Differentiation of Parkinson’s disease and non-Parkinsonian olfactory dysfunction with structural MRI data

Abstract

Despite the increasingly refined protocols in the diagnosis of Parkinson’s disease (PD), detection of PD at an early stage remains challenging. As one of the most common non-motor symptoms of PD, olfactory dysfunction yields a high prevalence of < 90% and is observed years prior to the onset of motor symptoms. Thus, olfaction-based assessment may help premotor diagnosis of PD. Nevertheless, diagnosis of PD based on olfactory dysfunction is not yet realized in clinical settings due to low specificity, therefore, it would be of high clinical relevance to differentiate PD-induced olfactory dysfunction from other forms of non-Parkinsonian olfactory dysfunction (NPOD). In the present study, we assess the feasibility of machine learning approaches in the classification of PD and NPOD patients and show that PD patients could be differentiated from NPOD patients using (a) morphometric measurements including cortical thickness, cortical surface area and volumetric measures of parcellated cortical and subcortical brain regions, or (b) T1-weighted axial magnetic resonance imaging (MRI) scans. The proposed methods give rise to efficient classification of PD and NPOD, and may thus improve the specificity of olfaction-based diagnosis of PD.