REST: A Toolkit for Resting-State Functional Magnetic Resonance Imaging Data Processing

By Xiaowei Song, Zhangye Dong, Xiangyu Long, Sufang Li, Xi-Nian Zuo, Chaozhe Zhu, Yong He, Chaogan Yan, Yufeng Zang. PLoS ONE, 2011

1,911 citations recorded by the source corpus.

Resting-state fMRI (RS-fMRI) has been drawing more and more attention in recent years. However, a publicly available, systematically integrated and easy-to-use tool for RS-fMRI data processing is still lacking. We developed a toolkit for the analysis of RS-fMRI data, namely the RESting-state fMRI data analysis Toolkit (REST). REST was developed in MATLAB with graphical user interface (GUI). After data preprocessing with SPM or AFNI, a few analytic methods can be performed in REST, including functional connectivity analysis based on linear correlation, regional homogeneity, amplitude of low frequency fluctuation (ALFF), and fractional ALFF. A few additional functions were implemented in REST, including a DICOM sorter, linear trend removal, bandpass filtering, time course extraction, regression of covariates, image calculator, statistical analysis, and slice viewer (for result visualization, multiple comparison correction, etc.). REST is an open-source package and is freely available at http://www.restfmri.net.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/b594809c33adc92b75bcb87a941941792c797f7c

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In Cognitive Neuroscience

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

DPABI: Data Processing & Analysis for (Resting-State) Brain Imaging (2016, methodology): “It is an open-source package (source code available at http://rfmri.org/DPABI) evolved from DPARSF and REST.”
Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank (2017, background): “Tools like Wei et al. (2002) (segmentation), Cook et al. (2006) (diﬀusion) or Song et al. (2011) (resting-state), are speciﬁc solutions for the processing of speciﬁc image modalities.”
RESTplus: an improved toolkit for resting-state functional magnetic resonance imaging data processing. (2019, methodology): “net) [3] is the earliest one dedicated to RS-fMRI analysis.”
Standardizing the intrinsic brain: Towards robust measurement of inter-individual variation in 1000 functional connectomes (2013, positive): “…the Data Processing Assistant for Resting-State fMRI (DPARSF, Yan and Zang, 2010, http://www.restfmri.net), which is based on Statistical Parametric Mapping (SPM8) (http://www.fil.ion.ucl.ac.uk/spm) and Resting-State fMRI Data Analysis Toolkit (REST, Song et al., 2011, http://www.restfmri. net).”
GRETNA: a graph theoretical network analysis toolbox for imaging connectomics (2015, methodology): “The three images are from the REST toolbox (Song et al., 2011) and separately correspond to brain masks of the whole brain, cerebral spinal fluid and WM in the standard MNI space.”
A Comprehensive Assessment of Regional Variation in the Impact of Head Micromovements on Functional Connectomics (2013, positive): “…the Data Processing Assistant for Resting-State fMRI (DPARSF, Yan and Zang, 2010, http://www.restfmri.net), which is based on Statistical Parametric Mapping (SPM8) (http://www.fil.ion.ucl.ac.uk/spm) and Resting-State fMRI Data Analysis Toolkit (REST; Song et al., 2011; http://www.restfmri.net).”
On the Plurality of (Methodological) Worlds: Estimating the Analytic Flexibility of fMRI Experiments (2012, methodology): “Activation maps were thresholded using three versions of a Monte Carlo simulation procedure, as implemented in the Resting-State fMRI Data Analysis Toolkit (REST; Song et al., 2011)2.”
Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia (2015, neutral): “These non-neuronal confounding factors were regressed out from the average BOLD TS for each AAL region via the least-squares error minimization scheme (Chai et al., 2012; Kim et al., 2015; Kim et al., 2015; Song et al., 2011).”
Toward reliable characterization of functional homogeneity in the human brain: Preprocessing, scan duration, imaging resolution and computational space (2012, methodology): “2004) and most applications published by using ReHo, preprocessing did not include removal of any nuisance signals although the functionality of removing head motion, white matter, CSF and global signal from BOLD time series is available in DPARSF (Yan and Zang, 2010) of REST software (Song et al., ”
Resting state fMRI: A review on methods in resting state connectivity analysis and resting state networks (2017, methodology): “The chart shows the processing of seed-based FC analysis in Figure 1.(20,26,27) Figures 2–7 shown in the present review were obtained from data acquired from a 1.”