Statistical Analysis of Functional MRI Data using Independent Component Analysis (original) (raw)
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Independent component analysis: a reliable alternative to general linear model for task-based fMRI
Frontiers in Psychiatry
BackgroundFunctional magnetic resonance imaging (fMRI) is a valuable tool for the presurgical evaluation of patients undergoing neurosurgeries. Although many pre-processing steps have been modified according to advances in recent years, statistical analysis has remained largely the same since the first days of fMRI. In this study, we examined the ability of Independent Component Analysis (ICA) to separate the activation of a language task in fMRI, and we compared it with the results of the General Lineal Model (GLM).MethodsSixty patients undergoing evaluation for brain surgery due to various brain lesions and/or epilepsy and 20 control subjects completed an fMRI language mapping protocol that included three tasks, resulting in 259 fMRI scans. Depending on brain lesion characteristics, patients were allocated to (1) static/chronic not-expanding lesions (Group 1) and (2) progressive/expanding lesions (Group 2). GLM and ICA statistical maps were evaluated by fMRI experts to assess the ...
Independent Component Analysis Applied to fMRI Data: A Generative Model for Validating Results
Journal of Vlsi Signal Processing Systems for Signal Image and Video Technology, 2004
We introduce and apply a synthesis/analysis model for analyzing functional Magnetic Resonance Imaging (fMRI) data using independent component analysis (ICA). Our model assumes statistically independent spatial sources in the brain. We also assume that the fMRI scanner acquires overdetermined data such that there are more time points than brain sources. We discuss the properties of each of the signals present in the model. The analysis portion of the model includes several candidates for spatial smoothing, ICA algorithm, and data reduction. We use the Kullback-Leibler divergence between the estimated source distributions and the "true" distributions as a measure of the optimality of the final ICA decomposition. Using this model, we generate fMRI-like data and optimize the analysis stage as a function of ICA algorithm, data reduction scheme, and spatial smoothing.
Magnetic Resonance Imaging, 2004
By measuring the changes of magnetic resonance signals during a stimulation, the functional magnetic resonance imaging (fMRI) is able to localize the neural activation in the brain. In this report, we discuss the fMRI application of the spatial independent component analysis (spatial ICA), which maximizes statistical independence over spatial images. Included simulations show the possibility of the spatial ICA on discriminating asynchronous activations or different response patterns in an fMRI data set. An in vivo visual stimulation fMRI test was conducted, and the result shows a proper sum of the separated components as the final image is better than a single component, using fMRI data analysis by spatial ICA. Our result means that spatial ICA is a useful tool for the detection of different response activations and suggests that a proper sum of the separated independent components should be used for the imaging result of fMRI data processing.
Frequency Domain Hybrid Independent Component Analysis of Functional Magnetic Resonance Imaging Data
Independent component analysis (ICA) of functional magnetic resonance imaging (fMRI) data reveals spatially independent patterns of functional activation. The purely datadriven approach of ICA makes statistical inference difficult. The purpose of this study was to develop a hybrid ICA in the frequency domain that enables statistical inference while preserving advantages of a data-driven ICA. Three normal volunteers were scanned with fMRI while they performed a working memory task. Their data were analyzed with frequency domain hybrid ICA. In each of the subjects, the patterns of activation corresponded to areas expected to be active during the fMRI task. This investigation demonstrates that a hybrid ICA in the frequency domain can statistically map functional activation while preserving the ability of ICA to blindly separate noise sources from the data.
Independent component analysis of fMRI data: examining the assumptions
1998
Independent component analysis (ICA), which separates fMRI data into spatially independent patterns of activity, has recently been shown to be a suitable method for exploratory fMRI analysis. The validity of the assumptions of ICA, mainly that the underlying components are spatially independent and add linearly, was explored with a representative fMRI data set by calculating the log-likelihood of observing each voxel's time course conditioned on the ICA model. The probability of observing the time courses from white-matter voxels was higher compared to other observed brain regions. Regions containing blood vessels had the lowest probabilities. The statistical distribution of probabilities over all voxels did not resemble that expected for a small number of independent components mixed with Gaussian noise. These results suggest the ICA model may more accurately represent the data in specific regions of the brain, and that both the activity-dependent sources of blood flow and noise are non-Gaussian.
