4H 1R3 Canada
- Department of Neurology and Neurosurgery, Faculty of Medicine, McGill University, Montreal, QC H4H 1R3 Canada
| | - Firoza Z Lussier
- Department of Neurology and Neurosurgery, Faculty of Medicine, McGill University, Montreal, QC H4H 1R3 Canada
| | - Thomas K Karikari
- Department of Psychiatry and Neurochemistry, The Sahlgrenska Academy at the University of Gothenburg, 40530 Mölndal, Sweden
| | - Jenna Stevenson
- Translational Neuroimaging Laboratory, McGill Research Centre for Studies in Aging, 6875 La Salle Blvd, FBC Room 3149, Montreal, QC H4H 1R3 Canada
| | - Christina Mayer
- Institute of Neuroimmunology and Multiple Sclerosis, University Medical Center Hamburg Eppendorf, Falkenried 94, 20251 Hamburg, Germany
| | - João Pedro Ferrari-Souza
- Department of Neurology and Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213 PA USA
- Graduate Program in Biological Sciences: Biochemistry, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS 91501-970 Brazil
| | - Eliane Kobayashi
- Department of Neurology and Neurosurgery, Faculty of Medicine, McGill University, Montreal, QC H4H 1R3 Canada
| | - Gassan Massarweh
- Department of Neurology and Neurosurgery, Faculty of Medicine, McGill University, Montreal, QC H4H 1R3 Canada
| | - Manuel A Friese
- Institute of Neuroimmunology and Multiple Sclerosis, University Medical Center Hamburg Eppendorf, Falkenried 94, 20251 Hamburg, Germany
| | - Tharick A Pascoal
- Department of Neurology and Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213 PA USA
| | - Serge Gauthier
- Translational Neuroimaging Laboratory, McGill Research Centre for Studies in Aging, 6875 La Salle Blvd, FBC Room 3149, Montreal, QC H4H 1R3 Canada
- Department of Neurology and Neurosurgery, Faculty of Medicine, McGill University, Montreal, QC H4H 1R3 Canada
| | - Henrik Zetterberg
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, 40530 Gothenburg, Sweden
- Department of Psychiatry and Neurochemistry, The Sahlgrenska Academy at the University of Gothenburg, 40530 Mölndal, Sweden
- Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, WC1E 6BT UK
- UK Dementia Research Institute at UCL, London, WC1E 6BT UK
- Hong Kong Center for Neurodegenerative Diseases, Clear Water Bay, Hong Kong, 518172 China
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI 53726 USA
| | - Kaj Blennow
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, 40530 Gothenburg, Sweden
- Department of Psychiatry and Neurochemistry, The Sahlgrenska Academy at the University of Gothenburg, 40530 Mölndal, Sweden
| | - Pedro Rosa-Neto
- Translational Neuroimaging Laboratory, McGill Research Centre for Studies in Aging, 6875 La Salle Blvd, FBC Room 3149, Montreal, QC H4H 1R3 Canada
- Department of Neurology and Neurosurgery, Faculty of Medicine, McGill University, Montreal, QC H4H 1R3 Canada
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12
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Mu R, Qin X, Zheng W, Yang P, Huang B, Li X, Liu F, Deng K, Zhu X. Amide proton transfer could be a surrogate imaging marker for predicting vascular cognitive impairment. Brain Res Bull 2023; 204:110793. [PMID: 37863439 DOI: 10.1016/j.brainresbull.2023.110793] [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: 07/13/2023] [Revised: 09/27/2023] [Accepted: 10/17/2023] [Indexed: 10/22/2023]
Abstract
BACKGROUD Emerging evidence suggests an overlap in the underlying pathways contributing to both cerebral small vessel disease (CSVD) and the neurodegenerative disease. Studies investigating the progression of CSVD should incorporate markers that reflect neurodegenerative lesions. OBJECTIVE We aim to investigate whether Amide proton transfer (APT) can serve as a potential marker for reflecting vascular cognitive impairment (VCI). METHOD Participants were categorized into one of three groups based on their Montreal Cognitive Assessment (MoCA) scores: normal control group (age,54.9 ± 7.9; male, 52.9%), mild cognitive impairment (MCI) group (age,55.7 ± 6.9; male, 42.6%), or vascular dementia (VaD) group (age,57.6 ± 5.5, male, 58.5%). One way analysis of variance was performed to compare the demographic and APT variables between groups. Multiple logistic regression analysis wwas constructed to examine the relationship between APT values and VCI grouping. A hierarchical linear regression model was employed to examine the associations between patients' demographic factors, imaging markers, APT values, and MoCA. RESULTS The APT values of frontal white matter, hippocampus, amygdala, and thalamus were significantly different among different groups (p < 0.05). The APT values of frontal white matter, amygdala, and thalamus indicate a significant positive effect on MCI grouping. the APT values of frontal white matter, amygdala, and thalamus indicate a significant positive effect on VaD grouping. The demographic data, CSVD imaging markers and APT values can account for 5.1%, 20.1% and 27.7% of the variation in MoCA, respectively. CONCLUSION APT imaging can partially identifying and predicting the occurrence of VCI.
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Affiliation(s)
- Ronghua Mu
- Department of Radiology, Nanxishan Hospital of Guangxi Zhuang Autonomous Region, 541004 Guilin, China
| | - Xiaoyan Qin
- Department of Radiology, Nanxishan Hospital of Guangxi Zhuang Autonomous Region, 541004 Guilin, China
| | - Wei Zheng
- Department of Radiology, Nanxishan Hospital of Guangxi Zhuang Autonomous Region, 541004 Guilin, China
| | - Peng Yang
- Department of Radiology, Nanxishan Hospital of Guangxi Zhuang Autonomous Region, 541004 Guilin, China
| | - Bingqin Huang
- Department of Radiology, Nanxishan Hospital of Guangxi Zhuang Autonomous Region, 541004 Guilin, China; Graduate School, Guilin Medical University, 541002 Guilin, China
| | - Xin Li
- Department of Radiology, Nanxishan Hospital of Guangxi Zhuang Autonomous Region, 541004 Guilin, China
| | - Fuzhen Liu
- Department of Radiology, Nanxishan Hospital of Guangxi Zhuang Autonomous Region, 541004 Guilin, China
| | - Kan Deng
- Philips (China) Investment Co., Ltd., Guangzhou Branch, 510000 Guangzhou, China
| | - Xiqi Zhu
- Department of Radiology, Nanxishan Hospital of Guangxi Zhuang Autonomous Region, 541004 Guilin, China.
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13
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Qu Y, Wang P, Yao H, Wang D, Song C, Yang H, Zhang Z, Chen P, Kang X, Du K, Fan L, Zhou B, Han T, Yu C, Zhang X, Zuo N, Jiang T, Zhou Y, Liu B, Han Y, Lu J, Liu Y. Reproducible Abnormalities and Diagnostic Generalizability of White Matter in Alzheimer's Disease. Neurosci Bull 2023; 39:1533-1543. [PMID: 37014553 PMCID: PMC10533766 DOI: 10.1007/s12264-023-01041-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 11/29/2022] [Indexed: 04/05/2023] Open
Abstract
Alzheimer's disease (AD) is associated with the impairment of white matter (WM) tracts. The current study aimed to verify the utility of WM as the neuroimaging marker of AD with multisite diffusion tensor imaging datasets [321 patients with AD, 265 patients with mild cognitive impairment (MCI), 279 normal controls (NC)], a unified pipeline, and independent site cross-validation. Automated fiber quantification was used to extract diffusion profiles along tracts. Random-effects meta-analyses showed a reproducible degeneration pattern in which fractional anisotropy significantly decreased in the AD and MCI groups compared with NC. Machine learning models using tract-based features showed good generalizability among independent site cross-validation. The diffusion metrics of the altered regions and the AD probability predicted by the models were highly correlated with cognitive ability in the AD and MCI groups. We highlighted the reproducibility and generalizability of the degeneration pattern of WM tracts in AD.
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Affiliation(s)
- Yida Qu
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Pan Wang
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin University, Tianjin, 300222, China
| | - Hongxiang Yao
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin University, Tianjin, 300222, China
| | - Dawei Wang
- Department of Radiology, Department of Epidemiology and Health Statistics, School of Public Health, Qilu Hospital of Shandong University, Ji'nan, 250063, China
| | - Chengyuan Song
- Department of Neurology, Qilu Hospital of Shandong University, Ji'nan, 250063, China
| | - Hongwei Yang
- Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China
| | - Zengqiang Zhang
- Branch of Chinese, PLA General Hospital, Sanya, 572022, China
| | - Pindong Chen
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Xiaopeng Kang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Kai Du
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Lingzhong Fan
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Bo Zhou
- Department of Neurology, The Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, 100089, China
| | - Tong Han
- Department of Radiology, Tianjin Huanhu Hospital, Tianjin, 300222, China
| | - Chunshui Yu
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Xi Zhang
- Department of Neurology, The Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, 100089, China
| | - Nianming Zuo
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Tianzi Jiang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Yuying Zhou
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin University, Tianjin, 300222, China
| | - Bing Liu
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
- State Key Lab of Cognition Neuroscience & Learning, Beijing Normal University, Beijing, 100091, China
| | - Ying Han
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China
- Beijing Institute of Geriatrics, Beijing, 100053, China
- National Clinical Research Center for Geriatric Disorders, Beijing, 100053, China
- Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, 100053, China
| | - Jie Lu
- Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China.
| | - Yong Liu
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100876, China.
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14
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Fislage M, Zacharias N, Feinkohl I. The Thalamus in Perioperative Neurocognitive Disorders. Neuropsychol Rev 2023:10.1007/s11065-023-09615-1. [PMID: 37736862 DOI: 10.1007/s11065-023-09615-1] [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: 01/25/2023] [Accepted: 08/21/2023] [Indexed: 09/23/2023]
Abstract
Thalamus function and structure are known predictors of individual differences in the risk of age-related neurocognitive disorders (NCD), such as dementia. However, to date, little is known about their role in the perioperative setting. Here, we provide a narrative review of brain-imaging studies of preoperative and postoperative thalamus scanning parameters associated with risks of developing perioperative NCD, such as postoperative delirium (POD) and postoperative cognitive dysfunction (POCD) during the postoperative phase. These findings are discussed in light of the concept of reserve capacity.
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Affiliation(s)
- Marinus Fislage
- Department of Anesthesiology and Operative Intensive Care Medicine (CCM, CVK), Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Charitéplatz 1, Berlin, 10117, Germany.
- Department of Neurology, National Taiwan University Hospital, Taipei City, 100225, Taiwan.
| | - Norman Zacharias
- Department of Anesthesiology and Operative Intensive Care Medicine (CCM, CVK), Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Charitéplatz 1, Berlin, 10117, Germany
| | - Insa Feinkohl
- Faculty of Health/School of Medicine, Witten/Herdecke University, Witten, Germany
- Max-Delbrueck-Center for Molecular Medicine in the Helmholtz Association (MDC), Molecular Epidemiology Research Group, Berlin, Germany
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15
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Chu C, Pan W, Ren Y, Mao P, Yang C, Liu C, Tang YL. Executive function deficits and medial temporal lobe atrophy in late-life depression and Alzheimer's disease: a comparative study. Front Psychiatry 2023; 14:1243894. [PMID: 37720905 PMCID: PMC10501151 DOI: 10.3389/fpsyt.2023.1243894] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 08/22/2023] [Indexed: 09/19/2023] Open
Abstract
Objectives Alzheimer's disease (AD) and late-life depression (LLD) frequently exhibit executive function deficits (EFD) and medial temporal lobe atrophy (MTA) as shared characteristics. The objective of this research was to examine the utility of the Trail Making Test (TMT) and the MTA scale in distinguishing between LLD and AD. Methods A study of 100 patients, 50 with AD and 50 with LLD, was conducted using a cross-sectional design. The individuals were subjected to clinical evaluations to assess their level of depression and overall cognitive abilities, which included the Geriatric Depression Scale (GDS), Mini-Mental State Examination (MMSE), and Montreal Cognitive Assessment (MoCA). We evaluated executive function deficits (EFD) through the use of the TMT, which includes both TMT-A and TMT-B. MTA was measured using magnetic resonance imaging. To evaluate the ability of TMT and MTA scale to distinguish between the two groups, a receiver operating characteristic (ROC) curve was utilized. To investigate the connections between MTA and neuropsychological measures, a correlation analysis was performed. Results AD patients exhibited notably reduced MMSE, MoCA, and GDS scores, as well as an increased MTA total scores, time spent on TMT-A, and TMT-B compared to LLD patients (p < 0.05). TMT-A and TMT-B both exhibited excellent discriminatory power between AD and LLD, achieving area under curve (AUC) values of 92.2 and 94.2%, respectively. In AD patients, there was a negative correlation between MMSE and MoCA scores and MTA scores, while in LLD patients, there was a positive correlation between time spent on TMT-A and GDS scores and MTA scores. Conclusion AD patients experience more severe EFD and MTA than LLD patients. The differential diagnosis of AD and LLD can be aided by the useful tool known as TMT. It is important to acknowledge that TMT is capable of capturing only a fraction of the executive function, thus necessitating a cautious interpretation of research findings.
