Elsevier

NeuroImage

Volume 76, 1 August 2013, Pages 386-399
NeuroImage

Review
Real-time fMRI neurofeedback: Progress and challenges

https://doi.org/10.1016/j.neuroimage.2013.03.033Get rights and content

Highlights

  • Substantial progress has been made in rtfMRI in recent years.

  • Use of rtfMRI neurofeedback in scientific investigation is just beginning.

  • Clinical studies are promising, with larger patient studies on the horizon.

  • Precise mechanisms of learning are still unknown and worth further study.

  • Multivariate and non-BOLD methods will be more accessible in future.

Abstract

In February of 2012, the first international conference on real time functional magnetic resonance imaging (rtfMRI) neurofeedback was held at the Swiss Federal Institute of Technology Zurich (ETHZ), Switzerland. This review summarizes progress in the field, introduces current debates, elucidates open questions, and offers viewpoints derived from the conference. The review offers perspectives on study design, scientific and clinical applications, rtfMRI learning mechanisms and future outlook.

Introduction

On February 16th and 17th, 2012, approximately 150 international researchers joined the first conference on an emerging discipline known as real-time functional magnetic resonance imaging (rtfMRI) neurofeedback at the Swiss Federal Institute of Technology in Zurich (ETHZ), Switzerland (www.relab.ethz.ch/rtfMRI2012). The purpose of this meeting was to provide a forum to share progress and discuss the challenges for future research and clinical applications. The meeting also inspired the creation of the following work, which reviews current progress and introduces open questions and controversies.

Functional MRI measures the blood oxygenation level dependent (BOLD) signal in the brain (Ogawa et al., 1990a, Ogawa et al., 1990b), a quantity that arises from several biophysical and physiological sources (Kim and Ogawa, 2012) and represents a vascular coupling to neural activity (Logothetis, 2008, Logothetis et al., 2001). FMRI provides specific advantages over other non-invasive neuroimaging methods such as electroencephalographic recordings (EEG), including whole brain coverage and finer spatial resolution on the order of several millimeters. We define rtfMRI, first published by Cox et al. (1995), as any process that uses functional information from a MRI scanner where the analysis and display of the fMRI keep pace with data acquisition. Although whole brain fMRI data sampling can now be performed in around half a second (Feinberg et al., 2010), typical protocols still use sampling rates covering the brain approximately every two seconds. Cox et al. described that real-time brain mapping could be used for quality assurance, faster protocol development and “interactive experimental paradigms”. At present, rtfMRI has additionally been applied to intraoperative surgical guidance (Hirsch et al., 2000), brain–computer interfaces (BCIs) (Sorger et al., 2012, Yoo et al., 2004), and neurofeedback.

While EEG neurofeedback has a long history (Elbert et al., 1980, Lynch et al., 1974, Rockstroh et al., 1984, Rockstroh et al., 1993), there has been a recent rise in attention to rtfMRI neurofeedback, providing a timely background for the conference. Fig. 1 shows that there were almost as many journal papers published on the topic in 2011–2012 (n = 73) than the preceding ten years combined (n = 75). The figure illustrates that recently neurofeedback and methods development currently comprise the plurality of the rtfMRI field, and as a result this paper focuses on neurofeedback approaches (Berman et al., 2011, Bray et al., 2007, Caria et al., 2007, Caria et al., 2010, Chiew et al., 2012, deCharms et al., 2004, deCharms et al., 2005, Frank et al., 2012 Haller et al., 2010, Hamilton et al., 2011 Hawkinson et al., 2011, Hawkinson et al., 2012, Hinds et al., 2011, Johnson et al., 2012, Johnston et al., 2010, Johnston et al., 2011 Lee et al., 2011, Lee et al., 2012, Li et al., 2012, Linden et al., 2012, McCaig et al., 2011 Posse et al., 2003, Rota et al., 2009, Scharnowski et al., 2012, Shibata et al., 2011, Subramanian et al., 2011; Sulzer et al.; Veit et al., 2012, Weiskopf et al., 2003, Weiskopf et al., 2004a, Yoo and Jolesz, 2002, Yoo et al., 2008, Zotev et al., 2011). Fig. 1 also clearly shows that review papers regarding this technology are rather plentiful (e.g. Caria et al., 2012, Chapin et al., 2012, deCharms, 2007, deCharms, 2008, LaConte, 2011, Linden, 2012b, Sitaram et al., 2010, Weiskopf, 2012, Weiskopf et al., 2004b). Therefore the purpose of this paper is to focus more on the open questions identified during the conference and the challenges that lie ahead. The paper is divided into five subsections that examine rtfMRI neurofeedback from different perspectives: 1) study design, 2) scientific applications, 3) clinical applications, 4) learning mechanisms and 5) the future of rtfMRI neurofeedback.

