Eigenvector Centrality Characterization on fMRI Data: Gender and Node Differences in Normal and ASD Subjects: Author name.
With the budding interests of structural and functional network characteristics as potential parameters for abnormal brains, an essential and thus simpler representation and evaluations have become necessary. Eigenvector centrality measure of functional magnetic resonance imaging (fMRI) offer region...
| Publicado en: | Journal of Autism & Developmental Disorders Vol. 54; no. 7; pp. 2757 - 2769 |
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| Autor principal: | |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jul2024
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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=178677507&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 178677507 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01623257 AUT jtl: Journal of Autism & Developmental Disorders issn: 01623257 maglogo: N pubinfo: dt: Jul2024 vid: 54 iid: 7 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 178677507 163504638 178677507 178677507 10.1007/s10803-023-05922-x 178677507 ppf: 2757 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Eigenvector Centrality Characterization on fMRI Data: Gender and Node Differences in Normal and ASD Subjects: Author name. aug: au: Saha, Papri affil: Department of Computer Science, Derozio Memorial College, Rajarhat Road, P.O. - R- Gopalpur, 700136, Kolkata, India sug: subj: Autism Spectrum Disorder Diagnosis Magnetic Resonance Imaging Methods Brain Physiology Brain Mapping Sex Factors Human Comparative Studies Regression Models, Theoretical Large-Scale Brain Networks Limbic System Machine Learning Algorithms Confidence Intervals Prefrontal Cortex Descriptive Statistics Research Methodology ab: With the budding interests of structural and functional network characteristics as potential parameters for abnormal brains, an essential and thus simpler representation and evaluations have become necessary. Eigenvector centrality measure of functional magnetic resonance imaging (fMRI) offer region wise network representations through fMRI diagnostic maps. The article investigates the suitability of network node centrality values to discriminate ASD subject groups compared to typically developing controls following a boxplot formalism and a classification and regression tree model. Region wise differences between normal and ASD subjects primarily belong to the frontoparietal, limbic, ventral attention, default mode and visual networks. The reduced number of regions-of-interests (ROI) clearly suggests the benefit of automated supervised machine learning algorithm over the manual classification method. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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