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Background: Phase four of the Alzheimer's Disease Neuroimaging Initiative (ADNI4) began in 2023. This time-period corresponded to MRI vendors introducing product sequences with compressed sensing (CS), cross-vendor adoption of arterial spin-labelling (ASL) and multi-band slice excitation, and hardware improvements (head-coils, increased gradient amplitudes). These advances enabled the acquisition of new imaging measures and reduced scan times.
View Article and Find Full Text PDFBiomarkers.
Alzheimers Dement
December 2024
Dementia Research Centre, UCL Queen Square Institute of Neurology, London, UK.
Background: With an aging population, it is essential to identify subtle features of brain pathology - both neurodegenerative and vascular - at an early stage, which may predict risk of future decline. We used diffusion MRI (dMRI) to assess grey matter cortical microstructure and investigate associations with 1) Alzheimer's disease (AD) pathology and 2) mid/late-life vascular risk (as measured by blood pressure (BP)).
Method: 151 asymptomatic individuals from the British 1946 birth cohort underwent combined PET/MR with [18F]florbetapir Aβ-PET at ∼73yrs, and [18F]MK-6240 tau-PET at ∼76yrs.
Biomarkers.
Alzheimers Dement
December 2024
Dementia Research Centre, UCL Queen Square Institute of Neurology, London, United Kingdom.
Background: Understanding when Aβ positive cognitively normal individuals develop tau pathology has important implications for treatment with anti-Aβ therapies. We employed a changepoint regression approach to estimate time from Aβ-PET positivity to regionally elevated tau-PET in a population-based cohort of primarily cognitively unimpaired individuals.
Method: Participants from Insight 46 (1946 British birth cohort) underwent two [F]florbetapir Aβ-PET scans and a sub-sample enriched for Aβ were also scanned with [F]MK-6240 tau-PET, characteristics are presented in Table 1.
Background: Phase four of the Alzheimer's Disease Neuroimaging Initiative (ADNI4) began in 2023. This time-period corresponded to MRI vendors introducing product sequences with compressed sensing (CS), cross-vendor adoption of arterial spin-labelling (ASL) and multi-band slice excitation, and hardware improvements (head-coils, increased gradient amplitudes). These advances enabled the acquisition of new imaging measures and reduced scan times.
View Article and Find Full Text PDFA Machine Learning Model to Harmonize Volumetric Brain MRI Data for Quantitative Neuroradiological Assessment of Alzheimer Disease.
Radiol Artif Intell
December 2024
From the Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Via Pilastroni 4, Brescia 25125, Italy (D.A., A.R.); Department of Neurology, Alzheimer Center Amsterdam, Vrije Universiteit, Amsterdam UMC, location VUmc, Amsterdam, the Netherlands (V.V., W.M.v.d.F., B.M.T.); Department of Neurodegeneration, Amsterdam Neuroscience, Amsterdam, the Netherlands (V.V., W.M.v.d.F., B.M.T.); Brain Imaging Centre, Research Centre for Natural Sciences, Budapest, Hungary (B.W., T.A., Z.V.); Biomatics and Applied Artificial Intelligence Institute, John von Neumann Faculty of Informatics, Óbuda University, Budapest, Hungary (B.W.); Department of CSIRO Health and Biosecurity, The Australian e-Health Research Centre, Brisbane, Australia (P.B.); School of Psychology, University of Surrey, Guildford, United Kingdom (T.A.); Sorbonne Université, Institut du Cerveau- Paris Brain Institute-ICM, CNRS, Inria, Inserm, AP-HP, Hôpital Pitié-Salpêtrière, Paris, France (S.D.); Department of Epidemiology and Data Science, Vrije Universiteit, Amsterdam UMC, location VUmc, Amsterdam, the Netherlands (W.M.v.d.F.); Department of Radiology & Nuclear Medicine, Amsterdam UMC, Vrije Universiteit, Amsterdam the Netherlands (F.B.); Queen Square Institute of Neurology, University College London, United Kingdom (F.B.); and UCL Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering and Department of Computer Science, University College London, London, United Kingdom (F.B., D.C.A., A.A., N.P.O.).
Purpose To extend a previously developed machine learning algorithm for harmonizing brain volumetric data of individuals undergoing neuroradiological assessment of Alzheimer disease not encountered during model training. Materials and Methods Neuroharmony is a recently developed method that uses image quality metrics (IQM) as predictors to remove scanner-related effects in brain-volumetric data using random forest regression. To account for the interactions between Alzheimer disease pathology and IQM during harmonization, the authors developed a multiclass extension of Neuroharmony for individuals with and without cognitive impairment.
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