Emotion Regulation Using Virtual Environments and Real-Time fMRI Neurofeedback

When virtual reality becomes psychoneuroendocrine reality: A stress(or) review

This review article was awarded the Dirk Hellhammer award from ISPNE in 2023. It explores the dynamic relationship between stressors and stress from a historical view as well as a vision towards the future of stress research via virtual reality (VR). We introduce the concept of a "syncytium," a permeable boundary that blurs the distinction between stress and stressor, in order to understand why the field of stress biology continues to inadequately measure stress alone as a proxy for the force of external stressors. Using Virtual Reality (VR) as an illustrative example to explicate the black box of stressors, we examine the distinction between 'immersion' and 'presence' as analogous terms for stressor and stress, respectively. We argue that the conventional psychological approaches to stress measurement and appraisal theory unfortunately fall short in quantifying the force of the stressor, leading to reverse causality fallacies. Further, the concept of affordances is introduced as an ecological or holistic tool to measure and design a stressor's force, bridging the gap between the external environment and an individual's physiological response to stress. Affordances also serve to ameliorate shortcomings in stress appraisal by integrating ecological interdependencies. By combining VR and psychobiological measures, this paper aims to unravel the complexity of the stressor-stress syncytium, highlighting the necessity of assessing both the internal and external facets to gain a holistic understanding of stress physiology and shift away from reverse causality reasoning. We find that the utility of VR extends beyond presence to include affordance-based measures of immersion, which can effectively model stressor force. Future research should prioritize the development of tools that can measure both immersion and presence, thereby providing a more comprehensive understanding of how external stressors interact with individual psychological states.

Neural underpinnings of individual differences in emotion regulation: A systematic review

This review synthesises individual differences in neural processes related to emotion regulation (ER). It comprises individual differences in self-reported and physiological regulation success, self-reported ER-related traits, and demographic variables, to assess their correlation with brain activation during ER tasks. Considering region-of-interest (ROI) and whole-brain analyses, the review incorporated data from 52 functional magnetic resonance imaging studies. Results can be summarized as follows: (1) Self-reported regulation success (assessed by emotional state ratings after regulation) and self-reported ER-related traits (assessed by questionnaires) correlated with brain activity in the lateral prefrontal cortex. (2) Amygdala activation correlated with ER-related traits only in ROI analyses, while it was associated with regulation success in whole-brain analyses. (3) For demographic and physiological measures, there was no systematic overlap in effects reported across studies. In showing that individual differences in regulation success and ER-related traits can be traced back to differences in the neural activity of brain regions associated with emotional reactivity (amygdala) and cognitive control (lateral prefrontal cortex), our findings can inform prospective personalised intervention models.

CAEVR: Biosignals-Driven Context-Aware Empathy in Virtual Reality

There is little research on how Virtual Reality (VR) applications can identify and respond meaningfully to users' emotional changes. In this paper, we investigate the impact of Context-Aware Empathic VR (CAEVR) on the emotional and cognitive aspects of user experience in VR. We developed a real-time emotion prediction model using electroencephalography (EEG), electrodermal activity (EDA), and heart rate variability (HRV) and used this in personalized and generalized models for emotion recognition. We then explored the application of this model in a context-aware empathic (CAE) virtual agent and an emotion-adaptive (EA) VR environment. We found a significant increase in positive emotions, cognitive load, and empathy toward the CAE agent, suggesting the potential of CAEVR environments to refine user-agent interactions. We identify lessons learned from this study and directions for future work.

Association between resting-state connectivity patterns in the defensive system network and treatment response in spider phobia-a replication approach

Although highly effective on average, exposure-based treatments do not work equally well for all patients with anxiety disorders. The identification of pre-treatment response-predicting patient characteristics may enable patient stratification. Preliminary research highlights the relevance of inhibitory fronto-limbic networks as such. We aimed to identify pre-treatment neural signatures differing between exposure treatment responders and non-responders in spider phobia and to validate results through rigorous replication. Data of a bi-centric intervention study comprised clinical phenotyping and pre-treatment resting-state functional connectivity (rsFC) data of n = 79 patients with spider phobia (discovery sample) and n = 69 patients (replication sample). RsFC data analyses were accomplished using the Matlab-based CONN-toolbox with harmonized analyses protocols at both sites. Treatment response was defined by a reduction of >30% symptom severity from pre- to post-treatment (Spider Phobia Questionnaire Score, primary outcome). Secondary outcome was defined by a reduction of >50% in a Behavioral Avoidance Test (BAT). Mean within-session fear reduction functioned as a process measure for exposure. Compared to non-responders and pre-treatment, results in the discovery sample seemed to indicate that responders exhibited stronger negative connectivity between frontal and limbic structures and were characterized by heightened connectivity between the amygdala and ventral visual pathway regions. Patients exhibiting high within-session fear reduction showed stronger excitatory connectivity within the prefrontal cortex than patients with low within-session fear reduction. Whereas these results could be replicated by another team using the same data (cross-team replication), cross-site replication of the discovery sample findings in the independent replication sample was unsuccessful. Results seem to support negative fronto-limbic connectivity as promising ingredient to enhance response rates in specific phobia but lack sufficient replication. Further research is needed to obtain a valid basis for clinical decision-making and the development of individually tailored treatment options. Notably, future studies should regularly include replication approaches in their protocols.

