Machine learning "diagnosis" of Tourette syndrome by resting brain activity
What is it about?
We applied a multivariate method–support vector machine (SVM) classification–to patterns in brain network activity, measured with resting state functional connectivity (RSFC) MRI in 42 children with Tourette syndrome (age 8–15 yrs) and 42 tic-free controls matched for age, IQ, and head movement during the scan. Strict methods were used to remove data potentially compromised by head movement in the scanner. Univariate tests at single voxels (volume elements) across the brain identified no significant group differences. By contrast, SVM correctly classified group membership in ~70% of subjects, a degree of accuracy that is very unlikely to happen by chance (p < .001).
Why is it important?
Our results support the contention that multivariate data analysis methods can better capture the complexity of some brain disorders, and hold promise for predicting prognosis and treatment outcome for individuals with TS. We also report a novel adaptation of SVM binary classification that provides a confidence measure for the classification of each individual.
The following have contributed to this page: Dr Kevin J. Black
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