Meta-analysis of real-time fMRI neurofeedback studies using individual participant data: How is brain regulation mediated?
Introduction
Neurofeedback using real-time functional magnetic resonance imaging (rt-fMRI) enables participants to obtain voluntary control over multiple brain regions. Studies using this technique have demonstrated that it may be possible to successfully manipulate brain areas including the anterior cingulate cortex (ACC, Weiskopf et al., 2003, Hamilton et al., 2011), the posterior cingulate cortex (Brewer and Garrison, 2014), the anterior insular cortex (AIC, Caria et al., 2007, Caria et al., 2010, Berman et al., 2013), posterior insular cortex (PIC, Rance et al., 2014), amygdala (Posse et al., 2003, Zotev et al., 2011, Bruhl et al., 2014), primary motor and somatosensory cortex cortices (Yoo and Jolesz, 2002, Berman et al., 2012), premotor area (Johnson et al., 2012), visual cortex (Shibata et al., 2011), auditory cortex (Yoo et al., 2006, Haller et al., 2013), substantia nigra/ventral tegmental area (Sulzer et al., 2013), nucleus accumbens (Greer et al., 2014) and inferior frontal gyrus (Rota et al., 2009; for a review see Ruiz et al., 2014).
Real-time fMRI neurofeedback has also been explored as a supplementary treatment for various neurological disorders. For instance, real-time fMRI neurofeedback has shown positive benefits for diseases such as schizophrenia (Ruiz et al., 2013), depression (Linden et al., 2012, Young et al., 2014), tinnitus (Haller et al., 2010), Parkinson's disease (Subramanian et al., 2011) and nicotine addiction (Canterberry et al., 2013, Hartwell et al., 2013, Li et al., 2013). However, effect size of neurofeedback varies and in a lot of studies some participants fail to attain self-regulation. The neural mechanisms of neurofeedback as used for self-regulation of bodily functions are not well understood, which may be a roadblock to achieving consistent outcomes between studies and successful translation into clinics.
One of the most important but least understood characteristics of neurofeedback is the specificity of activation during self-regulation. Previous investigations in real-time fMRI neurofeedback have attempted to control for specificity of the self-regulation using feedback from another region (deCharms et al., 2005), subtracting the mean activity of a reference slice that does not contain involved brain regions (Caria et al., 2007, Rota et al., 2009), or using post-hoc statistical methods (Blefari et al., 2015). In contrast, we are here interested in the regions that are additionally activated during self-regulation, that is, regions that are involved in the cognitively demanding task of neurofeedback regulation.
In their landmark study, deCharms et al. reported that reduced pain perception via ACC regulation may have resulted from the contribution of a higher order region despite rigorous controls (deCharms et al., 2005). If so, exactly which regions would be responsible for effects of self-regulation?
To answer this question, it is important to consider the cognitive processes involved during neurofeedback and the corresponding networks. One of these networks is the central executive network (CEN) that is active in most cognitively demanding task, likely reflecting working-memory involvement and decision-making (Koechlin and Summerfield, 2007, Miller and Cohen, 2001). It includes the dorsolateral prefrontal cortex (dlPFC) and the posterior parietal cortex (Sridharan et al., 2008). In addition, the saliency network that is comprised of the AIC and the ACC as main components will be involved in neurofeedback relevant tasks including attentional control and monitoring. Menon and Uddin (2010) suggest that this network coordinates task-related information processing by recruiting various other, more specialized networks. For neurofeedback, these might include reward-learning areas, recruiting the striatum (Hollerman et al., 1998, Samejima et al., 2005, Daniel and Pollmann, 2014), the frontal cortex (Watanabe, 1996, O'doherty et al., 2003) and areas responsible for interoception (Craig, 2002, Lerner et al., 2009) such as parts of the AIC. Neurofeedback will likely use subnetworks cutting through all the above-mentioned networks.
