Journal article
Generative adversarial network constrained multiple loss autoencoder: A deep learning-based individual atrophy detection for Alzheimer's disease and mild cognitive impairment
HUMAN BRAIN MAPPING, v 44(3), p1129
15 Feb 2023
PMID: 36394351
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Exploring individual brain atrophy patterns is of great value in precision medicine for Alzheimer's disease (AD) and mild cognitive impairment (MCI). However, the current individual brain atrophy detection models are deficient. Here, we proposed a framework called generative adversarial network constrained multiple loss autoencoder (GANCMLAE) for precisely depicting individual atrophy patterns. The GANCMLAE model was trained using normal controls (NCs) from the Alzheimer's Disease Neuroimaging Initiative cohort, and the Xuanwu cohort was employed to validate the robustness of the model. The potential of the model for identifying different atrophy patterns of MCI subtypes was also assessed. Furthermore, the clinical application potential of the GANCMLAE model was investigated. The results showed that the model can achieve good image reconstruction performance on the structural similarity index measure (0.929 +/- 0.003), peak signal-to-noise ratio (31.04 +/- 0.09), and mean squared error (0.0014 +/- 0.0001) with less latent loss in the Xuanwu cohort. The individual atrophy patterns extracted from this model are more precise in reflecting the clinical symptoms of MCI subtypes. The individual atrophy patterns exhibit a better discriminative power in identifying patients with AD and MCI from NCs than those of the t-test model, with areas under the receiver operating characteristic curve of 0.867 (95%: 0.837-0.897) and 0.752 (95%: 0.71-0.790), respectively. Similar findings are also reported in the AD and MCI subgroups. In conclusion, the GANCMLAE model can serve as an effective tool for individualised atrophy detection.
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Details
- Title
- Generative adversarial network constrained multiple loss autoencoder: A deep learning-based individual atrophy detection for Alzheimer's disease and mild cognitive impairment
- Publication Details
- HUMAN BRAIN MAPPING, v 44(3), p1129
- Publisher
- WILEY; HOBOKEN
- Grant note
- The authors would like to thank the Han Ying research team for their help in data collection and management as well as the dedication of each research participant. This work was supported by the National Natural Science Foundation of China (grant numbers 61633018, 82020108013, 61603236, and 81830059). National Key Research and Development Program of China (grant numbers 2016YFC1306300, 2018YFC1312000, and 2018YFC1707704). 111 Project (grant number D20031). Shanghai Municipal Science and Technology Major Project (grant number 017SHZDZX01). Beijing Municipal Commission of Health and Family Planning (grant number PXM2020_026283_000002). Science and Technology Innovation 2030 Major Projects (grant number 2022ZD0211600). Data collection and dissemination for this project were funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI): National Institutes of Health (grant number U01 AG024904), and Department of Defense (award number W81XWH-12-2-0012). ADNI is funded by the National Institute of Aging and the National Institute of Biomedical Imaging and Bioengineering and through generous contributions from the following organisations: AbbVie, Alzheimer's Association, Alzheimer's Drug Discovery Foundation, Araclon Biotech, BioClinica Inc., Biogen, Bristol-Myers Squibb Company, CereSpir Inc., 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 Institute of Health Research provides funds to support ADNI clinical sites in Canada. Private sector contributions were facilitated by the Foundation for the National Institutes of Health (). The grantee organisation was the Northern California Institute for Research and Education, and the study was coordinated by the Alzheimer's Disease Cooperative Study at the University of California, San Diego, CA. ADNI data were disseminated by the Laboratory for Neuro Imaging at the University of Southern California, CA.
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Drexel University
- Web of Science ID
- WOS:000888831400001
- Scopus ID
- 2-s2.0-85142286243
- Other Identifier
- 991021861176404721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Neuroimaging
- Neurosciences
- Radiology, Nuclear Medicine & Medical Imaging