Differentiating BOLD and Non-BOLD Signals in fMRI Time Series Using Multi-Echo EPI

By Prantik Kundu, Souheil J. Inati, Jennifer W. Evans, Wen-Ming Luh, Peter A. Bandettini. NeuroImage, 2011

655 citations recorded by the source corpus.

A central challenge in the fMRI based study of functional connectivity is distinguishing neuronally related signal fluctuations from the effects of motion, physiology, and other nuisance sources. Conventional techniques for removing nuisance effects include modeling of noise time courses based on external measurements followed by temporal filtering. These techniques have limited effectiveness. Previous studies have shown using multi-echo fMRI that neuronally related fluctuations are Blood Oxygen Level Dependent (BOLD) signals that can be characterized in terms of changes in R2* and initial signal intensity (S0) based on the analysis of echo-time (TE) dependence. We hypothesized that if TE-dependence could be used to differentiate BOLD and non-BOLD signals, non-BOLD signal could be removed to denoise data without conventional noise modeling. To test this hypothesis, whole brain multi-echo data were acquired at 3 TEs and decomposed with Independent Components Analysis (ICA) after spatially concatenating data across space and TE. Components were analyzed for the degree to which their signal changes fit models for R2* and S0 change, and summary scores were developed to characterize each component as BOLD-like or not BOLD-like. These scores clearly differentiated BOLD-like “functional network” components from non BOLD-like components related to motion, pulsatility, and other nuisance effects. Using non BOLD-like component time courses as noise regressors dramatically improved seed-based correlation mapping by reducing the effects of high and low frequency non-BOLD fluctuations. A comparison with seed-based correlation mapping using conventional noise regressors demonstrated the superiority of the proposed technique for both individual and group level seed-based connectivity analysis, especially in mapping subcortical-cortical connectivity. The differentiation of BOLD and non-BOLD components based on TE-dependence was highly robust, which allowed for the identification of BOLD-like components and the removal of non BOLD-like components to be implemented as a fully automated procedure.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/cdac47904831b789bf223ab51eb043dec4bb7ee4

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Citations in the corpus

Resting-state FMRI confounds and cleanup (2013, positive): “Multi-echo approaches in which M0 and T2* fluctuations can be separated effectively could further enhance this method (Barth et al., 1999; Kundu et al., 2012; Posse et al., 1999; Speck and Hennig, 1998).”
Benchmarking of participant‐level confound regression strategies for the control of motion artifact in studies of functional connectivity (2017, background): “…be noted that while improvements in image acquisition (including multi-echo techniques) may not salvage existing motion-contaminated data, it is likely that they will change the methodological landscape of connectivity research moving forward (Kundu et al., 2012, 2013; Bright and Murphy, 2013).”
Evaluation of slice accelerations using multiband echo planar imaging at 3 Tesla (2013, positive): “The increased temporal efficiency could be employed to achieve, within a given acquisition period, higher spatial resolution, increased fMRI statistical power, multiple TEs, faster sampling of temporal events in a resting state fMRI time series, increased sampling of q-space in diffusion imaging, or”
Building a Science of Individual Differences from fMRI. (2016, background): “Kundu P, et al. Differentiating BOLD and non-BOLD signals in fMRI time series using multiecho EPI.”
A wavelet method for modeling and despiking motion artifacts from resting-state fMRI time series (2014, background): “…which have not been explicitly discussed here, such as high-speed imaging (Setsompop et al., 2012), 3D imaging, multi-band imaging, multi-echo imaging (Poser et al., 2006), and combinations of these sequences with other pre-processing methods (Bright and Murphy, 2013; Kundu et al., 2012).”
Multi‐echo fMRI: A review of applications in fMRI denoising and analysis of BOLD signals (2017, methodology): “Classifying statistical components of fMRI datasets as problem and opportunity TE-dependence and TE-independence models can be applied to classify signal components of fMRI that are found using spatial independent component analysis (ICA) (Kundu et al., 2012).”
Hand classification of fMRI ICA noise components (2017, neutral): “Kundu et al. (2012) show TEdependence maps of ICA components as well as examples of signal components (functional networks) and artefacts.”
Methods for cleaning the BOLD fMRI signal (2016, neutral): “Similar to other ICA-based methods, the final step of ME-ICA is to regress out from the data those ICs that are classified as noise (Kundu et al., 2012; 2013; Olafsson et al., 2015).”
Evaluation of ICA-AROMA and alternative strategies for motion artifact removal in resting state fMRI (2015, background): “…2014), SLOMOCO (Beall and Lowe, 2014), RDI (Spisák et al., 2014) or alternative ICA-based strategies (Thomas et al., 2002; Kochiyama et al., 2005; De Martino et al., 2007; Perlbarg et al., 2007; Tohka et al., 2008; Kundu et al., 2012; Rummel et al., 2013; Storti et al., 2013; Sochat et al., 2014).”
Resting state fMRI: A review of methods and clinical applications (2012, neutral): “Some studies have aimed to automate this process and use ICA as a method for identifying noise within the BOLD signal.(33-36) Despite the differences in the 2 approaches, Rosazza et al(37) Fig 1.”
ICA-based artefact and accelerated fMRI acquisition for improved Resting State Network imaging (2014, methodology): “O F A related method uses a multi-echo approach that allows the separation of BOLD and non-BOLD signal com-147 ponents based on TE-dependence (Kundu et al., 2012).”
Methods to detect, characterize, and remove motion artifact in resting state fMRI (2013, methodology): “Although we are optimistic about improvements in MRI sequences and techniques at data acquisition with regard to motion correction (e.g., Bright and Murphy, 2013; Kundu et al., 2012), our focus is on data that have been already acquired.”
ICA-AROMA: A robust ICA-based strategy for removing motion artifacts from fMRI data (2015, neutral): “Multiple methods have been developed to automatically identify noise components based on temporal and/or spatial features (Thomas et al., 2002; Kochiyama et al., 2005; De Martino et al., 2007; Perlbarg et al., 2007; Tohka et al., 2008; Kundu et al., 2012; Bhaganagarapu et al., 2013; Rummel et al., 2”
Dynamic functional connectivity: Promise, issues, and interpretations (2013, background): “…RVHRCOR, PESTICA, CompCor, ME-ICA, censoring/“scrubbing” (Beall and Lowe, 2007; Behzadi et al., 2007; Chang et al., 2009; Glover et al., 2000; Kundu et al., 2012; Power et al., 2012)), residual noise inevitably remains, and dynamic studies of resting state would greatly benefit from a…”