Noise Reduction in BOLD-Based fMRI Using Component Analysis

Thomas, Christopher G.; Harshman, Richard A.; Menon, Ravi S.

doi:10.1006/nimg.2002.1200

Cited by 251 publications

(206 citation statements)

References 56 publications

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“…The chosen approach of modeling the signal by a mixture of Gaussian and Gamma functions allowed for an adequate representation of the ICA maps, but this is not equivalent to null hypothesis testing and family-wise false positive rate thresholding as used in the GLM. Moreover, the GLM noise estimation includes any unmodeled signal fluctuations such as cardiac artifacts, respiration, or residual movement, while the ICA residuals do not include these sources of structured noise, which are rather extracted into separate components (Thomas et al, 2002). This results in additional variability between the maps generated by GLM and ICA methods.…”

Section: Methodological Issuesmentioning

confidence: 99%

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Independent component analysis reveals dynamic ictal BOLD responses in EEG-fMRI data from focal epilepsy patients

LeVan¹,

Tyvaert²,

Moeller³

et al. 2010

NeuroImage

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Introduction-Seizures occur rarely during EEG-fMRI acquisitions of epilepsy patients, but can potentially offer a better estimation of the epileptogenic zone than interictal activity. Independent component analysis (ICA) is a data-driven method that imposes minimal constraints on the hemodynamic response function (HRF). In particular, the investigation of HRFs with clear peaks, but varying latency, may be used to differentiate the ictal focus from propagated activity.

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Section: Methodological Issuesmentioning

confidence: 99%

“…It can identify regions showing a non-canonical HRF and also yield de-noised time courses of cerebral activity as a result of the separation of artifactual components (Thomas et al, 2002).…”

Section: Introductionmentioning

confidence: 99%

Independent component analysis reveals dynamic ictal BOLD responses in EEG-fMRI data from focal epilepsy patients

LeVan¹,

Tyvaert²,

Moeller³

et al. 2010

NeuroImage

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show abstract

“…The goal of the ICA decomposition is to rotate the signal components so as to minimize the mutual information between each pair of components, which is equivalent to maximizing the deviation of each component from the Gaussian distribution expected on the Central Limit Theorem. In the fMRI domain, the application of ICA has usually been focused on extracting large effects due to causes unrelated to observer's task, such as head movements, heartbeat, breathing, instrument drift, drift due to general alertness levels, swallowing, etc (e.g., McKeown et al, 1998;Thomas et al, 2002;Calhoun et al, 2002a,b;2004a,b). This is a relatively straightforward analysis based on the assumption that the analyzed signals are statistically independent in time.…”

Section: Independent Components Analysismentioning

confidence: 99%

Independent components in stimulus-related BOLD signals and estimation of the underlying neural responses

2008

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This is the published version of the paper.This version of the publication may differ from the final published version. Permanent repository link A R T I C L E I N F O A B S T R A C TArticle history: IntroductionFunctional magnetic-resonance imaging (fMRI) based on the blood oxygenation-level dependent (BOLD) signal is a powerful technique for studying the neural response in the cortex. Most previous work has been based on the assumption that the BOLD signal is a unitary response to a single form of stimulus S(t), usually in the form of a linear convolution of a hemodynamic response function (HRF) with the temporal waveform of the stimulus time sequence (Bandettini et al., 1993;Friston et al., 1994;Boynton et al., 1996). In most cases, it is assumed that the HRF incorporates both the cortical response to the stimulus and the hemodynamic response of the blood to the oxygen requirements of the cortical response. However, the situation may be much more complicated, with multiple components of neural response to the stimulus weighted differentially across the cortex (Zacks et al., 2001;d'Avossa et al., 2003;Bellgowan et al., 2003;Calhoun et al., 2004a;Fox et al., 2005), such that any one voxel contains contributions from more than one neural time course. If these time course variations are small relative to the BOLD temporal resolution, they may be negligible for an overall analysis of response strength, but the cited studies show non-negligible variations that require a more detailed analysis. For this reason, consistent with Friston et al. (1998Friston et al. ( , 2000, Buxton et al. (2004) and others, we restrict the term HRF to the dynamics of the blood response to the metabolic demands of the neural processing and consider the neural response to the stimulus as a separate process. This leads to the model of the BOLD mechanisms shown in Fig. 1.

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“…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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Noise Reduction in BOLD-Based fMRI Using Component Analysis

Cited by 251 publications

References 56 publications

Independent component analysis reveals dynamic ictal BOLD responses in EEG-fMRI data from focal epilepsy patients

Independent component analysis reveals dynamic ictal BOLD responses in EEG-fMRI data from focal epilepsy patients

Independent components in stimulus-related BOLD signals and estimation of the underlying neural responses

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

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