Neurofeedback and Affect Regulation Circuitry in Depressed and Healthy Adolescents

Neurodevelopmental psychopathology seeks to understand higher-order emotion regulation circuitry to develop new therapies for adolescents with depression. Depressed (N = 34) and healthy youth (N = 19) completed neurofeedback (NF) training and exhibited increased bilateral amygdala and hippocampus activity in the region of interest (ROI) analyses by recalling positive autobiographical memories. We tested factors supportive of the engagement of emotion regulation's neural areas during NF (i.e., parental support, medication, and gender effects upon anterior cingulate cortex (ACC) engagement). Whole-brain analyses yielded effects of NF vs. control condition and effects of diagnosis. Youth showed higher amygdala and hippocampus (AMYHIPPO) activity during the NF vs. control condition, particularly in the left hippocampus. ACC's activity was also higher during NF vs. control. Higher average ACC activity was linked to better parental support, absent depression, female gender, and absent medication. Control youth showed higher average AMYHIPPO and ACC activity throughout the task and a faster decline in activity vs. depressed youths. Whole-brain level analyses showed higher activity in the frontotemporal network during the NF vs. control conditions, suggesting targeting their connectivity in future neurofeedback trials.

Selecting an optimal real-time fMRI neurofeedback method for alcohol craving control training

Real-time fMRI neurofeedback (rt-fMRI-NF) is a technique in which information about an individual's neural state is given back to them, typically to enable and reinforce neuromodulation. Its clinical potential has been demonstrated in several applications, but lack of evidence on optimal parameters limits clinical utility of the technique. This study aimed to identify optimal parameters for rt-fMRI-NF-aided craving regulation training in alcohol use disorder (AUD). Adults with AUD (n = 30) participated in a single-session study of four runs of rt-fMRI-NF where they downregulated "craving-related" brain activity. They received one of three types of neurofeedback: multi-region of interest (ROI), support vector machine with continuous feedback (cSVM), and support vector machine with intermittent feedback (iSVM). Performance was assessed on the success rate, change in neural downregulation, and change in self-reported craving for alcohol. Participants had more successful trials in run 4 versus 1, as well as improved downregulation of the insula, anterior cingulate, and dorsolateral prefrontal cortex (dlPFC). Greater downregulation of the latter two regions predicted greater reduction in craving. iSVM performed significantly worse than the other two methods. Downregulation of the striatum and dlPFC, enabled by ROI but not cSVM neurofeedback, was correlated with a greater reduction in craving. rt-fMRI-NF training for downregulation of alcohol craving in individuals with AUD shows potential for clinical use, though this pilot study should be followed with a larger randomized-control trial before clinical meaningfulness can be established. Preliminary results suggest an advantage of multi-ROI over SVM and intermittent feedback approaches.

"Managing emotion": Open label trial and waitlist controlled trial of an emotion regulation program for university students

Background: More than one-third of university students meet diagnostic criteria for a mental disorder, and three quarters experience role impairment in some aspect of their life. One determinant of whether young adults will experience mental health difficulties is their ability to regulate emotion. We conducted two pilot trials of a brief online program designed to teach emotion regulation skills to university students. Methods: In Study 1, we conducted an open-label trial (n = 104). In Study 2, we conducted a waitlist controlled trial (n = 167). In both studies, pre- and post-assessment of emotion regulation, psychological distress, and self-compassion were conducted. Results: In both trials, we observed improvements in emotion regulation, and reductions in symptoms of psychological distress. Acceptability and feasibility were also satisfactory. Conclusion: An online emotion regulation program may offer promise in improving emotion regulation and subsequent mental health concerns among university students. (ACTRN12620000390987; ACTRN12620000839909).

One session of fMRI-Neurofeedback training on motor imagery modulates whole-brain effective connectivity and dynamical complexity

In the past decade, several studies have shown that Neurofeedback (NFB) by functional magnetic resonance imaging can alter the functional coupling of targeted and non-targeted areas. However, the causal mechanisms underlying these changes remain uncertain. Here, we applied a whole-brain dynamical model to estimate Effective Connectivity (EC) profiles of resting-state data acquired before and immediately after a single-session NFB training for 17 participants who underwent motor imagery NFB training and 16 healthy controls who received sham feedback. Within-group and between-group classification analyses revealed that only for the NFB group it was possible to accurately discriminate between the 2 resting-state sessions. NFB training-related signatures were reflected in a support network of direct connections between areas involved in reward processing and implicit learning, together with regions belonging to the somatomotor, control, attention, and default mode networks, identified through a recursive-feature elimination procedure. By applying a data-driven approach to explore NFB-induced changes in spatiotemporal dynamics, we demonstrated that these regions also showed decreased switching between different brain states (i.e. metastability) only following real NFB training. Overall, our findings contribute to the understanding of NFB impact on the whole brain’s structure and function by shedding light on the direct connections between brain areas affected by NFB training.

Enhanced processing of aversive stimuli on embodied artificial limbs by the human amygdala

Body perception has been extensively investigated, with one particular focus being the integration of vision and touch within a neuronal body representation. Previous studies have implicated a distributed network comprising the extrastriate body area (EBA), posterior parietal cortex (PPC) and ventral premotor cortex (PMv) during illusory self-attribution of a rubber hand. Here, we set up an fMRI paradigm in virtual reality (VR) to study whether and how the self-attribution of (artificial) body parts is altered if these body parts are somehow threatened. Participants (N = 30) saw a spider (aversive stimulus) or a toy-car (neutral stimulus) moving along a 3D-rendered virtual forearm positioned like their real forearm, while tactile stimulation was applied on the real arm in the same (congruent) or opposite (incongruent) direction. We found that the PPC was more activated during congruent stimulation; higher visual areas and the anterior insula (aIns) showed increased activation during aversive stimulus presentation; and the amygdala was more strongly activated for aversive stimuli when there was stronger multisensory integration of body-related information (interaction of aversiveness and congruency). Together, these findings suggest an enhanced processing of aversive stimuli within the amygdala when they represent a bodily threat.

