Genetics Information with Functional Brain Networks for Dementia Classification

Uttam Khatri, Ji-In Kim and Goo-Rak Kwon ()
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Uttam Khatri: Department of Information and Communication Engineering, Chosun University, 309 Pilmun-Daero, Dong-gu, Gwangju 61452, Republic of Korea
Ji-In Kim: Department of Information and Communication Engineering, Chosun University, 309 Pilmun-Daero, Dong-gu, Gwangju 61452, Republic of Korea
Goo-Rak Kwon: Department of Information and Communication Engineering, Chosun University, 309 Pilmun-Daero, Dong-gu, Gwangju 61452, Republic of Korea

Mathematics, 2023, vol. 11, issue 6, 1-20

Abstract: Mild cognitive impairment (MCI) precedes the Alzheimer’s disease (AD) continuum, making it crucial for therapeutic care to identify patients with MCI at risk of progression. We aim to create generalized models to identify patients with MCI who advance to AD using high-dimensional-data resting state functional magnetic resonance imaging (rs-fMRI) brain networks and gene expression. Studies that integrate genetic traits with brain imaging for clinical examination are limited, compared with most current research methodologies, employing separate or multi-imaging features for disease prognosis. Healthy controls (HCs) and the two phases of MCI (convertible and stable MCI) along with AD can be effectively diagnosed using genetic markers. The rs-fMRI-based brain functional connectome provides various information regarding brain networks and is utilized in combination with genetic factors to distinguish people with AD from HCs. The most discriminating network nodes are identified using the least absolute shrinkage and selection operator (LASSO). The most common brain areas for nodal detection in patients with AD are the middle temporal, inferior temporal, lingual, hippocampus, amygdala, and middle frontal gyri. The highest degree of discriminative power is demonstrated by the nodal graph metrics. Similarly, we propose an ensemble feature-ranking algorithm for high-dimensional genetic information. We use a multiple-kernel learning support vector machine to efficiently merge multipattern data. Using the suggested technique to distinguish AD from HCs produced combined features with a leave-one-out cross-validation (LOOCV) classification accuracy of 93.07% and area under the curve (AUC) of 95.13%, making it the most state-of-the-art technique in terms of diagnostic accuracy. Therefore, our proposed approach has high accuracy and is clinically relevant and efficient for identifying AD.

Keywords: Alzheimer’s disease; brain networks node; ensemble features selection; MKL-SVM; genetics information (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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