Abstract
Background. As a chronic progressive disease, diabetes mellitus (DM) has a high incidence worldwide, and it impacts on cognitive and learning abilities in the lifetime even in the early stage, may degenerate memory in middle age, and perhaps increases the risk of Alzheimer’s disease. Method. In this work, we propose a convolutional neural network (CNN) based classification method to help classify diabetes by distinguishing the brains with abnormal functions from the normal ones on resting-state functional magnetic resonance imaging (rs-fMRI). The proposed classification model is based on the Inception-v4-Residual convolutional neural network architecture. In our workflow, the original rs-fMRI data are first mapped to generate amplitude of low-frequency fluctuation (ALFF) images and then fed into the CNN model to get the classification result to indicate the potential existence of DM. Result. We validate our method on a realistic clinical rs-fMRI dataset, and the achieved average accuracy is89.95% in fivefold cross-validation. Our model achieves a 0.8690 AUC with 77.50% and 77.51% sensitivity and specificity using our local dataset, respectively. Conclusion. It has the potential to become a novel clinical preliminary screening tool that provides help for the classification of different categories based on functional brain alteration caused by diabetes, benefiting from its accuracy and robustness, as well as efficiency and patient friendliness.
Highlights
As a chronic progressive disease, diabetes mellitus (DM) has a high incidence worldwide, and it impacts on cognitive and learning abilities in the lifetime even in the early stage, may degenerate memory in middle age, and perhaps increases the risk of Alzheimer’s disease
Children diagnosed with T1DM are more likely to perform poorly in school than their nondiabetic classmates and are vulnerable showing impaired results on cognitive tests, learning abilities, and affecting memory [3]. ere are several studies demonstrating a linkage between T2DM and mild cognitive impairment (MCI) and Alzheimer’s disease (AD). e coexistence of cerebrovascular disease and T2DM enhances the correlation with MCI and the development of dementia [4]
Diabetic encephalopathies are accepted complications of diabetes, which manifest themselves as a gradual decline of cognitive function and result in brain structural lesions [6, 7]. ere is a growing literature indicating that individuals with diabetes have impairments in recent memory [8], and the mechanism might be due to the fact that glucose transport is significantly reduced in diabetic animals [9]
Summary
As a chronic progressive disease, diabetes mellitus (DM) has a high incidence worldwide, and it impacts on cognitive and learning abilities in the lifetime even in the early stage, may degenerate memory in middle age, and perhaps increases the risk of Alzheimer’s disease. We propose a convolutional neural network (CNN) based classification method to help classify diabetes by distinguishing the brains with abnormal functions from the normal ones on resting-state functional magnetic resonance imaging (rs-fMRI). Diabetes affects more than 451 million people (18–99 years) worldwide, and this figure will rise to 693 million by 2045; more surprisingly still, it is estimated that almost half of all people living with diabetes are undiagnosed [1] It is a group of metabolic diseases characterized by hyperglycemia and frequently accompanied by complications. Diabetic encephalopathies are accepted complications of diabetes, which manifest themselves as a gradual decline of cognitive function and result in brain structural lesions (neural slowing, increased cortical atrophy, microstructural abnormalities in white matter tracts) [6, 7]. Most of the current research has formed a consensus that DM affects brain function and brain structure, and the changes in brain function often precede those in brain
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