Diagnosis of early Alzheimer's disease based on dynamic high order networks.
Machine learning methods have been widely used for early diagnosis of Alzheimer's disease (AD) via functional connectivity networks (FCNs) analysis from neuroimaging data. The conventional low-order FCNs are obtained by time-series correlation of the whole brain based on resting-state functional mag...
| Published in: | Brain Imaging & Behavior Vol. 15; no. 1; pp. 276 - 288 |
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| Main Authors: | , , , , , , , |
| Format: | Journal Article |
| Published: |
Springer Nature
2021
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=148318917&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 148318917 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 19317557 3GSC jtl: Brain Imaging & Behavior issn: 19317557 maglogo: N pubinfo: dt: 2021 vid: 15 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 148318917 148318917 NLM32789620 10.1007/s11682-019-00255-9 NLM32789620 148318917 ppf: 276 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Diagnosis of early Alzheimer's disease based on dynamic high order networks. aug: au: Lei, Baiying Yu, Shuangzhi Zhao, Xin Frangi, Alejandro F Tan, Ee-Leng Elazab, Ahmed Wang, Tianfu Wang, Shuqiang affil: National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University, 518060, Shenzhen, China sug: subj: Alzheimer's Disease Magnetic Resonance Imaging Brain Neuroradiography Clinical Assessment Tools ab: Machine learning methods have been widely used for early diagnosis of Alzheimer's disease (AD) via functional connectivity networks (FCNs) analysis from neuroimaging data. The conventional low-order FCNs are obtained by time-series correlation of the whole brain based on resting-state functional magnetic resonance imaging (R-fMRI). However, FCNs overlook inter-region interactions, which limits application to brain disease diagnosis. To overcome this drawback, we develop a novel framework to exploit the high-level dynamic interactions among brain regions for early AD diagnosis. Specifically, a sliding window approach is employed to generate some R-fMRI sub-series. The correlations among these sub-series are then used to construct a series of dynamic FCNs. High-order FCNs based on the topographical similarity between each pair of the dynamic FCNs are then constructed. Afterward, a local weight clustering method is used to extract effective features of the network, and the least absolute shrinkage and selection operation method is chosen for feature selection. A support vector machine is employed for classification, and the dynamic high-order network approach is evaluated on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Our experimental results demonstrate that the proposed approach not only achieves promising results for AD classification, but also successfully recognizes disease-related biomarkers. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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