Multilevel diffusion tensor imaging classification technique for characterizing neurobehavioral disorders.
This proposed novel method consists of three levels of analyses of diffusion tensor imaging data: 1) voxel level analysis of fractional anisotropy of white matter tracks, 2) connection level analysis, based on fiber tracks between specific brain regions, and 3) network level analysis, based connecti...
| Publicado en: | Brain Imaging & Behavior Vol. 14; no. 3; pp. 641 - 653 |
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| Autores principales: | , , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2020
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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=143612132&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 143612132 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 19317557 3GSC jtl: Brain Imaging & Behavior issn: 19317557 maglogo: N pubinfo: dt: Jun2020 vid: 14 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 143612132 143612132 NLM30519999 143612132 10.1007/s11682-018-0002-2 NLM30519999 143612132 ppf: 641 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Multilevel diffusion tensor imaging classification technique for characterizing neurobehavioral disorders. aug: au: Dalboni da Rocha, Josué Luiz Coutinho, Gabriel Bramati, Ivanei Moll, Fernanda Tovar Sitaram, Ranganatha affil: Brain and Language Lab, Department of Clinical Neuroscience, University of Geneva, Geneva, Switzerland sug: subj: Brain Alzheimer's Disease Magnetic Resonance Imaging Funding Source Human ab: This proposed novel method consists of three levels of analyses of diffusion tensor imaging data: 1) voxel level analysis of fractional anisotropy of white matter tracks, 2) connection level analysis, based on fiber tracks between specific brain regions, and 3) network level analysis, based connections among multiple brain regions. Machine-learning techniques of (Fisher score) feature selection, (Support Vector Machine) pattern classification, and (Leave-one-out) cross-validation are performed, for recognition of the neural connectivity patterns for diagnostic purposes. For validation proposes, this multilevel approach achieved an average classification accuracy of 90% between Alzheimer's disease and healthy controls, 83% between Alzheimer's disease and mild cognitive impairment, and 83% between mild cognitive impairment and healthy controls. The results indicate that the multilevel diffusion tensor imaging approach used in this analysis is a potential diagnostic tool for clinical evaluations of brain disorders. The presented pipeline is now available as a tool for scientifically applications in a broad range of studies from both clinical and behavioral spectrum, which includes studies about autism, dyslexia, schizophrenia, dementia, motor body performance, among others. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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