Automated Classification of Mild Cognitive Impairment by Machine Learning With Hippocampus-Related White Matter Network.
Background: Detection of mild cognitive impairment (MCI) is essential to screen high risk of Alzheimer's disease (AD). However, subtle changes during MCI make it challenging to classify in machine learning. The previous pathological analysis pointed out that the hippocampus is the critical hub for t...
| Publicado en: | Frontiers in Aging Neuroscience Vol. 14; pp. 1 - 15 |
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| Autores principales: | , , , , , , , , , |
| Formato: | diagnostic images pictorial research tables/charts Journal Article |
| Publicado: |
Frontiers Media S.A.
6/14/2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=157457347&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 157457347 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 16634365 BG2U jtl: Frontiers in Aging Neuroscience issn: 16634365 maglogo: N pubinfo: dt: 6/14/2022 vid: 14 pid: 40038 pub: Frontiers Media S.A. artinfo: ui: 157457347 157457347 157457347 10.3389/fnagi.2022.866230 157457347 ppf: 1 ppct: 14 formats: tig: atl: Automated Classification of Mild Cognitive Impairment by Machine Learning With Hippocampus-Related White Matter Network. aug: au: Zhou, Yu Si, Xiaopeng Chao, Yi-Ping Chen, Yuanyuan Lin, Ching-Po Li, Sicheng Zhang, Xingjian Sun, Yulin Ming, Dong Li, Qiang affil: School of Microelectronics, Tianjin University, Tianjin, China sug: subj: Mild Cognitive Impairment Classification Machine Learning Hippocampus White Matter Alzheimer's Disease Prevention and Control Health Screening Human Post Hoc Analysis Early Diagnosis Male Female Aged Aged, 80 and Over Neuropsychological Tests Descriptive Statistics Scales Questionnaires Magnetic Resonance Imaging Funding Source Aged: 65+ years Aged, 80 & over Male Female ab: Background: Detection of mild cognitive impairment (MCI) is essential to screen high risk of Alzheimer's disease (AD). However, subtle changes during MCI make it challenging to classify in machine learning. The previous pathological analysis pointed out that the hippocampus is the critical hub for the white matter (WM) network of MCI. Damage to the white matter pathways around the hippocampus is the main cause of memory decline in MCI. Therefore, it is vital to biologically extract features from the WM network driven by hippocampus-related regions to improve classification performance. Methods: Our study proposes a method for feature extraction of the whole-brain WM network. First, 42 MCI and 54 normal control (NC) subjects were recruited using diffusion tensor imaging (DTI), resting-state functional magnetic resonance imaging (rs-fMRI), and T1-weighted (T1w) imaging. Second, mean diffusivity (MD) and fractional anisotropy (FA) were calculated from DTI, and the whole-brain WM networks were obtained. Third, regions of interest (ROIs) with significant functional connectivity to the hippocampus were selected for feature extraction, and the hippocampus (HIP)-related WM networks were obtained. Furthermore, the rank sum test with Bonferroni correction was used to retain significantly different connectivity between MCI and NC, and significant HIP-related WM networks were obtained. Finally, the classification performances of these three WM networks were compared to select the optimal feature and classifier. Results: (1) For the features, the whole-brain WM network, HIP-related WM network, and significant HIP-related WM network are significantly improved in turn. Also, the accuracy of MD networks as features is better than FA. (2) For the classification algorithm, the support vector machine (SVM) classifier with radial basis function, taking the significant HIP-related WM network in MD as a feature, has the optimal classification performance (accuracy = 89.4%, AUC = 0.954). (3) For the pathologic mechanism, the hippocampus and thalamus are crucial hubs of the WM network for MCI. Conclusion: Feature extraction from the WM network driven by hippocampus-related regions provides an effective method for the early diagnosis of AD. pubtype: Academic Journal doctype: diagnostic images pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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