Magnetic resonance support vector machine discriminates essential tremor with rest tremor from tremor-dominant Parkinson disease.

BACKGROUND

The aim of the current study was to distinguish patients who had tremor-dominant Parkinson's disease (tPD) from those who had essential tremor with rest tremor (rET).

METHODS

We combined voxel-based morphometry-derived gray matter and white matter volumes and diffusion tensor imaging-derived mean diffusivity and fractional anisotropy in a support vector machine (SVM) to evaluate 15 patients with rET and 15 patients with tPD. Dopamine transporter single-photon emission computed tomography imaging was used as ground truth.

RESULTS

SVM classification of individual patients showed that no single predictor was able to fully discriminate patients with tPD from those with rET. By contrast, when all predictors were combined in a multi-modal algorithm, SVM distinguished patients with rET from those with tPD with an accuracy of 100%.

CONCLUSIONS

SVM is an operator-independent and automatic technique that may help distinguish patients with tPD from those with rET at the individual level.