Real-Time Functional MRI in the Treatment of Mental Health Disorders

Multiple mental disorders have been associated with dysregulation of precise brain processes. However, few therapeutic approaches can correct such specific patterns of brain activity. Since the late 1960s and early 1970s, many researchers have hoped that this feat could be achieved by closed-loop brain imaging approaches, such as neurofeedback, that aim to modulate brain activity directly. However, neurofeedback never gained mainstream acceptance in mental health, in part due to methodological considerations. In this review, we argue that, when contemporary methodological guidelines are followed, neurofeedback is one of the few intervention methods in psychology that can be assessed in double-blind placebo-controlled trials. Furthermore, using new advances in machine learning and statistics, it is now possible to target very precise patterns of brain activity for therapeutic purposes. We review the recent literature in functional magnetic resonance imaging neurofeedback and discuss current and future applications to mental health.

Expected final online publication date for the Annual Review of Clinical Psychology, Volume 18 is May 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Digital Technologies for Emotion-Regulation Assessment and Intervention: A Conceptual Review

The ability to regulate emotions in response to stress is central to healthy development. While early research in emotion regulation predominantly employed static, self-report measurement, the past decade has seen a shift in focus toward understanding the dynamic nature of regulation processes. This is reflected in recent refinements in the definition of emotion regulation, which emphasize the importance of the ability to flexibly adapt regulation efforts across contexts. The latest proliferation of digital technologies employed in mental health research offers the opportunity to capture the state- and context-sensitive nature of emotion regulation. In this conceptual review, we examine the use of digital technologies (ecological momentary assessment; wearable and smartphone technology, physical activity, acoustic data, visual data, and geo-location; smart home technology; virtual reality; social media) in the assessment of emotion regulation and describe their application to interventions. We also discuss challenges and ethical considerations, and outline areas for future research.

Research on the Emotions Based on Brain-Computer Technology: A Bibliometric Analysis and Research Agenda

This study conducts a scientific analysis of 249 literature on the application of brain-computer technology in emotion research. We find that existing researches mainly focus on engineering, computer science, neurosciences neurology and psychology. PR China, United States, and Germany have the largest number of publications. Authors can be divided into four groups: real-time functional magnetic resonance imaging (rtfMRI) research group, brain-computer interface (BCI) impact factors analysis group, brain-computer music interfacing (BCMI) group, and user status research group. Clustering results can be divided into five categories, including external stimulus and event-related potential (ERP), electroencephalography (EEG), and information collection, support vector machine (SVM) and information processing, deep learning and emotion recognition, neurofeedback, and self-regulation. Based on prior researches, this study points out that individual differences, privacy risk, the extended study of BCI application scenarios and others deserve further research.

Classification of Complex Emotions Using EEG and Virtual Environment: Proof of Concept and Therapeutic Implication

During the last decades, neurofeedback training for emotional self-regulation has received significant attention from scientific and clinical communities. Most studies have investigated emotions using functional magnetic resonance imaging (fMRI), including the real-time application in neurofeedback training. However, the electroencephalogram (EEG) is a more suitable tool for therapeutic application. Our study aims at establishing a method to classify discrete complex emotions (e.g., tenderness and anguish) elicited through a near-immersive scenario that can be later used for EEG-neurofeedback. EEG-based affective computing studies have mainly focused on emotion classification based on dimensions, commonly using passive elicitation through single-modality stimuli. Here, we integrated both passive and active elicitation methods. We recorded electrophysiological data during emotion-evoking trials, combining emotional self-induction with a multimodal virtual environment. We extracted correlational and time-frequency features, including frontal-alpha asymmetry (FAA), using Complex Morlet Wavelet convolution. Thinking about future real-time applications, we performed within-subject classification using 1-s windows as samples and we applied trial-specific cross-validation. We opted for a traditional machine-learning classifier with low computational complexity and sufficient validation in online settings, the Support Vector Machine. Results of individual-based cross-validation using the whole feature sets showed considerable between-subject variability. The individual accuracies ranged from 59.2 to 92.9% using time-frequency/FAA and 62.4 to 92.4% using correlational features. We found that features of the temporal, occipital, and left-frontal channels were the most discriminative between the two emotions. Our results show that the suggested pipeline is suitable for individual-based classification of discrete emotions, paving the way for future personalized EEG-neurofeedback training.

EEG Markers in Emotionally Unstable Personality Disorder-A Possible Outcome Measure for Neurofeedback: A Narrative Review

Objectives. There is growing evidence for the use of biofeedback (BF) in affective disorders, dissocial personality disorder, and in children with histories of abuse. Electroencephalogram (EEG) markers could be used as neurofeedback in emotionally unstable personality disorder (EUPD) management especially for those at high risk of suicide when emotionally aroused. This narrative review investigates the evidence for EEG markers in EUPD. Methods. PRISMA guidelines were used to conduct a narrative review. A structured search method was developed and implemented in collaboration with an information specialist. Studies were identified via 3 electronic database searches of MEDLINE, Embase, and PsycINFO. A predesigned inclusion/exclusion criterion was applied to selected papers. A thematic analysis approach with 5 criteria was used. Results. From an initial long list of 5250 papers, 229 studies were identified and screened, of which 44 met at least 3 of the predesigned inclusion criteria. No research to date investigates EEG-based neurofeedback in EUPD. A number of different EEG biomarkers are identified but there is poor consistency between studies. Conclusions. The findings heterogeneity may be due to the disorder complexity and the variable EEG related parameters studied. An alternative explanation may be that there are a number of different neuromarkers, which could be clustered together with clinical symptomatology, to give new subdomains. Quantitative EEGs in particular may be helpful to identify more specific abnormalities. EEG standardization of neurofeedback protocols based on specific EEG abnormalities detected may facilitate targeted use of neurofeedback as an intervention in EUPD.

