title: Multivariate pattern classification of pediatric Tourette syndrome using functional connectivity MRI.
abstract: Tourette syndrome (TS) is a developmental neuropsychiatric disorder characterized by motor and vocal tics. Individuals with TS would benefit greatly from advances in prediction of symptom timecourse and treatment effectiveness. As a first step, we applied a multivariate method - support vector machine (SVM) classification - to test whether patterns in brain network activity, measured with resting state functional connectivity (RSFC) MRI, could predict diagnostic group membership for individuals. RSFC data from 42 children with TS (8-15 yrs) and 42 unaffected controls (age, IQ, in-scanner movement matched) were included. While univariate tests identified no significant group differences, SVM classified group membership with ~70% accuracy (p < .001). We also report a novel adaptation of SVM binary classification that, in addition to an overall accuracy rate for the SVM, 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 TS.
authors.affiliation: Department of Psychiatry, Washington University School of Medicine, USA.|Department of Radiology, Washington University School of Medicine, USA.
authors.forename: Deanna J
authors.initials: DJ
authors.lastname: Greene
authors.affiliation: Department of Psychology, The University of Texas at Austin, USA.
authors.forename: Jessica A
authors.initials: JA
authors.lastname: Church
authors.affiliation: Department of Neurology, Washington University School of Medicine, USA.
authors.forename: Nico U F
authors.initials: NU
authors.lastname: Dosenbach
authors.affiliation: Department of Neurology, Washington University School of Medicine, USA.
authors.forename: Ashley N
authors.initials: AN
authors.lastname: Nielsen
authors.affiliation: Department of Neurology, Washington University School of Medicine, USA.
authors.forename: Babatunde
authors.initials: B
authors.lastname: Adeyemo
authors.affiliation: Department of Neurology, Washington University School of Medicine, USA.
authors.forename: Binyam
authors.initials: B
authors.lastname: Nardos
authors.affiliation: Department of Radiology, Washington University School of Medicine, USA.|Department of Neurology, Washington University School of Medicine, USA.|Department of Neuroscience, Washington University School of Medicine, USA.
authors.forename: Steven E
authors.initials: SE
authors.lastname: Petersen
authors.affiliation: Department of Psychiatry, Washington University School of Medicine, USA.|Department of Radiology, Washington University School of Medicine, USA.|Department of Neurology, Washington University School of Medicine, USA.|Department of Neuroscience, Washington University School of Medicine, USA.
authors.forename: Kevin J
authors.initials: KJ
authors.lastname: Black
authors.affiliation: Department of Psychiatry, Washington University School of Medicine, USA.|Department of Radiology, Washington University School of Medicine, USA.|Department of Neurology, Washington University School of Medicine, USA.|Department of Neuroscience, Washington University School of Medicine, USA.|Department of Pediatrics, Washington University School of Medicine, USA.
authors.forename: Bradley L
authors.initials: BL
authors.lastname: Schlaggar
mesh_terms: D000293:Adolescent; D016022:Case-Control Studies; D002648:Child; D005260:Female; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008959:Models, Neurological; D015999:Multivariate Analysis; D009415:Nerve Net; D011379:Prognosis; D060388:Support Vector Machine / Q000592:standards*; D005879:Tourette Syndrome / Q000145:classification* / Q000000981:diagnostic imaging*
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