Aberrant Changes in Cortical Complexity in Right-Onset Versus Left-Onset Parkinson's Disease in Early-Stage

Does the side of onset influence symptom severity in Parkinson's disease? A systematic review and meta-analysis

Parkinson's disease (PD) is a neurodegenerative movement disorder characterized by motor symptoms that initially manifest unilaterally. Whilst some studies indicate that right-side onset is associated with greater symptom severity, others report no differences between right-side and left-side onset patients. The present meta-analysis was thus designed to reconcile inconsistencies in the literature and determine whether side of onset affects PD symptom severity. Following the PRISMA guidelines 1013 studies were initially identified in database and grey literature searches; following title and abstract, and full text, screening 34 studies met the stringent inclusion criteria (n = 2210). Results of the random-effects meta-analysis indicated no difference in symptom severity between PD patients with left-side (n = 1104) and right-side (n = 1106) onset. As such, the meta-analysis suggests that the side of onset should not be used to predict symptom trajectory or to formulate prognoses for PD patients. The current meta-analysis was the first to focus on the relationship between the side of onset and symptom severity in PD. However, the studies included were limited by the common exclusion of left-handed participants. Future research would benefit from exploring other factors that may influence symptom severity and disease progression in PD, such as asymmetric loss of nigrostriatal dopaminergic neurons.

Machine Learning Models for Diagnosis of Parkinson's Disease Using Multiple Structural Magnetic Resonance Imaging Features

Purpose

This study aimed to develop machine learning models for the diagnosis of Parkinson's disease (PD) using multiple structural magnetic resonance imaging (MRI) features and validate their performance.

Methods

Brain structural MRI scans of 60 patients with PD and 56 normal controls (NCs) were enrolled as development dataset and 69 patients with PD and 71 NCs from Parkinson's Progression Markers Initiative (PPMI) dataset as independent test dataset. First, multiple structural MRI features were extracted from cerebellar, subcortical, and cortical regions of the brain. Then, the Pearson's correlation test and least absolute shrinkage and selection operator (LASSO) regression were used to select the most discriminating features. Finally, using logistic regression (LR) classifier with the 5-fold cross-validation scheme in the development dataset, the cerebellar, subcortical, cortical, and a combined model based on all features were constructed separately. The diagnostic performance and clinical net benefit of each model were evaluated with the receiver operating characteristic (ROC) analysis and the decision curve analysis (DCA) in both datasets.

Results

After feature selection, 5 cerebellar (absolute value of left lobule crus II cortical thickness (CT) and right lobule IV volume, relative value of right lobule VIIIA CT and lobule VI/VIIIA gray matter volume), 3 subcortical (asymmetry index of caudate volume, relative value of left caudate volume, and absolute value of right lateral ventricle), and 4 cortical features (local gyrification index of right anterior circular insular sulcus and anterior agranular insula complex, local fractal dimension of right middle insular area, and CT of left supplementary and cingulate eye field) were selected as the most distinguishing features. The area under the curve (AUC) values of the cerebellar, subcortical, cortical, and combined models were 0.679, 0.555, 0.767, and 0.781, respectively, for the development dataset and 0.646, 0.632, 0.690, and 0.756, respectively, for the independent test dataset, respectively. The combined model showed higher performance than the other models (Delong's test, all p-values < 0.05). All models showed good calibration, and the DCA demonstrated that the combined model has a higher net benefit than other models.

Conclusion

The combined model showed favorable diagnostic performance and clinical net benefit and had the potential to be used as a non-invasive method for the diagnosis of PD.