期刊
JOURNAL OF NEUROSCIENCE METHODS
卷 339, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.jneumeth.2020.108701
关键词
Alzheimer's disease; MCI to AD progression; Deep learning; Residual neural networks
资金
- NIH [2R01EB005846, P20GM103472, R01REB020407]
- NSF [1539067, IIS-1318759]
- National Natural Science Foundation of China [61703253]
- Natural Science Foundation of Shanxi Province in China [2016021077]
- Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health) [U01 AG024904]
- DOD ADNI (Department of Defense) [W81XWH-12-2-0012]
- National Institute on Aging
- National Institute of Biomedical Imaging and Bioengineering
- 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
- 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
- Transition Therapeutics
- Canadian Institutes of Health Research
Background: The unparalleled performance of deep learning approaches in generic image processing has motivated its extension to neuroimaging data. These approaches learn abstract neuroanatomical and functional brain alterations that could enable exceptional performance in classification of brain disorders, predicting disease progression, and localizing brain abnormalities. New Method: This work investigates the suitability of a modified form of deep residual neural networks (ResNet) for studying neuroimaging data in the specific application of predicting progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD). Prediction was conducted first by training the deep models using MCI individuals only, followed by a domain transfer learning version that additionally trained on AD and controls. We also demonstrate a network occlusion based method to localize abnormalities. Results: The implemented framework captured non-linear features that successfully predicted AD progression and also conformed to the spectrum of various clinical scores. In a repeated cross-validated setup, the learnt predictive models showed highly similar peak activations that corresponded to previous AD reports. Comparison with existing methods: The implemented architecture achieved a significant performance improvement over the classical support vector machine and the stacked autoencoder frameworks (p < 0.005), numerically better than state-of-the-art performance using sMRI data alone ( > 7% than the second-best performing method) and within 1% of the state-of-the-art performance considering learning using multiple neuroimaging modalities as well. Conclusions: The explored frameworks reflected the high potential of deep learning architectures in learning subtle predictive features and utility in critical applications such as predicting and understanding disease progression.
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