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Distinct subtypes of spatial brain metabolism patterns in Alzheimer’s disease identified by deep learning-based FDG PET clusters

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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.

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Data availability

All raw data including FDG PET, CDR-SB score, demographic, cognitive, and biomarker variables are available through the ADNI data archive (http://adni.loni.usc.edu/). The custom codes for the deep learning model may be available for research purposes from the corresponding authors on reasonable request.

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Acknowledgements

This paper is based on the first author’s Ph.D. thesis in the Graduate School of Convergence Science and Technology, and College of Medicine or College of Pharmacy, Seoul National University, Seoul, Republic of Korea.

Funding

This research was supported by the National Research Foundation of Korea grant funded by the Korea Government (NRF-2019K1A3A1A14065446), and Korea Medical Device Development Fund grant funded by the Korea government (the Ministry of Science and ICT, the Ministry of Trade, Industry and Energy, the Ministry of Health & Welfare, the Ministry of Food and Drug Safety) (Project Number: 1711137868, RS-2020-KD000006).

Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

Author information

Authors and Affiliations

  1. Department of Nuclear Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea

    Hyun Gee Ryoo

  2. Department of Nuclear Medicine, Seoul National University Hospital, 101, Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea

    Hyun Gee Ryoo, Hongyoon Choi & Dong Soo Lee

  3. Department 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

    Hyun Gee Ryoo & Dong Soo Lee

  4. Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea

    Hongyoon Choi & Dong Soo Lee

  5. Department of Nuclear Medicine, Inselspital, University of Bern, Freiburgstrasse 18, 3010, Bern, Switzerland

    Kuangyu Shi & Axel Rominger

  6. Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea

    Dong Young Lee

  7. Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea

    Dong Young Lee

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  1. Hyun Gee Ryoo
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Consortia

for the Alzheimer’s Disease Neuroimaging Initiative

Contributions

H.C. designed the study. H.G.R. and H.C. developed the model. H.G.R. performed experiments. A.R. and D.S.L. contributed to analyze PET images. D.Y.L. and D.S.L. contributed to analyze clinical data. K.S. supported developing the model. K.S., A.R., D.Y.L., and D.S.L. contributed to data interpretation and analysis. H.G.R. wrote the manuscript mainly and all authors critically reviewed and edited the manuscript.

Corresponding author

Correspondence to Hongyoon Choi.

Ethics declarations

Ethics approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Informed consent

Written informed consent for cognitive testing and neuroimaging prior to participation of the ADNI cohort was obtained from all subjects, and the study protocols were approved by the institutional review boards of all participating institutions.

Competing interests

Dr. Choi is a co-founder of Portrai, Inc. All other authors declare no competing interests.

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Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.

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Ryoo, H.G., Choi, H., Shi, K. et al. Distinct subtypes of spatial brain metabolism patterns in Alzheimer’s disease identified by deep learning-based FDG PET clusters. Eur J Nucl Med Mol Imaging 51, 443–454 (2024). https://doi.org/10.1007/s00259-023-06440-9

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