Affective Neurofeedback Under Naturalistic Conditions: A Mini-Review of Current Achievements and Open Challenges

Affective neurofeedback training allows for the self-regulation of the putative circuits of emotion regulation. This approach has recently been studied as a possible additional treatment for psychiatric disorders, presenting positive effects in symptoms and behaviors. After neurofeedback training, a critical aspect is the transference of the learned self-regulation strategies to outside the laboratory and how to continue reinforcing these strategies in non-controlled environments. In this mini-review, we discuss the current achievements of affective neurofeedback under naturalistic setups. For this, we first provide a brief overview of the state-of-the-art for affective neurofeedback protocols. We then discuss virtual reality as a transitional step toward the final goal of "in-the-wild" protocols and current advances using mobile neurotechnology. Finally, we provide a discussion of open challenges for affective neurofeedback protocols in-the-wild, including topics such as convenience and reliability, environmental effects in attention and workload, among others.

Brain-Computer Interfaces, Open-Source, and Democratizing the Future of Augmented Consciousness

Accessibility, adaptability, and transparency of Brain-Computer Interface (BCI) tools and the data they collect will likely impact how we collectively navigate a new digital age. This discussion reviews some of the diverse and transdisciplinary applications of BCI technology and draws speculative inferences about the ways in which BCI tools, combined with machine learning (ML) algorithms may shape the future. BCIs come with substantial ethical and risk considerations, and it is argued that open source principles may help us navigate complex dilemmas by encouraging experimentation and making developments public as we build safeguards into this new paradigm. Bringing open-source principles of adaptability and transparency to BCI tools can help democratize the technology, permitting more voices to contribute to the conversation of what a BCI-driven future should look like. Open-source BCI tools and access to raw data, in contrast to black-box algorithms and limited access to summary data, are critical facets enabling artists, DIYers, researchers and other domain experts to participate in the conversation about how to study and augment human consciousness. Looking forward to a future in which augmented and virtual reality become integral parts of daily life, BCIs will likely play an increasingly important role in creating closed-loop feedback for generative content. Brain-computer interfaces are uniquely situated to provide artificial intelligence (AI) algorithms the necessary data for determining the decoding and timing of content delivery. The extent to which these algorithms are open-source may be critical to examine them for integrity, implicit bias, and conflicts of interest.

Converging Robotic Technologies in Targeted Neural Rehabilitation: A Review of Emerging Solutions and Challenges

Recent advances in the field of neural rehabilitation, facilitated through technological innovation and improved neurophysiological knowledge of impaired motor control, have opened up new research directions. Such advances increase the relevance of existing interventions, as well as allow novel methodologies and technological synergies. New approaches attempt to partially overcome long-term disability caused by spinal cord injury, using either invasive bridging technologies or noninvasive human-machine interfaces. Muscular dystrophies benefit from electromyography and novel sensors that shed light on underlying neuromotor mechanisms in people with Duchenne. Novel wearable robotics devices are being tailored to specific patient populations, such as traumatic brain injury, stroke, and amputated individuals. In addition, developments in robot-assisted rehabilitation may enhance motor learning and generate movement repetitions by decoding the brain activity of patients during therapy. This is further facilitated by artificial intelligence algorithms coupled with faster electronics. The practical impact of integrating such technologies with neural rehabilitation treatment can be substantial. They can potentially empower nontechnically trained individuals-namely, family members and professional carers-to alter the programming of neural rehabilitation robotic setups, to actively get involved and intervene promptly at the point of care. This narrative review considers existing and emerging neural rehabilitation technologies through the perspective of replacing or restoring functions, enhancing, or improving natural neural output, as well as promoting or recruiting dormant neuroplasticity. Upon conclusion, we discuss the future directions for neural rehabilitation research, diagnosis, and treatment based on the discussed technologies and their major roadblocks. This future may eventually become possible through technological evolution and convergence of mutually beneficial technologies to create hybrid solutions.

Emotional regulation in eating disorders and gambling disorder: A transdiagnostic approach

BACKGROUND AND AIMS

Difficulties in Emotion Regulation (ER) are related to the etiology and maintenance of several psychological disorders, including Eating Disorders (ED) and Gambling Disorder (GD). This study explored the existence of latent empirical groups between both disorders, based on ER difficulties and considering a set of indicators of personality traits, the severity of the disorder, and psychopathological distress.

METHODS

The sample included 1,288 female and male participants, diagnosed with ED (n = 906) and GD (n = 382). Two-step clustering was used for the empirical classification, while analysis of variance and chi-square tests were used for the comparison between the latent groups.

RESULTS

Three empirical groups were identified, from the most disturbed ER profile (Subgroup 1) to the most functional (Subgroup 3). The ER state showed a linear relationship with the severity of each disorder and the psychopathological state. Different personality traits were found to be related to the level of emotion dysregulation.

DISCUSSION AND CONCLUSION

In this study, three distinct empirical groups based on ER were identified across ED and GD, suggesting that ER is a transdiagnostic construct. These findings may lead to the development of common treatment strategies and more tailored approaches.

