Machine Support to Discrimination of Parkinson’s Disease and Essential Tremor

Abstract

Pathological tremor is a common but also highly complex movement disorder, affecting about 5% of population over the age of 65. Different methodologies have been proposed for its quantification and analysis. Nevertheless, the discrimination between Parkinsonian and Essential tremor remains a looming challenge in clinical practice, greatly impacting both patient treatment and the development of new interventions. In this study, the discriminative powers of cortical thickness features, extracted from magnetic resonance imaging of 21 ET, 15 PD patients and 18 healthy control subjects have been presented and discussed. A total of 129 volumetric features (whole brain, except cerebellum) and 152 cortical thickness features (average plus standard deviation of the thickness of the different cortical areas) have been extracted from each subject. Data mining of these characteristics has been employed to identify the most discriminative structural features for tremor diagnosis. These features were then combined in advanced classification models that yielded accuracy of up to 100% in discrimination of examined ET and PD patients, with the volume and thickness of enthorinal cortex as the most discriminative feature. Noteworthy, when compared to healthy age-matched controls, the identified MRI-based features indicate the structural changes of the brain and cortex in both PD and ET group of patients, potentially illuminating the neurodegenerative nature of both pathologies studied. This is an important finding by itself as the neurodegenerative aspects of ET have not yet been rigorously proven.