Data-driven FDG-PET subtypes of Alzheimer’s disease-related neurodegeneration

Abstract

BackgroundPrevious research has described distinct subtypes of Alzheimer’s disease (AD) based on 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.

Differential diagnosis of amnestic dementia patients based on an FDG‐PET signature of autopsy‐confirmed LATE‐NC

“…For reference, we included normative FDG-PET data of a group of 179 healthy controls in ADNI that was also used in our previous FDG-PET analyses. 23 All participants provided written informed consent and data collection and sharing were approved by the institutional review board of each institution participating in ADNI.…”

“…27 We estimated LATE-NC-typical and AD-typical FDG-PET patterns by contrasting FDG-PET data of the pure LATE-NC and AD groups against normative data from healthy controls using voxel-wise 2-sample t-tests in SPM. 23 To better under- stand the effect of comorbid pathology on the FDG-PET pattern, we also studied the FDG-PET pattern of the AD+LATE-NC group compared to healthy controls as well as compared to the pure AD group. FDG-PET images were scaled to a pons reference region (manually drawn in MNI space), smoothed with an 8mm isotropic kernel, and regions with less than 50% gray matter probability in the segmented MNI152 template were excluded.…”

“…FDG-PET images were scaled to a pons reference region (manually drawn in MNI space), smoothed with an 8mm isotropic kernel, and regions with less than 50% gray matter probability in the segmented MNI152 template were excluded. 23,28 Statistical parametric maps of the group differences were expressed as Cohen's d effect size maps to reveal the brain-wide hypometabolic patterns (using the formula…”

Differential Effects of Tau Stage, Lewy Body Pathology, and Substantia Nigra Degeneration on18F-FDG PET Patterns in Clinical Alzheimer Disease

“…Domain-specific composite scores were used for assessing memory (ADNI-MEM) (30) and executive function (ADNI-EF) (31). In addition, we calculated a "cognitive profile" variable ∆(MEM-EXEC) to characterize relative impairments between these two domains (32). The average interval between neuropsychological evaluation and death was 1.9 ± 2.0 years.…”

Recent update on the heterogeneity of the Alzheimer’s disease spectrum

“…(3) a relatively rare "cortical-predominant" subtype (6.8%), characterized by younger age and more severe executive dysfunction (Levin et al 2021).…”