2015
DOI: 10.1007/s11682-015-9359-7
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Characterizing and differentiating task-based and resting state fMRI signals via two-stage sparse representations

Abstract: A relatively underexplored question in fMRI is whether there are intrinsic differences in terms of signal composition patterns that can effectively characterize and differentiate task-based or resting state fMRI (tfMRI or rsfMRI) signals. In this paper, we propose a novel two-stage sparse representation framework to examine the fundamental difference between tfMRI and rsfMRI signals. Specifically, in the first stage, the whole-brain tfMRI or rsfMRI signals of each subject were composed into a big data matrix, … Show more

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Cited by 73 publications
(44 citation statements)
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“…Therefore, we sought to analyze multimodal data jointly and provide an insight into abnormal local function within selective networks underlying NSS in SSD. Furthermore, we chose to investigate intrinsic neural activity (INA), because INA explores the intrinsically (functionally) segregation or specialization of brain regions/networks (Logothetis, ; Zhang et al, ). For this particular reason, we used fractional amplitude of low‐frequency fluctuations (fALFF), because fALFF captures the relative magnitude of blood oxygen level‐dependent (BOLD) signal changes on INA and might help to identify brain regions/networks with aberrant local functioning (Egorova, Veldsman, Cumming, & Brodtmann, ; Hirjak et al, ; Kubera et al, ).…”
Section: Introductionmentioning
confidence: 99%
“…Therefore, we sought to analyze multimodal data jointly and provide an insight into abnormal local function within selective networks underlying NSS in SSD. Furthermore, we chose to investigate intrinsic neural activity (INA), because INA explores the intrinsically (functionally) segregation or specialization of brain regions/networks (Logothetis, ; Zhang et al, ). For this particular reason, we used fractional amplitude of low‐frequency fluctuations (fALFF), because fALFF captures the relative magnitude of blood oxygen level‐dependent (BOLD) signal changes on INA and might help to identify brain regions/networks with aberrant local functioning (Egorova, Veldsman, Cumming, & Brodtmann, ; Hirjak et al, ; Kubera et al, ).…”
Section: Introductionmentioning
confidence: 99%
“…Thus, faithful reconstruction and quantitative modeling of those concurrent neural networks from noisy fMRI data has been of a major neuroscientific research topic for years (Bullmore and Sporns, 2009; Dosenbach et al, 2006; Duncan, 2010; Fedorenko et al, 2013; Fox et al, 2005; Huettel, Scott A., Allen W. Song, 2004; Pessoa et al, 2012). Popular brain network reconstruction techniques based on fMRI data include general linear model (GLM) (Friston et al, 1994; Worsley, 1997) for task-based fMRI (tfMRI), independent component analysis (ICA) (Beckmann et al, 2005; Calhoun et al, 2004) for resting state fMRI (rsfMRI), and dictionary learning/sparse representation (Ge et al, 2016; Jiang et al, 2015; Li et al, 2016; Lv et al, 2015a, 2015b, 2015c, 2015d; Xintao Hu et al, 2015; Zhang et al, 2016; Zhao et al, 2016, 2015) for both tfMRI and rsfMRI, all of which can effectively reconstruct concurrent network maps from whole brain fMRI data. For instance, by using the dictionary learning and sparse coding algorithms (Ge et al, 2016; Jiang et al, 2015; Li et al, 2016; Lv et al, 2015a, 2015b, 2015c, 2015d; Mairal et al, 2010; Xintao Hu et al, 2015; Zhang et al, 2016; Zhao et al, 2015), several hundred of concurrent functional brain networks, characterized by both spatial maps and associated temporal time series, can be effectively decomposed from either tfMRI or rsfMRI data of an individual brain.…”
Section: Introductionmentioning
confidence: 99%
“…Popular brain network reconstruction techniques based on fMRI data include general linear model (GLM) (Friston et al, 1994; Worsley, 1997) for task-based fMRI (tfMRI), independent component analysis (ICA) (Beckmann et al, 2005; Calhoun et al, 2004) for resting state fMRI (rsfMRI), and dictionary learning/sparse representation (Ge et al, 2016; Jiang et al, 2015; Li et al, 2016; Lv et al, 2015a, 2015b, 2015c, 2015d; Xintao Hu et al, 2015; Zhang et al, 2016; Zhao et al, 2016, 2015) for both tfMRI and rsfMRI, all of which can effectively reconstruct concurrent network maps from whole brain fMRI data. For instance, by using the dictionary learning and sparse coding algorithms (Ge et al, 2016; Jiang et al, 2015; Li et al, 2016; Lv et al, 2015a, 2015b, 2015c, 2015d; Mairal et al, 2010; Xintao Hu et al, 2015; Zhang et al, 2016; Zhao et al, 2015), several hundred of concurrent functional brain networks, characterized by both spatial maps and associated temporal time series, can be effectively decomposed from either tfMRI or rsfMRI data of an individual brain. Pooling and integrating the spatial maps of those functional networks from many brains such as those of Human Connectome Project (HCP) subjects can significantly advance our understanding of the regularity and variability of brain functions across individuals and populations (Lv et al, 2015a, 2015b; Zhao et al, 2016)(Zhao et al, 2016)(Zhao et al, 2016)(Zhao et al, 2016)(Zhao et al, 2016)(Zhao et al, 2016).…”
Section: Introductionmentioning
confidence: 99%
“…Although there are no golden criteria for defining the sparsity λ , previous studies regarding the sparse decomposition of fMRI signals have suggested a sparsity of 0.1–1 provided a good tradeoff between the involved noise and the sparse level of atoms [Lv et al, ; Zhang et al, ]. Hence, a grid search procedure with λ from 0.1 to 0.5 and k from 8 to 40 was used to determine the optimal model parameters herein.…”
Section: Resultsmentioning
confidence: 99%