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Affiliation(s)
- Changbiao Chu
- Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, Capital Medical University, National Clinical Research Center for Geriatric Diseases, Beijing, China
| | - Weigang Pan
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders and National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Yanping Ren
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders and National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Peixian Mao
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders and National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Chunlin Yang
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders and National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Chaomeng Liu
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders and National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Yi-lang Tang
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, United States
- Mental Health Service Line, Atlanta VA Medical Center, Decatur, GA, United States
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16
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Almgren H, Camacho M, Hanganu A, Kibreab M, Camicioli R, Ismail Z, Forkert ND, Monchi O. Machine learning-based prediction of longitudinal cognitive decline in early Parkinson's disease using multimodal features. Sci Rep 2023; 13:13193. [PMID: 37580407 PMCID: PMC10425414 DOI: 10.1038/s41598-023-37644-6] [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: 11/03/2022] [Accepted: 06/25/2023] [Indexed: 08/16/2023] Open
Abstract
Patients with Parkinson's Disease (PD) often suffer from cognitive decline. Accurate prediction of cognitive decline is essential for early treatment of at-risk patients. The aim of this study was to develop and evaluate a multimodal machine learning model for the prediction of continuous cognitive decline in patients with early PD. We included 213 PD patients from the Parkinson's Progression Markers Initiative (PPMI) database. Machine learning was used to predict change in Montreal Cognitive Assessment (MoCA) score using the difference between baseline and 4-years follow-up data as outcome. Input features were categorized into four sets: clinical test scores, cerebrospinal fluid (CSF) biomarkers, brain volumes, and genetic variants. All combinations of input feature sets were added to a basic model, which consisted of demographics and baseline cognition. An iterative scheme using RReliefF-based feature ranking and support vector regression in combination with tenfold cross validation was used to determine the optimal number of predictive features and to evaluate model performance for each combination of input feature sets. Our best performing model consisted of a combination of the basic model, clinical test scores and CSF-based biomarkers. This model had 12 features, which included baseline cognition, CSF phosphorylated tau, CSF total tau, CSF amyloid-beta1-42, geriatric depression scale (GDS) scores, and anxiety scores. Interestingly, many of the predictive features in our model have previously been associated with Alzheimer's disease, showing the importance of assessing Alzheimer's disease pathology in patients with Parkinson's disease.
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Affiliation(s)
- Hannes Almgren
- Department of Clinical Neurosciences, University of Calgary, 2500 University Drive NW, Calgary, AB, T2N 1N4, Canada.
- Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, 3330 Hospital Dr NW, Calgary, AB, T2N 4N1, Canada.
| | - Milton Camacho
- Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, 3330 Hospital Dr NW, Calgary, AB, T2N 4N1, Canada
- Department of Radiology, University of Calgary, 2500 University Drive NW, Calgary, AB, T2N 1N4, Canada
| | - Alexandru Hanganu
- Département de Psychologie, Université de Montréal, Pavillon Marie-Victorin, 90 Vincent d'Indy Ave, Montreal, QC, H2V 2S9, Canada
- Centre de recherche de l'Institut universitaire de gériatrie de Montréal, 4565 chemin Queen Mary, Montreal, QC, H3W 1W5, Canada
| | - Mekale Kibreab
- Department of Clinical Neurosciences, University of Calgary, 2500 University Drive NW, Calgary, AB, T2N 1N4, Canada
- Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, 3330 Hospital Dr NW, Calgary, AB, T2N 4N1, Canada
| | - Richard Camicioli
- Division of Neurology, Department of Medicine, and Neuroscience and Mental Health Institute, University of Alberta, 7-112 Clinical Sciences Building 11350 83rd Avenue, Edmonton, AB, T6G 2G3, Canada
| | - Zahinoor Ismail
- Department of Clinical Neurosciences, University of Calgary, 2500 University Drive NW, Calgary, AB, T2N 1N4, Canada
- Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, 3330 Hospital Dr NW, Calgary, AB, T2N 4N1, Canada
- Department of Psychiatry, University of Calgary, 3280 Hospital Dr NW, Calgary, AB, T2N 4Z6, Canada
| | - Nils D Forkert
- Department of Clinical Neurosciences, University of Calgary, 2500 University Drive NW, Calgary, AB, T2N 1N4, Canada
- Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, 3330 Hospital Dr NW, Calgary, AB, T2N 4N1, Canada
- Department of Radiology, University of Calgary, 2500 University Drive NW, Calgary, AB, T2N 1N4, Canada
- Alberta Children's Hospital Research Institute, Heritage Medical Research Building, University of Calgary, 3330 Hospital Dr. NW, Calgary, AB, T2N 4N1, Canada
| | - Oury Monchi
- Department of Clinical Neurosciences, University of Calgary, 2500 University Drive NW, Calgary, AB, T2N 1N4, Canada
- Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, 3330 Hospital Dr NW, Calgary, AB, T2N 4N1, Canada
- Centre de recherche de l'Institut universitaire de gériatrie de Montréal, 4565 chemin Queen Mary, Montreal, QC, H3W 1W5, Canada
- Department of Radiology, University of Calgary, 2500 University Drive NW, Calgary, AB, T2N 1N4, Canada
- Département de radiologie, radio-oncologie et médecine nucléaire, Faculté de médecine, Université de Montréal, Pavillon Roger-Gaudry, 2900 Boulevard. Édouard-Montpetit, Montreal, QC, H3T 1A4, Canada
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Ford E, Milne R, Curlewis K. Ethical issues when using digital biomarkers and artificial intelligence for the early detection of dementia. WILEY INTERDISCIPLINARY REVIEWS. DATA MINING AND KNOWLEDGE DISCOVERY 2023; 13:e1492. [PMID: 38439952 PMCID: PMC10909482 DOI: 10.1002/widm.1492] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 01/12/2023] [Accepted: 01/13/2023] [Indexed: 03/06/2024]
Abstract
Dementia poses a growing challenge for health services but remains stigmatized and under-recognized. Digital technologies to aid the earlier detection of dementia are approaching market. These include traditional cognitive screening tools presented on mobile devices, smartphone native applications, passive data collection from wearable, in-home and in-car sensors, as well as machine learning techniques applied to clinic and imaging data. It has been suggested that earlier detection and diagnosis may help patients plan for their future, achieve a better quality of life, and access clinical trials and possible future disease modifying treatments. In this review, we explore whether digital tools for the early detection of dementia can or should be deployed, by assessing them against the principles of ethical screening programs. We conclude that while the importance of dementia as a health problem is unquestionable, significant challenges remain. There is no available treatment which improves the prognosis of diagnosed disease. Progression from early-stage disease to dementia is neither given nor currently predictable. Available technologies are generally not both minimally invasive and highly accurate. Digital deployment risks exacerbating health inequalities due to biased training data and inequity in digital access. Finally, the acceptability of early dementia detection is not established, and resources would be needed to ensure follow-up and support for those flagged by any new system. We conclude that early dementia detection deployed at scale via digital technologies does not meet standards for a screening program and we offer recommendations for moving toward an ethical mode of implementation. This article is categorized under:Application Areas > Health CareCommercial, Legal, and Ethical Issues > Ethical ConsiderationsTechnologies > Artificial Intelligence.
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Affiliation(s)
- Elizabeth Ford
- Department of Primary Care and Public HealthBrighton and Sussex Medical SchoolBrightonUK
| | - Richard Milne
- Kavli Centre for Ethics, Science and the PublicUniversity of CambridgeCambridgeUK
- Engagement and SocietyWellcome Connecting ScienceCambridgeUK
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18
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Piao S, Chen K, Wang N, Bao Y, Liu X, Hu B, Lu Y, Yang L, Geng D, Li Y. Modular Level Alterations Of Structural-Functional Connectivity Coupling in Mild Cognitive Impairment Patients and Interactions with Age Effect. J Alzheimers Dis 2023; 92:1439-1450. [PMID: 36911934 DOI: 10.3233/jad-220837] [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: 03/12/2023]
Abstract
BACKGROUND Structural-functional connectivity (SC- FC) coupling is related to various cognitive functions and more sensitive for the detection of subtle brain alterations. OBJECTIVE To investigate whether decoupling of SC-FC was detected in mild cognitive impairment (MCI) patients on a modular level, the interaction effect of aging and disease, and its relationship with network efficiency. METHODS 73 patients with MCI and 65 healthy controls were enrolled who underwent diffusion tensor imaging and resting-state functional MRI to generate structural and functional networks. Five modules were defined based on automated anatomical labeling 90 atlas, including default mode network (DMN), frontoparietal attention network (FPN), sensorimotor network (SMN), subcortical network (SCN), and visual network (VIS). Intra-module and inter-module SC-FC coupling were compared between two groups. The interaction effect of aging and group on modular SC-FC coupling was further analyzed by two-way ANOVA. The correlation between the coupling and network efficiency was finally calculated. RESULTS In MCI patients, aberrant intra-module coupling was noted in SMN, and altered inter-module coupling was found in the other four modules. Intra-module coupling exhibited significant age-by-group effects in DMN and SMN, and inter-module coupling showed significant age-by-group effects in DMN and FPN. In MCI patients, both positive or negative correlations between coupling and network efficiency were found in DMN, FPN, SCN, and VIS. CONCLUSION SC-FC coupling could reflect the association of SC and FC, especially in modular levels. In MCI, SC-FC coupling could be affected by the interaction effect of aging and disease, which may shed light on advancing the pathophysiological mechanisms of MCI.
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Affiliation(s)
- Sirong Piao
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Keliang Chen
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
| | - Na Wang
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China.,Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
| | - Yifang Bao
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China.,Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
| | - Xueling Liu
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China.,Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
| | - Bin Hu
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China.,Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
| | - Yucheng Lu
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China.,Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
| | - Liqin Yang
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China.,Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
| | - Daoying Geng
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Yuxin Li
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
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Advanced Overview of Biomarkers and Techniques for Early Diagnosis of Alzheimer's Disease. Cell Mol Neurobiol 2023:10.1007/s10571-023-01330-y. [PMID: 36847930 DOI: 10.1007/s10571-023-01330-y] [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: 12/05/2022] [Accepted: 02/15/2023] [Indexed: 03/01/2023]
Abstract
The development of early non-invasive diagnosis methods and identification of novel biomarkers are necessary for managing Alzheimer's disease (AD) and facilitating effective prognosis and treatment. AD has multi-factorial nature and involves complex molecular mechanism, which causes neuronal degeneration. The primary challenges in early AD detection include patient heterogeneity and lack of precise diagnosis at the preclinical stage. Several cerebrospinal fluid (CSF) and blood biomarkers have been proposed to show excellent diagnosis ability by identifying tau pathology and cerebral amyloid beta (Aβ) for AD. Intense research endeavors are being made to develop ultrasensitive detection techniques and find potent biomarkers for early AD diagnosis. To mitigate AD worldwide, understanding various CSF biomarkers, blood biomarkers, and techniques that can be used for early diagnosis is imperative. This review attempts to provide information regarding AD pathophysiology, genetic and non-genetic factors associated with AD, several potential blood and CSF biomarkers, like neurofilament light, neurogranin, Aβ, and tau, along with biomarkers under development for AD detection. Besides, numerous techniques, such as neuroimaging, spectroscopic techniques, biosensors, and neuroproteomics, which are being explored to aid early AD detection, have been discussed. The insights thus gained would help in finding potential biomarkers and suitable techniques for the accurate diagnosis of early AD before cognitive dysfunction.
