Genetics Information with Functional Brain Networks for Dementia Classification
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...
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MDPI AG
2023-03-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/11/6/1529 |
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| author | Uttam Khatri Ji-In Kim Goo-Rak Kwon |
| author_facet | Uttam Khatri Ji-In Kim Goo-Rak Kwon |
| author_sort | Uttam Khatri |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T06:13:42Z |
| format | Article |
| id | doaj.art-c507b055e0f344559895665ca8a78587 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-11T06:13:42Z |
| publishDate | 2023-03-01 |
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| series | Mathematics |
| spelling | doaj.art-c507b055e0f344559895665ca8a785872023-11-17T12:29:55ZengMDPI AGMathematics2227-73902023-03-01116152910.3390/math11061529Genetics Information with Functional Brain Networks for Dementia ClassificationUttam Khatri0Ji-In Kim1Goo-Rak Kwon2Department of Information and Communication Engineering, Chosun University, 309 Pilmun-Daero, Dong-gu, Gwangju 61452, Republic of KoreaDepartment of Information and Communication Engineering, Chosun University, 309 Pilmun-Daero, Dong-gu, Gwangju 61452, Republic of KoreaDepartment of Information and Communication Engineering, Chosun University, 309 Pilmun-Daero, Dong-gu, Gwangju 61452, Republic of KoreaMild 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.https://www.mdpi.com/2227-7390/11/6/1529Alzheimer’s diseasebrain networks nodeensemble features selectionMKL-SVMgenetics information |
| spellingShingle | Uttam Khatri Ji-In Kim Goo-Rak Kwon Genetics Information with Functional Brain Networks for Dementia Classification Mathematics Alzheimer’s disease brain networks node ensemble features selection MKL-SVM genetics information |
| title | Genetics Information with Functional Brain Networks for Dementia Classification |
| title_full | Genetics Information with Functional Brain Networks for Dementia Classification |
| title_fullStr | Genetics Information with Functional Brain Networks for Dementia Classification |
| title_full_unstemmed | Genetics Information with Functional Brain Networks for Dementia Classification |
| title_short | Genetics Information with Functional Brain Networks for Dementia Classification |
| title_sort | genetics information with functional brain networks for dementia classification |
| topic | Alzheimer’s disease brain networks node ensemble features selection MKL-SVM genetics information |
| url | https://www.mdpi.com/2227-7390/11/6/1529 |
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