Abstract
BackgroundPrevious research has described distinct subtypes of Alzheimer’s disease (AD) based on the differences in regional patterns of brain atrophy on MRI. We conducted a data-driven exploration of distinct AD neurodegeneration subtypes using FDG-PET as a sensitive molecular imaging marker of neurodegenerative processes.MethodsHierarchical clustering of voxel-wise FDG-PET data from 177 amyloid-positive patients with AD dementia enrolled in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) was used to identify distinct hypometabolic subtypes of AD, which were then further characterized with respect to clinical and biomarker characteristics. We then classified FDG-PET scans of 217 amyloid-positive patients with mild cognitive impairment (“prodromal AD”) according to the identified subtypes and studied their domain-specific cognitive trajectories and progression to dementia over a follow-up interval of up to 72 months.ResultsThree main hypometabolic subtypes were identified: (i) “typical” (48.6%), showing a classic posterior temporo-parietal hypometabolic pattern; (ii) “limbic-predominant” (44.6%), characterized by old age and a memory-predominant cognitive profile; and (iii) a relatively rare “cortical-predominant” subtype (6.8%) characterized by younger age and more severe executive dysfunction. Subtypes classified in the prodromal AD sample demonstrated similar subtype characteristics as in the AD dementia sample and further showed differential courses of cognitive decline.ConclusionsThese findings complement recent research efforts on MRI-based identification of distinct AD atrophy subtypes and may provide a potentially more sensitive molecular imaging tool for early detection and characterization of AD-related neurodegeneration variants at prodromal disease stages.
Highlights
Previous research has demonstrated heterogeneity in Alzheimer’s disease (AD) which is linked to distinct neuropathological subtypes of AD characterized by limbic-predominant, hippocampal sparing, or rather balanced (“typical”) spatial distributions of neurofibrillary tangle pathology [1]
Recent research has used clustering methods on structural Magnetic resonance imaging (MRI) data to identify similar regional atrophy subtypes in AD in a data-driven manner [3]. These atrophy subtypes could already be detected in patients with prodromal AD, who showed similar biomarker characteristics as the subtypes identified in patients with AD dementia and were associated with differential clinical trajectories [4, 5]
Identification of hypometabolic subtypes in patients with AD dementia We used objective criteria for evaluating optimal clustering solutions to select the level of cutoff for the hierarchical clustering dendrogram supported by the data
Summary
Previous research has demonstrated heterogeneity in Alzheimer’s disease (AD) which is linked to distinct neuropathological subtypes of AD characterized by limbic-predominant, hippocampal sparing, or rather balanced (“typical”) spatial distributions of neurofibrillary tangle pathology [1]. Recent research has used clustering methods on structural MRI data to identify similar regional atrophy subtypes in AD in a data-driven manner [3]. These atrophy subtypes could already be detected in patients with prodromal AD (i.e. amyloid-beta [Aβ]-positive patients with mild cognitive impairment [MCI]), who showed similar biomarker characteristics as the subtypes identified in patients with AD dementia and were associated with differential clinical trajectories [4, 5]. While previous hypothesis-driven studies have reported differential hypometabolic FDG-PET patterns amongst AD dementia patients [11,12,13], to our knowledge, FDGPET has not yet been used for identifying neurodegeneration subtypes of AD in a data-driven manner. We conducted a data-driven exploration of distinct AD neurodegeneration subtypes using FDG-PET as a sensitive molecular imaging marker of neurodegenerative processes
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have