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ObjectiveThis study aimed to investigate the association of altered cortical thickness and functional connectivity (FC) with depression in Parkinson’s disease (PD).Materials and methodsA total of 26 non-depressed PD patients (PD-ND), 30 PD patients with minor depression (PD-MnD), 32 PD patients with major depression (PD-MDD), and 30 healthy controls (HC) were enrolled. Differences in cortical thickness among the four groups were assessed, and the results were used to analyze FC differences in regions of cortical atrophy. Binary logistic regression and receiver operating characteristic (ROC) curve analyses were also performed to identify clinical features and neuroimaging biomarkers that might help in the prediction of PD-MDD.ResultsPatients with PD-MDD showed decreased cortical thickness compared to patients with PD-ND in the left superior temporal and right rostral middle frontal gyri (RMFG), as well as weak FC between the left superior temporal gyrus and right cerebellum posterior lobe and between right RMFG and right inferior frontal gyrus and insula. The combination of cortical thickness, FC, and basic clinical features showed strong potential for predicting PD-MDD based on the area under the ROC curve (0.927, 95% CI 0.854–0.999, p < 0.001).ConclusionPatients with PD-MDD show extensive cortical atrophy and FC alterations, suggesting that cortical thickness and FC may be neuroimaging-based diagnostic biomarkers for PD-MDD.

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Objectives Freezing of gait (FOG) is a common and complex disabling episodic gait disturbance in patients with Parkinson's disease (PD). Currently, the treatment of FOG remains a challenge for clinicians. The aim of our study was to develop a nomogram for FOG risk based on data collected from Chinese patients with PD. Materials & Methods A total of 379 PD patients (197 with FOG) from Kunming Medical University were recruited as a training cohort. Additionally, 339 PD patients (166 with FOG) were recruited from West China Hospital of Sichuan University, to serve as the validation cohort. The least absolute shrinkage and selection operator regression model was used to select clinical and demographic characteristics as well as blood markers, which were incorporated into a predictive model using multivariate logistic regression to predict the risk of developing FOG. The model was validated using the validation dataset, and model performance was evaluated using the C‐index, calibration plot, and decision curve analyses. Results The final predictive model included the REM Sleep Behavior Disorder Screening Questionnaire (RBDSQ) score, Parkinson's Disease Questionnaire (PDQ39), H‐Y stage, and visuospatial function. The model showed good calibration and good discrimination, with a C‐index value of 0.772 against the training cohort and 0.766 against the validation cohort. Decision curve analysis demonstrated the clinical utility of the nomogram. Conclusion A nomogram incorporating RBDSQ, PDQ39, H‐Y stage, and visuospatial function may reliably predict the risk of FOG in PD patients.

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