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      Distinct patterns of voxel‐ and connection‐based white matter hyperintensity distribution and associated factors in early‐onset and late‐onset Alzheimer's disease

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          Abstract

          Introduction

          The distribution of voxel‐ and connection‐based white matter hyperintensity (WMH) patterns in early‐onset Alzheimer's disease (EOAD) and late‐onset Alzheimer's disease (LOAD), as well as factors associated with these patterns, remain unclear.

          Method

          We analyzed the WMH distribution patterns in EOAD and LOAD at the voxel and connection levels, each compared with their age‐matched cognitively unimpaired participants. Linear regression assessed the independent effects of amyloid and vascular risk factors on WMH distribution patterns in both groups.

          Results

          Patients with EOAD showed increased WMH burden in the posterior region at the voxel level, and in occipital region tracts and visual network at the connection level, compared to controls. LOAD exhibited extensive involvement across various brain areas in both levels. Amyloid accumulation was associated WMH distribution in the early‐onset group, whereas the late‐onset group demonstrated associations with both amyloid and vascular risk factors.

          Discussion

          EOAD showed posterior‐focused WMH distribution pattern, whereas LOAD was with a wider distribution. Amyloid accumulation was associated with connection‐based WMH patterns in both early‐onset and late‐onset groups, with additional independent effects of vascular risk factors in late‐onset group.

          Highlights

          1. Both early‐onset Alzheimer's disease (EOAD) and late‐onset AD (LOAD) showed increased white matter hyperintensity (WMH) volume compared with their age‐matched cognitively unimpaired participants.

          2. EOAD and LOAD exhibited distinct patterns of WMH distribution, with EOAD showing a posterior‐focused pattern and LOAD displaying a wider distribution across both voxel‐ and connection‐based levels.

          3. In both EOAD and LOAD, amyloid accumulation was associated with connection‐based WMH patterns, with additional independent effects of vascular risk factors observed in LOAD.

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          Most cited references40

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          A multi-modal parcellation of human cerebral cortex

          Understanding the amazingly complex human cerebral cortex requires a map (or parcellation) of its major subdivisions, known as cortical areas. Making an accurate areal map has been a century-old objective in neuroscience. Using multi-modal magnetic resonance images from the Human Connectome Project (HCP) and an objective semi-automated neuroanatomical approach, we delineated 180 areas per hemisphere bounded by sharp changes in cortical architecture, function, connectivity, and/or topography in a precisely aligned group average of 210 healthy young adults. We characterized 97 new areas and 83 areas previously reported using post-mortem microscopy or other specialized study-specific approaches. To enable automated delineation and identification of these areas in new HCP subjects and in future studies, we trained a machine-learning classifier to recognize the multi-modal ‘fingerprint’ of each cortical area. This classifier detected the presence of 96.6% of the cortical areas in new subjects, replicated the group parcellation, and could correctly locate areas in individuals with atypical parcellations. The freely available parcellation and classifier will enable substantially improved neuroanatomical precision for studies of the structural and functional organization of human cerebral cortex and its variation across individuals and in development, aging, and disease.
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            Bayesian analysis of neuroimaging data in FSL.

            Typically in neuroimaging we are looking to extract some pertinent information from imperfect, noisy images of the brain. This might be the inference of percent changes in blood flow in perfusion FMRI data, segmentation of subcortical structures from structural MRI, or inference of the probability of an anatomical connection between an area of cortex and a subthalamic nucleus using diffusion MRI. In this article we will describe how Bayesian techniques have made a significant impact in tackling problems such as these, particularly in regards to the analysis tools in the FMRIB Software Library (FSL). We shall see how Bayes provides a framework within which we can attempt to infer on models of neuroimaging data, while allowing us to incorporate our prior belief about the brain and the neuroimaging equipment in the form of biophysically informed or regularising priors. It allows us to extract probabilistic information from the data, and to probabilistically combine information from multiple modalities. Bayes can also be used to not only compare and select between models of different complexity, but also to infer on data using committees of models. Finally, we mention some analysis scenarios where Bayesian methods are impractical, and briefly discuss some practical approaches that we have taken in these cases.
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              Introduction to the recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease.

              Criteria for the clinical diagnosis of Alzheimer's disease (AD) were established in 1984. A broad consensus now exists that these criteria should be revised to incorporate state-of-the-art scientific knowledge. The National Institute on Aging (NIA) and the Alzheimer's Association sponsored a series of advisory round table meetings in 2009 whose purpose was to establish a process for revising diagnostic and research criteria for AD. The recommendation from these advisory meetings was that three separate work groups should be formed with each assigned the task of formulating diagnostic criteria for one phase of the disease: the dementia phase; the symptomatic, pre-dementia phase; and the asymptomatic, preclinical phase of AD. Two notable differences from the AD criteria published in 1984 are incorporation of biomarkers of the underlying disease state and formalization of different stages of disease in the diagnostic criteria. There was a broad consensus within all three workgroups that much additional work is needed to validate the application of biomarkers for diagnostic purposes. In the revised NIA-Alzheimer's Association criteria, a semantic and conceptual distinction is made between AD pathophysiological processes and clinically observable syndromes that result, whereas this distinction was blurred in the 1984 criteria. The new criteria for AD are presented in three documents. The core clinical criteria of the recommendations regarding AD dementia and MCI due to AD are intended to guide diagnosis in the clinical setting. However, the recommendations of the preclinical AD workgroup are intended purely for research purposes. Copyright © 2011 The Alzheimer's Association. All rights reserved.
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                Author and article information

                Contributors
                luoxiao1990@zju.edu.cn
                zhangminming@zju.edu.cn
                Journal
                Alzheimers Dement (Amst)
                Alzheimers Dement (Amst)
                10.1002/(ISSN)2352-8729
                DAD2
                Alzheimer's & Dementia : Diagnosis, Assessment & Disease Monitoring
                John Wiley and Sons Inc. (Hoboken )
                2352-8729
                22 April 2024
                Apr-Jun 2024
                : 16
                : 2 ( doiID: 10.1002/dad2.v16.2 )
                : e12585
                Affiliations
                [ 1 ] Department of Radiology The Second Affiliated Hospital of Zhejiang University School of Medicine Hangzhou China
                [ 2 ] Department of Clinical Neurosciences University of Cambridge Cambridge UK
                Author notes
                [*] [* ] Correspondence

                Minming Zhang, MD PhD and Xiao Luo, PhD, Department of Radiology, The Second Affiliated Hospital of Zhejiang University, School of Medicine, Hangzhou, China.

                Email: zhangminming@ 123456zju.edu.cn and luoxiao1990@ 123456zju.edu.cn

                Author information
                https://orcid.org/0000-0003-2084-203X
                Article
                DAD212585
                10.1002/dad2.12585
                11033836
                38651161
                de0f64be-92a6-4239-868c-73c0b3c1361d
                © 2024 The Authors. Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring published by Wiley Periodicals LLC on behalf of Alzheimer's Association.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

                History
                : 12 February 2024
                : 08 November 2023
                : 15 March 2024
                Page count
                Figures: 4, Tables: 4, Pages: 12, Words: 5775
                Funding
                Funded by: National Natural Science Foundation of China , doi 10.13039/501100001809;
                Award ID: 81971577
                Award ID: 82271936
                Categories
                Research Article
                Research Articles
                Custom metadata
                2.0
                April‐June 2024
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.4.0 mode:remove_FC converted:22.04.2024

                early‐onset alzheimer's disease,late‐onset alzheimer's disease,white matter hyperintensities

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