Analysis of fMRI Data by Blind Separation into Independent Spatial Components

Howard Hughes Medical Institute, Salk Institute for Biological Studies, La Jolla, California 92186-5800, USA.
Human Brain Mapping (Impact Factor: 5.97). 01/1998; 6(3):160-88. DOI: 10.1002/(SICI)1097-0193(1998)6:3<160::AID-HBM5>3.0.CO;2-1
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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 work/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 (Comon [1994]: 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 assumptions about their spatial distributions and without a priori assumptions about their time courses. ICA appears to be a highly promising method for the analysis of fMRI data from normal and clinical populations, especially for uncovering unpredictable transient patterns of brain activity associated with performance of psychomotor tasks.

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Available from: Scott Makeig, Oct 09, 2015
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    • "Such statistical methods as principal component analysis (PCA), general linear models (GLM), and independent component analysis (ICA), have been developed to extract both spatial and temporal patterns of interest from functional signals, and to understand how different brain regions interact with each other. For instance, ICA has been widely used in single-subject fMRI/EEG studies to separate spatially or temporally independent components (McKeown et al. (1998); Beckmann and Smith (2004)). However, the extension of these methods to group inference is not straightforward due to striking neuroanatomic variations, and thus it remains an active research topic (Calhoun, Liu, and Adalı (2009)). "
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    ABSTRACT: In spatial-temporal neuroimaging studies, there is an evolving literature on the analysis of functional imaging data in order to learn the intrinsic functional connectivity patterns among different brain regions. However, there are only few efficient approaches for integrating functional connectivity pattern across subjects, while accounting for spatial-temporal functional variation across multiple groups of subjects. The objective of this paper is to develop a new sparse reduced rank (SRR) modeling framework for carrying out functional connectivity analysis across multiple groups of subjects in the frequency domain. Our new framework not only can extract both frequency and spatial factors across subjects, but also imposes sparse constraints on the frequency factors. It thus leads to the identification of important frequencies with high power spectra. In addition, we propose two novel adaptive criteria for automatic selection of sparsity level and model rank. Using simulated data, we demonstrate that SRR outperforms several existing methods. Finally, we apply SRR to detect group differences between controls and two subtypes of attention deficit hyperactivity disorder (ADHD) patients, through analyzing the ADHD-200 data.
    Statistica Sinica 09/2015; 25(1). DOI:10.5705/ss.2013.232w · 1.16 Impact Factor
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    • "As brain function is still poorly understood, and fMRI data are noisy, data-driven approaches exhibit great potential in extracting spatial and temporal components from fMRI data with little to no prior information about the brain. Among others, independent component analysis (ICA) (McKeown et al., 1998; Vigario and Oja, 2008; Calhoun et al., 2001; Guo and Pagnonib, 2008; Du and Fan, 2013; Lee et al., 2008; Michael et al., 2014; Risk et al., 2014) and tensor decomposition (TD) (Andersen and Rayens, 2004; Beckmann and Smith, 2005; Mørup et al., 2008; Cichocki et al., 2009, 2015; Mørup et al., 2011), two key approaches of blind source separation (BSS), have provided promising results in multi-subject fMRI analysis. When comparing ICA and TD, ICA emphasizes statistical independence , whereas TD stresses multiway data structure. "
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    ABSTRACT: Canonical polyadic decomposition (CPD) may face a local optimal problem when analyzing multi-subject fMRI data with inter-subject variability. Beckmann and Smith proposed a tensor PICA approach that incorporated an independence constraint to the spatial modality by combining CPD with ICA, and alleviated the problem of inter-subject spatial map (SM) variability. This study extends tensor PICA to incorporate additional inter-subject time course (TC) variability and to connect CPD and ICA in a new way. Assuming multiple subjects share common TCs but with different time delays, we accommodate subject-dependent TC delays into the CP model based on the idea of shift-invariant CP (SCP). We use ICA as an initialization step to provide the aggregating mixing matrix for shift-invariant CPD to estimate shared TCs with subject-dependent delays and intensities. We then estimate shared SMs using a least-squares fit post shift-invariant CPD. Using simulated fMRI data as well as actual fMRI data we demonstrate that the proposed approach improves the estimates of the shared SMs and TCs, and the subject-dependent TC delays and intensities. The default mode component illustrates larger TC delays than the task-related component. The proposed approach shows improvements over tensor PICA in particular when TC delays are large, and also outperforms SCP with SM orthogonality constraint and SCP with ICA-based SM initialization. TCs with subject-dependent delays conform to the true situation of multi-subject fMRI data. The proposed approach is suitable for decomposing multi-subject fMRI data with large inter-subject temporal and spatial variability. Copyright © 2015. Published by Elsevier B.V.
    Journal of Neuroscience Methods 08/2015; DOI:10.1016/j.jneumeth.2015.08.023 · 2.05 Impact Factor
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    • "ROIs may be based on clustering of fMRI data (McKeown et al., 1997), or it could even be that instead of ROIs the network is formed between individual voxels (Craddock et al., 2012). "
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    ABSTRACT: Recent progress in diffusion MRI and tractography algorithms as well as the launch of the Human Connectome Project (HCP) have provided brain research with an abundance of structural connectivity data. In this work, we describe and evaluate a method that can infer the structural brain network that interconnects a given set of Regions of Interest (ROIs) from tractography data. The proposed method, referred to as Minimum Asymmetry Network Inference Algorithm (MANIA), differs from prior work because it does not determine the connectivity between two ROIs based on an arbitrary connectivity threshold. Instead, we exploit a basic limitation of the tractography process: the observed streamlines from a source to a target do not provide any information about the polarity of the underlying white matter, and so if there are some fibers connecting two voxels (or two ROIs) X and Y tractography should be able in principle to follow this connection in both directions, from X to Y and from Y to X. We leverage this limitation to formulate the network inference process as an optimization problem that minimizes the (appropriately normalized) asymmetry of the observed network. We evaluate the proposed method on a noise model that randomly corrupts the observed connectivity of synthetic networks. As a case-study, we apply MANIA on diffusion MRI data from 28 healthy subjects to infer the structural network between 18 corticolimbic ROIs that are associated with various neuropsychiatric conditions including depression, anxiety and addiction.
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