Section snippets

Considerations in study design

The design of a study depends on its objectives. The experimental objectives of neurofeedback studies may range from demonstrating neurofeedback induced learning of self-regulation to specific behavioral effects (e.g. Rota et al., 2009, Shibata et al., 2011) or clinical improvement in patients (e.g. deCharms et al., 2005, Ruiz et al., 2013, Subramanian et al., 2011). However, the majority of neurofeedback studies employ a similar experimental framework and schedule, primarily consisting of:

  • 1.

Scientific applications

Neurofeedback as a scientific tool was pioneered by a number of researchers in the late 1960's (Fetz, 1969, Fox and Rudell, 1968, Olds, 1965, Wyrwicka and Sterman, 1968), using electrophysiological recordings in animals either noninvasively (EEG) or invasively. These research lines continue into the present time (Jackson et al., 2006, Moritz et al., 2008, Schafer and Moore, 2011). In humans, a number of studies have demonstrated the feasibility of learning to control local brain activity using

Clinical applications

Disorders of the brain, ranging from stroke to addiction to autism, represent one of the crucial public health challenges for rtfMRI neurofeedback. The following section describes the steps to be taken and risks to be considered if neurofeedback is to play a role in addressing this challenge.

Although a large variety of brain disorders could be imagined in principle as targets for neurofeedback, robust and well-controlled studies on patients based on well-founded pathophysiological models must

Learning mechanisms

While there are several studies demonstrating rtfMRI as a scientific tool or a therapeutic method, there are very few studies targeted at testing specific theoretical hypotheses about the mechanism of operant and cognitive control of neural activity with feedback. Gaining an understanding of and then exploiting these learning mechanisms could help standardize and quantify methods used in the field. In this chapter, we discuss some fundamental questions raised in the conference regarding what

Where is the future of rtfMRI neurofeedback?

Since its introduction in 1995 (Cox et al., 1995) rtfMRI has inspired research leading towards neural intervention, intraoperative procedures, brain–computer interfaces and quality assurance. While the future of rtfMRI neurofeedback can lead towards some exciting applications in a multitude of neurological and psychiatric disorders, we are currently just beginning to scratch the surface of where it can be applied. This section discusses both the immediate future and long-term future of

Conclusions from the meeting

Over the past decade much work has shown promise for rtfMRI in neurofeedback and other applications. Some key successes, including showing relevant behavioral effects of neurofeedback, exhibiting its use as a scientific tool, and identifying online brain states have led to a recent spike in interest in the field. Yet despite clear progress, fundamental issues remain such as the minimum discernible signal-to-noise ratio of feedback, imagery strategy, effect size, transfer, and how participants

Acknowledgments

The authors would like to thank the sponsors of the event, including the Swiss National Science Foundation (project #31CO30_139955), the Zurich Neuroscience Center (ZNZ), Philips Medical, ETHZ, and the University of Zurich. NB is supported by the Deutsche Forschungsgemeinschaft (DFG, Koselleck Grant) and a European Research Council (ERC) grant and a Computational Neuroscience Grant (Bernstein) from the German Ministry of Education and Research (BMBF). FS is supported by the Swiss National Fund

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