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20
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Hao X, Zhang W, Jiao B, Yang Q, Zhang X, Chen R, Wang X, Xiao X, Zhu Y, Liao W, Wang D, Shen L. Correlation between retinal structure and brain multimodal magnetic resonance imaging in patients with Alzheimer's disease. Front Aging Neurosci 2023; 15:1088829. [PMID: 36909943 PMCID: PMC9992546 DOI: 10.3389/fnagi.2023.1088829] [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: 11/03/2022] [Accepted: 02/06/2023] [Indexed: 02/24/2023] Open
Abstract
Background The retina imaging and brain magnetic resonance imaging (MRI) can both reflect early changes in Alzheimer's disease (AD) and may serve as potential biomarker for early diagnosis, but their correlation and the internal mechanism of retinal structural changes remain unclear. This study aimed to explore the possible correlation between retinal structure and visual pathway, brain structure, intrinsic activity changes in AD patients, as well as to build a classification model to identify AD patients. Methods In the study, 49 AD patients and 48 healthy controls (HCs) were enrolled. Retinal images were obtained by optical coherence tomography (OCT). Multimodal MRI sequences of all subjects were collected. Spearman correlation analysis and multiple linear regression models were used to assess the correlation between OCT parameters and multimodal MRI findings. The diagnostic value of combination of retinal imaging and brain multimodal MRI was assessed by performing a receiver operating characteristic (ROC) curve. Results Compared with HCs, retinal thickness and multimodal MRI findings of AD patients were significantly altered (p < 0.05). Significant correlations were presented between the fractional anisotropy (FA) value of optic tract and mean retinal thickness, macular volume, macular ganglion cell layer (GCL) thickness, inner plexiform layer (IPL) thickness in AD patients (p < 0.01). The fractional amplitude of low frequency fluctuations (fALFF) value of primary visual cortex (V1) was correlated with temporal quadrant peripapillary retinal nerve fiber layer (pRNFL) thickness (p < 0.05). The model combining thickness of GCL and temporal quadrant pRNFL, volume of hippocampus and lateral geniculate nucleus, and age showed the best performance to identify AD patients [area under the curve (AUC) = 0.936, sensitivity = 89.1%, specificity = 87.0%]. Conclusion Our study demonstrated that retinal structure change was related to the loss of integrity of white matter fiber tracts in the visual pathway and the decreased LGN volume and functional metabolism of V1 in AD patients. Trans-synaptic axonal retrograde lesions may be the underlying mechanism. Combining retinal imaging and multimodal MRI may provide new insight into the mechanism of retinal structural changes in AD and may serve as new target for early auxiliary diagnosis of AD.
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Affiliation(s)
- Xiaoli Hao
- Department of Neurology, Xiangya Hospital of Central South University, Changsha, China
| | - Weiwei Zhang
- Department of Radiology, Xiangya Hospital of Central South University, Changsha, China
| | - Bin Jiao
- Department of Neurology, Xiangya Hospital of Central South University, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Central South University, Changsha, China.,Engineering Research Center of Hunan Province in Cognitive Impairment Disorders, Central South University, Changsha, China.,Hunan International Scientific and Technological Cooperation Base of Neurodegenerative and Neurogenetic Diseases, Changsha, China.,Key Laboratory of Hunan Province in Neurodegenerative Disorders, Central South University, Changsha, China
| | - Qijie Yang
- Department of Neurology, Xiangya Hospital of Central South University, Changsha, China
| | - Xinyue Zhang
- Department of Neurology, Xiangya Hospital of Central South University, Changsha, China
| | - Ruiting Chen
- Department of Radiology, Xiangya Hospital of Central South University, Changsha, China
| | - Xin Wang
- Department of Neurology, Xiangya Hospital of Central South University, Changsha, China
| | - Xuewen Xiao
- Department of Neurology, Xiangya Hospital of Central South University, Changsha, China
| | - Yuan Zhu
- Department of Neurology, Xiangya Hospital of Central South University, Changsha, China
| | - Weihua Liao
- Department of Radiology, Xiangya Hospital of Central South University, Changsha, China
| | - Dongcui Wang
- Department of Radiology, Xiangya Hospital of Central South University, Changsha, China
| | - Lu Shen
- Department of Neurology, Xiangya Hospital of Central South University, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Central South University, Changsha, China.,Engineering Research Center of Hunan Province in Cognitive Impairment Disorders, Central South University, Changsha, China.,Hunan International Scientific and Technological Cooperation Base of Neurodegenerative and Neurogenetic Diseases, Changsha, China.,Key Laboratory of Hunan Province in Neurodegenerative Disorders, Central South University, Changsha, China.,Key Laboratory of Organ Injury, Aging and Regenerative Medicine of Hunan Province, Changsha, China
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21
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Environmental neuroscience linking exposome to brain structure and function underlying cognition and behavior. Mol Psychiatry 2023; 28:17-27. [PMID: 35790874 DOI: 10.1038/s41380-022-01669-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/07/2021] [Revised: 06/02/2022] [Accepted: 06/09/2022] [Indexed: 01/07/2023]
Abstract
Individual differences in human brain structure, function, and behavior can be attributed to genetic variations, environmental exposures, and their interactions. Although genome-wide association studies have identified many genetic variants associated with brain imaging phenotypes, environmental exposures associated with these phenotypes remain largely unknown. Here, we propose that environmental neuroscience should pay more attention on exploring the associations between lifetime environmental exposures (exposome) and brain imaging phenotypes and identifying both cumulative environmental effects and their vulnerable age windows during the life course. Exposome-neuroimaging association studies face several challenges including the accurate measurement of the totality of environmental exposures varied in space and time, the highly correlated structure of the exposome, and the lack of standardized approaches for exposome-wide association studies. By agnostically scanning the effects of environmental exposures on brain imaging phenotypes and their interactions with genomic variations, exposome-neuroimaging association analyses will improve our understanding of causal factors associated with individual differences in brain structure and function as well as their relations with cognitive abilities and neuropsychiatric disorders.
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Wang Q, Zang F, He C, Zhang Z, Xie C. Dyslipidemia induced large-scale network connectivity abnormality facilitates cognitive decline in the Alzheimer's disease. J Transl Med 2022; 20:567. [PMID: 36474263 PMCID: PMC9724298 DOI: 10.1186/s12967-022-03786-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 11/23/2022] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Although lipid metabolite dysfunction contributes substantially to clinical signs and pathophysiology of Alzheimer's disease (AD), how dyslipidemia promoting neuropathological processes and brain functional impairment subsequently facilitates the progression of AD remains unclear. METHODS We combined large-scale brain resting-state networks (RSNs) approaches with canonical correlation analysis to explore the accumulating effects of lipid gene- and protein-centric levels on cerebrospinal fluid (CSF) biomarkers, dynamic trajectory of large-scale RSNs, and cognitive performance across entire AD spectrum. Support vector machine model was used to distinguish AD spectrum and pathway analysis was used to test the influences among these variables. RESULTS We found that the effects of accumulation of lipid-pathway genetic variants and lipoproteins were significantly correlated with CSF biomarkers levels and cognitive performance across the AD spectrum. Dynamic trajectory of large-scale RSNs represented a rebounding mode, which is characterized by a weakened network cohesive connector role and enhanced network incohesive provincial role following disease progression. Importantly, the fluctuating large-scale RSNs connectivity was significantly correlated with the summative effects of lipid-pathway genetic variants and lipoproteins, CSF biomarkers, and cognitive performance. Moreover, SVM model revealed that the lipid-associated twenty-two brain network connections represented higher capacity to classify AD spectrum. Pathway analysis further identified dyslipidemia directly influenced brain network reorganization or indirectly affected the CSF biomarkers and subsequently caused cognitive decline. CONCLUSIONS Dyslipidemia exacerbated cognitive decline and increased the risk of AD via mediating large-scale brain networks integrity and promoting neuropathological processes. These findings reveal a role for lipid metabolism in AD pathogenesis and suggest lipid management as a potential therapeutic target for AD.
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Affiliation(s)
- Qing Wang
- grid.263826.b0000 0004 1761 0489Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, 210009 Jiangsu China
| | - Feifei Zang
- grid.263826.b0000 0004 1761 0489Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, 210009 Jiangsu China
| | - Cancan He
- grid.263826.b0000 0004 1761 0489Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, 210009 Jiangsu China
| | - Zhijun Zhang
- grid.263826.b0000 0004 1761 0489Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, 210009 Jiangsu China ,grid.263826.b0000 0004 1761 0489Institute of Neuropsychiatry, Affiliated ZhongDa Hospital, Southeast University, Nanjing, 210009 Jiangsu China ,grid.263826.b0000 0004 1761 0489The Key Laboratory of Developmental Genes and Human Disease, Southeast University, Nanjing, 210009 Jiangsu China
| | - Chunming Xie
- grid.263826.b0000 0004 1761 0489Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, 210009 Jiangsu China ,grid.263826.b0000 0004 1761 0489Institute of Neuropsychiatry, Affiliated ZhongDa Hospital, Southeast University, Nanjing, 210009 Jiangsu China ,grid.263826.b0000 0004 1761 0489The Key Laboratory of Developmental Genes and Human Disease, Southeast University, Nanjing, 210009 Jiangsu China
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23
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Touron E, Moulinet I, Kuhn E, Sherif S, Ourry V, Landeau B, Mézenge F, Vivien D, Klimecki OM, Poisnel G, Marchant NL, Chételat G. Depressive symptoms in cognitively unimpaired older adults are associated with lower structural and functional integrity in a frontolimbic network. Mol Psychiatry 2022; 27:5086-5095. [PMID: 36258017 PMCID: PMC9763117 DOI: 10.1038/s41380-022-01772-8] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/25/2021] [Revised: 08/19/2022] [Accepted: 08/30/2022] [Indexed: 01/14/2023]
Abstract
Subclinical depressive symptoms are associated with increased risk of Alzheimer's disease (AD), but the brain mechanisms underlying this relationship are still unclear. We aimed to provide a comprehensive overview of the brain substrates of subclinical depressive symptoms in cognitively unimpaired older adults using complementary multimodal neuroimaging data. We included cognitively unimpaired older adults from the baseline data of the primary cohort Age-Well (n = 135), and from the replication cohort ADNI (n = 252). In both cohorts, subclinical depressive symptoms were assessed using the 15-item version of the Geriatric Depression Scale; based on this scale, participants were classified as having depressive symptoms (>0) or not (0). Voxel-wise between-group comparisons were performed to highlight differences in gray matter volume, glucose metabolism and amyloid deposition; as well as white matter integrity (only available in Age-Well). Age-Well participants with subclinical depressive symptoms had lower gray matter volume in the hippocampus and lower white matter integrity in the fornix and the posterior parts of the cingulum and corpus callosum, compared to participants without symptoms. Hippocampal atrophy was recovered in ADNI, where participants with subclinical depressive symptoms also showed glucose hypometabolism in the hippocampus, amygdala, precuneus/posterior cingulate cortex, medial and dorsolateral prefrontal cortex, insula, and temporoparietal cortex. Subclinical depressive symptoms were not associated with brain amyloid deposition in either cohort. Subclinical depressive symptoms in ageing are linked with neurodegeneration biomarkers in the frontolimbic network including brain areas particularly sensitive to AD. The relationship between depressive symptoms and AD may be partly underpinned by neurodegeneration in common brain regions.
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Affiliation(s)
- Edelweiss Touron
- Unité 1237 PhIND "Physiopathology and Imaging of Neurological Disorders", Institut National de la Santé et de la Recherche Médicale, Blood and Brain @ Caen-Normandie, GIP Cyceron, Normandie Université, Université de Caen, Caen, France
| | - Inès Moulinet
- Unité 1237 PhIND "Physiopathology and Imaging of Neurological Disorders", Institut National de la Santé et de la Recherche Médicale, Blood and Brain @ Caen-Normandie, GIP Cyceron, Normandie Université, Université de Caen, Caen, France
| | - Elizabeth Kuhn
- Unité 1237 PhIND "Physiopathology and Imaging of Neurological Disorders", Institut National de la Santé et de la Recherche Médicale, Blood and Brain @ Caen-Normandie, GIP Cyceron, Normandie Université, Université de Caen, Caen, France
| | - Siya Sherif
- Unité 1237 PhIND "Physiopathology and Imaging of Neurological Disorders", Institut National de la Santé et de la Recherche Médicale, Blood and Brain @ Caen-Normandie, GIP Cyceron, Normandie Université, Université de Caen, Caen, France
| | - Valentin Ourry
- Unité 1237 PhIND "Physiopathology and Imaging of Neurological Disorders", Institut National de la Santé et de la Recherche Médicale, Blood and Brain @ Caen-Normandie, GIP Cyceron, Normandie Université, Université de Caen, Caen, France
- Unité 1077 NIMH "Neuropsychologie et Imagerie de la Mémoire Humaine," Institut National de la Santé et de la Recherche Médicale, Normandie Université, Université de Caen, PSL Université, EPHE, CHU de Caen-Normandie, GIP Cyceron, Caen, France
| | - Brigitte Landeau
- Unité 1237 PhIND "Physiopathology and Imaging of Neurological Disorders", Institut National de la Santé et de la Recherche Médicale, Blood and Brain @ Caen-Normandie, GIP Cyceron, Normandie Université, Université de Caen, Caen, France
| | - Florence Mézenge
- Unité 1237 PhIND "Physiopathology and Imaging of Neurological Disorders", Institut National de la Santé et de la Recherche Médicale, Blood and Brain @ Caen-Normandie, GIP Cyceron, Normandie Université, Université de Caen, Caen, France
| | - Denis Vivien
- Unité 1237 PhIND "Physiopathology and Imaging of Neurological Disorders", Institut National de la Santé et de la Recherche Médicale, Blood and Brain @ Caen-Normandie, GIP Cyceron, Normandie Université, Université de Caen, Caen, France
- Département de Recherche Clinique, CHU de Caen-Normandie, Caen, France
| | - Olga M Klimecki
- Clinical Psychology and Behavioral Neuroscience, Faculty of Psychology, Technische Universität Dresden, 01187, Dresden, Germany
| | - Géraldine Poisnel
- Unité 1237 PhIND "Physiopathology and Imaging of Neurological Disorders", Institut National de la Santé et de la Recherche Médicale, Blood and Brain @ Caen-Normandie, GIP Cyceron, Normandie Université, Université de Caen, Caen, France
| | | | - Gaël Chételat
- Unité 1237 PhIND "Physiopathology and Imaging of Neurological Disorders", Institut National de la Santé et de la Recherche Médicale, Blood and Brain @ Caen-Normandie, GIP Cyceron, Normandie Université, Université de Caen, Caen, France.