Indeed, studies using a single region of interest suggest involvement of the dorsolateral prefrontal cortex (dlPFC), the dorsomedial prefrontal cortex (dmPFC, Zotev et al., 2013), the ventromedial prefrontal cortex (vmPFC, Haller et al., 2010) and the anterior mid-cingulate cortex (Lee et al., 2012) to anterior cingulate cortex (Lawrence et al., 2013, Zotev et al., 2013) in the regulation process. A number of feedback studies show activation of the posterior ACC (pACC,), although this area was not targeted (e.g. Caria et al., 2007, Rota et al., 2009, Lee et al., 2012, Veit et al., 2012, Lawrence et al., 2013). Similarly, several studies reported activation of the insula during neurofeedback runs (e.g. Rota et al., 2009, Haller et al., 2010, Lee et al., 2012, Paret et al., 2014).
In the current investigation, we assess the brain network mediating regulation in real-time fMRI neurofeedback. We hypothesized that regardless of the target region used, a common brain network is involved in the regulation process itself. Consequently, we performed a meta-analysis using individual participant data (IPD meta-analysis) across multiple previously reported rt-fMRI neurofeedback studies with different target regions in order to cancel out target region-specific effects and identify those activations commonly related to the regulation process. It should be noted that, at the current stage, we cannot distinguish between self-regulation processes and other processes involved in neurofeedback including feedback processing and learning as the current study does not include control runs without feedback (“transfer runs”). Our results suggest the existence of a neurofeedback network consisting of the anterior insula, basal ganglia, dorsal parts of the parietal lobe extending to the temporo-parietal junction, ACC, dlPFC, ventrolateral prefrontal cortex (vlPFC) and visual association areas including the temporo-occipital junction.
Section snippets
Study selection
Studies were selected based on a Web of Knowledge (https://apps.webofknowledge.com) search for the keywords: “real time fMRI”, “real time functional” or “rtfMRI” (in January 2014) as well as studies indicated in the real-time community ([email protected], updated in August 2015 to [email protected]) literature updates. This search provided us with a total of 316 publications. Next, we used the following selection criteria, 1) rt-fMRI neurofeedback, 2) 1.5 or 3.0 T static field
Results
The third level mixed effects analysis of all 12 studies yielded two main regions that are consistently activated during neurofeedback: the bilateral anterior insula and the basal ganglia. Considering the subsample analysis with a larger field of view (n = 8 studies) additional significant areas include the posterior ACC (pACC), the bilateral ventrolateral prefrontal cortex (vlPFC) and an area in the bilateral dorsolateral prefrontal cortex (dlPFC) extending to the premotor cortex (PMC), a large
Discussion
The IPD meta-analysis of rt-fMRI neurofeedback studies with a variety of target regions identified a regulation network that includes notably the anterior insula, the basal ganglia, the temporo-parietal area, the ACC, the dlPFC, the vlPFC and the visual association area including the temporo-occipital junction (see Fig. 2).
Anterior insula activation is known to occur during interoceptive cognition and self-awareness processes (Craig, 2002, Critchley et al., 2004). Additionally, specifically the
Conclusion
Brain self-regulation during rt-fMRI neurofeedback involves a complex regulation network, including notably AIC, BG and the ACC. Taking into account the limitation that the current investigation is a retrospective IPD meta-analysis of rt-fMRI studies, which were not specifically designed for this purpose, our results suggest that some target regions of rt-fMRI neurofeedback studies (notably insula and ACC) are also implicated in the process of regulation per se. This may therefore represent a
Support
This work was supported by the Swiss National Science Foundation (project 320030_147126/1, 320030_127079/1 and the Marie Heim-Vögtlin grants PMCDP2_145442 and PMCDP2_162223) and the Center for Biomedical Imaging (CIBM, Geneva, Switzerland).
We are very grateful to all of the researchers who kindly supplied us with data from their studies. This work was supported by data from Brian Berman & Silvina Horovitz, Markus Breimhorst, Annette Brühl, Andrea Caria, Sabine Frank, Steve Johnston & David
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Both authors contributed equally.