Multi-echo fMRI: A review of applications in fMRI denoising and analysis of BOLD signals

Highlights

• An emerging fMRI technology, multi-echo (ME)-fMRI, makes fMRI more interpretable by enabling the detection of BOLD or artifact origins of acquired signals.

• This review covers recent multi-echo fMRI acquisition methods, and the analysis steps for this data that make fMRI at once more principled, straightforward, and powerful.

• After an overview of history and theory of ME-fMRI, T₂* modeling and applications is discussed.

• A focus is on the use of ME-fMRI data to extract and classify components from spatial ICA, called multi-echo ICA (ME-ICA).

• New imaging methods such as multi-band multi-echo fMRI and imaging at 7 T are demonstrated throughout the review, and a practical analysis pipeline is described.

Abstract

In recent years the field of fMRI research has enjoyed expanded technical abilities related to resolution, as well as use across many fields of brain research. At the same time, the field has also dealt with uncertainty related to many known and unknown effects of artifact in fMRI data. In this review we discuss an emerging fMRI technology, called multi-echo (ME)-fMRI, which focuses on improving the fidelity and interpretability of fMRI. Where the essential problem of standard single-echo fMRI is the indeterminacy of sources of signals, whether BOLD or artifact, this is not the case for ME-fMRI. By acquiring multiple echo images per slice, the ME approach allows T₂* decay to be modeled at every voxel at every time point. Since BOLD signals arise by changes in T₂* over time, an fMRI experiment sampling the T₂* signal decay can be analyzed to distinguish BOLD from artifact signal constituents. While the ME approach has a long history of use in theoretical and validation studies, modern MRI systems enable whole-brain multi-echo fMRI at high resolution. This review covers recent multi-echo fMRI acquisition methods, and the analysis steps for this data to make fMRI at once more principled, straightforward, and powerful. After a brief overview of history and theory, T₂* modeling and applications will be discussed. These applications include T₂* mapping and combining echoes from ME data to increase BOLD contrast and mitigate dropout artifacts. Next, the modeling of fMRI signal changes to detect signal origins in BOLD-related T₂* versus artifact-related S₀ changes will be reviewed. A focus is on the use of ME-fMRI data to extract and classify components from spatial ICA, called multi-echo ICA (ME-ICA). After describing how ME-fMRI and ME-ICA lead to a general model for analysis of fMRI signals, applications in animal and human imaging will be discussed. Applications include removing motion artifacts in resting state data at subject and group level. New imaging methods such as multi-band multi-echo fMRI and imaging at 7 T are demonstrated throughout the review, and a practical analysis pipeline is described. The review culminates with evidence from recent studies of major boosts in statistical power from using multi-echo fMRI for detecting activation and connectivity in healthy individuals and patients with neuropsychiatric disease. In conclusion, the review shows evidence that the multi-echo approach expands the range of experiments that is practicable using fMRI. These findings suggest a compelling future role of the multi-echo approach in subject-level and clinical fMRI.

Introduction

Blood oxygenation level dependent (BOLD) functional MRI (fMRI) is widely used to study brain activity based on hemodynamic signals (Bandettini et al., 1992, Bullmore et al., 1996, Buxton et al., 1998, Friston et al., 1995, Ogawa et al., 1990). However, recent studies show that fMRI data can be severely affected by artifacts (Power et al., 2012). These artifacts relate to subject head motion, cardiac and respiratory effects, and hardware (Glover et al., 2000, Jo et al., 2010). Studies on the effects of fMRI artifacts have brought into question many of the compelling findings on brain function based on fMRI, for example, relating to human brain development (Fair et al., 2009). fMRI artifacts also reduce the statistical power of fMRI studies and lead to spurious findings, which has been associated with a crisis of confidence in fMRI research (Button et al., 2013). Thus, while in recent years the field of fMRI research has both enjoyed advanced technology and expanded use, it also deals with a deep discomfort related to many known and unknown effects of artifact. In this review, we discuss an emerging fMRI approach, called multi-echo (ME)-fMRI, which focuses on improving the fidelity of fMRI signals through a physically-driven determination of the origins of fMRI signals as arising from either BOLD contrast or artifact. We also discuss how ME methods can be combined with emerging multi-band acceleration methods to create fMRI strategies with both high resolution and fidelity. Altogether, it is shown that ME-fMRI involves a few relatively small changes from standard fMRI acquisition technique, but enables analyses that support major improvements in fMRI data quality.

To date, the challenges in controlling fMRI artifacts have been met with generic time series signal processing methods such as regression and frequency-restricting bandpass filters (Satterthwaite et al., 2012). Head motion artifacts, modeled from shifts in brain images over time, are regressed out of fMRI time series. Recordings of physiology during MRI are used to model cardiac and respiratory artifact regressors (Glover et al., 2000). Spontaneous activity is band pass filtered to retain a narrow range of frequencies in order to exclude hardware related signal drifts and high-frequency noise (Carp, 2013). Time series models of noise and usually their temporal derivatives are linearly regressed out of data. Despite the significant reduction in information in data after applying these steps, it is now clear that much artifact remains. Of late, simple deletion of volumes from fMRI datasets has been suggested (Power et al., 2012). This altogether means that artifact signals are not characterized well enough by modeling artifact time courses and regressing them out of data. This reality also suggests that artifacts are themselves various and complex, and may interact in unpredictable ways. At the heart of the issue, however, is that standard fMRI approaches do not have a strong and general ground truth to precisely relate signals to biophysical signal mechanisms versus artifacts. Given information on how signals scale across the echo images of an ME-fMRI experiment, however, valuable insight on the origins of fMRI signals in BOLD contrast or artifact can be gained.