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dc.contributor.authorLee, Jungsunen_US
dc.contributor.authorChon, Myong-Wuken_US
dc.contributor.authorKim, Harinen_US
dc.contributor.authorRathi, Yogeshen_US
dc.contributor.authorBouix, Sylvainen_US
dc.contributor.authorShenton, Martha E.en_US
dc.contributor.authorKubicki, Mareken_US
dc.date.accessioned2018-07-25T14:22:11Z
dc.date.issued2018en_US
dc.identifier.citationLee, Jungsun, Myong-Wuk Chon, Harin Kim, Yogesh Rathi, Sylvain Bouix, Martha E. Shenton, and Marek Kubicki. 2018. “Diagnostic value of structural and diffusion imaging measures in schizophrenia.” NeuroImage : Clinical 18 (1): 467-474. doi:10.1016/j.nicl.2018.02.007. http://dx.doi.org/10.1016/j.nicl.2018.02.007.en
dc.identifier.issnen
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:37298276
dc.description.abstractObjectives: Many studies have attempted to discriminate patients with schizophrenia from healthy controls by machine learning using structural or functional MRI. We included both structural and diffusion MRI (dMRI) and performed random forest (RF) and support vector machine (SVM) in this study. Methods: We evaluated the performance of classifying schizophrenia using RF method and SVM with 504 features (volume and/or fractional anisotropy and trace) from 184 brain regions. We enrolled 47 patients and 23 age- and sex-matched healthy controls and resampled our data into a balanced dataset using a Synthetic Minority Oversampling Technique method. We randomly permuted the classification of all participants as a patient or healthy control 100 times and ran the RF and SVM with leave one out cross validation for each permutation. We then compared the sensitivity and specificity of the original dataset and the permuted dataset. Results: Classification using RF with 504 features showed a significantly higher rate of performance compared to classification by chance: sensitivity (87.6% vs. 47.0%) and specificity (95.9 vs. 48.4%) performed by RF, sensitivity (89.5% vs. 48.0%) and specificity (94.5% vs. 47.1%) performed by SVM. Conclusions: Machine learning using RF and SVM with both volume and diffusion measures can discriminate patients with schizophrenia with a high degree of performance. Further replications are required.en
dc.language.isoen_USen
dc.publisherElsevieren
dc.relation.isversionofdoi:10.1016/j.nicl.2018.02.007en
dc.relation.hasversionhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC5987843/pdf/en
dash.licenseLAAen_US
dc.subjectClassificationen
dc.subjectDiffusion MRIen
dc.subjectRandom foresten
dc.subjectSupport vector machineen
dc.subjectSchizophreniaen
dc.titleDiagnostic value of structural and diffusion imaging measures in schizophreniaen
dc.typeJournal Articleen_US
dc.description.versionVersion of Recorden
dc.relation.journalNeuroImage : Clinicalen
dash.depositing.authorRathi, Yogeshen_US
dc.date.available2018-07-25T14:22:11Z
dc.identifier.doi10.1016/j.nicl.2018.02.007*
dash.identifier.orcid0000-0003-4235-7879en_US
dash.contributor.affiliatedBouix, Sylvain
dash.contributor.affiliatedRathi, Yogesh
dash.contributor.affiliatedShenton, Martha
dash.contributor.affiliatedKubicki, Marek
dc.identifier.orcid0000-0003-4235-7879


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