ICA of Functional MRI Data: An Overview
in Proceedings of the …, 2003
Independent component analysis (ICA) has found a fruitful application in the analysis of functional magnetic resonance imaging (fMRI) data. A principal advantage of this approach is its applicability to cognitive paradigms for which detailed a priori models of brain activity are not ...
2009
In this paper, our aim is analyzing functional magnetic resonance imaging (fMRI) data by independent component analysis (ICA) in order to find regions of brain which were activated by neural activity in human brain. Usually by applying ICA algorithm for whole dataset, independent components can be estimated but we can't understand the procedure of activation. Here, we propose a method to detect active components in different time intervals. Spatial ICA is applied in sliding time windows. We find active components in each window by applying a criteria which measure two kind of cross-correlation coefficients: the correlation between components in each window and reference function in that time interval and the correlation between components in adjacent windows. Finally, we detect active regions of active components in each window. In order to investigate the advantage of using this method, we perform some experiments for simulated and experimental fMRI datasets and show the results. Receiver operating characteristic (ROC) curve shows the performance of this method.
Independent Component Analysis applied to fMRI data of elderly normal and demented subjects
We introduce and apply a synthesis/analysis model for analyzing functional Magnetic Resonance Imaging (fMRI) data using independent component analysis (ICA). Our model assumes statistically independent spatial sources in the brain. We also assume that the fMRI scanner acquires overdetermined data such that there are more time points than brain sources. We discuss the properties of each of the signals present in the model. The analysis portion of the model includes several candidates for spatial smoothing, ICA algorithm, and data reduction. We use the Kullback-Leibler divergence between the estimated source distributions and the "true" distributions as a measure of the optimality of the final ICA decomposition. Using this model, we generate fMRI-like data and optimize the analysis stage as a function of ICA algorithm, data reduction scheme, and spatial smoothing.
Analysis of fMRI data by blind separation into independent spatial components
Human Brain Mapping, 1998
Current analytical techniques applied to functional magnetic resonance imaging (fMRI) data require a priori knowledge or specific assumptions about the time courses of processes contributing to the measured signals. Here we describe a new method for analyzing fMRI data based on the independent component analysis (ICA) algorithm of Bell and Sejnowski ([1995]: Neural Comput 7:1129-1159). We decomposed eight fMRI data sets from 4 normal subjects performing Stroop color-naming, the Brown and Peterson word/number task, and control tasks into spatially independent components. Each component consisted of voxel values at fixed three-dimensional locations (a component ''map''), and a unique associated time course of activation. Given data from 144 time points collected during a 6-min trial, ICA extracted an equal number of spatially independent components. In all eight trials, ICA derived one and only one component with a time course closely matching the time course of 40-sec alternations between experimental and control tasks. The regions of maximum activity in these consistently task-related components generally overlapped active regions detected by standard correlational analysis, but included frontal regions not detected by correlation. Time courses of other ICA components were transiently task-related, quasiperiodic, or slowly varying. By utilizing higher-order statistics to enforce successively stricter criteria for spatial independence between component maps, both the ICA algorithm and a related fourth-order decomposition technique : Signal Processing 36:11-20) were superior to principal component analysis (PCA) in determining the spatial and temporal extent of task-related activation. For each subject, the time courses and active regions of the task-related ICA components were consistent across trials and were robust to the addition of simulated noise. Simulated movement artifact and simulated task-related activations added to actual fMRI data were clearly separated by the algorithm. ICA can be used to distinguish between nontask-related signal components, movements, and other artifacts, as well as consistently or transiently task-related fMRI activations, based on only weak 1998 Wiley-Liss, Inc.
Unmixing fMRI with independent component analysis
IEEE Engineering in Medicine and Biology Magazine, 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 mixtures of independent sources and works with higher-order statistics to achieve independence. ICA has recently demonstrated considerable promise in characterizing functional magnetic resonance imaging (fMRI) data, primarily due to its intuitive nature and ability for flexible characterization of the brain function. In this article, ICA is introduced and its application to fMRI data analysis is reviewed.