The Current Evidence Levels for Biofeedback and Neurofeedback Interventions in Treating Depression: A Narrative Review

This article is aimed at showing the current level of evidence for the usage of biofeedback and neurofeedback to treat depression along with a detailed review of the studies in the field and a discussion of rationale for utilizing each protocol. La Vaque et al. criteria endorsed by the Association for Applied Psychophysiology and Biofeedback and International Society for Neuroregulation & Research were accepted as a means of study evaluation. Heart rate variability (HRV) biofeedback was found to be moderately supportable as a treatment of MDD while outcome measure was a subjective questionnaire like Beck Depression Inventory (level 3/5, “probably efficacious”). Electroencephalographic (EEG) neurofeedback protocols, namely, alpha-theta, alpha, and sensorimotor rhythm upregulation, all qualify for level 2/5, “possibly efficacious.” Frontal alpha asymmetry protocol also received limited evidence of effect in depression (level 2/5, “possibly efficacious”). Finally, the two most influential real-time functional magnetic resonance imaging (rt-fMRI) neurofeedback protocols targeting the amygdala and the frontal cortices both demonstrate some effectiveness, though lack replications (level 2/5, “possibly efficacious”). Thus, neurofeedback specifically targeting depression is moderately supported by existing studies (all fit level 2/5, “possibly efficacious”). The greatest complication preventing certain protocols from reaching higher evidence levels is a relatively high number of uncontrolled studies and an absence of accurate replications arising from the heterogeneity in protocol details, course lengths, measures of improvement, control conditions, and sample characteristics.

A Novel Brain-Computer Interface Virtual Environment for Neurofeedback During Functional MRI

Virtual environments (VEs), in the recent years, have become more prevalent in neuroscience. These VEs can offer great flexibility, replicability, and control over the presented stimuli in an immersive setting. With recent developments, it has become feasible to achieve higher-quality visuals and VEs at a reasonable investment. Our aim in this project was to develop and implement a novel real-time functional magnetic resonance imaging (rt-fMRI)-based neurofeedback (NF) training paradigm, taking into account new technological advances that allow us to integrate complex stimuli into a visually updated and engaging VE. We built upon and developed a first-person shooter in which the dynamic change of the VE was the feedback variable in the brain-computer interface (BCI). We designed a study to assess the feasibility of the BCI in creating an immersive VE for NF training. In a randomized single-blinded fMRI-based NF-training session, 24 participants were randomly allocated into one of two groups: active and reduced contingency NF. All participants completed three runs of the shooter-game VE lasting 10 min each. Brain activity in a supplementary motor area region of interest regulated the possible movement speed of the player's avatar and thus increased the reward probability. The gaming performance revealed that the participants were able to actively engage in game tasks and improve across sessions. All 24 participants reported being able to successfully employ NF strategies during the training while performing in-game tasks with significantly higher perceived NF control ratings in the NF group. Spectral analysis showed significant differential effects on brain activity between the groups. Connectivity analysis revealed significant differences, showing a lowered connectivity in the NF group compared to the reduced contingency-NF group. The self-assessment manikin ratings showed an increase in arousal in both groups but failed significance. Arousal has been linked to presence, or feelings of immersion, supporting the VE's objective. Long paradigms, such as NF in MRI settings, can lead to mental fatigue; therefore, VEs can help overcome such limitations. The rewarding achievements from gaming targets can lead to implicit learning of self-regulation and may broaden the scope of NF applications.

Putamen volume predicts real-time fMRI neurofeedback learning success across paradigms and neurofeedback target regions

Real-time fMRI guided neurofeedback training has gained increasing interest as a noninvasive brain regulation technique with the potential to modulate functional brain alterations in therapeutic contexts. Individual variations in learning success and treatment response have been observed, yet the neural substrates underlying the learning of self-regulation remain unclear. Against this background, we explored potential brain structural predictors for learning success with pooled data from three real-time fMRI data sets. Our analysis revealed that gray matter volume of the right putamen could predict neurofeedback learning success across the three data sets (n = 66 in total). Importantly, the original studies employed different neurofeedback paradigms during which different brain regions were trained pointing to a general association with learning success independent of specific aspects of the experimental design. Given the role of the putamen in associative learning this finding may reflect an important role of instrumental learning processes and brain structural variations in associated brain regions for successful acquisition of fMRI neurofeedback-guided self-regulation.

Clinical Application of Real-Time fMRI-Based Neurofeedback for Depression

Real-time functional magnetic resonance imaging-based neurofeedback (rt-fMRI NF) is a recent technique used to train self-regulation of circumscribed brain areas or networks. For clinical applications in depression, NF training targets brain areas with disturbed activation patterns, such as heightened reactivity of amygdala in response to negative stimuli, in order to normalize the neurophysiology and their behavioral correlates. Recent studies have targeted emotion processing areas such as the amygdala, the salience network, and top-down control areas such as the lateral prefrontal cortex. Different methods of rt-fMRI-based NF in depression, their potential for clinical improvement, and most recent advancements of this technology are discussed considering their role for future clinical applications. Initial findings of randomized controlled trials show promising results. However, for lasting treatment effects, clinical efficiency and optimal target regions, tasks, control conditions, and duration of training need to be established.

