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The magnetic resonance imaging (MRI) Core has been operating since Alzheimer's Disease Neuroimaging Initiative's (ADNI) inception, providing 20 years of data including reliable, multi-platform standardized protocols, carefully curated image data, and quantitative measures provided by expert investigators. The overarching purposes of the MRI Core include: (1) optimizing and standardizing MRI acquisition methods, which have been adopted by many multicenter studies and trials worldwide and (2) providing curated images and numeric summary values from relevant MRI sequences/contrasts to the scientific community. Over time, ADNI MRI has become increasingly complex. To remain technically current, the ADNI MRI protocol has changed substantially over the past two decades. The ADNI 4 protocol contains nine different imaging types (e.g., three dimensional [3D] T1-weighted and fluid-attenuated inversion recovery [FLAIR]). Our view is that the ADNI MRI data are a greatly underutilized resource. The purpose of this paper is to educate the scientific community on ADNI MRI methods and content to promote greater awareness, accessibility, and use. HIGHLIGHTS: The MRI Core provides multi-platform standardized protocols, carefully curated image data, and quantitative analysis by expert groups. The ADNI MRI protocol has undergone major changes over the past two decades to remain technically current. As of April 25, 2024, the following numbers of image series are available: 17,141 3D T1w; 6877 FLAIR; 3140 T2/PD; 6623 GRE; 3237 dMRI; 2846 ASL; 2968 TF-fMRI; and 2861 HighResHippo (see Table 1 for abbreviations). As of April 25, 2024, the following numbers of quantitative analyses are available: FreeSurfer 10,997; BSI 6120; tensor based morphometry (TBM) and TBM-SYN 12,019; WMH 9944; dMRI 1913; ASL 925; TF-fMRI NFQ 2992; and medial temporal subregion volumes 2726 (see Table 4 for abbreviations). ADNI MRI is an underutilized resource that could be more useful to the research community.
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http://dx.doi.org/10.1002/alz.14166 | DOI Listing |
Neuroscience
September 2025
Department of Medicine, LSU Health Shreveport, Shreveport, LA, USA. Electronic address:
Early and accurate Alzheimer's disease (AD) diagnosis is critical for effective intervention, but it is still challenging due to neurodegeneration's slow and complex progression. Recent studies in brain imaging analysis have highlighted the crucial roles of deep learning techniques in computer-assisted interventions for diagnosing brain diseases. In this study, we propose AlzFormer, a novel deep learning framework based on a space-time attention mechanism, for multiclass classification of AD, MCI, and CN individuals using structural MRI scans.
View Article and Find Full Text PDFFront Bioinform
August 2025
Artificial Intelligence and Cyber Futures Institute, Charles Stuart University, Bathurst, NSW, Australia.
Introduction: Alzheimer's disease (AD) is one of the most common neurodegenerative disabilities that often leads to memory loss, confusion, difficulty in language and trouble with motor coordination. Although several machine learning (ML) and deep learning (DL) algorithms have been utilized to identify Alzheimer's disease (AD) from MRI scans, precise classification of AD categories remains challenging as neighbouring categories share common features.
Methods: This study proposes transfer learning-based methods for extracting features from MRI scans for multi-class classification of different AD categories.
Diffusion MRI (dMRI) is a powerful tool to assess white matter (WM) microstructural abnormalities in Alzheimer's disease (AD). The fourth phase of the Alzheimer's Disease Neuroimaging Initiative (ADNI) now includes multiple multishell dMRI protocols, enabling both traditional and advanced dMRI model analyses. There is a need to evaluate whether multishell data offer deeper insights into WM pathology in AD than more widely available single-shell data by overcoming single-shell model limitations.
View Article and Find Full Text PDFComput Biol Med
September 2025
Department of Clinical Psychology and Psychobiology, Universidade de Santiago de Compostela (USC), Rúa Xosé María Suárez Núñez S/N, 15782, Santiago de Compostela, Spain; Cognitive Neuroscience Research Group (Neucoga-Aging), Instituto de Psicoloxía (IPsiUS), USC, Health Research Institute of
Alzheimer's disease (AD) is a leading cause of dementia worldwide, characterized by heterogeneous neuropathological changes and progressive cognitive decline. Despite the numerous studies, there are still no effective treatments beyond those that aim to slow progression and compensate the impairment. Neuroimaging techniques provide a comprehensive view of brain changes, with magnetic resonance imaging (MRI) playing a key role due to its non-invasive nature and wide availability.
View Article and Find Full Text PDFJ Alzheimers Dis
September 2025
Department of Neurology, Second Faculty of Medicine, Charles University and Motol University Hospital, Praha, Czech Republic.
BackgroundAlzheimer's disease (AD) is a progressive neurodegenerative disorder with extensive neuropathological and clinical heterogeneity.ObjectiveWe assessed empirically derived brain atrophy profiles in relation to incident AD dementia.MethodsA secondary data analysis of two prospective cohort studies was conducted, including participants without dementia from the Alzheimer's Disease Neuroimaging Initiative (ADNI; = 1703) and the Czech Brain Aging Study (CBAS; = 385).
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