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Hedderich DM, Schmitz-Koep B, Schuberth M, Schultz V, Schlaeger SJ, Schinz D, Rubbert C, Caspers J, Zimmer C, Grimmer T, Yakushev I. Impact of normative brain volume reports on the diagnosis of neurodegenerative dementia disorders in neuroradiology: A real-world, clinical practice study. Front Aging Neurosci 2022; 14:971863. [PMID: 36313028 PMCID: PMC9597632 DOI: 10.3389/fnagi.2022.971863] [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: 06/17/2022] [Accepted: 09/20/2022] [Indexed: 12/02/2022] Open
Abstract
Background: Normative brain volume reports (NBVR) are becoming more available in the work-up of patients with suspected dementia disorders, potentially leveraging the value of structural MRI in clinical settings. The present study aims to investigate the impact of NBVRs on the diagnosis of neurodegenerative dementia disorders in real-world clinical practice. Methods: We retrospectively analyzed data of 112 memory clinic patients, who were consecutively referred for MRI and 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) during a 12-month period. Structural MRI was assessed by two residents with 2 and 3 years of neuroimaging experience. Statements and diagnostic confidence regarding the presence of a neurodegenerative disorder in general (first level) and Alzheimer’s disease (AD) pattern in particular (second level) were recorded without and with NBVR information. FDG-PET served as the reference standard. Results: Overall, despite a trend towards increased accuracy, the impact of NBVRs on diagnostic accuracy was low and non-significant. We found a significant drop of sensitivity (0.75–0.58; p < 0.001) and increase of specificity (0.62–0.85; p < 0.001) for rater 1 at identifying patients with neurodegenerative dementia disorders. Diagnostic confidence increased for rater 2 (p < 0.001). Conclusions: Overall, NBVRs had a limited impact on diagnostic accuracy in real-world clinical practice. Potentially, NBVR might increase diagnostic specificity and confidence of neuroradiology residents. To this end, a well-defined framework for integration of NBVR in the diagnostic process and improved algorithms of NBVR generation are essential.
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Affiliation(s)
- Dennis M. Hedderich
- Department of Neuroradiology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
- *Correspondence: Dennis M. Hedderich
| | - Benita Schmitz-Koep
- Department of Neuroradiology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Madeleine Schuberth
- Department of Neuroradiology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Vivian Schultz
- Department of Neuroradiology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Sarah J. Schlaeger
- Department of Neuroradiology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - David Schinz
- Department of Neuroradiology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Christian Rubbert
- Department of Diagnostic and Interventional Radiology, Medical Faculty, University Dusseldorf, Dusseldorf, Germany
| | - Julian Caspers
- Department of Diagnostic and Interventional Radiology, Medical Faculty, University Dusseldorf, Dusseldorf, Germany
| | - Claus Zimmer
- Department of Neuroradiology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Timo Grimmer
- Department of Psychiatry and Psychotherapy, Klinikum rechts der Isar, Sch, Munich, Germany
| | - Igor Yakushev
- Department of Nuclear Medicine, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
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Zhang Y, Wang J, Zhang Y, Wang L, Wei J. Characteristics Scanning of Brain Structure and Function Changes in Patients with Different Degrees of Alzheimer's Disease. CONTRAST MEDIA & MOLECULAR IMAGING 2022; 2022:5238941. [PMID: 36262986 PMCID: PMC9546702 DOI: 10.1155/2022/5238941] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 08/20/2022] [Accepted: 09/08/2022] [Indexed: 01/26/2023]
Abstract
Through the case control study on structural magnetic resonance imaging (sMRI) scanning, MR spectrum (MRS), and neuropsychological assessment of the intracranial structures of Alzheimer's disease (AD), patients of different degrees (early, middle, and late), the early clinical features, imaging features, and neuropsychological characteristics of patients with AD were analyzed to provide help for the early diagnosis of AD. The data of MR scanning of the brain, bilateral MRS scan of the hippocampus, thyroid function and other laboratory indicators, and neuropsychological evaluation analysis were collected in 50 patients who had been diagnosed with AD. According to CDR, 50 patients were divided into the early AD group and the middle and advanced AD group, with 23 patients in the early AD group and 27 patients in the middle and advanced AD group. Retrospective study was conducted to analyze the general conditions, medial temporal lobe atrophy (MTA) grading, and the metabolic changes of bilateral MRS in the hippocampus of patients in both groups, so did the mini-mental state examination (MMSE), activities of daily living scale (ADL), and other neuropsychological assessment results. Moreover, the comparative analysis was carried out. The results showed that the MTA grade of medial temporal atrophy increased with the progressive severity of the disease in both groups. A statistical test was conducted on the reduction of hippocampal volume in the two groups, and the P was less than 0.05. Therefore, the MTA scale was of great value in the diagnosis and staging of early AD. However, when the diagnosis of early AD was treated by MTA visual evaluation alone, there was 23.8% false negative diagnosis. If the judgment of early AD only depended on the metabolic changes of hippocampus MRS or MR scanning of intracranial structures, it was likely to cause false negative diagnosis. Therefore, the combination of MRS analysis and MR scanning of intracranial structures was favorable for the early diagnosis and treatment of AD. Combined with neuropsychological assessment, AD patients were staged more effectively, which greatly improved the accuracy of AD diagnosis in the early stage.
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Affiliation(s)
- Yamin Zhang
- Department of Neurology, Gansu Provincial Hospital, Lanzhou 730000, Gansu, China
| | - Jianping Wang
- Emergency Department, Gansu Provincial Hospital, Lanzhou 730000, Gansu, China
| | - Yi Zhang
- Department of Neurology, Gansu Provincial Hospital, Lanzhou 730000, Gansu, China
| | - Lujun Wang
- Neonatal Intensive Care Unit, Gansu Provincial Maternity and Child-Care Hospital, Lanzhou 730000, Gansu, China
| | - Jia Wei
- Functional Department, Gansu Provincial Hospital, Lanzhou 730000, Gansu, China
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Mejia AF. Discussion on "distributional independent component analysis for diverse neuroimaging modalities" by Ben Wu, Subhadip Pal, Jian Kang, and Ying Guo. Biometrics 2022; 78:1109-1112. [PMID: 34897649 PMCID: PMC9188627 DOI: 10.1111/biom.13592] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 06/21/2021] [Accepted: 06/28/2021] [Indexed: 11/30/2022]
Abstract
I applaud the authors on their innovative generalized independent component analysis (ICA) framework for neuroimaging data. Although ICA has enjoyed great popularity for the analysis of functional magnetic resonance imaging (fMRI) data, its applicability to other modalities has been limited because standard ICA algorithms may not be directly applicable to a diversity of data representations. This is particularly true for single-subject structural neuroimaging, where only a single measurement is collected at each location in the brain. The ingenious idea of Wu et al. (2021) is to transform the data to a vector of probabilities via a mixture distribution with K components, which (following a simple transformation toR K - 1 $\mathbb {R}^{K-1}$ ) can be directly analyzed with standard ICA algorithms, such as infomax (Bell and Sejnowski, 1995) or fastICA (Hyvarinen, 1999). The underlying distribution forming the basis of the mixture is customized to the particular modality being analyzed. This framework, termed distributional ICA (DICA), is applicable in theory to nearly any neuroimaging modality. This has substantial implications for ICA as a general tool for neuroimaging analysis, with particular promise for structural modalities and multimodal studies. This invited commentary focuses on the applicability and potential of DICA for different neuroimaging modalities, questions around details of implementation and performance, and limitations of the validation study presented in the paper.
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Affiliation(s)
- Amanda F. Mejia
- Department of Statistics, Indiana University, Myles Brand Hall E104 901
E. 10th Street Bloomington, IN, 47408, USA
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Ganz T, Fainstein N, Ben-Hur T. When the infectious environment meets the AD brain. Mol Neurodegener 2022; 17:53. [PMID: 35986296 PMCID: PMC9388962 DOI: 10.1186/s13024-022-00559-3] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 08/08/2022] [Indexed: 11/23/2022] Open
Abstract
Background The Amyloid theory of Alzheimer’s disease (AD) suggests that the deposition of Amyloid β (Aβ) in the brain triggers a chain of events, involving the deposition of phosphorylated Tau and other misfolded proteins, leading to neurodegeneration via neuroinflammation, oxidative stress, and neurovascular factors. The infectious theory linked various infectious agents with the development of AD, raising the possibility that they serve as etiological causes of the disease. Are these theories mutually exclusive, or do they coincide? Main body In this review, we will discuss how the two theories converge. We present a model by which (1) the systemic infectious burden accelerates the development of AD brain pathology via bacterial Amyloids and other pathogen-associated molecular patterns (PAMPs), and (2) the developing AD brain pathology increases its susceptibility to the neurotoxicity of infectious agents -derived PAMPs, which drive neurodegeneration via activated microglia. Conclusions The reciprocal effects of amyloid deposition and systemic infectious burden may lead to a vicious cycle fueling Alzheimer’s disease pathogenesis.
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Classification of Alzheimer’s Disease Using Dual-Phase 18F-Florbetaben Image with Rank-Based Feature Selection and Machine Learning. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12157355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
18F-florbetaben (FBB) positron emission tomography is a representative imaging test that observes amyloid deposition in the brain. Compared to delay-phase FBB (dFBB), early-phase FBB shows patterns related to glucose metabolism in 18F-fluorodeoxyglucose perfusion images. The purpose of this study is to prove that classification accuracy is higher when using dual-phase FBB (dual FBB) versus dFBB quantitative analysis by using machine learning and to find an optimal machine learning model suitable for dual FBB quantitative analysis data. The key features of our method are (1) a feature ranking method for each phase of FBB with a cross-validated F1 score and (2) a quantitative diagnostic model based on machine learning methods. We compared four classification models: support vector machine, naïve Bayes, logistic regression, and random forest (RF). In composite standardized uptake value ratio, RF achieved the best performance (F1: 78.06%) with dual FBB, which was 4.83% higher than the result with dFBB. In conclusion, regardless of the two quantitative analysis methods, using the dual FBB has a higher classification accuracy than using the dFBB. The RF model is the machine learning model that best classifies a dual FBB. The regions that have the greatest influence on the classification of dual FBB are the frontal and temporal lobes.
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Koppelmans V, Silvester B, Duff K. Neural Mechanisms of Motor Dysfunction in Mild Cognitive Impairment and Alzheimer’s Disease: A Systematic Review. J Alzheimers Dis Rep 2022; 6:307-344. [PMID: 35891638 PMCID: PMC9277676 DOI: 10.3233/adr-210065] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 05/23/2022] [Indexed: 12/20/2022] Open
Abstract
Background: Despite the prevalence of motor symptoms in mild cognitive impairment (MCI) and Alzheimer’s disease (AD), their underlying neural mechanisms have not been thoroughly studied. Objective: This review summarizes the neural underpinnings of motor deficits in MCI and AD. Methods: We searched PubMed up until August of 2021 and identified 37 articles on neuroimaging of motor function in MCI and AD. Study bias was evaluated based on sample size, availability of control samples, and definition of the study population in terms of diagnosis. Results: The majority of studies investigated gait, showing that slower gait was associated with smaller hippocampal volume and prefrontal deactivation. Less prefrontal activation was also observed during cognitive-motor dual tasking, while more activation in cerebellar, cingulate, cuneal, somatosensory, and fusiform brain regions was observed when performing a hand squeezing task. Excessive subcortical white matter lesions in AD were associated with more signs of parkinsonism, poorer performance during a cognitive and motor dual task, and poorer functional mobility. Gait and cognitive dual-tasking was furthermore associated with cortical thickness of temporal lobe regions. Most non-gait motor measures were only reported in one study in relation to neural measures. Conclusion: Cross-sectional designs, lack of control groups, mixing amnestic- and non-amnestic MCI, disregard of sex differences, and small sample sizes limited the interpretation of several studies, which needs to be addressed in future research to progress the field.
