The amplitude of low-frequency fluctuation predicts levodopa treatment response in patients with Parkinson's disease. (November 2021)

• Record Type: Journal Article
• Authors: Yang, Bowen; Wang, Xiu; Mo, Jiajie; Li, Zilin; Gao, Dongmei; Bai, Yutong; Zou, Liangying; Zhang, Xin; Zhao, Xuemin; Wang, Yao; Liu, Chang; Zhao, Baotian; Guo, Zhihao; Zhang, Chao; Hu, Wenhan; Zhang, Jianguo; Zhang, Kai
• Is Part Of: Parkinsonism & related disorders. Volume 92(2021)
• Page Start: 26
• Page End: 32
• Publication Date: 2021-11
• Subjects: Parkinson's disease -- Machine learning -- Levodopa response -- Motor symptom -- Resting-state functional magnetic resonance imaging; Parkinson's disease -- Periodicals; Movement disorders -- Periodicals; Movement Disorders -- Periodicals; Nerve Degeneration -- Periodicals; Nervous System Diseases -- Periodicals; Parkinson Disease -- Periodicals; Tremor -- Periodicals; Parkinson, Maladie de -- Périodiques; Parkinson's disease; 616.833
• DOI: 10.1016/j.parkreldis.2021.10.003
• Languages: English
• ISSNs: 1353-8020

Abstract

Abstract: Introduction: Levodopa has become the main therapy for motor symptoms of Parkinson's disease (PD). This study aimed to test whether the amplitude of low-frequency fluctuation (ALFF) computed by fMRI could predict individual patient's response to levodopa treatment. Methods: We included 40 patients. Treatment efficacy was defined based on motor symptoms improvement from the state of medication off to medication on, as assessed by the Unified Parkinson's Disease Rating Scale score III. Two machine learning models were constructed to test the prediction ability of ALFF. First, the ensemble method was implemented to predict individual treatment responses. Second, the categorical boosting (CatBoost) classification was used to predict individual levodopa responses in patients classified as moderate and superior responders, according to the 50% threshold of improvement. The age, disease duration and treatment dose were controlled as covariates. Results: No significant difference in clinical data were observed between moderate and superior responders. Using the ensemble method, the regression model showed a significant correlation between the predicted and the observed motor symptoms improvement (r = 0.61, p < 0.01, mean absolute error = 0.11 ± 0.02), measured as a continuous variable. The use of the Catboost algorithm revealed that ALFF was able to differentiate between moderate and superior responders (area under the curve = 0.90). The mainly contributed regions for both models included the bilateral primary motor cortex, the occipital cortex, the cerebellum, and the basal ganglia. Conclusion: Both continuous and binary ALFF values have the potential to serve as promising predictive markers of dopaminergic therapy response in patients with PD. Highlights: The ALFF could predict the motor improvement induced by levodopa in PD patients. In the prediction model, predicted and observed improvements correlate significantly. In the classification model, moderate and superior responders were discriminated. Bilateral primary motor cortex, cerebellum, basal ganglia are critical in prediction.