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37
Regional homogeneity approach to fMRI data analysis
 NeuroImage
, 2004
"... Kendall’s coefficient concordance (KCC) can measure the similarity of a number of time series. It has been used for purifying a given cluster in functional MRI (fMRI). In the present study, a new method was developed based on the regional homogeneity (ReHo), in which KCC was used to measure the simi ..."
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Cited by 88 (9 self)
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Kendall’s coefficient concordance (KCC) can measure the similarity of a number of time series. It has been used for purifying a given cluster in functional MRI (fMRI). In the present study, a new method was developed based on the regional homogeneity (ReHo), in which KCC was used to measure the similarity of the time series of a given voxel to those of its nearest neighbors in a voxelwise way. Six healthy subjects performed left and right finger movement tasks in eventrelated design; five of them were additionally scanned in a rest condition. KCC was compared among the three conditions (left finger movement, right finger movement, and the rest). Results show that bilateral primary motor cortex (M1) had higher KCC in either left or right finger movement condition than in rest condition. Contrary to prediction and to activation pattern, KCC of ipsilateral M1 is significantly higher than contralateral M1 in unilateral finger movement conditions. These results support the previous electrophysiologic findings of increasing ipsilateral M1 excitation during unilateral movement. ReHo can consider as a complementary method to modeldriven method, and it could help reveal the complexity of the human brain function. More work is needed to understand the neural mechanism underlying ReHo.
Unmixing fMRI with Independent Component Analysis  Using ICA to Characterize HighDimensional fMRI Data in a Concise Manner
, 2006
"... Independent component analysis (ICA) is a statistical method used to discover hidden factors (sources or features) from a set of measurements or observed data such that the sources are maximally independent. Typically, it assumes a generative model where observations are assumed to be linear mixtu ..."
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Cited by 41 (17 self)
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Independent component analysis (ICA) is a statistical method used to discover hidden factors (sources or features) from a set of measurements or observed data such that the sources are maximally independent. Typically, it assumes a generative model where observations are assumed to be linear mixtures of independent sources, and unlike principal component analysis (PCA), which uncorrelates the data, ICA works with higherorder statistics to achieve independence. An intuitive example of ICA can be given by a scatterplot of two independent signals s1 and s2. Figure 1(a) shows a plot of the two independent signals (s1, s2) in a scatter plot. Figure 1(b) and (c) shows the projections for PCA and ICA, respectively, for a linear mixture of s1 and s2. PCA finds the orthogonal vectors u1, u2 but does not find independent vectors. In contrast, ICA is able to find the independent vectors a1, a2 of the linear mixed signals (s1, s2) and is thus able to restore the original sources. A typical ICA model assumes that the source signals are not observable, are statistically independent, and are nonGaussian, with an unknown but linear mixing process. Consider an observed Mdimensional random vector denoted by x = (x1,... xM) T, which is generated by the ICA model: x = As, (1) where s = [s1, s2,... sN] T is an Ndimensional vector whose elements are assumed independent sources and AM×N is an unknown mixing matrix. Typically M> = N, so A is usually of full rank. The goal of ICA is to estimate an unmixing matrix WN×M such that y [defined in (2)] is a good approximation to the true sources: s. y = Wx (2) ICA is hence an approach to solving the blind source separation problem, which traditionally addresses the solution of the cocktail party problem in which several people are speaking simultaneously in the same room. The problem is to separate the voices of the different speakers by using recordings of several microphones in the room [2]. The basic ICA model for blind source separation is shown
Bayesian secondlevel analysis of functional magnetic resonance images
 Neuroimage
, 2003
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Realtime independent component analysis of fMRI timeseries
, 2003
"... Realtime functional magnetic resonance imaging (fMRI) enables one to monitor a subject’s brain activity during an ongoing session. The availability of online information about brain activity is essential for developing and refining interactive fMRI paradigms in research and clinical trials and for ..."
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Cited by 14 (1 self)
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Realtime functional magnetic resonance imaging (fMRI) enables one to monitor a subject’s brain activity during an ongoing session. The availability of online information about brain activity is essential for developing and refining interactive fMRI paradigms in research and clinical trials and for neurofeedback applications. Data analysis for realtime fMRI has traditionally been based on hypothesisdriven processing methods. Offline data analysis, conversely, may be usefully complemented by datadriven approaches, such as independent component analysis (ICA), which can identify brain activity without a priori temporal assumptions on brain activity. However, ICA is commonly considered a timeconsuming procedure and thus unsuitable to process the high flux of fMRI data while they are acquired. Here, by specific choices regarding the implementation, we exported the ICA framework and implemented it into realtime fMRI data analysis. We show that, reducing the ICA input to a few points within a timeseries in a slidingwindow approach, computational times become compatible with realtime settings. Our technique produced accurate dynamic readouts of brain activity as well as a precise spatiotemporal history of quasistationary patterns in the form of cumulative activation maps and time courses. Results from real and simulated motor activation data show comparable performances for the proposed ICA implementation and standard linear regression analysis applied either
Identifying regional activity associated with temporally separated components of working memory using eventrelated functional MRI
 NeuroImage
, 2003
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The spatiotemporal MEG covariance matrix modeled as a sum of Kronecker products
 NeuroImage
, 2005
"... The single Kronecker product (KP) model for the spatiotemporal covariance of MEG residuals is extended to a sum of Kronecker products. This sum of KP is estimated such that it approximates the spatiotemporal sample covariance best in matrix norm. Contrary to the single KP, this extension allows for ..."
