Reproducibility and Sensitivity of Resting‐State fMRI in Patients With Parkinson's Disease Using Cross Validation‐Based Data Censoring

Abstract

BackgroundUncontrollable 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.PurposeTo determine data censoring criteria using a quantitative cross validation‐based data censoring (CVDC) method and to improve the detection of rsFC deficits in PD.Study TypeProspective.SubjectsForty‐one PD patients (68.63 ± 9.17 years, 44% female) and 20 healthy controls (66.83 ± 12.94 years, 55% female).Field Strength/Sequence3‐T, T1‐weighted gradient echo and EPI sequences.AssessmentClusters 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.Statistical TestsGroup 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.ResultsWith 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.Data ConclusionThe CVDC method may provide superior sensitivity and improved reproducibility for detecting rsFC deficits in PD.Level of Evidence1Technical EfficacyStage 2