Multivariate pattern classification of pediatric Tourette syndrome using functional connectivity MRI

Deanna J. Greene, Jessica A. Church, Nico U.F. Dosenbach, Ashley N. Nielsen, Babatunde Adeyemo, Binyam Nardos, Steven E. Petersen, Kevin J. Black, Bradley L. Schlaggar

Research output: Contribution to journalArticlepeer-review

41 Scopus citations


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.

Original languageEnglish
Pages (from-to)581-598
Number of pages18
JournalDevelopmental Science
Issue number4
StatePublished - Jul 1 2016


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