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Affiliation(s)
- Vincent Koppelmans
- Department of Psychiatry, University of Utah, SaltLake City, UT, USA
- Huntsman Mental Health Institute, University of Utah, Salt Lake City, UT, USA
| | - Benjamin Silvester
- Department of Psychiatry, University of Utah, SaltLake City, UT, USA
- Huntsman Mental Health Institute, University of Utah, Salt Lake City, UT, USA
| | - Kevin Duff
- Department of Neurology, University of Utah, SaltLake City, UT, USA
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Zamani J, Sadr A, Javadi AH. Classification of early-MCI patients from healthy controls using evolutionary optimization of graph measures of resting-state fMRI, for the Alzheimer's disease neuroimaging initiative. PLoS One 2022; 17:e0267608. [PMID: 35727837 PMCID: PMC9212187 DOI: 10.1371/journal.pone.0267608] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Accepted: 04/11/2022] [Indexed: 11/21/2022] Open
Abstract
Identifying individuals with early mild cognitive impairment (EMCI) can be an effective strategy for early diagnosis and delay the progression of Alzheimer's disease (AD). Many approaches have been devised to discriminate those with EMCI from healthy control (HC) individuals. Selection of the most effective parameters has been one of the challenging aspects of these approaches. In this study we suggest an optimization method based on five evolutionary algorithms that can be used in optimization of neuroimaging data with a large number of parameters. Resting-state functional magnetic resonance imaging (rs-fMRI) measures, which measure functional connectivity, have been shown to be useful in prediction of cognitive decline. Analysis of functional connectivity data using graph measures is a common practice that results in a great number of parameters. Using graph measures we calculated 1155 parameters from the functional connectivity data of HC (n = 72) and EMCI (n = 68) extracted from the publicly available database of the Alzheimer's disease neuroimaging initiative database (ADNI). These parameters were fed into the evolutionary algorithms to select a subset of parameters for classification of the data into two categories of EMCI and HC using a two-layer artificial neural network. All algorithms achieved classification accuracy of 94.55%, which is extremely high considering single-modality input and low number of data participants. These results highlight potential application of rs-fMRI and efficiency of such optimization methods in classification of images into HC and EMCI. This is of particular importance considering that MRI images of EMCI individuals cannot be easily identified by experts.
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Affiliation(s)
- Jafar Zamani
- School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran
| | - Ali Sadr
- School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran
| | - Amir-Homayoun Javadi
- School of Psychology, University of Kent, Canterbury, United Kingdom
- School of Rehabilitation, Tehran University of Medical Sciences, Tehran, Iran
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31
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Predictive Scale for Amyloid PET Positivity Based on Clinical and MRI Variables in Patients with Amnestic Mild Cognitive Impairment. J Clin Med 2022; 11:jcm11123433. [PMID: 35743503 PMCID: PMC9224873 DOI: 10.3390/jcm11123433] [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: 04/11/2022] [Revised: 06/11/2022] [Accepted: 06/12/2022] [Indexed: 12/05/2022] Open
Abstract
The presence of amyloid-β (Aβ) deposition is considered important in patients with amnestic mild cognitive impairment (aMCI), since they can progress to Alzheimer’s disease dementia. Amyloid positron emission tomography (PET) has been used for detecting Aβ deposition, but its high cost is a significant barrier for clinical usage. Therefore, we aimed to develop a new predictive scale for amyloid PET positivity using easily accessible tools. Overall, 161 aMCI patients were recruited from six memory clinics and underwent neuropsychological tests, brain magnetic resonance imaging (MRI), apolipoprotein E (APOE) genotype testing, and amyloid PET. Among the potential predictors, verbal and visual memory tests, medial temporal lobe atrophy, APOE genotype, and age showed significant differences between the Aβ-positive and Aβ-negative groups and were combined to make a model for predicting amyloid PET positivity with the area under the curve (AUC) of 0.856. Based on the best model, we developed the new predictive scale comprising integers, which had an optimal cutoff score ≥ 3. The new predictive scale was validated in another cohort of 98 participants and showed a good performance with AUC of 0.835. This new predictive scale with accessible variables may be useful for predicting Aβ positivity in aMCI patients in clinical practice.
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32
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Kim J, Jeong M, Stiles WR, Choi HS. Neuroimaging Modalities in Alzheimer's Disease: Diagnosis and Clinical Features. Int J Mol Sci 2022; 23:6079. [PMID: 35682758 PMCID: PMC9181385 DOI: 10.3390/ijms23116079] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 05/25/2022] [Accepted: 05/26/2022] [Indexed: 11/17/2022] Open
Abstract
Alzheimer's disease (AD) is a neurodegenerative disease causing progressive cognitive decline until eventual death. AD affects millions of individuals worldwide in the absence of effective treatment options, and its clinical causes are still uncertain. The onset of dementia symptoms indicates severe neurodegeneration has already taken place. Therefore, AD diagnosis at an early stage is essential as it results in more effective therapy to slow its progression. The current clinical diagnosis of AD relies on mental examinations and brain imaging to determine whether patients meet diagnostic criteria, and biomedical research focuses on finding associated biomarkers by using neuroimaging techniques. Multiple clinical brain imaging modalities emerged as potential techniques to study AD, showing a range of capacity in their preciseness to identify the disease. This review presents the advantages and limitations of brain imaging modalities for AD diagnosis and discusses their clinical value.
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Affiliation(s)
- JunHyun Kim
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; (J.K.); (M.J.); (W.R.S.)
- Department of Cogno-Mechatronics Engineering, Pusan National University, Busan 46241, Korea
| | - Minhong Jeong
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; (J.K.); (M.J.); (W.R.S.)
| | - Wesley R. Stiles
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; (J.K.); (M.J.); (W.R.S.)
| | - Hak Soo Choi
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; (J.K.); (M.J.); (W.R.S.)
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33
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Current trends in blood biomarker detection and imaging for Alzheimer’s disease. Biosens Bioelectron 2022; 210:114278. [DOI: 10.1016/j.bios.2022.114278] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 03/21/2022] [Accepted: 04/09/2022] [Indexed: 12/28/2022]
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34
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Reyes-Leiva D, Dols-Icardo O, Sirisi S, Cortés-Vicente E, Turon-Sans J, de Luna N, Blesa R, Belbin O, Montal V, Alcolea D, Fortea J, Lleó A, Rojas-García R, Illán-Gala I. Pathophysiological Underpinnings of Extra-Motor Neurodegeneration in Amyotrophic Lateral Sclerosis: New Insights From Biomarker Studies. Front Neurol 2022; 12:750543. [PMID: 35115992 PMCID: PMC8804092 DOI: 10.3389/fneur.2021.750543] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 12/09/2021] [Indexed: 11/13/2022] Open
Abstract
Amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration (FTLD) lie at opposing ends of a clinical, genetic, and neuropathological continuum. In the last decade, it has become clear that cognitive and behavioral changes in patients with ALS are more frequent than previously recognized. Significantly, these non-motor features can impact the diagnosis, prognosis, and management of ALS. Partially overlapping neuropathological staging systems have been proposed to describe the distribution of TAR DNA-binding protein 43 (TDP-43) aggregates outside the corticospinal tract. However, the relationship between TDP-43 inclusions and neurodegeneration is not absolute and other pathophysiological processes, such as neuroinflammation (with a prominent role of microglia), cortical hyperexcitability, and synaptic dysfunction also play a central role in ALS pathophysiology. In the last decade, imaging and biofluid biomarker studies have revealed important insights into the pathophysiological underpinnings of extra-motor neurodegeneration in the ALS-FTLD continuum. In this review, we first summarize the clinical and pathophysiological correlates of extra-motor neurodegeneration in ALS. Next, we discuss the diagnostic and prognostic value of biomarkers in ALS and their potential to characterize extra-motor neurodegeneration. Finally, we debate about how biomarkers could improve the diagnosis and classification of ALS. Emerging imaging biomarkers of extra-motor neurodegeneration that enable the monitoring of disease progression are particularly promising. In addition, a growing arsenal of biofluid biomarkers linked to neurodegeneration and neuroinflammation are improving the diagnostic accuracy and identification of patients with a faster progression rate. The development and validation of biomarkers that detect the pathological aggregates of TDP-43 in vivo are notably expected to further elucidate the pathophysiological underpinnings of extra-motor neurodegeneration in ALS. Novel biomarkers tracking the different aspects of ALS pathophysiology are paving the way to precision medicine approaches in the ALS-FTLD continuum. These are essential steps to improve the diagnosis and staging of ALS and the design of clinical trials testing novel disease-modifying treatments.
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Affiliation(s)
- David Reyes-Leiva
- Neuromuscular Diseases Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras, CIBERER, Valencia, Spain
| | - Oriol Dols-Icardo
- Sant Pau Memory Unit, Department of Neurology, Biomedical Research Institute Sant Pau, Hospital de la Santa Creu i Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas, CIBERNED, Madrid, Spain
| | - Sonia Sirisi
- Sant Pau Memory Unit, Department of Neurology, Biomedical Research Institute Sant Pau, Hospital de la Santa Creu i Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas, CIBERNED, Madrid, Spain
| | - Elena Cortés-Vicente
- Neuromuscular Diseases Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras, CIBERER, Valencia, Spain
| | - Janina Turon-Sans
- Neuromuscular Diseases Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras, CIBERER, Valencia, Spain
| | - Noemi de Luna
- Neuromuscular Diseases Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras, CIBERER, Valencia, Spain
| | - Rafael Blesa
- Sant Pau Memory Unit, Department of Neurology, Biomedical Research Institute Sant Pau, Hospital de la Santa Creu i Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas, CIBERNED, Madrid, Spain
| | - Olivia Belbin
- Sant Pau Memory Unit, Department of Neurology, Biomedical Research Institute Sant Pau, Hospital de la Santa Creu i Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas, CIBERNED, Madrid, Spain
| | - Victor Montal
- Sant Pau Memory Unit, Department of Neurology, Biomedical Research Institute Sant Pau, Hospital de la Santa Creu i Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas, CIBERNED, Madrid, Spain
| | - Daniel Alcolea
- Sant Pau Memory Unit, Department of Neurology, Biomedical Research Institute Sant Pau, Hospital de la Santa Creu i Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas, CIBERNED, Madrid, Spain
| | - Juan Fortea
- Sant Pau Memory Unit, Department of Neurology, Biomedical Research Institute Sant Pau, Hospital de la Santa Creu i Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas, CIBERNED, Madrid, Spain
| | - Alberto Lleó
- Sant Pau Memory Unit, Department of Neurology, Biomedical Research Institute Sant Pau, Hospital de la Santa Creu i Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas, CIBERNED, Madrid, Spain
| | - Ricard Rojas-García
- Neuromuscular Diseases Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras, CIBERER, Valencia, Spain
| | - Ignacio Illán-Gala
- Sant Pau Memory Unit, Department of Neurology, Biomedical Research Institute Sant Pau, Hospital de la Santa Creu i Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas, CIBERNED, Madrid, Spain
- *Correspondence: Ignacio Illán-Gala
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Han H, Li X, Gan JQ, Yu H, Wang H. Biomarkers Derived from Alterations in Overlapping Community Structure of Resting-state Brain Functional Networks for Detecting Alzheimer's Disease. Neuroscience 2021; 484:38-52. [PMID: 34973385 DOI: 10.1016/j.neuroscience.2021.12.031] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 12/17/2021] [Accepted: 12/23/2021] [Indexed: 11/27/2022]
Abstract
Recent studies show that overlapping community structure is an important feature of the brain functional network. However, alterations in such overlapping community structure in Alzheimer's disease (AD) patients have not been examined yet. In this study, we investigate the overlapping community structure in AD by using resting-state functional magnetic resonance imaging (rs-fMRI) data. The collective sparse symmetric non-negative matrix factorization (cssNMF) is adopted to detect the overlapping community structure. Experimental results on 28 AD patients and 32 normal controls (NCs) from the ADNI2 dataset show that the two groups have remarkable differences in terms of the optimal number of communities, the hierarchy of communities detected at different scales, network functional segregation, and nodal functional diversity. In particular, the frontal-parietal and basal ganglia networks exhibit significant differences between the two groups. A machine learning framework proposed in this paper for AD detection achieved an accuracy of 76.7% when using the detected community strengths of the frontal-parietal and basal ganglia networks only as input features. These findings provide novel insights into the understanding of pathological changes in the brain functional network organization of AD and show the potential of the community structure-related features for AD detection.
