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Abstract
Purpose: Alzheimer's disease (AD) is a heterogeneous disease that presents a broad spectrum of clinicopathologic profiles. To date, objective subtyping of AD independent of disease progression using brain imaging has been required. Our study aimed to extract representations of unique brain metabolism patterns different from disease progression to identify objective subtypes of AD.

Methods: A total of 3620 FDG brain PET images with AD, mild cognitive impairment (MCI), and cognitively normal (CN) were obtained from the ADNI database from 1607 participants at enrollment and follow-up visits. A conditional variational autoencoder model was trained on FDG brain PET images of AD patients with the corresponding condition of AD severity score. The k-means algorithm was applied to generate clusters from the encoded representations. The trained deep learning-based cluster model was also transferred to FDG PET of MCI patients and predicted the prognosis of subtypes for conversion from MCI to AD. Spatial metabolism patterns, clinical and biological characteristics, and conversion rate from MCI to AD were compared across the subtypes.

Results: Four distinct subtypes of spatial metabolism patterns in AD with different brain pathologies and clinical profiles were identified: (i) angular, (ii) occipital, (iii) orbitofrontal, and (iv) minimal hypometabolic patterns. The deep learning model was also successfully transferred for subtyping MCI, and significant differences in frequency (P < 0.001) and risk of conversion (log-rank P < 0.0001) from MCI to AD were observed across the subtypes, highest in S2 (35.7%) followed by S1 (23.4%).

Conclusion: We identified distinct subtypes of AD with different clinicopathologic features. The deep learning-based approach to distinguish AD subtypes on FDG PET could have implications for predicting individual outcomes and provide a clue to understanding the heterogeneous pathophysiology of AD.

Affiliations

Hyun Gee Ryoo 1 2 3, Hongyoon Choi 4 5, Kuangyu Shi 6, Axel Rominger 6, Dong Young Lee 7 8, Dong Soo Lee 2 3 9; Alzheimer’s Disease Neuroimaging Initiative
1Department of Nuclear Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea.
2Department of Nuclear Medicine, Seoul National University Hospital, 101, Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.
3Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, and College of Medicine or College of Pharmacy, Seoul National University, Seoul, Republic of Korea.
4Department of Nuclear Medicine, Seoul National University Hospital, 101, Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea. chy1000@snu.ac.kr.
5Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea. chy1000@snu.ac.kr.
6Department of Nuclear Medicine, Inselspital, University of Bern, Freiburgstrasse 18, 3010, Bern, Switzerland.
7Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea.
8Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea.
9Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea.