title: Data-Driven and Predefined ROI-Based Quantification of Long-Term Resting-State fMRI Reproducibility.
abstract: Resting-state functional magnetic resonance imaging (fMRI) is a promising tool for neuroscience and clinical studies. However, there exist significant variations in strength and spatial extent of resting-state functional connectivity over repeated sessions in a single or multiple subjects with identical experimental conditions. Reproducibility studies have been conducted for resting-state fMRI where the reproducibility was usually evaluated in predefined regions-of-interest (ROIs). It was possible that reproducibility measures strongly depended on the ROI definition. In this work, this issue was investigated by comparing data-driven and predefined ROI-based quantification of reproducibility. In the data-driven analysis, the reproducibility was quantified using functionally connected voxels detected by a support vector machine (SVM)-based technique. In the predefined ROI-based analysis, all voxels in the predefined ROIs were included when estimating the reproducibility. Experimental results show that (1) a moderate to substantial within-subject reproducibility and a reasonable between-subject reproducibility can be obtained using functionally connected voxels identified by the SVM-based technique; (2) in the predefined ROI-based analysis, an increase in ROI size does not always result in higher reproducibility measures; (3) ROI pairs with high connectivity strength have a higher chance to exhibit high reproducibility; (4) ROI pairs with high reproducibility do not necessarily have high connectivity strength; (5) the reproducibility measured from the identified functionally connected voxels is generally higher than that measured from all voxels in predefined ROIs with typical sizes. The findings (2) and (5) suggest that conventional ROI-based analyses would underestimate the resting-state fMRI reproducibility.
authors.affiliation: 1 Department of Electrical Engineering, School of Engineering, Widener University , Chester, Pennsylvania.
authors.forename: Xiaomu
authors.initials: X
authors.lastname: Song
authors.affiliation: 2 Department of Radiology, Brigham and Women's Hospital , Harvard Medical School, Boston, Massachusetts.
authors.forename: Lawrence P
authors.initials: LP
authors.lastname: Panych
authors.affiliation: 3 Brain Imaging and Analysis Center, Duke University Medical Center , Durham, North Carolina.
authors.forename: Nan-Kuei
authors.initials: NK
authors.lastname: Chen
mesh_terms: D000328:Adult; D001921:Brain / Q000502:physiology; D001931:Brain Mapping / Q000379:methods; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods; D008297:Male; D008564:Membrane Potentials / Q000502:physiology*; D008875:Middle Aged; D009415:Nerve Net / Q000502:physiology; D009434:Neural Pathways / Q000502:physiology; D015203:Reproducibility of Results; D012146:Rest / Q000502:physiology; D060388:Support Vector Machine
keywords: functional network; intra-class correlation coefficient; long-term; reproducibility; resting-state fMRI; support vector machine