title: A Study of Long-Term fMRI Reproducibility Using Data-Driven Analysis Methods.
abstract: The reproducibility of functional magnetic resonance imaging (fMRI) is important for fMRI-based neuroscience research and clinical applications. Previous studies show considerable variation in amplitude and spatial extent of fMRI activation across repeated sessions on individual subjects even using identical experimental paradigms and imaging conditions. Most existing fMRI reproducibility studies were typically limited by time duration and data analysis techniques. Particularly, the assessment of reproducibility is complicated by a fact that fMRI results may depend on data analysis techniques used in reproducibility studies. In this work, the long-term fMRI reproducibility was investigated with a focus on the data analysis methods. Two spatial smoothing techniques, including a wavelet-domain Bayesian method and the Gaussian smoothing, were evaluated in terms of their effects on the long-term reproducibility. A multivariate support vector machine (SVM)-based method was used to identify active voxels, and compared to a widely used general linear model (GLM)-based method at the group level. The reproducibility study was performed using multisession fMRI data acquired from eight healthy adults over 1.5 years' period of time. Three regions-of-interest (ROI) related to a motor task were defined based upon which the long-term reproducibility were examined. Experimental results indicate that different spatial smoothing techniques may lead to different reproducibility measures, and the wavelet-based spatial smoothing and SVM-based activation detection is a good combination for reproducibility studies. On the basis of the ROIs and multiple numerical criteria, we observed a moderate to substantial within-subject long-term reproducibility. A reasonable long-term reproducibility was also observed from the inter-subject study. It was found that the short-term reproducibility is usually higher than the long-term reproducibility. Furthermore, the results indicate that brain regions with high contrast-to-noise ratio do not necessarily exhibit high reproducibility. These findings may provide supportive information for optimal design/implementation of fMRI studies and data interpretation.
authors.affiliation: Department of Electrical Engineering, School of Engineering, Widener University, Chester, PA 19013.
authors.forename: Xiaomu
authors.initials: X
authors.lastname: Song
authors.affiliation: Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115.
authors.forename: Lawrence P
authors.initials: LP
authors.lastname: Panych
authors.affiliation: Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC 27710.
authors.forename: Ying-Hui
authors.initials: YH
authors.lastname: Chou
authors.affiliation: Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC 27710.
authors.forename: Nan-Kuei
authors.initials: NK
authors.lastname: Chen
keywords: quantitative fMRI; reproducibility; support vector machine; wavelet