Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China.
Department of Physics and Statistics, Addis Ababa Science and Technology University, Addis Ababa, Ethiopia.
J Magn Reson Imaging. 2024 May;59(5):1630-1642. doi: 10.1002/jmri.28958. Epub 2023 Aug 16.
Uncontrollable body movements are typical symptoms of Parkinson's disease (PD), which results in inconsistent findings regarding resting-state functional connectivity (rsFC) networks, especially for group difference clusters. Systematically identifying the motion-associated data was highly demanded.
To determine data censoring criteria using a quantitative cross validation-based data censoring (CVDC) method and to improve the detection of rsFC deficits in PD.
Prospective.
Forty-one PD patients (68.63 ± 9.17 years, 44% female) and 20 healthy controls (66.83 ± 12.94 years, 55% female).
FIELD STRENGTH/SEQUENCE: 3-T, T1-weighted gradient echo and EPI sequences.
Clusters with significant differences between groups were found in three visual networks, default network, and right sensorimotor network. Five-fold cross-validation tests were performed using multiple motion exclusion criteria, and the selected criteria were determined based on cluster sizes, significance values, and Dice coefficients among the cross-validation tests. As a reference method, whole brain rsFC comparisons between groups were analyzed using a FMRIB Software Library (FSL) pipeline with default settings.
Group difference clusters were calculated using nonparametric permutation statistics of FSL-randomize. The family-wise error was corrected. Demographic information was evaluated using independent sample t-tests and Pearson's Chi-squared tests. The level of statistical significance was set at P < 0.05.
With the FSL processing pipeline, the mean Dice coefficient of the network clusters was 0.411, indicating a low reproducibility. With the proposed CVDC method, motion exclusion criteria were determined as frame-wise displacement >0.55 mm. Group-difference clusters showed a mean P-value of 0.01 and a 72% higher mean Dice coefficient compared to the FSL pipeline. Furthermore, the CVDC method was capable of detecting subtle rsFC deficits in the medial sensorimotor network and auditory network that were unobservable using the conventional pipeline.
The CVDC method may provide superior sensitivity and improved reproducibility for detecting rsFC deficits in PD.
1 TECHNICAL EFFICACY: Stage 2.
无法控制的身体运动是帕金森病（PD）的典型症状，这导致静息态功能连接（rsFC）网络的结果不一致，尤其是对于组间差异簇。因此，人们高度需要系统地识别与运动相关的数据。
使用基于定量交叉验证的数据剔除（CVDC）方法确定数据剔除标准，并改善 PD 中 rsFC 缺陷的检测。
前瞻性。
41 名 PD 患者（68.63±9.17 岁，44%为女性）和 20 名健康对照者（66.83±12.94 岁，55%为女性）。
磁场强度/序列：3-T，T1 加权梯度回波和 EPI 序列。
在三个视觉网络、默认网络和右侧感觉运动网络中发现了组间有显著差异的聚类。使用多种运动剔除标准进行五重交叉验证测试，并根据交叉验证测试中的聚类大小、显著性值和 Dice 系数选择剔除标准。作为参考方法，使用 FMRIB 软件库（FSL）管道的默认设置对组间全脑 rsFC 进行分析。
使用 FSL-randomize 的非参数置换统计量计算组间差异聚类。采用 F 检验校正了组间差异聚类的错误发现率。采用独立样本 t 检验和 Pearson χ2 检验对人口统计学信息进行评估。统计显著性水平设为 P<0.05。
使用 FSL 处理管道，网络聚类的平均 Dice 系数为 0.411，表明可重复性较低。使用提出的 CVDC 方法，将帧位移>0.55mm 确定为运动剔除标准。与 FSL 管道相比，组间差异聚类的平均 P 值为 0.01，平均 Dice 系数提高了 72%。此外，CVDC 方法能够检测到使用传统管道无法观察到的内侧感觉运动网络和听觉网络中的细微 rsFC 缺陷。
CVDC 方法可能为 PD 中 rsFC 缺陷的检测提供更高的敏感性和改善的可重复性。
1 技术功效：2 级。
