Tagmazian, A. A. | Search bioRxiv

bioRxiv2026

petVAE: A Data-Driven Model for Identifying Amyloid PET Subgroups Across the Alzheimer's Disease Continuum

Amyloid-{beta} (A{beta}) PET imaging is a core biomarker and is sufficient for the biological diagnosis of Alzheimers disease (AD). Here, we aimed to identify biologically meaningful subgroups across the continuum of A{beta} accumulation using a data-driven deep learning approach, without imposing predefined thresholds for A{beta} negativity or positivity. We analyzed 3,110 A{beta} PET scans from the Alzheimers Disease Neuroimaging Initiative and Anti-Amyloid Treatment in Asymptomatic Alzheimers Disease studies to develop petVAE, a two-dimensional variational autoencoder. The model accurately reconstructed scans without prior labeling, selection by scanner or region of interest. Latent representations of scans extracted from petVAE were used to visualize and cluster the AD continuum. Clustering yielded four groups: two predominantly A{beta} negative (A{beta}-, A{beta}-+) and two predominantly A{beta} positive (A{beta}+, A{beta}++). All clusters differed in standardized uptake value ratio (p < 1.64x10-) and cerebrospinal fluid (CSF) A{beta} (p < 0.02), demonstrating petVAEs ability to assign scans along the A{beta} continuum. Extreme clusters (A{beta}-, A{beta}++) resembled conventional A{beta} negative and positive groups and differed in cognition, APOE {varepsilon}4 prevalence, A{beta} and tau CSF biomarkers (p < 3x10-). Intermediate clusters (A{beta}-+, A{beta}+) showed higher odds of carrying at least one APOE {varepsilon}4 allele versus A{beta}- (p < 0.03). Participants in A{beta}+ or A{beta}++ clusters exhibited faster progression to AD (A{beta}+ hazard ratio = 2.42, A{beta}++ HR = 9.43; p < 1.17x10-). Thus, petVAE was capable of reconstructing PET scans while extracting latent features that capture the AD continuum and define biologically meaningful subgroups, enabling data-driven characterization of preclinical disease stages.

bioinformatics↗

bioRxiv2023

ArcheD, a residual neural network for prediction of cerebrospinal fluid amyloid-beta from amyloid PET images

Detection and measurement of amyloid-beta (A{beta}) aggregation in the brain is a key factor for early identification and diagnosis of Alzheimers disease (AD). We aimed to develop a deep learning model to predict A{beta} cerebrospinal fluid (CSF) concentration directly from amyloid PET images, independent of tracers, brain reference regions or preselected regions of interest. We used 1870 A{beta} PET images and CSF measurements to train and validate a convolutional neural network ("ArcheD"). We evaluated the ArcheD performance in relation to episodic memory and the standardized uptake value ratio (SUVR) of cortical A{beta}. We also compared the brain regions relevance for the models CSF prediction within clinical-based and biological-based classifications. ArcheD-predicted A{beta} CSF values correlated strongly with measured A{beta} CSF values (r=0.81; p<0.001) and showed correlations with SUVR and episodic memory measures in all participants except in those with AD. For both clinical and biological classifications, cerebral white matter significantly contributed to CSF prediction (q<0.01), specifically in non-symptomatic and early stages of AD. However, in late-stage disease, brain stem, subcortical areas, cortical lobes, limbic lobe, and basal forebrain made more significant contributions (q<0.01). Considering cortical gray matter separately, the parietal lobe was the strongest predictor of CSF amyloid levels in those with prodromal or early AD, while the temporal lobe played a more crucial role for those with AD. In summary, ArcheD reliably predicted A{beta} CSF concentration from A{beta} PET scans, offering potential clinical utility for A{beta} level determination and early AD detection.

neuroscience↗