Quantifying cerebral blood flow changes using arterial spin labeling: A comparative study of idiopathic rapid eye movement sleep behavior disorder and Parkinson's disease

• Published in: Magnetic resonance imaging Vol. 109; pp. 158 - 164
• Main Authors: Zhi, Yuqi, Chen, Mingshen, Zhou, Chunshan, Yang, Yongxu, Huang, Yan, Liang, Xiaoyun, Wang, Ping, Cheng, Xiaoyu, Mao, Chengjie, Jiang, Zhen, Dai, Yakang, Peng, Bo, Zhu, Jiangtao
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier Inc 01.06.2024
• Subjects: Arterial spin labeling; Idiopathic rapid eye movement sleep behavior disorder; Machine learning; Parkinson's disease; Parkinson's disease; Idiopathic rapid eye movement sleep behavior disorder; Arterial spin labeling; Machine learning
• ISSN: 0730-725X; 1873-5894; 1873-5894
• DOI: 10.1016/j.mri.2024.03.026

Idiopathic rapid eye movement sleep behavior disorder (iRBD) and Parkinson's disease (PD) have been found to have changes in cerebral perfusion and overlap of some of the lesioned brain areas. However, a consensus regarding the specific location and diagnostic significance of these cerebral blood perfusion alternations remains elusive in both iRBD and PD. The present study evaluated the patterns of cerebral blood flow changes in iRBD and PD. A total of 59 right-handed subjects were enrolled, including 15 patients with iRBD, 20 patients with PD, and 24 healthy controls (HC). They were randomly divided into groups at a ratio of 4 to 1 for training and testing. A PASL sequence was employed to obtain quantitative cerebral blood flow (CBF) maps. The CBF values were calculated from these acquired maps. In addition, AutoGluon was employed to construct a classifier for CBF features selection and classification. An independent t-test was performed for CBF variations, with age and sex as nuisance variables. The performance of the feature was evaluated using receiver operating characteristic (ROC) curves. A significance level of P < 0.05 was considered significant. CBF in several brain regions, including the left median cingulate and paracingulate gyri and the right middle occipital gyrus (MOG), showed significant differences between PD and HC, demonstrating good classification performance. The combined model that integrates all features achieved even higher performance with an AUC of 0.9380. Additionally, CBF values in multiple brain regions, including the right MOG and the left angular gyrus, displayed significant differences between PD and iRBD. Particularly, CBF values in the left angular gyrus exhibited good performance in classifying PD and iRBD. The combined model achieved improved performance, with an AUC of 0.8533. No significant differences were found in brain regions when comparing CBF values between iRBD and HC subjects. ASL-based quantitative CBF change features can offer reliable biomarkers to assist in the diagnosis of PD. Regarding the characteristic of CBF in the right MOG, it is anticipated to serve as an imaging biomarker for predicting the progression of iRBD to PD.