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. 2023 Jul 8;13(7):1046.
doi: 10.3390/brainsci13071046.

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

Nishant Chauhan  1 , Byung-Jae Choi  1
Affiliations

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

Nishant Chauhan et al. Brain Sci. .

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.

Keywords: alzheimer’s disease; deep learning; extreme learning machine; fMRI; functional connectivity; machine learning; maximal information coefficient; pearson correlation.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Pathophysiology of AD in the brain. The metabolism of APP sometimes follows a non-amyloidogenic pathway and forms amyloid plaques. Tau, a microtubule-associated protein, generates insoluble filaments that congregate as neurofibrillary tangles in AD.
Figure 2
Figure 2
Architecture of proposed method.
Figure 3
Figure 3
The architecture of multiple hidden layer extreme learning machine.
Figure 4
Figure 4
Functional connectivity matrices based on PCC, MIC, and eMIC (for CN and AD group). (a) FC Matrices of CN and AD group using PCC (b) FC Matrices of CN and AD group using MIC (c) FC Matrices of CN and AD group using eMIC.
Figure 5
Figure 5
Classification accuracy graph using multilayer ELM. Horizontal axes represent the number of features. (a) CN and MCI. (b) MCI and AD. (c) CN and AD.
Figure 6
Figure 6
Sensitivity and specificity graph of AD classification. CN and MCI. MCI and AD. CN and AD.
See this image and copyright information in PMC

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