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Affiliation(s)
- Hongfang Han
- Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science & Medical Engineering, Southeast University, Nanjing 210096, Jiangsu, PR China; Institute of Artificial Intelligence of Hefei Comprehensive National Science Center, Hefei 230094, Anhui, PR China
| | - Xuan Li
- Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science & Medical Engineering, Southeast University, Nanjing 210096, Jiangsu, PR China; School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, UK; Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, 52425 Jülich, Germany
| | - John Q Gan
- School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, UK
| | - Hua Yu
- Department of Cardiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230001, Anhui, PR China
| | - Haixian Wang
- Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science & Medical Engineering, Southeast University, Nanjing 210096, Jiangsu, PR China; Institute of Artificial Intelligence of Hefei Comprehensive National Science Center, Hefei 230094, Anhui, PR China.
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Lutz A, Chételat G, Collette F, Klimecki OM, Marchant NL, Gonneaud J. The protective effect of mindfulness and compassion meditation practices on ageing: Hypotheses, models and experimental implementation. Ageing Res Rev 2021; 72:101495. [PMID: 34718153 DOI: 10.1016/j.arr.2021.101495] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 09/09/2021] [Accepted: 10/18/2021] [Indexed: 10/20/2022]
Abstract
Alzheimer's disease (AD) represents a major health and societal issue; there is no treatment to date and the pathophysiological mechanisms underlying this disease are not well understood. Yet, there is hope that AD risk factors and thus the number of AD cases can be significantly reduced by prevention measures based on lifestyle modifications as targeted by non-pharmacological preventive interventions. So far, these interventions have rarely targeted the psycho-affective risk factors related to depression, stress, anxiety, and feeling of loneliness, which are all prevalent in ageing. This paper presents the hypothesis that the regular practice of mindfulness meditation (MM) and loving-kindness and compassion meditation (LKCM) in the ageing population constitutes a lifestyle that is protective against AD. In this model, these practices can promote cognition, mental health, and well-being by strengthening attention control, metacognitive monitoring, emotion regulation and pro-social capacities. Training these capacities could reduce the risk of AD by upregulating beneficial age-related factors such as cognitive reserve, and down-regulating detrimental age-related factors, such as stress, or depression. As an illustration, we present the Medit-Ageing study (public name Silver Santé Study), an on-going European project that assesses the impact and mechanisms of non-pharmacological interventions including meditation, in the ageing population.
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Dyrba M, Hanzig M, Altenstein S, Bader S, Ballarini T, Brosseron F, Buerger K, Cantré D, Dechent P, Dobisch L, Düzel E, Ewers M, Fliessbach K, Glanz W, Haynes JD, Heneka MT, Janowitz D, Keles DB, Kilimann I, Laske C, Maier F, Metzger CD, Munk MH, Perneczky R, Peters O, Preis L, Priller J, Rauchmann B, Roy N, Scheffler K, Schneider A, Schott BH, Spottke A, Spruth EJ, Weber MA, Ertl-Wagner B, Wagner M, Wiltfang J, Jessen F, Teipel SJ. Improving 3D convolutional neural network comprehensibility via interactive visualization of relevance maps: evaluation in Alzheimer's disease. Alzheimers Res Ther 2021; 13:191. [PMID: 34814936 PMCID: PMC8611898 DOI: 10.1186/s13195-021-00924-2] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 10/25/2021] [Indexed: 11/29/2022]
Abstract
Background Although convolutional neural networks (CNNs) achieve high diagnostic accuracy for detecting Alzheimer’s disease (AD) dementia based on magnetic resonance imaging (MRI) scans, they are not yet applied in clinical routine. One important reason for this is a lack of model comprehensibility. Recently developed visualization methods for deriving CNN relevance maps may help to fill this gap as they allow the visualization of key input image features that drive the decision of the model. We investigated whether models with higher accuracy also rely more on discriminative brain regions predefined by prior knowledge. Methods We trained a CNN for the detection of AD in N = 663 T1-weighted MRI scans of patients with dementia and amnestic mild cognitive impairment (MCI) and verified the accuracy of the models via cross-validation and in three independent samples including in total N = 1655 cases. We evaluated the association of relevance scores and hippocampus volume to validate the clinical utility of this approach. To improve model comprehensibility, we implemented an interactive visualization of 3D CNN relevance maps, thereby allowing intuitive model inspection. Results Across the three independent datasets, group separation showed high accuracy for AD dementia versus controls (AUC ≥ 0.91) and moderate accuracy for amnestic MCI versus controls (AUC ≈ 0.74). Relevance maps indicated that hippocampal atrophy was considered the most informative factor for AD detection, with additional contributions from atrophy in other cortical and subcortical regions. Relevance scores within the hippocampus were highly correlated with hippocampal volumes (Pearson’s r ≈ −0.86, p < 0.001). Conclusion The relevance maps highlighted atrophy in regions that we had hypothesized a priori. This strengthens the comprehensibility of the CNN models, which were trained in a purely data-driven manner based on the scans and diagnosis labels. The high hippocampus relevance scores as well as the high performance achieved in independent samples support the validity of the CNN models in the detection of AD-related MRI abnormalities. The presented data-driven and hypothesis-free CNN modeling approach might provide a useful tool to automatically derive discriminative features for complex diagnostic tasks where clear clinical criteria are still missing, for instance for the differential diagnosis between various types of dementia. Supplementary Information The online version contains supplementary material available at 10.1186/s13195-021-00924-2.
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Affiliation(s)
- Martin Dyrba
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany.
| | - Moritz Hanzig
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany.,Institute of Visual and Analytic Computing, University of Rostock, Rostock, Germany
| | - Slawek Altenstein
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany.,Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Campus Charité Mitte, Berlin, Germany
| | - Sebastian Bader
- Institute of Visual and Analytic Computing, University of Rostock, Rostock, Germany
| | | | - Frederic Brosseron
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department for Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, Bonn, Germany
| | - Katharina Buerger
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany.,Institute for Stroke and Dementia Research (ISD), University Hospital, Ludwig Maximilian University, Munich, Germany
| | - Daniel Cantré
- Institute of Diagnostic and Interventional Radiology, Pediatric Radiology and Neuroradiology, Rostock University Medical Center, Rostock, Germany
| | - Peter Dechent
- MR-Research in Neurosciences, Department of Cognitive Neurology, Georg-August-University, Goettingen, Germany
| | - Laura Dobisch
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Emrah Düzel
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany.,Institute of Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University, Magdeburg, Germany
| | - Michael Ewers
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany.,Institute for Stroke and Dementia Research (ISD), University Hospital, Ludwig Maximilian University, Munich, Germany
| | - Klaus Fliessbach
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department for Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, Bonn, Germany
| | - Wenzel Glanz
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | | | - Michael T Heneka
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department for Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, Bonn, Germany
| | - Daniel Janowitz
- Institute for Stroke and Dementia Research (ISD), University Hospital, Ludwig Maximilian University, Munich, Germany
| | - Deniz B Keles
- Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Campus Benjamin Franklin, Berlin, Germany
| | - Ingo Kilimann
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany.,Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock, Germany
| | - Christoph Laske
- German Center for Neurodegenerative Diseases (DZNE), Tuebingen, Germany.,Section for Dementia Research, Hertie Institute for Clinical Brain Research, Tuebingen, Germany.,Department of Psychiatry and Psychotherapy, University of Tuebingen, Tuebingen, Germany
| | - Franziska Maier
- Department of Psychiatry, Medical Faculty, University of Cologne, Cologne, Germany
| | - Coraline D Metzger
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany.,Institute of Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University, Magdeburg, Germany.,Department of Psychiatry and Psychotherapy, Otto-von-Guericke University, Magdeburg, Germany
| | - Matthias H Munk
- German Center for Neurodegenerative Diseases (DZNE), Tuebingen, Germany.,Department of Psychiatry and Psychotherapy, University of Tuebingen, Tuebingen, Germany.,Systems Neurophysiology, Department of Biology, Darmstadt University of Technology, Darmstadt, Germany
| | - Robert Perneczky
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany.,Department of Psychiatry and Psychotherapy, University Hospital, Ludwig Maximilian University, Munich, Germany.,Munich Cluster for Systems Neurology (SyNergy), Ludwig Maximilian University, Munich, Germany.,Ageing Epidemiology Research Unit (AGE), School of Public Health, Imperial College London, London, UK
| | - Oliver Peters
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany.,Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Campus Benjamin Franklin, Berlin, Germany
| | - Lukas Preis
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany.,Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Campus Benjamin Franklin, Berlin, Germany
| | - Josef Priller
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany.,Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Campus Charité Mitte, Berlin, Germany.,Department of Psychiatry and Psychotherapy, Klinikum rechts der Isar, Technical University Munich, Munich, Germany
| | - Boris Rauchmann
- Department of Psychiatry and Psychotherapy, University Hospital, Ludwig Maximilian University, Munich, Germany
| | - Nina Roy
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Klaus Scheffler
- Department for Biomedical Magnetic Resonance, University of Tuebingen, Tuebingen, Germany
| | - Anja Schneider
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department for Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, Bonn, Germany
| | - Björn H Schott
- German Center for Neurodegenerative Diseases (DZNE), Goettingen, Germany.,Department of Psychiatry and Psychotherapy, University Medical Center Goettingen, Goettingen, Germany.,Leibniz Institute for Neurobiology, Magdeburg, Germany
| | - Annika Spottke
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department of Neurology, University Hospital Bonn, Bonn, Germany
| | - Eike J Spruth
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany.,Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Campus Charité Mitte, Berlin, Germany
| | - Marc-André Weber
- Institute of Diagnostic and Interventional Radiology, Pediatric Radiology and Neuroradiology, Rostock University Medical Center, Rostock, Germany
| | - Birgit Ertl-Wagner
- Institute for Clinical Radiology, Ludwig Maximilian University, Munich, Germany.,Department of Medical Imaging, University of Toronto, Toronto, Canada
| | - Michael Wagner
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department for Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, Bonn, Germany
| | - Jens Wiltfang
- German Center for Neurodegenerative Diseases (DZNE), Goettingen, Germany.,Department of Psychiatry and Psychotherapy, University Medical Center Goettingen, Goettingen, Germany.,Neurosciences and Signaling Group, Institute of Biomedicine (iBiMED), Department of Medical Sciences, University of Aveiro, Aveiro, Portugal
| | - Frank Jessen
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department of Psychiatry, Medical Faculty, University of Cologne, Cologne, Germany.,Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Cologne, Germany
| | - Stefan J Teipel
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany.,Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock, Germany
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Wakasugi N, Hanakawa T. It Is Time to Study Overlapping Molecular and Circuit Pathophysiologies in Alzheimer's and Lewy Body Disease Spectra. Front Syst Neurosci 2021; 15:777706. [PMID: 34867224 PMCID: PMC8637125 DOI: 10.3389/fnsys.2021.777706] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 10/28/2021] [Indexed: 12/30/2022] Open
Abstract
Alzheimer's disease (AD) is the leading cause of dementia due to neurodegeneration and is characterized by extracellular senile plaques composed of amyloid β1 - 42 (Aβ) as well as intracellular neurofibrillary tangles consisting of phosphorylated tau (p-tau). Dementia with Lewy bodies constitutes a continuous spectrum with Parkinson's disease, collectively termed Lewy body disease (LBD). LBD is characterized by intracellular Lewy bodies containing α-synuclein (α-syn). The core clinical features of AD and LBD spectra are distinct, but the two spectra share common cognitive and behavioral symptoms. The accumulation of pathological proteins, which acquire pathogenicity through conformational changes, has long been investigated on a protein-by-protein basis. However, recent evidence suggests that interactions among these molecules may be critical to pathogenesis. For example, Aβ/tau promotes α-syn pathology, and α-syn modulates p-tau pathology. Furthermore, clinical evidence suggests that these interactions may explain the overlapping pathology between AD and LBD in molecular imaging and post-mortem studies. Additionally, a recent hypothesis points to a common mechanism of prion-like progression of these pathological proteins, via neural circuits, in both AD and LBD. This suggests a need for understanding connectomics and their alterations in AD and LBD from both pathological and functional perspectives. In AD, reduced connectivity in the default mode network is considered a hallmark of the disease. In LBD, previous studies have emphasized abnormalities in the basal ganglia and sensorimotor networks; however, these account for movement disorders only. Knowledge about network abnormalities common to AD and LBD is scarce because few previous neuroimaging studies investigated AD and LBD as a comprehensive cohort. In this paper, we review research on the distribution and interactions of pathological proteins in the brain in AD and LBD, after briefly summarizing their clinical and neuropsychological manifestations. We also describe the brain functional and connectivity changes following abnormal protein accumulation in AD and LBD. Finally, we argue for the necessity of neuroimaging studies that examine AD and LBD cases as a continuous spectrum especially from the proteinopathy and neurocircuitopathy viewpoints. The findings from such a unified AD and Parkinson's disease (PD) cohort study should provide a new comprehensive perspective and key data for guiding disease modification therapies targeting the pathological proteins in AD and LBD.
