期刊
BMC BIOINFORMATICS
卷 21, 期 -, 页码 -出版社
BMC
DOI: 10.1186/s12859-020-03848-0
关键词
Alzheimer’ s disease; Tau PET; Deep learning
类别
资金
- National Institutes of Health (NIH) [U01 AG024904, DOD W81XWH-12-2-0012, P30 AG010133, R01 AG019771, R01 LM013463, DOD W81XWH-14-2-0151, R01 AG057739, R01 CA129769, R01 LM012535, R03 AG063250, U01 AG068057, K01 AG049050, R01 AG061788]
Background Alzheimer's disease (AD) is the most common type of dementia, typically characterized by memory loss followed by progressive cognitive decline and functional impairment. Many clinical trials of potential therapies for AD have failed, and there is currently no approved disease-modifying treatment. Biomarkers for early detection and mechanistic understanding of disease course are critical for drug development and clinical trials. Amyloid has been the focus of most biomarker research. Here, we developed a deep learning-based framework to identify informative features for AD classification using tau positron emission tomography (PET) scans. Results The 3D convolutional neural network (CNN)-based classification model of AD from cognitively normal (CN) yielded an average accuracy of 90.8% based on five-fold cross-validation. The LRP model identified the brain regions in tau PET images that contributed most to the AD classification from CN. The top identified regions included the hippocampus, parahippocampus, thalamus, and fusiform. The layer-wise relevance propagation (LRP) results were consistent with those from the voxel-wise analysis in SPM12, showing significant focal AD associated regional tau deposition in the bilateral temporal lobes including the entorhinal cortex. The AD probability scores calculated by the classifier were correlated with brain tau deposition in the medial temporal lobe in MCI participants (r = 0.43 for early MCI and r = 0.49 for late MCI). Conclusion A deep learning framework combining 3D CNN and LRP algorithms can be used with tau PET images to identify informative features for AD classification and may have application for early detection during prodromal stages of AD.
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