Image-level trajectory inference of tau pathology using variational autoencoder for Flortaucipir PET

• Published in: European journal of nuclear medicine and molecular imaging 2022-07, Vol.49 (9), p.3061-3072
• Main Authors: Hong, Jimin, Kang, Seung Kwan, Alberts, Ian, Lu, Jiaying, Sznitman, Raphael, Lee, Jae Sung, Rominger, Axel, Choi, Hongyoon, Shi, Kuangyu
• Format: Article
• Language: English
• Subjects: Advanced Image Analyses (Radiomics and Artificial Intelligence); Alzheimer's disease; Amygdala; Amyloid; Biomarkers; Brain; Cardiology; Clustering; Cognitive ability; Continuity (mathematics); Deposition; Glucose metabolism; Graph theory; Imaging; Medical imaging; Medicine; Medicine & Public Health; Neurodegenerative diseases; Neuroimaging; Nuclear Medicine; Oncology; Original; Original Article; Orthopedics; Parahippocampal gyrus; Positron emission; Positron emission tomography; Radiology; Tau protein; Temporal lobe; Tomography
• ISSN: 1619-7070; 1619-7089
• DOI: 10.1007/s00259-021-05662-z

Purpose Alzheimer’s disease (AD) studies revealed that abnormal deposition of tau spreads in a specific spatial pattern, namely Braak stage. However, Braak staging is based on post mortem brains, each of which represents the cross section of the tau trajectory in disease progression, and numerous studies were reported that do not conform to that model. This study thus aimed to identify the tau trajectory and quantify the tau progression in a data-driven approach with the continuous latent space learned by variational autoencoder (VAE). Methods A total of 1080 [ 18 F]Flortaucipir brain positron emission tomography (PET) images were collected from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. VAE was built to compress the hidden features from tau images in latent space. Hierarchical agglomerative clustering and minimum spanning tree (MST) were applied to organize the features and calibrate them to the tau progression, thus deriving pseudo-time . The image-level tau trajectory was inferred by continuously sampling across the calibrated latent features. We assessed the pseudo-time with regard to tau standardized uptake value ratio (SUVr) in AD-vulnerable regions, amyloid deposit, glucose metabolism, cognitive scores, and clinical diagnosis. Results We identified four clusters that plausibly capture certain stages of AD and organized the clusters in the latent space. The inferred tau trajectory agreed with the Braak staging. According to the derived pseudo-time , tau first deposits in the parahippocampal and amygdala, and then spreads to the fusiform, inferior temporal lobe, and posterior cingulate. Prior to the regional tau deposition, amyloid accumulates first. Conclusion The spatiotemporal trajectory of tau progression inferred in this study was consistent with Braak staging. The profile of other biomarkers in disease progression agreed well with previous findings. We addressed that this approach additionally has the potential to quantify tau progression as a continuous variable by taking a whole-brain tau image into account.