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

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 regions-of-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.

[1]  Thomas E. Nichols,et al.  Estimation efficiency and statistical power in arterial spin labeling fMRI , 2006, NeuroImage.

[2]  H. Stødkilde-Jørgensen,et al.  State Space Models for Physiological Noise in fMRI Time Series , 1998, NeuroImage.

[3]  X Hu,et al.  Retrospective estimation and correction of physiological fluctuation in functional MRI , 1995, Magnetic resonance in medicine.

[4]  G. Glover,et al.  Physiological noise in oxygenation‐sensitive magnetic resonance imaging , 2001, Magnetic resonance in medicine.

[5]  G. Crelier,et al.  Investigation of BOLD signal dependence on cerebral blood flow and oxygen consumption: The deoxyhemoglobin dilution model , 1999, Magnetic resonance in medicine.

[6]  R W Cox,et al.  AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. , 1996, Computers and biomedical research, an international journal.

[7]  Lee M. Miller,et al.  Measuring interregional functional connectivity using coherence and partial coherence analyses of fMRI data , 2004, NeuroImage.

[8]  R. Buxton,et al.  Quantitative imaging of perfusion using a single subtraction (QUIPSS and QUIPSS II) , 1998 .

[9]  D. Heeger,et al.  Linear Systems Analysis of Functional Magnetic Resonance Imaging in Human V1 , 1996, The Journal of Neuroscience.

[10]  Thomas E. Nichols,et al.  Non-white noise in fMRI: Does modelling have an impact? , 2006, NeuroImage.

[11]  Richard A. Harshman,et al.  Noise Reduction in BOLD-Based fMRI Using Component Analysis , 2002, NeuroImage.

[12]  Thomas T. Liu,et al.  A signal processing model for arterial spin labeling functional MRI , 2005, NeuroImage.

[13]  Peter A. Bandettini,et al.  Separating respiratory-variation-related fluctuations from neuronal-activity-related fluctuations in fMRI , 2006, NeuroImage.

[14]  Stephen M. Smith,et al.  Probabilistic independent component analysis for functional magnetic resonance imaging , 2004, IEEE Transactions on Medical Imaging.

[15]  L. K. Hansen,et al.  Independent component analysis of functional MRI: what is signal and what is noise? , 2003, Current Opinion in Neurobiology.

[16]  Karl J. Friston,et al.  Analysis of fMRI Time-Series Revisited—Again , 1995, NeuroImage.

[17]  E. DeYoe,et al.  Reduction of physiological fluctuations in fMRI using digital filters , 1996, Magnetic resonance in medicine.

[18]  T. Lund,et al.  Physiological noise reduction in fMRI using vessel time-series as covariates in a general linear model , 2001, NeuroImage.

[19]  Luke Bloy,et al.  Spatial and temporal characteristics of physiological noise in fMRI at 3T. , 2006, Academic radiology.

[20]  Mark W. Woolrich,et al.  Advances in functional and structural MR image analysis and implementation as FSL , 2004, NeuroImage.

[21]  Thomas T. Liu,et al.  Physiological noise reduction for arterial spin labeling functional MRI , 2006, NeuroImage.

[22]  Donald A. Jackson.  STOPPING RULES IN PRINCIPAL COMPONENTS ANALYSIS: A COMPARISON OF HEURISTICAL AND STATISTICAL APPROACHES' , 1993 .

[23]  G H Glover,et al.  Image‐based method for retrospective correction of physiological motion effects in fMRI: RETROICOR , 2000, Magnetic resonance in medicine.

[24]  Stephen M. Smith,et al.  Temporal Autocorrelation in Univariate Linear Modeling of FMRI Data , 2001, NeuroImage.

[25]  A M Dale,et al.  Estimation and detection of event‐related fMRI signals with temporally correlated noise: A statistically efficient and unbiased approach , 2000, Human brain mapping.

[26]  J. Haxby,et al.  Localization of Cardiac-Induced Signal Change in fMRI , 1999, NeuroImage.

[27]  G. Glover,et al.  Correction of physiologically induced global off‐resonance effects in dynamic echo‐planar and spiral functional imaging , 2002, Magnetic resonance in medicine.