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Affiliation(s)
- Noritaka Wakasugi
- Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Takashi Hanakawa
- Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Tokyo, Japan
- Department of Integrated Neuroanatomy and Neuroimaging, Graduate School of Medicine, Kyoto University, Kyoto, Japan
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Pursuit of precision medicine: Systems biology approaches in Alzheimer's disease mouse models. Neurobiol Dis 2021; 161:105558. [PMID: 34767943 PMCID: PMC10112395 DOI: 10.1016/j.nbd.2021.105558] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 11/05/2021] [Accepted: 11/08/2021] [Indexed: 12/12/2022] Open
Abstract
Alzheimer's disease (AD) is a complex disease that is mediated by numerous factors and manifests in various forms. A systems biology approach to studying AD involves analyses of various body systems, biological scales, environmental elements, and clinical outcomes to understand the genotype to phenotype relationship that potentially drives AD development. Currently, there are many research investigations probing how modifiable and nonmodifiable factors impact AD symptom presentation. This review specifically focuses on how imaging modalities can be integrated into systems biology approaches using model mouse populations to link brain level functional and structural changes to disease onset and progression. Combining imaging and omics data promotes the classification of AD into subtypes and paves the way for precision medicine solutions to prevent and treat AD.
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40
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Müller EG, Edwin TH, Strand BH, Stokke C, Revheim ME, Knapskog AB. Is Amyloid Burden Measured by 18F-Flutemetamol PET Associated with Progression in Clinical Alzheimer's Disease? J Alzheimers Dis 2021; 85:197-205. [PMID: 34776444 PMCID: PMC8842772 DOI: 10.3233/jad-215046] [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] [Indexed: 11/15/2022]
Abstract
Background: Patients with Alzheimer’s disease (AD) show heterogeneity in clinical progression rate, and we have limited tools to predict prognosis. Amyloid burden from 18F-Flutemetamol positron emission tomography (PET), as measured by standardized uptake value ratios (SUVR), might provide prognostic information. Objective: We investigate whether 18F-Flutemetamol PET composite or regional SUVRs are associated with trajectories of clinical progression. Methods: This observational longitudinal study included 94 patients with clinical AD. PET images were semi-quantified with normalization to pons. Group-based trajectory modeling was applied to identify trajectory groups according to change in the Clinical Dementia Rating Scale Sum of Boxes (CDR-SB) over time. Multinomial logistic regression models assessed the association of SUVRs with trajectory group membership. Results: Three trajectory groups were identified. In the regression models, neither composite nor regional SUVRs were associated with trajectory group membership. Conclusion: There were no associations between CDR progression and 18F-Flutemetamol PET-derived composite SUVRs or regional SUVRs in clinical AD.
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Affiliation(s)
- Ebba Gløersen Müller
- Division of Radiology and Nuclear Medicine, Department of Nuclear Medicine, Oslo University Hospital, Oslo, Norway.,Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Trine Holt Edwin
- Institute of Health and Society, Faculty of Medicine, University of Oslo, Oslo, Norway.,Department of Geriatric Medicine, Oslo University Hospital, Oslo, Norway.,Norwegian National Advisory Unit on Ageing and Health, Vestfold Hospital Trust, Tønsberg, Norway
| | - Bjørn Heine Strand
- Department of Geriatric Medicine, Oslo University Hospital, Oslo, Norway.,Norwegian National Advisory Unit on Ageing and Health, Vestfold Hospital Trust, Tønsberg, Norway.,Department of Chronic Diseases and Ageing, Norwegian Institute of Public Health, Oslo, Norway
| | - Caroline Stokke
- Division of Radiology and Nuclear Medicine, Department of Nuclear Medicine, Oslo University Hospital, Oslo, Norway.,Department of Physics, University of Oslo, Oslo, Norway
| | - Mona Elisabeth Revheim
- Division of Radiology and Nuclear Medicine, Department of Nuclear Medicine, Oslo University Hospital, Oslo, Norway.,Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
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41
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Ho SH, Yang DW. Risk Factors Predicting Amyloid PET Positivity in Patients with Mild Cognitive Impairment and Apolipoprotein E ɛ3/ɛ3 Genotypes. J Alzheimers Dis 2021; 77:1017-1024. [PMID: 32804143 DOI: 10.3233/jad-200439] [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] [Indexed: 11/15/2022]
Abstract
BACKGROUND The apolipoprotein E (APOE) ɛ4 allele is a well-known risk factor for AD and is associated with higher amyloid deposition and earlier dementia onset. However, the relationship between amyloid pathology and the most common APOE allele, ɛ3, has not been well studied. OBJECTIVE In this study, we aimed to identify the risk factors predicting amyloid PET positivity in patients with mild cognitive impairment (MCI) and APOEɛ3/ɛ3 genotypes. METHODS We retrospectively reviewed the medical records of MCI patients with APOEɛ3/ɛ3 genotypes who underwent amyloid PET scanning. Demographics, neuropsychological tests, and brain MRI were obtained. We analyzed which risk factors could affect amyloid PET positivity in MCI patients with APOEɛ3/ɛ3 genotypes using logistic regression models. RESULTS We recruited 171 MCI patients with APOEɛ3/ɛ3 genotypes in this study. Out of 171 patients, 49 patients (28.65%) showed positive results in the amyloid PET scans. In a multivariate logistic regression model, amyloid positivity was associated with frontal atrophy (OR = 2.63, p = 0.009), and CDR-SOB scores (OR = 2.46, p = 0.013). The odds ratio for amyloid PET positivity in patients older than and equal to 75 years with both frontal atrophy and CDR-SOB scores >1.0 was 3.63. CONCLUSION Our study demonstrated that frontal atrophy, high CDR-SOB scores, and old age were risk factors associated with amyloid PET positivity in MCI with APOEɛ3/ɛ3 genotypes.
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Affiliation(s)
- Seong Hee Ho
- Department of Neurology, The Catholic University of Korea Seoul St. Mary's hospital, Seoul, Republic of Korea
| | - Dong-Won Yang
- Department of Neurology, The Catholic University of Korea Seoul St. Mary's hospital, Seoul, Republic of Korea
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42
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Raji CA, Torosyan N, Silverman DHS. Optimizing Use of Neuroimaging Tools in Evaluation of Prodromal Alzheimer's Disease and Related Disorders. J Alzheimers Dis 2021; 77:935-947. [PMID: 32804147 DOI: 10.3233/jad-200487] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Alzheimer's disease (AD) is the most common neurodegenerative disease and is characterized by preclinical, pre-dementia, and dementia phases. Progression of the disease leads to cognitive decline and is associated with loss of functional independence, personality changes, and behavioral disturbances. Current guidelines for AD diagnosis include the use of neuroimaging tools as biomarkers for identifying and monitoring pathological changes. Various imaging modalities, namely magnetic resonance imaging (MRI), fluorodeoxyglucose-positron emission tomography (FDG-PET) and PET with amyloid-beta tracers are available to facilitate early accurate diagnoses. Enhancing diagnosis in the early stages of the disease can allow for timely interventions that can delay progression of the disease. This paper will discuss the characteristic findings associated with each of the imaging tools for patients with AD, with a focus on FDG-PET due to its established accuracy in assisting with the differential diagnosis of dementia and discussion of other methods including MRI. Diagnostically-relevant features to aid clinicians in making a differential diagnosis will also be pointed out and multimodal imaging will be reviewed. We also discuss the role of quantification software in interpretation of brain imaging. Lastly, to guide evaluation of patients presenting with cognitive deficits, an algorithm for optimal integration of these imaging tools will be shared. Molecular imaging modalities used in dementia evaluations hold promise toward identifying AD-related pathology before symptoms are fully in evidence. The work describes state of the art functional and molecular imaging methods for AD. It will also overview a clinically applicable quantitative method for reproducible assessments of such scans in the early identification of AD.
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Affiliation(s)
- Cyrus A Raji
- Ahmanson Translational Imaging Division, Department of Molecular and Medical Pharmacology, David Geffen School of Medicine at the University of California, Los Angeles, CA, USA.,Mallinckrodt Institute of Radiology, Division of Neuroradiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Nare Torosyan
- Ahmanson Translational Imaging Division, Department of Molecular and Medical Pharmacology, David Geffen School of Medicine at the University of California, Los Angeles, CA, USA
| | - Daniel H S Silverman
- Ahmanson Translational Imaging Division, Department of Molecular and Medical Pharmacology, David Geffen School of Medicine at the University of California, Los Angeles, CA, USA
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43
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Evaluation of Class IIa Histone Deacetylases Expression and In Vivo Epigenetic Imaging in a Transgenic Mouse Model of Alzheimer's Disease. Int J Mol Sci 2021; 22:ijms22168633. [PMID: 34445342 PMCID: PMC8395513 DOI: 10.3390/ijms22168633] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 08/01/2021] [Accepted: 08/09/2021] [Indexed: 12/15/2022] Open
Abstract
Epigenetic regulation by histone deacetylase (HDAC) is associated with synaptic plasticity and memory formation, and its aberrant expression has been linked to cognitive disorders, including Alzheimer's disease (AD). This study aimed to investigate the role of class IIa HDAC expression in AD and monitor it in vivo using a novel radiotracer, 6-(tri-fluoroacetamido)-1-hexanoicanilide ([18F]TFAHA). A human neural cell culture model with familial AD (FAD) mutations was established and used for in vitro assays. Positron emission tomography (PET) imaging with [18F]TFAHA was performed in a 3xTg AD mouse model for in vivo evaluation. The results showed a significant increase in HDAC4 expression in response to amyloid-β (Aβ) deposition in the cell model. Moreover, treatment with an HDAC4 selective inhibitor significantly upregulated the expression of neuronal memory-/synaptic plasticity-related genes. In [18F]TFAHA-PET imaging, whole brain or regional uptake was significantly higher in 3xTg AD mice compared with WT mice at 8 and 11 months of age. Our study demonstrated a correlation between class IIa HDACs and Aβs, the therapeutic benefit of a selective inhibitor, and the potential of using [18F]TFAHA as an epigenetic radiotracer for AD, which might facilitate the development of AD-related neuroimaging approaches and therapies.
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Soni N, Ora M, Bathla G, Nagaraj C, Boles Ponto LL, Graham MM, Saini J, Menda Y. Multiparametric magnetic resonance imaging and positron emission tomography findings in neurodegenerative diseases: Current status and future directions. Neuroradiol J 2021; 34:263-288. [PMID: 33666110 PMCID: PMC8447818 DOI: 10.1177/1971400921998968] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Neurodegenerative diseases (NDDs) are characterized by progressive neuronal loss, leading to dementia and movement disorders. NDDs broadly include Alzheimer's disease, frontotemporal lobar degeneration, parkinsonian syndromes, and prion diseases. There is an ever-increasing prevalence of mild cognitive impairment and dementia, with an accompanying immense economic impact, prompting efforts aimed at early identification and effective interventions. Neuroimaging is an essential tool for the early diagnosis of NDDs in both clinical and research settings. Structural, functional, and metabolic imaging modalities, including magnetic resonance imaging (MRI) and positron emission tomography (PET), are widely available. They show encouraging results for diagnosis, monitoring, and treatment response evaluation. The current review focuses on the complementary role of various imaging modalities in relation to NDDs, the qualitative and quantitative utility of newer MRI techniques, novel radiopharmaceuticals, and integrated PET/MRI in the setting of NDDs.