Prevent breaking bad: A proof of concept study of rebalancing the brain's rumination circuit with real-time fMRI functional connectivity neurofeedback

Rumination, repetitively thinking about the causes, consequences, and one's negative affect, has been considered as an important factor of depression. The intrusion of ruminative thoughts is not easily controlled, and it may be useful to visualize one's neural activity related to rumination and to use that information to facilitate one's self‐control. Real‐time fMRI neurofeedback (rtfMRI‐nf) enables one to see and regulate the fMRI signal from their own brain. This proof‐of concept study utilized connectivity‐based rtfMRI‐nf (cnf) to normalize brain functional connectivity (FC) associated with rumination. Healthy participants were instructed to brake or decrease FC between the precuneus and the right temporoparietal junction (rTPJ), associated with high levels of rumination, while engaging in a self‐referential task. The cnf group (n = 14) showed a linear decrease in the precuneus‐rTPJ FC across neurofeedback training (trend [112] = −0.180, 95% confidence interval [CI] −0.330 to −0.031, while the sham group (n = 14) showed a linear increase in the target FC (trend [112] = 0.151, 95% CI 0.017 to 0.299). Although the cnf group showed a greater reduction in state‐rumination compared to the sham group after neurofeedback training (p < .05), decoupled precuneus‐rTPJ FC did not predict attenuated state‐rumination. We did not find any significant aversive effects of rtfMRI‐nf in all study participants. These results suggest that cnf has the capacity to influence FC among precuneus and rTPJ of a ruminative brain circuit. This approach can be applied to mood and anxiety patients to determine the clinical benefits of reduction in maladaptive rumination.

EEG-Based Emotion Recognition: A State-of-the-Art Review of Current Trends and Opportunities

Emotions are fundamental for human beings and play an important role in human cognition. Emotion is commonly associated with logical decision making, perception, human interaction, and to a certain extent, human intelligence itself. With the growing interest of the research community towards establishing some meaningful "emotional" interactions between humans and computers, the need for reliable and deployable solutions for the identification of human emotional states is required. Recent developments in using electroencephalography (EEG) for emotion recognition have garnered strong interest from the research community as the latest developments in consumer-grade wearable EEG solutions can provide a cheap, portable, and simple solution for identifying emotions. Since the last comprehensive review was conducted back from the years 2009 to 2016, this paper will update on the current progress of emotion recognition using EEG signals from 2016 to 2019. The focus on this state-of-the-art review focuses on the elements of emotion stimuli type and presentation approach, study size, EEG hardware, machine learning classifiers, and classification approach. From this state-of-the-art review, we suggest several future research opportunities including proposing a different approach in presenting the stimuli in the form of virtual reality (VR). To this end, an additional section devoted specifically to reviewing only VR studies within this research domain is presented as the motivation for this proposed new approach using VR as the stimuli presentation device. This review paper is intended to be useful for the research community working on emotion recognition using EEG signals as well as for those who are venturing into this field of research.

A Pilot Adaptive Neurofeedback Investigation of the Neural Mechanisms of Implicit Emotion Regulation Among Women With PTSD

Posttraumatic stress disorder (PTSD) is widely associated with deficits in implicit emotion regulation. Recently, adaptive fMRI neurofeedback (A-NF) has been developed as a methodology that offers a unique probe of brain networks that mediate implicit emotion regulation and their impairment in PTSD. We designed an A-NF paradigm in which difficulty of an emotional conflict task (i.e., embedding trauma distractors onto a neutral target stimulus) was controlled by a whole-brain classifier trained to differentiate attention to the trauma distractor vs. target. We exploited this methodology to test whether PTSD was associated with: (1) an altered brain state that differentiates attention towards vs. away from trauma cues; and (2) an altered ability to use concurrent feedback about brain states during an implicit emotion regulation task. Adult women with a current diagnosis of PTSD (n = 10) and healthy control (n = 9) women participated in this task during 3T fMRI. During two initial non-feedback runs used to train a whole-brain classifier, we observed: (1) poorer attention performance in PTSD; and (2) a linear relationship between brain state discrimination and attention performance, which was significantly attenuated among the PTSD group when the task contained trauma cues. During the A-NF phase, the PTSD group demonstrated poorer ability to regulate brain states as per attention instructions, and this poorer ability was related to PTSD symptom severity. Further, PTSD was associated with the heightened encoding of feedback in the insula and hippocampus. These results suggest a novel understanding of whole-brain states and their regulation that underlie emotion regulation deficits in PTSD.

Reinforcement and Punishment Shape the Learning Dynamics in fMRI Neurofeedback

Introduction

Neurofeedback (NF) using real-time functional magnetic resonance imaging (fMRI) has proven to be a valuable neuroscientific tool for probing cognition and promising therapeutic approach for several psychiatric disorders. Even though learning constitutes an elementary aspect of NF, the question whether certain training schemes might positively influence its dynamics has largely been neglected.

Methods

To address this issue, participants were trained to exert control on their subgenual anterior cingulate cortex (sgACC) blood-oxygenation-level-dependent signal, receiving either exclusively positive reinforcement (PR, “positive feedback”) or also positive punishment (PP, “negative feedback”). The temporal dynamics of the learning process were investigated by individually modeling the feedback periods and trends, offering the possibility to assess activation changes within and across blocks, runs and sessions.

Results

The results show faster initial learning of the PR + PP group by significantly lower deactivations of the sgACC in the first session and stronger regulation trends during the first runs. Independent of the group, significant control over the sgACC could further be shown with but not without feedback.

Conclusion

The beneficial effect of PP is supported by previous findings of multiple research domains suggesting that error avoidance represents an important motivational factor of learning, which complements the reward spectrum. This hypothesis warrants further investigation with respect to NF, as it could offer a way to generally facilitate the process of gaining volitional control over brain activity.

