关键词: 帕金森病, 冻结步态, 静息态功能磁共振成像, 低频振幅, 局部一致性, 度中心性, 前馈神经网络, 机器学习, 接收者操作特征曲线, 海马旁回

Abstract: Freezing of gait is a significant and debilitating motor symptom often observed in individuals with Parkinson’s disease. Resting-state functional magnetic resonance imaging, along with its multi-level feature indices, has provided a fresh perspective and valuable insight into the study of freezing of gait in Parkinson’s disease. It has been revealed that Parkinson’s disease is accompanied by widespread irregularities in inherent brain network activity. However, the effective integration of the multi-level indices of resting-state functional magnetic resonance imaging into clinical settings for the diagnosis of freezing of gait in Parkinson’s disease remains a challenge. Although previous studies have demonstrated that radiomics can extract optimal features as biomarkers to identify or predict diseases, a knowledge gap still exists in the field of freezing of gait in Parkinson’s disease. This cross-sectional study aimed to evaluate the ability of radiomics features based on multi-level indices of resting-state functional magnetic resonance imaging, along with clinical features, to distinguish between Parkinson’s disease patients with and without freezing of gait. We recruited 28 patients with Parkinson’s disease who had freezing of gait (15 men and 13 women, average age 63 years) and 30 patients with Parkinson’s disease who had no freezing of gait (16 men and 14 women, average age 64 years). Magnetic resonance imaging scans were obtained using a 3.0T scanner to extract the mean amplitude of low-frequency fluctuations, mean regional homogeneity, and degree centrality. Neurological and clinical characteristics were also evaluated. We used the least absolute shrinkage and selection operator algorithm to extract features and established feedforward neural network models based solely on resting-state functional magnetic resonance imaging indicators. We then performed predictive analysis of three distinct groups based on resting-state functional magnetic resonance imaging indicators indicators combined with clinical features. Subsequently, we conducted 100 additional five-fold cross-validations to determine the most effective model for each classification task and evaluated the performance of the model using the area under the receiver operating characteristic curve. The results showed that when differentiating patients with Parkinson’s disease who had freezing of gait from those who did not have freezing of gait, or from healthy controls, the models using only the mean regional homogeneity values achieved the highest area under the receiver operating characteristic curve values of 0.750 (with an accuracy of 70.9%) and 0.759 (with an accuracy of 65.3%), respectively. When classifying patients with Parkinson’s disease who had freezing of gait from those who had no freezing of gait, the model using the mean amplitude of low-frequency fluctuation values combined with two clinical features achieved the highest area under the receiver operating characteristic curve of 0.847 (with an accuracy of 74.3%). The most significant features for patients with Parkinson’s disease who had freezing of gait were amplitude of low-frequency fluctuation alterations in the left parahippocampal gyrus and two clinical characteristics: Montreal Cognitive Assessment and Hamilton Depression Scale scores. Our findings suggest that radiomics features derived from resting-state functional magnetic resonance imaging indices and clinical information can serve as valuable indices for the identification of freezing of gait in Parkinson’s disease.

Key words: amplitude of low-frequency fluctuation, degree centrality, feedforward neural network, freezing of gait, machine learning, parahippocampal gyrus, Parkinson’s disease, receiver operating characteristic, regional homogeneity, resting-state functional magnetic resonance imaging