RT Journal Article
SR Electronic
T1 Cerebellar Atrophy in Essential Tremor Using an Automated Segmentation Method
JF American Journal of Neuroradiology
JO Am. J. Neuroradiol.
FD American Society of Neuroradiology
SP 1240
OP 1243
DO 10.3174/ajnr.A1544
VO 30
IS 6
A1 A. Cerasa
A1 D. Messina
A1 G. Nicoletti
A1 F. Novellino
A1 P. Lanza
A1 F. Condino
A1 G. Arabia
A1 M. Salsone
A1 A. Quattrone
YR 2009
UL http://www.ajnr.org/content/30/6/1240.abstract
AB BACKGROUND AND PURPOSE: Essential tremor (ET) is a slowly progressive disorder characterized by postural and kinetic tremors most commonly affecting the forearms and hands. Several lines of evidence from physiologic and neuroimaging studies point toward a major role of the cerebellum in this disease. Recently, voxel-based morphometry (VBM) has been proposed to quantify cerebellar atrophy in ET. However, VBM was not originally designed to study subcortical structures, and the complicated anatomy of the cerebellum may hamper the automatic processing of VBM. The aim of this study was to determine the efficacy and utility of using automated subcortical segmentation to identify atrophy of the cerebellum and other subcortical structures in patients with ET.MATERIALS AND METHODS: We used a recently developed automated volumetric method (FreeSurfer) to quantify subcortical atrophy in ET by comparing results obtained with this method with those provided by previous evidence. The study included T1-weighted MR images of 46 patients with ET grouped into those having arm ET (n = 27, a-ET) or head ET (n = 19, h-ET) and 28 healthy controls.RESULTS: Results revealed the expected reduction of cerebellar volume in patients with h-ET with respect to healthy controls after controlling for intracranial volume. No significant difference was detected in any other subcortical area.CONCLUSIONS: Volumetric data obtained with automated segmentation of subcortical and cerebellar structures approximate data from a previous study based on VBM. The current findings extend the literature by providing initial validation for using fully automated segmentation to derive cerebellar volumetric information from patients with ET.