Abstract
Resting-state functional magnetic resonance imaging (fMRI) has attracted more and more attention because of its effectiveness, simplicity and non-invasiveness in exploration of the intrinsic functional architecture of the human brain. However, user-friendly toolbox for “pipeline” data analysis of resting-state fMRI is still lacking. Based on some functions in Statistical Parametric Mapping (SPM) and Resting-State fMRI Data Analysis Toolkit (REST), we have developed a MATLAB toolbox called Data Processing Assistant for Resting-State fMRI (DPARSF) for “pipeline” data analysis of resting-state fMRI. After the user arranges the Digital Imaging and Communications in Medicine (DICOM) files and click a few buttons to set parameters, DPARSF will then give all the preprocessed (slice timing, realign, normalize, smooth) data and results for functional connectivity, regional homogeneity, amplitude of low-frequency fluctuation (ALFF), and fractional ALFF. DPARSF can also create a report for excluding subjects with excessive head motion and generate a set of pictures for easily checking the effect of normalization. In addition, users can also use DPARSF to extract time courses from regions of interest.
Highlights
Resting-state functional magnetic resonance imaging has been more and more widely used since Biswal et al (1995) firstly reported the presence of spatially coherent activity in the resting-state blood oxygen level-dependent (BOLD) fMRI signal
While Functional connectivity (FC) measures the signal synchrony among remote brain areas, the regional spontaneous activity could be examined by several metrics, such as the regional homogeneity (ReHo, Zang et al, 2004), the amplitude of low-frequency fluctuation (ALFF, Zang et al, 2007) and the fractional amplitude of lowfrequency fluctuation (ALFF)
Data Processing Assistant for Resting-State fMRI (DPARSF) can create a report for excluding subjects with excessive head motion and generate a set of pictures for checking the effect of normalization
Summary
Resting-state functional magnetic resonance imaging (fMRI) has been more and more widely used since Biswal et al (1995) firstly reported the presence of spatially coherent activity in the resting-state blood oxygen level-dependent (BOLD) fMRI signal. Resting-state fMRI is considered as a powerful tool for investigating the spontaneous neuronal activity which consumes most of the brain’s energy (Fox and Raichle, 2007). Functional connectivity (FC) is widely used in resting-state fMRI studies (Biswal et al, 1995; Lowe et al, 1998; Xiong et al, 1999; Cordes et al, 2000; Greicius et al, 2003; Fox et al, 2005, 2006; Fransson, 2005; Vincent et al, 2006). SPM is a powerful tool, lots of complicated and timeconsuming operations are needed when analyzing large sample data set.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
