Emotion Regulation Using Virtual Environments and Real-Time fMRI Neurofeedback

Abstract

Neurofeedback (NFB) enables the voluntary regulation of brain activity, with promising applications to enhance and recover emotion and cognitive processes, and their underlying neurobiology. It remains unclear whether NFB can be used to aid and sustain complex emotions, with ecological validity implications. We provide a technical proof of concept of a novel real-time functional magnetic resonance imaging (rtfMRI) NFB procedure. Using rtfMRI-NFB, we enabled participants to voluntarily enhance their own neural activity while they experienced complex emotions. The rtfMRI-NFB software (FRIEND Engine) was adapted to provide a virtual environment as brain computer interface (BCI) and musical excerpts to induce two emotions (tenderness and anguish), aided by participants' preferred personalized strategies to maximize the intensity of these emotions. Eight participants from two experimental sites performed rtfMRI-NFB on two consecutive days in a counterbalanced design. On one day, rtfMRI-NFB was delivered to participants using a region of interest (ROI) method, while on the other day using a support vector machine (SVM) classifier. Our multimodal VR/NFB approach was technically feasible and robust as a method for real-time measurement of the neural correlates of complex emotional states and their voluntary modulation. Guided by the color changes of the virtual environment BCI during rtfMRI-NFB, participants successfully increased in real time, the activity of the septo-hypothalamic area and the amygdala during the ROI based rtfMRI-NFB, and successfully evoked distributed patterns of brain activity classified as tenderness and anguish during SVM-based rtfMRI-NFB. Offline fMRI analyses confirmed that during tenderness rtfMRI-NFB conditions, participants recruited the septo-hypothalamic area and other regions ascribed to social affiliative emotions (medial frontal / temporal pole and precuneus). During anguish rtfMRI-NFB conditions, participants recruited the amygdala and other dorsolateral prefrontal and additional regions associated with negative affect. These findings were robust and were demonstrable at the individual subject level, and were reflected in self-reported emotion intensity during rtfMRI-NFB, being observed with both ROI and SVM methods and across the two sites. Our multimodal VR/rtfMRI-NFB protocol provides an engaging tool for brain-based interventions to enhance emotional states in healthy subjects and may find applications in clinical conditions associated with anxiety, stress and impaired empathy among others.

Cognitive reappraisal during unpleasant picture processing: Subjective self‐report and peripheral physiology

“…A large and growing body of work has examined the relationship between negative affect and cognitive reappraisal using subjective measures (i.e., self-report questionnaires), even when these correlates might be vulnerable to the experimenter demands and participant bias (Andreotti et al, 2013;Ray, McRae, Ochsner, & Gross, 2010). Similarly, a number of human neuroscience studies over the past two decades have used functional brain imaging techniques (fMRI) and electrophysiological measures (positron emission tomography [PET] and EEG/ERP), trying to delineate the neural mechanisms underlying the capacity to flexibly alter and refine the experience of emotions in humans (Etkin et al, 2015;Lorenzetti et al, 2018;Ochsner & Gross, 2005;Ochsner et al, 2004;Phan & Sripada, 2013). However, only a few studies have focused on peripheral psychophysiological correlates of these processes.…”

A Roadmap for Integrating Neuroscience Into Addiction Treatment: A Consensus of the Neuroscience Interest Group of the International Society of Addiction Medicine

“…There is not a consensus on a framework that specifically defines (i) key neuromodulation targets, (ii) their relevant substance use, cognitive, and clinical outcomes, as different brain pathways are ascribed to heterogeneous neurobehavioral processes ( with transcranial alternating current (tACS) stimulation (217). Additionally, personalized brain treatment targets can be identified using neurofeedback machine learning approaches that discriminate distinct patterns of brain function within each individual, instead of a priori brain regions (or their connectivity) across various individuals (218).…”

“…Real-time fMRI (rtfMRI)-based neurofeedback has the potential to provide insight in understanding the mechanisms of SUD underpinned by deep brain nuclei [e.g., striatum, amygdala (80)] the activity of which is unlikely to be robustly measured via surface EEG. Feedback can be provided on the level of activity of single or multiple a priori regions of interest, the strength of the connectivity between multiple regions, and patterns of brain activity identified with machine learning methods (e.g., support vector machine) (218). A handful of studies have used rtfMRI neurofeedback in SUD [for a review, see (12)].…”

An artistic approach to neurofeedback for emotion regulation

“…In the context of emotions and the management of emotional disorders, representing feedback related to the pathology is much more complex because it raises the question about the possibility to represent visually or auditorily an emotion. Since emotion is a central part of people's dealings with artworks, first approaches have been tested in this direction, for example with color schemes that vary when one must feel tenderness or anxiety (Lorenzetti et al 2018). Ramirez and colleagues performed a musical neurofeedback task for treating depression in elderly people.…”