Classification of Alzheimer's Disease Using Maximal Information Coefficient-Based Functional Connectivity with an Extreme Learning Machine.

Abstract

Alzheimer's disease (AD) is a progressive chronic illness that leads to cognitive decline and dementia. Neuroimaging technologies, such as functional magnetic resonance imaging (fMRI), and deep learning approaches offer promising avenues for AD classification. In this study, we investigate the use of fMRI-based functional connectivity (FC) measures, including the Pearson correlation coefficient (PCC), maximal information coefficient (MIC), and extended maximal information coefficient (eMIC), combined with extreme learning machines (ELM) for AD classification. Our findings demonstrate that employing non-linear techniques, such as MIC and eMIC, as features for classification yields accurate results. Specifically, eMIC-based features achieve a high accuracy of 94% for classifying cognitively normal (CN) and mild cognitive impairment (MCI) individuals, outperforming PCC (81%) and MIC (85%). For MCI and AD classification, MIC achieves higher accuracy (81%) compared to PCC (58%) and eMIC (78%). In CN and AD classification, eMIC exhibits the best accuracy of 95% compared to MIC (90%) and PCC (87%). These results underscore the effectiveness of fMRI-based features derived from non-linear techniques in accurately differentiating AD and MCI individuals from CN individuals, emphasizing the potential of neuroimaging and machine learning methods for improving AD diagnosis and classification.