Baseline cerebral structural morphology predict freezing of gait in early drug-naïve Parkinson’s disease

Recent neuroimaging evidence of morphological alterations in early Parkinson’s disease (PD) is heterogeneous. Moreover, predictors of freezing of gait (FOG) in early PD are limited. We aimed to investigate alterations in cerebral morphology in early PD and develop a model that could predict the occurrence of FOG at the individual level using machine learning with clinical, laboratory and cerebral structural imaging information of early drug-naïve PD. Data from 73 healthy controls (HCs) and 158 early drug-naïve PD patients at baseline were obtained from the Parkinson’s Progression Markers Initiative cohort. The CIVET pipeline was used to generate structural morphological features with T1-weighted imaging (T1WI). Five machine learning algorithms were calculated to assess the predictive performance of future FOG in early PD during a five-year follow-up period. We found that the bilateral olfactory cortex (OLF) showed a significantly higher surface area in PD patients than in HCs. Models trained with structural morphological features showed fair to good performance (accuracy range, 0.67–0.73). Performance improved when models were trained with structural morphological, clinical and laboratory features (accuracy range, 0.71–0.78). For machine learning algorithms, elastic net-support vector machine models (accuracy range, 0.69–0.78) performed the best. The main features used to predict FOG based on elastic net-support vector machine models were the left lingual gyrus, left anterior cingulate and paracingulate gyri and left angular gyrus. Overall, we found that the OLF exhibits predominantly cortical expansion in early PD. T1WI morphometric markers help predict future FOG in patients with early drug-naïve PD at an individual level.