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Affiliation(s)
- Neetu Soni
- University of Iowa Hospitals and Clinics, USA
| | - Manish Ora
- Department of Nuclear Medicine, SGPGIMS, India
| | - Girish Bathla
- Neuroradiology Department, University of Iowa Hospitals and
Clinics, USA
| | - Chandana Nagaraj
- Department of Neuro Imaging and Interventional Radiology,
NIMHANS, India
| | | | - Michael M Graham
- Division of Nuclear Medicine, University of Iowa Hospitals and
Clinics, USA
| | - Jitender Saini
- Department of Neuro Imaging and Interventional Radiology,
NIMHANS, India
| | - Yusuf Menda
- University of Iowa Hospitals and Clinics, USA
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Vanhoutte M, Landeau B, Sherif S, de la Sayette V, Dautricourt S, Abbas A, Manrique A, Chocat A, Chételat G. Evaluation of the early-phase [ 18F]AV45 PET as an optimal surrogate of [ 18F]FDG PET in ageing and Alzheimer's clinical syndrome. Neuroimage Clin 2021; 31:102750. [PMID: 34247116 PMCID: PMC8274342 DOI: 10.1016/j.nicl.2021.102750] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 06/27/2021] [Accepted: 06/28/2021] [Indexed: 12/05/2022]
Abstract
Dual-phase [18F]AV45 positron emission tomography (PET) is highly promising in the assessment of neurodegenerative diseases, allowing to obtain information on both neurodegeneration (early-phase; eAV45) and amyloid deposition (late-phase; lAV45) which are highly complementary; yet eAV45 needs further evaluation. This study aims at validating eAV45 as an optimal proxy of [18F]FDG PET in a large mixed-population of healthy ageing and Alzheimer's clinical syndrome participants (n = 191) who had [18F]FDG PET, eAV45 and lAV45 scans. We found early time frame 0-4 min to give maximal correlation with [18F]FDG PET and minimal correlation with lAV45. Moreover, maximal overlap of [18F]FDG PET versus eAV45 associations with clinical diagnosis and cognition was obtained with pons scaling. Across reference regions, classification performance between clinical subgroups was similar for both eAV45 and [18F]FDG PET. These findings highlight the optimal use of eAV45 to assess neurodegeneration as a validated proxy of [18F]FDG PET. On top of this purpose, this study showed that combined [18F]AV45 PET dual-biomarker even outperformed [18F]FDG PET or lAV45 alone.
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Affiliation(s)
- Matthieu Vanhoutte
- Inserm UMR-S U1237, Caen-Normandie University, GIP Cyceron, Caen, France.
| | - Brigitte Landeau
- Inserm UMR-S U1237, Caen-Normandie University, GIP Cyceron, Caen, France
| | - Siya Sherif
- Inserm UMR-S U1237, Caen-Normandie University, GIP Cyceron, Caen, France
| | - Vincent de la Sayette
- Inserm U1077, Caen-Normandie University, École Pratique des Hautes Études, Caen, France; University Hospital, Neurology Department, Caen, France
| | - Sophie Dautricourt
- Inserm UMR-S U1237, Caen-Normandie University, GIP Cyceron, Caen, France; University Hospital, Neurology Department, Caen, France
| | - Ahmed Abbas
- Inserm U1077, Caen-Normandie University, École Pratique des Hautes Études, Caen, France
| | - Alain Manrique
- University Hospital, Nuclear Medicine Department, Caen, France
| | - Anne Chocat
- Inserm UMR-S U1237, Caen-Normandie University, GIP Cyceron, Caen, France
| | - Gaël Chételat
- Inserm UMR-S U1237, Caen-Normandie University, GIP Cyceron, Caen, France; Inserm U1077, Caen-Normandie University, École Pratique des Hautes Études, Caen, France.
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Chen W, Li S, Ma Y, Lv S, Wu F, Du J, Wu H, Wang S, Zhao Q. A simple nomogram prediction model to identify relatively young patients with mild cognitive impairment who may progress to Alzheimer's disease. J Clin Neurosci 2021; 91:62-68. [PMID: 34373060 DOI: 10.1016/j.jocn.2021.06.026] [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: 08/08/2020] [Revised: 05/16/2021] [Accepted: 06/14/2021] [Indexed: 12/25/2022]
Abstract
AIM Construct a clinical predictive model based on easily accessible clinical features and imaging data to identify patients 65 years of age and younger with mild cognitive impairment(MCI) who may progress to Alzheimer's disease(AD). METHODS From the ADNI database, patients with MCI who were less than or equal to 65 years of age and who had been followed for 6-60 months were selected.We collected demographic data, neuropsychological test scale scores, and structural magnetic images of these patients. Clinical characteristics were then screened, and VBM and SBM analyses were performed using structural nuclear magnetic images to obtain imaging histology characteristics. Finally, predictive models were constructed combining the clinical and imaging histology characteristics. RESULTS The constructed nomogram has a cross-validated AUC of 0.872 in the training set and 0.867 in the verification set, and the calibration curve fits well.We also provide an online model-based forecasting tool. CONCLUSION The model has good performance and uses convenience,it should be able to provide assistance in clinical work to screen relatively young MCI patients who may progress to AD.
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Affiliation(s)
- Wenhong Chen
- Department of Neurology, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Songtao Li
- Department of Neurology, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Yangyang Ma
- Department of Neurology, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Shuyue Lv
- Department of Neurology, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Fan Wu
- Department of Neurology, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Jianshi Du
- Department of Vascular Surgery, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Honglin Wu
- Department of Gastroenterology, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Shuai Wang
- Department of Gastroenterology, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Qing Zhao
- Department of Neurology, China-Japan Union Hospital of Jilin University, Changchun, China.
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Early-phase 18F-FP-CIT and 18F-flutemetamol PET were significantly correlated. Sci Rep 2021; 11:12297. [PMID: 34112926 PMCID: PMC8192502 DOI: 10.1038/s41598-021-91891-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 06/02/2021] [Indexed: 02/02/2023] Open
Abstract
Little is known about whether early-phase PET images of 18F-FP-CIT match those of amyloid PET. Here, we compared early-phase 18F-FP-CIT and 18F-flutemetamol PET images in patients who underwent both within a 1-month interval. The SUVR on early-phase 18F-FP-CIT PET (median, 0.86) was significantly lower than that of 18F-flutemetamol PET (median, 0.91, p < 0.001) for total brain regions including all cerebral lobes and central structures. This significant difference persisted for each brain region except central structures (p = 0.232). The SUVR of total brain regions obtained from early 18F-FP-CIT PET showed a very strong correlation with that of 18F-flutemetamol PET (rho = 0.80, p < 0.001). Among the kinetic parameters, only R1 showed a statistically significant correlation between the two techniques for all brain regions (rho = 0.89, p < 0.001). R1 from 18F-FP-CIT (median, 0.77) was significantly lower in all areas of the brain compared to R1 from 18F-flutemetamol PET (median, 0.81, p < 0.001).18F-FP-CIT demonstrated lower uptake in cortical brain regions than 18F-flutemetamol on early-phase PET. However, both early-phase PETs demonstrated significant correlation of uptake.
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48
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Ezzati A, Harvey DJ, Habeck C, Golzar A, Qureshi IA, Zammit AR, Hyun J, Truelove-Hill M, Hall CB, Davatzikos C, Lipton RB. Predicting Amyloid-β Levels in Amnestic Mild Cognitive Impairment Using Machine Learning Techniques. J Alzheimers Dis 2021; 73:1211-1219. [PMID: 31884486 DOI: 10.3233/jad-191038] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
BACKGROUND Amyloid-β positivity (Aβ+) based on PET imaging is part of the enrollment criteria for many of the clinical trials of Alzheimer's disease (AD), particularly in trials for amyloid-targeted therapy. Predicting Aβ positivity prior to PET imaging can decrease unnecessary patient burden and costs of running these trials. OBJECTIVE The aim of this study was to evaluate the performance of a machine learning model in estimating the individual risk of Aβ+ based on gold-standard of PET imaging. METHODS We used data from an amnestic mild cognitive impairment (aMCI) subset of the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort to develop and validate the models. The predictors of Aβ status included demographic and ApoE4 status in all models plus a combination of neuropsychological tests (NP), MRI volumetrics, and cerebrospinal fluid (CSF) biomarkers. RESULTS The models that included NP and MRI measures separately showed an area under the receiver operating characteristics (AUC) of 0.74 and 0.72, respectively. However, using NP and MRI measures jointly in the model did not improve prediction. The models including CSF biomarkers significantly outperformed other models with AUCs between 0.89 to 0.92. CONCLUSIONS Predictive models can be effectively used to identify persons with aMCI likely to be amyloid positive on a subsequent PET scan.
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Affiliation(s)
- Ali Ezzati
- Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, USA.,Department of Neurology, Montefiore Medical Center, Bronx, NY, USA
| | - Danielle J Harvey
- Department of Public Health Sciences, University of California-Davis, Davis, CA, USA
| | - Christian Habeck
- Cognitive Neuroscience Division, Department of Neurology, Columbia University, New York, NY, USA
| | | | - Irfan A Qureshi
- Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, USA.,Biohaven Pharmaceuticals, New Haven, CT, USA
| | - Andrea R Zammit
- Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Jinshil Hyun
- Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, USA
| | | | | | | | - Richard B Lipton
- Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, USA.,Department of Neurology, Montefiore Medical Center, Bronx, NY, USA
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Bao W, Xie F, Zuo C, Guan Y, Huang YH. PET Neuroimaging of Alzheimer's Disease: Radiotracers and Their Utility in Clinical Research. Front Aging Neurosci 2021; 13:624330. [PMID: 34025386 PMCID: PMC8134674 DOI: 10.3389/fnagi.2021.624330] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2020] [Accepted: 02/23/2021] [Indexed: 12/14/2022] Open
Abstract
Alzheimer's Disease (AD), the leading cause of senile dementia, is a progressive neurodegenerative disorder affecting millions of people worldwide and exerting tremendous socioeconomic burden on all societies. Although definitive diagnosis of AD is often made in the presence of clinical manifestations in late stages, it is now universally believed that AD is a continuum of disease commencing from the preclinical stage with typical neuropathological alterations appearing decades prior to its first symptom, to the prodromal stage with slight symptoms of amnesia (amnestic mild cognitive impairment, aMCI), and then to the terminal stage with extensive loss of basic cognitive functions, i.e., AD-dementia. Positron emission tomography (PET) radiotracers have been developed in a search to meet the increasing clinical need of early detection and treatment monitoring for AD, with reference to the pathophysiological targets in Alzheimer's brain. These include the pathological aggregations of misfolded proteins such as β-amyloid (Aβ) plagues and neurofibrillary tangles (NFTs), impaired neurotransmitter system, neuroinflammation, as well as deficient synaptic vesicles and glucose utilization. In this article we survey the various PET radiotracers available for AD imaging and discuss their clinical applications especially in terms of early detection and cognitive relevance.
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Affiliation(s)
- Weiqi Bao
- PET Center, Huanshan Hospital, Fudan University, Shanghai, China
| | - Fang Xie
- PET Center, Huanshan Hospital, Fudan University, Shanghai, China
| | - Chuantao Zuo
- PET Center, Huanshan Hospital, Fudan University, Shanghai, China
| | - Yihui Guan
- PET Center, Huanshan Hospital, Fudan University, Shanghai, China
| | - Yiyun Henry Huang
- Department of Radiology and Biomedical Imaging, PET Center, Yale University School of Medicine, New Haven, CT, United States
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50
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Lou B, Jiang Y, Li C, Wu PY, Li S, Qin B, Chen H, Wang R, Wu B, Chen M. Quantitative Analysis of Synthetic Magnetic Resonance Imaging in Alzheimer's Disease. Front Aging Neurosci 2021; 13:638731. [PMID: 33912023 PMCID: PMC8072384 DOI: 10.3389/fnagi.2021.638731] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Accepted: 03/18/2021] [Indexed: 12/15/2022] Open
Abstract
Objectives: The purpose of this study was to evaluate the feasibility and whether synthetic MRI can benefit diagnosis of Alzheimer’s disease (AD). Materials and Methods: Eighteen patients and eighteen age-matched normal controls (NCs) underwent MR examination. The mini-mental state examination (MMSE) scores were obtained from all patients. The whole brain volumetric characteristics, T1, T2, and proton density (PD) values of different cortical and subcortical regions were obtained. The volumetric characteristics and brain regional relaxation values between AD patients and NCs were compared using independent-samples t-test. The correlations between these quantitative parameters and MMSE score were assessed by the Pearson correlation in AD patients. Results: Although the larger volume of cerebrospinal fluid (CSF), lower brain parenchymal volume (BPV), and the ratio of brain parenchymal volume to intracranial volume (BPV/ICV) were found in AD patients compared with NCs, there were no significant differences (p > 0.05). T1 values of right insula cortex and T2 values of left hippocampus and right insula cortex were significantly higher in AD patients than in NCs, but T1 values of left caudate showed a reverse trend (p < 0.05). As the MMSE score decreased in AD patients, the BPV and BPV/ICV decreased, while the volume of CSF and T1 values of bilateral insula cortex and bilateral hippocampus as well as T2 values of bilateral hippocampus increased (p < 0.05). Conclusion: Synthetic MRI not only provides more information to differentiate AD patients from normal controls, but also reflects the disease severity of AD.
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Affiliation(s)
- Baohui Lou
- Department of Radiology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Yuwei Jiang
- Department of Radiology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Chunmei Li
- Department of Radiology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | | | - Shuhua Li
- Department of Neurology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Bin Qin
- Department of Neurology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Haibo Chen
- Department of Neurology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Rui Wang
- Department of Radiology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Bing Wu
- GE Healthcare, Beijing, China
| | - Min Chen
- Department of Radiology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
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