A Guide to Literature Informed Decisions in the Design of Real Time fMRI Neurofeedback Studies: A Systematic Review

Background: Although biofeedback using electrophysiology has been explored extensively, the approach of using neurofeedback corresponding to hemodynamic response is a relatively young field. Real time functional magnetic resonance imaging-based neurofeedback (rt-fMRI-NF) uses sensory feedback to operantly reinforce patterns of neural response. It can be used, for example, to alter visual perception, increase brain connectivity, and reduce depression symptoms. Within recent years, interest in rt-fMRI-NF in both research and clinical contexts has expanded considerably. As such, building a consensus regarding best practices is of great value. Objective: This systematic review is designed to describe and evaluate the variations in methodology used in previous rt-fMRI-NF studies to provide recommendations for rt-fMRI-NF study designs that are mostly likely to elicit reproducible and consistent effects of neurofeedback. Methods: We conducted a database search for fMRI neurofeedback papers published prior to September 26th, 2019. Of 558 studies identified, 146 met criteria for inclusion. The following information was collected from each study: sample size and type, task used, neurofeedback calculation, regulation procedure, feedback, whether feedback was explicitly related to changing brain activity, feedback timing, control group for active neurofeedback, how many runs and sessions of neurofeedback, if a follow-up was conducted, and the results of neurofeedback training. Results: rt-fMRI-NF is typically upregulation practice based on hemodynamic response from a specific region of the brain presented using a continually updating thermometer display. Most rt-fMRI-NF studies are conducted in healthy samples and half evaluate its effect on immediate changes in behavior or affect. The most popular control group method is to provide sham signal from another region; however, many studies do not compare use a comparison group. Conclusions: We make several suggestions for designs of future rt-fMRI-NF studies. Researchers should use feedback calculation methods that consider neural response across regions (i.e., SVM or connectivity), which should be conveyed as intermittent, auditory feedback. Participants should be given explicit instructions and should be assessed on individual differences. Future rt-fMRI-NF studies should use clinical samples; effectiveness of rt-fMRI-NF should be evaluated on clinical/behavioral outcomes at follow-up time points in comparison to both a sham and no feedback control group.

The Benefits of emotion Regulation Interventions in Virtual Reality for the Improvement of Wellbeing in Adults and Older Adults: A Systematic Review

The impact of emotion regulation interventions on wellbeing has been extensively documented in literature, although only in recent years virtual reality (VR) technologies have been incorporated in the design of such interventions, in both clinical and non-clinical settings. A systematic search, following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines, was therefore carried out to explore the state of the art in emotion regulation interventions for wellbeing using virtual reality. The literature on this topic was queried, 414 papers were screened, and 11 studies were included, covering adults and older adults. Our findings offer an overview of the current use of VR technologies for the enhancement of emotion regulation (ER) and wellbeing. The results are promising and suggest that VR-based emotion regulation training can facilitate the promotion of wellbeing. An overview of VR-based training interventions is crucial for better understanding how to use these tools in the clinical settings. This review offers a critical debate on the structure of such intervention protocols. It also analyzes and highlights the crucial role played by the selection of the objective and subjective wellbeing assessment measures of said intervention protocols.

Virtual Reality for Anxiety Disorders: Rethinking a Field in Expansion

The principal aim to this chapter is to present the latest ideas in virtual reality (VR), some of which have already been applied to the field of anxiety disorders, and others are still pending to be materialized. More than 20 years ago, VR emerged as an exposure tool in order to provide patients and therapists with more appealing ways of delivering a technique that was undoubtedly effective but also rejected and thus underused. Throughout these years, many improvements were achieved. The first section of the chapter describes those improvements, both considering the research progresses and the applications in the real world. In a second part, our main interest is to expand the discussion of the new applications of VR beyond its already known role as an exposure tool. In particular, VR is enabling the materialization of numerous ideas that were previously confined to a merely philosophical discussion in the field of cognitive sciences. That is, VR has the enormous potential of providing feasible ways to explore nonclassical ways of cognition, such as embodied and situated information processing. Despite the fact that many of these developments are not fully developed, and not specifically designed for anxiety disorders, we want to introduce these new ideas in a context in which VR is experiencing an enormous transformation.

Mental Control in Musical Imagery: A Dual Component Model

Hearing music in your head is a ubiquitous experience, but the role mental control plays in these experiences has not been deeply addressed. In this conceptual analysis, a dual-component model of mental control in musical imagery experiences is developed and discussed. The first component, initiation, refers to whether the musical imagery experience began voluntarily or involuntarily. The second component, management, refers to instances of control that occur after the experience has begun (e.g., changing the song, stopping the experience). Given the complex nature of this inner experience, we propose a new model combining and integrating four literatures: lab-based auditory imagery research using musical stimuli; involuntary musical imagery; mental rehearsal and composition in musicians; and in vivo studies of musical imagery in everyday environments. These literatures support the contention that mental control of musical imagery is multi-faceted. Future research should investigate these two components of mental control and better integrate the diverse literatures on musical imagery.

New Technologies for the Understanding, Assessment, and Intervention of Emotion Regulation

In the last decades, emotion regulation (ER) received increasing attention and became one of the most studied topics within the psychological field. Nevertheless, this construct has not been fully updated with the latest technological advancements. In this perspective, we will show how diverse technologies, such as virtual reality (VR), wearable biosensors, smartphones, or biofeedback techniques, can be applied to the understanding, assessment, and intervention of ER. After providing a brief overview of the currently available technological developments, we will discuss the benefits of incorporating new technologies in ER field, including ecological validity, intervention personalization, and the integration of understudied facets of ER, such as the implicit and interpersonal dimension.

