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Sökning: WFRF:(Muehlboeck JS)

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1.
  • Ferreira, Daniel, et al. (författare)
  • The interactive effect of demographic and clinical factors on hippocampal volume : A multicohort study on 1958 cognitively normal individuals
  • 2017
  • Ingår i: Hippocampus. - : John Wiley and Sons. - 1050-9631 .- 1098-1063. ; 27:6, s. 653-667
  • Tidskriftsartikel (refereegranskat)abstract
    • Alzheimer's disease is characterized by hippocampal atrophy. Other factors also influence the hippocampal volume, but their interactive effect has not been investigated before in cognitively healthy individuals. The aim of this study is to evaluate the interactive effect of key demographic and clinical factors on hippocampal volume, in contrast to previous studies frequently investigating these factors in a separate manner. Also, to investigate how comparable the control groups from ADNI, AIBL, and AddNeuroMed are with five population-based cohorts. In this study, 1958 participants were included (100 AddNeuroMed, 226 ADNI, 155 AIBL, 59 BRC, 295 GENIC, 279 BioFiNDER, 398 PIVUS, and 446 SNAC-K). ANOVA and random forest were used for testing between-cohort differences in demographic-clinical variables. Multiple regression was used to study the influence of demographic-clinical variables on hippocampal volume. ANCOVA was used to analyze whether between-cohort differences in demographic-clinical variables explained between-cohort differences in hippocampal volume. Age and global brain atrophy were the most important variables in explaining variability in hippocampal volume. These variables were not only important themselves but also in interaction with gender, education, MMSE, and total intracranial volume. AddNeuroMed, ADNI, and AIBL differed from the population-based cohorts in several demographic-clinical variables that had a significant effect on hippocampal volume. Variability in hippocampal volume in individuals with normal cognition is high. Differences that previously tended to be related to disease mechanisms could also be partly explained by demographic and clinical factors independent from the disease. Furthermore, cognitively normal individuals especially from ADNI and AIBL are not representative of the general population. These findings may have important implications for future research and clinical trials, translating imaging biomarkers to the general population, and validating current diagnostic criteria for Alzheimer's disease and predementia stages.
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2.
  • Brusini, Irene, et al. (författare)
  • Shape Information Improves the Cross-Cohort Performance of Deep Learning-Based Segmentation of the Hippocampus
  • 2020
  • Ingår i: Frontiers in Neuroscience. - : Frontiers Media S.A.. - 1662-4548 .- 1662-453X. ; 14
  • Tidskriftsartikel (refereegranskat)abstract
    • Performing an accurate segmentation of the hippocampus from brain magnetic resonance images is a crucial task in neuroimaging research, since its structural integrity is strongly related to several neurodegenerative disorders, including Alzheimer's disease (AD). Some automatic segmentation tools are already being used, but, in recent years, new deep learning (DL)-based methods have been proven to be much more accurate in various medical image segmentation tasks. In this work, we propose a DL-based hippocampus segmentation framework that embeds statistical shape of the hippocampus as context information into the deep neural network (DNN). The inclusion of shape information is achieved with three main steps: (1) a U-Net-based segmentation, (2) a shape model estimation, and (3) a second U-Net-based segmentation which uses both the original input data and the fitted shape model. The trained DL architectures were tested on image data of three diagnostic groups [AD patients, subjects with mild cognitive impairment (MCI) and controls] from two cohorts (ADNI and AddNeuroMed). Both intra-cohort validation and cross-cohort validation were performed and compared with the conventional U-net architecture and some variations with other types of context information (i.e., autocontext and tissue-class context). Our results suggest that adding shape information can improve the segmentation accuracy in cross-cohort validation, i.e., when DNNs are trained on one cohort and applied to another. However, no significant benefit is observed in intra-cohort validation, i.e., training and testing DNNs on images from the same cohort. Moreover, compared to other types of context information, the use of shape context was shown to be the most successful in increasing the accuracy, while keeping the computational time in the order of a few minutes.
