Differentiating BOLD and non-BOLD signals in fMRI time series using multi-echo EPI

Abstract

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.

Integrated strategy for improving functional connectivity mapping using multiecho fMRI

“…These results illustrate that removing non-BOLD signals is a more effective means of removing motion artifact than linear regression of head movement parameters while also reiterating that other non-BOLD artifacts such as cardiac pulsation can be removed equivalently (11).…”

Compulsive sexual behavior: Prefrontal and limbic volume and interactions

“…BOLD signal percent signal change is linearly dependent on TE, a characteristic of the T2* decay. This TE‐dependence is measured using the pseudo‐ F ‐statistic, kappa, with components that scale strongly with TE having high kappa scores [Kundu et al, 2012]. Non‐BOLD components are identified by TE independence measured by the pseudo‐ F ‐statistic, rho.…”

“…Non‐BOLD components are identified by TE independence measured by the pseudo‐ F ‐statistic, rho. Components are thus categorized as BOLD or non‐BOLD based on their kappa and rho value weightings, respectively [Kundu et al, 2012]. Non‐BOLD components are removed by projection, de‐noising data for motion, physiological and scanner artifacts in a robust manner based on physical principles.…”

Age-Related Differences in the Dynamic Architecture of Intrinsic Networks

“…Our interpretation is that motion introduces a small amount of noise into the individual factor scores, which is difficult to control for. Longer resting-state acquisitions that would permit removal of windows with motion, or the use of multi-echo EPI denoising (Kundu et al, 2012) may reduce the impact of noise.…”