Application of advanced machine learning methods on resting-state fMRI network for identification of mild cognitive impairment and Alzheimer's disease.
The study of brain networks by resting-state functional magnetic resonance imaging (rs-fMRI) is a promising method for identifying patients with dementia from healthy controls (HC). Using graph theory, different aspects of the brain network can be efficiently characterized by calculating measures of...
| Publicado en: | Brain Imaging & Behavior Vol. 10; no. 3; pp. 799 - 818 |
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| Autores principales: | , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Sep2016
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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=117723654&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 117723654 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 19317557 3GSC jtl: Brain Imaging & Behavior issn: 19317557 maglogo: N pubinfo: dt: Sep2016 vid: 10 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 117723654 117723654 NLM26363784 117723654 10.1007/s11682-015-9448-7 NLM26363784 117723654 ppf: 799 ppct: 19 formats: fmt: @attributes: type: P tig: atl: Application of advanced machine learning methods on resting-state fMRI network for identification of mild cognitive impairment and Alzheimer's disease. aug: au: Khazaee, Ali Ebrahimzadeh, Ata Babajani-Feremi, Abbas affil: Department of Electrical and Computer Engineering , Babol University of Technology , Babol Iran sug: subj: Magnetic Resonance Imaging Methods Brain Alzheimer's Disease Brain Mapping Methods Aged Alzheimer's Disease Physiopathology Relaxation Alzheimer's Disease Classification Brain Physiopathology Neural Pathways Physiology Databases Image Interpretation, Computer Assisted Female Male Neural Pathways Physiopathology Brain Physiology Neural Pathways Sensitivity and Specificity Human Aged: 65+ years Female Male ab: The study of brain networks by resting-state functional magnetic resonance imaging (rs-fMRI) is a promising method for identifying patients with dementia from healthy controls (HC). Using graph theory, different aspects of the brain network can be efficiently characterized by calculating measures of integration and segregation. In this study, we combined a graph theoretical approach with advanced machine learning methods to study the brain network in 89 patients with mild cognitive impairment (MCI), 34 patients with Alzheimer's disease (AD), and 45 age-matched HC. The rs-fMRI connectivity matrix was constructed using a brain parcellation based on a 264 putative functional areas. Using the optimal features extracted from the graph measures, we were able to accurately classify three groups (i.e., HC, MCI, and AD) with accuracy of 88.4 %. We also investigated performance of our proposed method for a binary classification of a group (e.g., MCI) from two other groups (e.g., HC and AD). The classification accuracies for identifying HC from AD and MCI, AD from HC and MCI, and MCI from HC and AD, were 87.3, 97.5, and 72.0 %, respectively. In addition, results based on the parcellation of 264 regions were compared to that of the automated anatomical labeling atlas (AAL), consisted of 90 regions. The accuracy of classification of three groups using AAL was degraded to 83.2 %. Our results show that combining the graph measures with the machine learning approach, on the basis of the rs-fMRI connectivity analysis, may assist in diagnosis of AD and MCI. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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