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3.
  • Ekman, Urban, et al. (författare)
  • The MemClin project : a prospective multi memory clinics study targeting early stages of cognitive impairment
  • 2020
  • Ingår i: BMC Geriatrics. - : BMC. - 1471-2318 .- 1471-2318. ; 20
  • Tidskriftsartikel (refereegranskat)abstract
    • Background: There remains a lack of large-scale clinical studies of cognitive impairment that aim to increase diagnostic and prognostic accuracy as well as validate previous research findings. The MemClin project will amass large quantities of cross-disciplinary data allowing for the construction of robust models to improve diagnostic accuracy, expand our knowledge on differential diagnostics, strengthen longitudinal prognosis, and harmonise examination protocols across centres. The current article describes the Memory Clinic (MemClin) project's study-design, materials and methods, and patient characteristics. In addition, we present preliminary descriptive data from the ongoing data collection.Methods: Nine out of ten memory clinics in the greater Stockholm area, which largely use the same examination methods, are included. The data collection of patients with different stages of cognitive impairment and dementia is coordinated centrally allowing for efficient and secure large-scale database construction. The MemClin project rest directly on the memory clinics examinations with cognitive measures, health parameters, and biomarkers.Results: Currently, the MemClin project has informed consent from 1543 patients. Herein, we present preliminary data from 835 patients with confirmed cognitive diagnosis and neuropsychological test data available. Of those, 239 had dementia, 487 mild cognitive impairment (MCI), and 104 subjective cognitive impairment (SCI). In addition, we present descriptive data on visual ratings of brain atrophy and cerebrospinal fluid markers.Conclusions: Based on our current progress and preliminary data, the MemClin project has a high potential to provide a large-scale database of 1200-1500 new patients annually. This coordinated data collection will allow for the construction of improved diagnostic and prognostic models for neurodegenerative disorders and other cognitive conditions in their naturalistic setting.
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4.
  • Ferreira, Daniel, et al. (författare)
  • Practical cut-offs for visual rating scales of medial temporal, frontal and posterior atrophy in Alzheimer's disease and mild cognitive impairment
  • 2015
  • Ingår i: Journal of Internal Medicine. - 0954-6820 .- 1365-2796. ; 278:3, s. 277-290
  • Tidskriftsartikel (refereegranskat)abstract
    • BACKGROUND:Atrophy in the medial temporal lobe, frontal lobe and posterior cortex can be measured with visual rating scales such as the medial temporal atrophy (MTA), global cortical atrophy - frontal subscale (GCA-F) and posterior atrophy (PA) scales, respectively. However, practical cut-offs are urgently needed, especially now that different presentations of Alzheimer's disease (AD) are included in the revised diagnostic criteria.AIMS:The aim of this study was to generate a list of practical cut-offs for the MTA, GCA-F and PA scales, both for diagnosis of AD and determining prognosis in mild cognitive impairment (MCI), and to evaluate the influence of key demographic and clinical factors on these cut-offs.METHODS:AddNeuroMed and ADNI cohorts were combined giving a total of 1147 participants (322 AD patients, 480 MCI patients and 345 control subjects). The MTA, GCA-F and PA scales were applied and a broad range of cut-offs was evaluated.RESULTS:The MTA scale showed better diagnostic and predictive performances than the GCA-F and PA scales. Age, apolipoprotein E (ApoE) ε4 status and age at disease onset influenced all three scales. For the age ranges 45-64, 65-74, 75-84 and 85-94 years, the following cut-offs should be used. MTA: ≥1.5, ≥1.5, ≥2 and ≥2.5; GCA-F, ≥1, ≥1, ≥1 and ≥1; and PA, ≥1, ≥1, ≥1 and ≥1, respectively, with an adjustment for early-onset ApoE ε4 non-carrier AD patients (MTA: ≥2, ≥2, ≥3 and ≥3; and GCA-F: ≥1, ≥1, ≥2 and ≥2, respectively).CONCLUSIONS:If successfully validated in clinical settings, the list of practical cut-offs proposed here might be useful in clinical practice. Their use might also (i) promote research on atrophy subtypes, (ii) increase the understanding of different presentations of AD, (iii) improve diagnosis and prognosis and (iv) aid population selection and enrichment for clinical trials. This article is protected by copyright. All rights reserved.