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Cited by 9 (0 self)
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The single Kronecker product (KP) model for the spatiotemporal covariance of MEG residuals is extended to a sum of Kronecker products. This sum of KP is estimated such that it approximates the spatiotemporal sample covariance best in matrix norm. Contrary to the single KP, this extension allows for describing multiple, independent phenomena in the ongoing background activity. Whereas the single KP model can be interpreted by assuming that background activity is generated by randomly distributed dipoles with certain spatial and temporal characteristics, the sum model can be physiologically interpreted by assuming a composite of such processes. Taking enough terms into account, the spatiotemporal sample covariance matrix can be described exactly by this extended model. In the estimation of the sum of KP model, it appears that the sum of the first 2 KP describes between 67 % and 93%. Moreover, these first two terms describe two physiological processes in the background activity: focal, frequencyspecific alpha activity, and more widespread nonfrequencyspecific activity. Furthermore, temporal nonstationarities due to trialtotrial variations are not clearly visible in the first two terms, and, hence, play only a minor role in the sample covariance matrix in terms of matrix power. Considering the dipole localization, the single KP model appears to describe around 80 % of the noise and seems therefore adequate. The emphasis of further improvement of localization accuracy should be on improving the source model rather than the covariance model.
Séparation aveugle de sources en ingénierie biomédicale  Blind source separation in biomedical engineering
 IRBM, ELSEVIER
, 2007
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Unmixing' Functional Magnetic Resonance Imaging With Independent Component Analysis
 IEEE Eng. in Medicine and Biology
, 2006
"... Independent component analysis (ICA) has recently demonstrated considerable promise in characterizing fMRI data, primarily due to its intuitive nature and ability for flexible characterization of the brain function. As typically applied, spatial brain networks are assumed to be systematically nonov ..."
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Cited by 4 (2 self)
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Independent component analysis (ICA) has recently demonstrated considerable promise in characterizing fMRI data, primarily due to its intuitive nature and ability for flexible characterization of the brain function. As typically applied, spatial brain networks are assumed to be systematically nonoverlapping. Often temporal coherence of brain networks is also assumed, although convolutive and other models can be utilized to relax this assumption. ICA has been successfully utilized in a number of exciting fMRI applications including the identification of various signaltypes such as task and transiently taskrelated and physiologyrelated signals in the spatial or temporal domain. Additional applications include the analysis of multisubject fMRI data, the incorporation of a priori information, and the analysis of complexvalued fMRI data. In this paper, we first introduce ICA and its application to fMRI data analysis, and then What an antithetical mind! tenderness, roughness delicacy, coarseness sentiment, sensuality soaring and groveling, dirt and deity all mixed up in that one compound of inspired clay!Lord Byron
ICA Denoising for EventRelated fMRI Studies
"... Abstract — The poor SNR of fMRI data requires that many repetitive trials be performed during an eventrelated experiment to obtain statistically significant levels of inferred brain activity. This is costly in terms of scanner time, necessitates that subjects perform the behavioural task(s) for lon ..."
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Abstract — The poor SNR of fMRI data requires that many repetitive trials be performed during an eventrelated experiment to obtain statistically significant levels of inferred brain activity. This is costly in terms of scanner time, necessitates that subjects perform the behavioural task(s) for long durations which may induce fatique, and vastly increases the amount of data generated. In this paper, we present a method to enhance the statistical effect size using ICA, so that the same level of significance can be obtained with shorter scanning times. We perform ICA on fMRI data from a simple eventrelated motor task by projecting the original data onto the linear subspace defined by the taskrelated ICA components. This essentially denoises the signal and results in significant improvement in the effect size. Using simulations we demonstrate that the proposed ICAdenoising procedure is robust to a variety of realistic noise models and enhances the performance of Least Squares estimates of the evoked hemodynamic response. I.
Unraveling Spatiotemporal Dynamics in fMRI Recordings Using Complex ICA
"... Abstract. Independent component analysis (ICA) of functional magnetic resonance imaging (fMRI) data is commonly carried out under the assumption that each source may be represented as a spatially fixed pattern of activation, which leads to the instantaneous mixing model. To allow modeling patterns o ..."
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Abstract. Independent component analysis (ICA) of functional magnetic resonance imaging (fMRI) data is commonly carried out under the assumption that each source may be represented as a spatially fixed pattern of activation, which leads to the instantaneous mixing model. To allow modeling patterns of spatiotemporal dynamics, in particular, the flow of oxygenated blood, we have developed a convolutive ICA approach: spatial complex ICA applied to frequencydomain fMRI data. In several frequencybands, we identify components pertaining to activity in primary visual cortex (V1) and blood supply vessels. One such component, obtained in the 0.10Hz band, is analyzed in detail and found to likely reflect flow of oxygenated blood in V1 1. 1