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

Increased attention among the research community in exploring underlying mechanisms of emotion regulation has prompted a growth of experimental works in this field. Empirical studies have mainly focused on self-reports, brain imaging, and electrophysiological measures, with only a few works exploring peripheral physiology. Additionally, most of such studies have not considered the specific stimuli content, even though prior literature has shown relevant differences in psychophysiological and subjective responses depending on picture categories. The current study assessed several peripheral correlates (startle amplitude, electrodermal changes, heart rate) of emotion regulation processes in a sample of 122 healthy participants. The task consisted of voluntary reappraisal of negative emotions prompted by unpleasant pictures (threat to others and victims), compared to a nonregulation control condition (looking at exemplars of the same categories and household objects). Results showed an effect of emotion regulation instructions in all psychophysiological and subjective measures. In peripheral physiology, greater responses were observed specifically when increasing negative emotions, concurring with previous research. Regarding specific content, our findings evidence a similar emotion regulation pattern, independently of the unpleasant category, suggesting a plausible effect of cognitive variables (such as cognitive effort) during voluntary reappraisal for both categories.

Amygdala Modulation During Emotion Regulation Training With fMRI-Based Neurofeedback

Available evidence suggests that individuals can enhance their ability to modulate brain activity in target regions, within the Emotion Regulation network, using fMRI-based neurofeedback. However, there is no systematic review that investigates the effectiveness of this method on amygdala modulation, a core region within this network. The major goal of this study was to systematically review and analyze the effects of real-time fMRI-Neurofeedback concerning the neuromodulation of the amygdala during Emotion Regulation training. A search was performed in PubMed, Science Direct, and Web of Science with the following key terms: ≪(“neurofeedback” or “neuro feedback” or “neuro-feedback”) and (“emotion regulation”) and (fMRI OR “functional magnetic resonance”),≫ and afterwards two additional searches were performed, replacing the term “emotion regulation” for “amygdala” and “neurofeedback” for “feedback.” Of the 531 identified articles, only 19 articles reported results of amygdala modulation during Emotional Regulation training through rtfMRI-NF, using healthy participants or patients, in original research articles. The results, systematically reviewed here, provide evidence for amygdala's modulation during rtfMRI-NF training, although studies' heterogeneity precluded a quantitative meta-analysis—the included studies relied on different outcome measures to infer the success of neurofeedback intervention. Thus, a qualitative analysis was done instead. We identified critical features influencing inference on the quality of the intervention as: the inclusion of a Practice Run, a Transfer Run and a Control Group in the protocol, and to choose adequate Emotion Regulation strategies—in particular, the effective recall of autobiographic memories. Surprisingly, the Regulated vs. Control Condition was lacking in most of the studies, precluding valid inference of amygdala neuromodulation within Session. The best controlled studies nevertheless showed positive effects. The type of stimulus/interface did not seem critical for amygdala modulation. We also identified potential effects of lateralization of amygdala responses following Up- or Down-Regulation, and the impact of fMRI parameters for data acquisition and analysis. Despite qualitative evidence for amygdala modulation during rtfMRI-NF, there are still important limitations in the design of a clear conceptual framework of NF-training research. Future studies should focus on more homogeneous guidelines concerning design, protocol structure and, particularly, harmonized outcome measures to provide quantitative estimates of neuromodulatory effects in the amygdala.

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

Although there is general consensus that altered brain structure and function underpins addictive disorders, clinicians working in addiction treatment rarely incorporate neuroscience-informed approaches into their practice. We recently launched the Neuroscience Interest Group within the International Society of Addiction Medicine (ISAM-NIG) to promote initiatives to bridge this gap. This article summarizes the ISAM-NIG key priorities and strategies to achieve implementation of addiction neuroscience knowledge and tools for the assessment and treatment of substance use disorders. We cover two assessment areas: cognitive assessment and neuroimaging, and two interventional areas: cognitive training/remediation and neuromodulation, where we identify key challenges and proposed solutions. We reason that incorporating cognitive assessment into clinical settings requires the identification of constructs that predict meaningful clinical outcomes. Other requirements are the development of measures that are easily-administered, reliable, and ecologically-valid. Translation of neuroimaging techniques requires the development of diagnostic and prognostic biomarkers and testing the cost-effectiveness of these biomarkers in individualized prediction algorithms for relapse prevention and treatment selection. Integration of cognitive assessments with neuroimaging can provide multilevel targets including neural, cognitive, and behavioral outcomes for neuroscience-informed interventions. Application of neuroscience-informed interventions including cognitive training/remediation and neuromodulation requires clear pathways to design treatments based on multilevel targets, additional evidence from randomized trials and subsequent clinical implementation, including evaluation of cost-effectiveness. We propose to address these challenges by promoting international collaboration between researchers and clinicians, developing harmonized protocols and data management systems, and prioritizing multi-site research that focuses on improving clinical outcomes.

Existence of Initial Dip for BCI: An Illusion or Reality

A tight coupling between the neuronal activity and the cerebral blood flow (CBF) is the motivation of many hemodynamic response (HR)-based neuroimaging modalities. The increase in neuronal activity causes the increase in CBF that is indirectly measured by HR modalities. Upon functional stimulation, the HR is mainly categorized in three durations: (i) initial dip, (ii) conventional HR (i.e., positive increase in HR caused by an increase in the CBF), and (iii) undershoot. The initial dip is a change in oxygenation prior to any subsequent increase in CBF and spatially more specific to the site of neuronal activity. Despite additional evidence from various HR modalities on the presence of initial dip in human and animal species (i.e., cat, rat, and monkey); the existence/occurrence of an initial dip in HR is still under debate. This article reviews the existence and elusive nature of the initial dip duration of HR in intrinsic signal optical imaging (ISOI), functional magnetic resonance imaging (fMRI), and functional near-infrared spectroscopy (fNIRS). The advent of initial dip and its elusiveness factors in ISOI and fMRI studies are briefly discussed. Furthermore, the detection of initial dip and its role in brain-computer interface using fNIRS is examined in detail. The best possible application for the initial dip utilization and its future implications using fNIRS are provided.