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5.
  • Mangialasche, Francesca, et al. (författare)
  • Classification and prediction of clinical diagnosis of Alzheimer's disease based on MRI and plasma measures of α-/γ-tocotrienols and γ-tocopherol.
  • 2013
  • Ingår i: Journal of Internal Medicine. - 0954-6820 .- 1365-2796. ; 273:6, s. 602-621
  • Tidskriftsartikel (refereegranskat)abstract
    • Background: The aim of this study was to evaluate the accuracy of combined structural magnetic resonance imaging (MRI) measures and plasma levels of vitamin E forms, including all eight natural vitamin E congeners (four tocopherols and four tocotrienols) and markers of vitamin E oxidative/nitrosative damage, in differentiating individuals with Alzheimer's disease (AD) and mild cognitive impairment (MCI) from cognitively intact control (CTL) subjects.Methods: Overall, 81 patients with AD, 86 with MCI and 86 CTL individuals were enrolled from the longitudinal multicentre AddNeuroMed study. MRI and plasma vitamin E data were acquired at baseline. MRI scans were analysed using Freesurfer, an automated segmentation scheme which generates regional volume and cortical thickness measures. Orthogonal partial least squares to latent structures (OPLS), a multivariate data analysis technique, was used to analyse MRI and vitamin E measures in relation to AD and MCI diagnosis.Results: The joint evaluation of MRI and plasma vitamin E measures enhanced the accuracy of differentiating individuals with AD and MCI from CTL subjects: 98.2% (sensitivity 98.8%, specificity 97.7%) for AD versus CTL, and 90.7% (sensitivity 91.8%, specificity 89.5%) for MCI versus CTL. This combination of measures also identified 85% of individuals with MCI who converted to clinical AD at follow-up after 1 year.Conclusions: Plasma levels of tocopherols and tocotrienols together with automated MRI measures can help to differentiate AD and MCI patients from CTL subjects, and to prospectively predict MCI conversion into AD. Our results suggest the potential role of nutritional biomarkers detected in plasma–tocopherols and tocotrienols–as indirect indicators of AD pathology, and the utility of a multimodality approach.
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6.
  • Mårtensson, Gustav, et al. (författare)
  • AVRA : Automatic visual ratings of atrophy from MRI images using recurrent convolutional neural networks
  • 2019
  • Ingår i: NeuroImage. - : ELSEVIER SCI LTD. - 0353-8842 .- 2213-1582. ; 23
  • Tidskriftsartikel (refereegranskat)abstract
    • Quantifying the degree of atrophy is done clinically by neuroradiologists following established visual rating scales. For these assessments to be reliable the rater requires substantial training and experience, and even then the rating agreement between two radiologists is not perfect. We have developed a model we call AVRA (Automatic Visual Ratings of Atrophy) based on machine learning methods and trained on 2350 visual ratings made by an experienced neuroradiologist. It provides fast and automatic ratings for Scheltens' scale of medial temporal atrophy (MTA), the frontal subscale of Pasquier's Global Cortical Atrophy (GCA-F) scale, and Koedam's scale of Posterior Atrophy (PA). We demonstrate substantial inter-rater agreement between AVRA's and a neuroradiologist ratings with Cohen's weighted kappa values of kappa(w) = 0.74/0.72 (MTA left/right), kappa(w) = 0.62 (GCA-F) and kappa(w) = 0.74 (PA). We conclude that automatic visual ratings of atrophy can potentially have great scientific value, and aim to present AVRA as a freely available toolbox.
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  • Resultat 1-10 av 27
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