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Individuals with <jats:styled-content style=\"fixed-case\">TS<\/jats:styled-content> would benefit greatly from advances in prediction of symptom timecourse and treatment effectiveness. As a first step, we applied a multivariate method \u2013 support vector machine (<jats:styled-content style=\"fixed-case\">SVM<\/jats:styled-content>) classification \u2013 to test whether patterns in brain network activity, measured with resting state functional connectivity (<jats:styled-content style=\"fixed-case\">RSFC<\/jats:styled-content>) <jats:styled-content style=\"fixed-case\">MRI<\/jats:styled-content>, could predict diagnostic group membership for individuals. <jats:styled-content style=\"fixed-case\">RSFC<\/jats:styled-content> data from 42 children with <jats:styled-content style=\"fixed-case\">TS<\/jats:styled-content> (8\u201315\u00a0yrs) and 42 unaffected controls (age, <jats:styled-content style=\"fixed-case\">IQ<\/jats:styled-content>, in\u2010scanner movement matched) were included. While univariate tests identified no significant group differences, <jats:styled-content style=\"fixed-case\">SVM<\/jats:styled-content> classified group membership with ~70% accuracy (<jats:italic>p<\/jats:italic>\u00a0&lt;\u00a0.001). We also report a novel adaptation of <jats:styled-content style=\"fixed-case\">SVM<\/jats:styled-content> binary classification that, in addition to an overall accuracy rate for the <jats:styled-content style=\"fixed-case\">SVM<\/jats:styled-content>, provides a confidence measure for the accurate classification of each individual. Our results support the contention that multivariate methods can better capture the complexity of some brain disorders, and hold promise for predicting prognosis and treatment outcome for individuals with <jats:styled-content style=\"fixed-case\">TS<\/jats:styled-content>.<\/jats:p>","DOI":"10.1111\/desc.12407","type":"journal-article","created":{"date-parts":[[2016,2,1]],"date-time":"2016-02-01T08:59:12Z","timestamp":1454317152000},"page":"581-598","source":"Crossref","is-referenced-by-count":63,"title":["Multivariate pattern classification of pediatric Tourette syndrome using functional connectivity <scp>MRI<\/scp>"],"prefix":"10.1111","volume":"19","author":[{"given":"Deanna J.","family":"Greene","sequence":"first","affiliation":[{"name":"Department of Psychiatry Washington University School of Medicine USA"},{"name":"Department of Radiology Washington University School of Medicine USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jessica 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