Machine learning of cerebello-cerebral functional networks for mild cognitive impairment detection

Abstract

Background: Early identification of degenerative processes in Alzheimer’s disease (AD) is essential. Cerebello-cerebral network changes can be used for early diagnosis of dementia and its stages, namely mild cognitive impairment (MCI) and AD. Methods: Features of cortical thickness (CT) and cerebello-cerebral functional connectivity (FC) extracted from MRI data were used to analyze structural and functional changes, and machine learning for the disease progression classification. Results: CT features have an accuracy of 92.05% for AD vs. HC, 88.64% for MCI vs. HC, and 83.13% for MCI vs. AD. Additionally, combined with convolutional CT and cerebello-cerebral FC features, the accuracy of the classifier reached 94.12% for MCI vs. HC, 90.91% for AD vs. HC, and 89.16% for MCI vs. AD, evaluated using support vector machines. Conclusions: The proposed pipeline offers a promising low-cost alternative for the diagnosis of preclinical AD and can be useful for other degenerative brain disorders.