Cortical graph neural network for AD and MCI diagnosis and transfer learning across populations
- PMID: 31491832
- PMCID: PMC6627731
- DOI: 10.1016/j.nicl.2019.101929
Cortical graph neural network for AD and MCI diagnosis and transfer learning across populations
Abstract
Combining machine learning with neuroimaging data has a great potential for early diagnosis of mild cognitive impairment (MCI) and Alzheimer's disease (AD). However, it remains unclear how well the classifiers built on one population can predict MCI/AD diagnosis of other populations. This study aimed to employ a spectral graph convolutional neural network (graph-CNN), that incorporated cortical thickness and geometry, to identify MCI and AD based on 3089 T1-weighted MRI data of the ADNI-2 cohort, and to evaluate its feasibility to predict AD in the ADNI-1 cohort (n = 3602) and an Asian cohort (n = 347). For the ADNI-2 cohort, the graph-CNN showed classification accuracy of controls (CN) vs. AD at 85.8% and early MCI (EMCI) vs. AD at 79.2%, followed by CN vs. late MCI (LMCI) (69.3%), LMCI vs. AD (65.2%), EMCI vs. LMCI (60.9%), and CN vs. EMCI (51.8%). We demonstrated the robustness of the graph-CNN among the existing deep learning approaches, such as Euclidean-domain-based multilayer network and 1D CNN on cortical thickness, and 2D and 3D CNNs on T1-weighted MR images of the ADNI-2 cohort. The graph-CNN also achieved the prediction on the conversion of EMCI to AD at 75% and that of LMCI to AD at 92%. The find-tuned graph-CNN further provided a promising CN vs. AD classification accuracy of 89.4% on the ADNI-1 cohort and >90% on the Asian cohort. Our study demonstrated the feasibility to transfer AD/MCI classifiers learned from one population to the other. Notably, incorporating cortical geometry in CNN has the potential to improve classification performance.
Keywords: Convolutional neural networks; Cortical thickness; Dementia classification; Graph; Transfer learning.
Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.
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References
-
- Aderghal K., Benois-Pineau J., Afdel K. Approach and Fusion on ADNI. Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval. ACM; Bucharest, Romania: 2017. Classification of sMRI for Alzheimer’s disease diagnosis with CNN: single siamese networks with 2D+? pp. 494–498.
-
- Alzheimer’s Association 2015 Alzheimer’s disease facts and figures. Alzheimers Dement. 2015;11:332–384. - PubMed
-
- Barandela R., Sánchez J.S., Garcia V., Rangel E. Strategies for learning in class imbalance problems. Pattern Recogn. 2003;36:849–851.
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