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. 2019:23:101929.
doi: 10.1016/j.nicl.2019.101929. Epub 2019 Jul 4.

Cortical graph neural network for AD and MCI diagnosis and transfer learning across populations

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Cortical graph neural network for AD and MCI diagnosis and transfer learning across populations

Chong-Yaw Wee et al. Neuroimage Clin. 2019.

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.

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Figures

Fig. 1
Fig. 1
Graph-CNN Architecture. (A) The graph-CNN model used in this study. (B) The coarsening and pooling operations for an input graph in a convolutional layer.
Fig. 2
Fig. 2
Classification performance of the graph-CNN model directly trained based on the ADNI-1 cohort (Scratch) and trained based on the ADNI-2 cohort (Pre-trained) with respect to the number of training epoch for the CN vs. AD classification.
Fig. 3
Fig. 3
Classification performance of the graph-CNN model directly trained based on the ADNI-1 cohort (Scratch) and trained based on the ADNI-2 cohort (Pre-trained) with respect to the number of training epoch for the CN vs. MCI classification.
Fig. 4
Fig. 4
Classification performance of the graph-CNN model directly trained based on the ADNI-1 cohort (Scratch) and trained based on the ADNI-2 cohort (Pre-trained) with respect to the number of training epoch for the MCI vs. AD classification.
Fig. 5
Fig. 5
Classification performance of the graph-CNN model directly trained based on the Asian cohort (Scratch) and trained based on the ADNI-2 cohort (Pre-trained) with respect to the number of training epoch for the CN vs. Moderate MCI classification.
Fig. 6
Fig. 6
Top 10 cortical regions for most discriminating (A) AD and (B) LMCI from CN.

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References

    1. 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.
    1. Alzheimer’s Association 2015 Alzheimer’s disease facts and figures. Alzheimers Dement. 2015;11:332–384. - PubMed
    1. Barandela R., Sánchez J.S., Garcia V., Rangel E. Strategies for learning in class imbalance problems. Pattern Recogn. 2003;36:849–851.
    1. Basaia S., Agosta F., Wagner L., Canu E., Magnani G., Santangelo R., Filippi M., Alzheimer's Disease Neuroimaging, I Automated classification of Alzheimer's disease and mild cognitive impairment using a single MRI and deep neural networks. Neuroimage Clin. 2019;21 - PMC - PubMed
    1. Casanova R., Hsu F.-C., Sink K.M., Rapp S.R., Williamson J.D., Resnick S.M., Espeland M.A., for the Alzheimer’s Disease Neuroimaging Initiative Alzheimer’s Disease risk assessment using large-scale machine learning methods. PLoS ONE. 2013;8 - PMC - PubMed

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