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 T
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-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 T
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-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.
Ageing is associated with grey matter atrophy and changes in task-related neural activations. This study investigated the effects of age and education on neural activation during a spatial working memory task in 189 participants aged between 20–80 years old, whilst controlling for grey matter density. Age was related to linear decreases in neural activation in task activated areas, and this effect was no longer significant when adjusting for education or accuracy. Age was also related to cubic increases in neural activation in non-task related areas, such as the temporal gyrus, cuneus and cerebellum when adjusting for accuracy and education. These findings support previous lifespan datasets indicating linear age-related decreases in task activation, but non-linear increases in non-task related areas during episodic memory tasks. The findings also support past studies indicating education offers a form of cognitive reserve through providing a form of neural compensation and highlights the need to consider education in ageing studies.
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