Independent component analysis of functional MRI: what is signal and what is noise?

McKeown, Martin J.

doi:10.1016/j.conb.2003.09.012

Cited by 371 publications

(272 citation statements)

References 63 publications

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“…We see a major utility for parallel ICA in this context as it provides the means to disentangle and visualize these networks both in their spatial and temporal form (Calhoun, et al, 2006a;Debener, et al, 2006;Makeig, et al, 2004a;McKeown, et al, 2003;Onton, et al, 2006). However, some limitations apply: Infomax assumes sources to have non-normal, either superor subgaussian distributions (Bell, et al, 1995;Lee, et al, 1999), and this seems to hold for a great variety of physiological signals as well as technical artefacts.…”

Section: Area Of Applicationmentioning

confidence: 99%

“…The scalp EEG samples a volume-conducted, spatially degraded version of the responses, where the potential at any location and latency can be considered a mixture of multiple independent timecourses that stem from large-scale synchronous field potentials (Makeig, et al, 2004a;Onton, et al, 2006). Similarly, the neurovascular transformation of the distributed neuronal activity into hemodynamic signals (Lauritzen, et al, 2003;Logothetis, 2003) affords detection of blood oxygenation level dependent responses (BOLD, Ogawa, et al, 1990) that are temporally degraded and spatially mixed across the fMRI volume (Calhoun, et al, 2006a;McKeown, et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

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Unmixing concurrent EEG-fMRI with parallel independent component analysis

Eichele

Calhoun

Moosmann

et al. 2008

International Journal of Psychophysiology

104

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Concurrent event-related EEG-fMRI recordings pick up volume-conducted and hemodynamically convoluted signals from latent neural sources that are spatially and temporally mixed across the brain, i.e. the observed data in both modalities represent multiple, simultaneously active, regionally overlapping neuronal mass responses. This mixing process decreases the sensitivity of voxel-byvoxel prediction of hemodynamic activation by the EEG when multiple sources contribute to either the predictor and/or the response variables. In order to address this problem, we used independent component analysis (ICA) to recover maps from the fMRI and timecourses from the EEG, and matched these components across the modalities by correlating their trial-to-trial modulation. The analysis was implemented as a group-level ICA that extracts a single set of components from the data and directly allows for population inferences about consistently expressed function-relevant spatiotemporal responses. We illustrate the utility of this method by extracting a previously undetected but relevant EEG-fMRI component from a concurrent auditory target detection experiment. §Corresponding Author:

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Section: Area Of Applicationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Unmixing concurrent EEG-fMRI with parallel independent component analysis

Eichele

Calhoun

Moosmann

et al. 2008

International Journal of Psychophysiology

104

View full text Add to dashboard Cite

show abstract

“…However, since this technique removes only the average timeseries, it is unable to account for voxel-specific phase differences in the noise due to physiological fluctuations. Additionally, component based techniques, utilizing independent component analysis (ICA) or principal components analysis (PCA), have shown potential in identifying spatial and temporal patterns of structured noise (Thomas et al 2002;McKeown et al 2003;Beckmann and Smith 2004). However, the utility of component based methods has been limited to BOLD studies with sampling times short enough to clearly differentiate cardiac and respiratory elements from evoked responses (Thomas et al 2002), in which case a temporal band pass filter would be adequate for noise removal.…”

Section: Introductionmentioning

confidence: 99%

A component based noise correction method (CompCor) for BOLD and perfusion based fMRI

et al. 2007

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A component based method (CompCor) for the reduction of noise in both blood oxygenation level dependent (BOLD) and perfusion-based functional magnetic resonance imaging (fMRI) data is presented. In the proposed method, significant principal components are derived from noise regionsof-interest (ROI) in which the time series data are unlikely to be modulated by neural activity. These components are then included as nuisance parameters within general linear models for BOLD and perfusion-based fMRI time-series data. Two approaches for the determination of the noise ROI are considered. The first method uses high-resolution anatomical data to define a region of interest composed primarily of white matter and cerebrospinal fluid, while the second method defines a region based upon the temporal standard deviation of the time series data. With the application of CompCor, the temporal standard deviation of resting state perfusion and BOLD data in gray matter regions was significantly reduced as compared to either no correction or the application of a previously described retrospective image based correction scheme (RETROICOR). For both functional perfusion and BOLD data, the application of CompCor significantly increased the number of activated voxels as compared to no correction. In addition, for functional BOLD data, there were significantly more activated voxels detected with CompCor as compared to RETROICOR. In comparison to RETROICOR, CompCor has the advantage of not requiring external monitoring of physiological fluctuations.

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“…The ICA model assumes that the data are linear mixtures of statistically independent sources and attempts to decompose the data into maximally independent components and their mixing coefficients (also called loading parameters). The ICA method is being increasingly utilized for fMRI data analysis to reveal hidden structure in the spatial and temporal dimensions of these data [Calhoun et al, 2003;McKeown et al, 2003]. In contrast to a first-level ICA approach (i.e., analysis of the preprocessing fMRI time series data for each task and each subject separately), we instead introduce the idea of a second-level (group), feature-based analysis of the fMRI activation maps (the "features") generated from a first-level analysis.…”

Section: Introductionmentioning

confidence: 99%

A method for multitask fMRI data fusion applied to schizophrenia

Calhoun

Adalı

Kiehl

et al. 2005

Human Brain Mapping

155

122

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Abstract:It is becoming common to collect data from multiple functional magnetic resonance imaging (fMRI) paradigms on a single individual. The data from these experiments are typically analyzed separately and sometimes directly subtracted from one another on a voxel-by-voxel basis. These comparative approaches, although useful, do not directly attempt to examine potential commonalities between tasks and between voxels. To remedy this we propose a method to extract maximally spatially independent maps for each task that are "coupled" together by a shared loading parameter. We first compute an activation map for each task and each individual as "features," which are then used to perform joint independent component analysis (jICA) on the group data. We demonstrate our approach on a data set derived from healthy controls and schizophrenia patients, each of which carried out an auditory oddball task and a Sternberg working memory task. Our analysis approach revealed two interesting findings in the data that were missed with traditional analyses. First, consistent with our hypotheses, schizophrenia patients demonstrate "decreased" connectivity in a joint network including portions of regions implicated in two prevalent models of schizophrenia. A second finding is that for the voxels identified by the jICA analysis, the correlation between the two tasks was significantly higher in patients than in controls. This finding suggests that schizophrenia patients activate "more similarly" for both tasks than do controls. A possible synthesis of both findings is that patients are activating less, but also activating with a less-unique set of regions for these very different tasks. Both of the findings described support the claim that examination of joint activation across multiple tasks can enable new questions to be posed about fMRI data. Our approach can also be applied to data using more than two tasks. It thus provides a way to integrate and probe brain networks using a variety of tasks and may increase our understanding of coordinated brain networks and the impact of pathology upon them. Hum Brain Mapp 27:598-610, 2006.

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Independent component analysis of functional MRI: what is signal and what is noise?

Cited by 371 publications

References 63 publications

Unmixing concurrent EEG-fMRI with parallel independent component analysis

Unmixing concurrent EEG-fMRI with parallel independent component analysis

A component based noise correction method (CompCor) for BOLD and perfusion based fMRI

A method for multitask fMRI data fusion applied to schizophrenia

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