Neurofeedback-mediated self-regulation of the dopaminergic midbrain

The Mesocortical System Encodes the Strength of Subsequent Force Generation

Our minds impact motor outputs. Such mind-motor interactions are critical for understanding motor control mechanisms and optimizing motor performance. In particular, incentive motivation strongly enhances motor performance. Dopaminergic neurons located in the ventral midbrain (VM) are believed to be the center of incentive motivation. Direct projections from the VM to the primary motor cortex constitute a mesocortical pathway. However, the functional role of this pathway in humans remains unclear. Recently, we demonstrated the functional role of the mesocortical pathway in human motor control in the context of incentive motivation by using functional magnetic resonance imaging (fMRI). Incentive motivation remarkably improved not only reaction times but also the peak grip force in subsequent grip responses. Although the reaction time has been used as a proxy for incentive motivation mediated by dopaminergic midbrain activity, the premovement activity of the mesocortical pathway is involved in controlling the force strength rather than the initiation of subsequent force generation. In this commentary, we review our recent findings and discuss remaining questions regarding the functional role of the mesocortical pathway in mind-motor interactions.

Impact of Mobile Neurofeedback on Internet Addiction and Neurocognitive Function in Neurotypical Children: Double-Blind, Sham-Controlled Randomized Clinical Trial

Objective

The purpose of this study was to evaluate the positive impact of mobile neurofeedback (MNF) in neurotypical children compared to sham mobile neurofeedback.

Methods

Neurotypical children aged 10-15 participated in the study. All subjects were assessed using the Kiddie Schedule for Affective Disorders and Schizophrenia Present and Lifetime Version Korean Version (K-SADS-PL-K) and confirmed to have no psychiatric symptoms. The participants were randomly assigned to the MNF active (N=31) or sham control (N=30) groups. The MNF program was administered using a mobile app for 30 min/day, 3 days/week, for 3 months. All participants and their parents completed self-report scales and participants complete neurocognitive function assessments including the continuous performance test, Stroop, children's color trails test-1 and 2, and intelligence test at baseline and after the 3-month MNF program.

Results

This study involved 61 participants (mean [SD] age, 11.24 [1.84] years; 30 male participants [49.2%]). To verify the difference between the MNF group and the sham group, 2(MNF-Sham) X 2(Pre-Post) repeated measures ANOVA was performed. The main effect of the K-scale (Korea Internet addiction scale) between-group factor (MNF vs Sham) was not significant, but the main effect of the within-group factor (Pre vs Post) was significant (F=7.595, p=0.008). The interaction effect of between-group factors and within-group factors was also significant (F=5.979, p=0.017). In other self-reported scales of children and parents and neurocognitive function assessments, there was no significant difference between the two groups.

Conclusion

Active mobile neurofeedback significantly improved children's K-scale score compared to the sham group. Therefore, mobile neurofeedback could be an easy-to-access therapeutic option for children at risk of Internet addiction. On the other hand, there was no significant difference in other scales and neurocognitive function. A 3-month intervention may not have been long enough to cause change, so longer interventions are needed for confirmation.

Simulated operant reflex conditioning environment reveals effects of feedback parameters

Operant conditioning of neural activation has been researched for decades in humans and animals. Many theories suggest two parallel learning processes, implicit and explicit. The degree to which feedback affects these processes individually remains to be fully understood and may contribute to a large percentage of non-learners. Our goal is to determine the explicit decision-making processes in response to feedback representing an operant conditioning environment. We developed a simulated operant conditioning environment based on a feedback model of spinal reflex excitability, one of the simplest forms of neural operant conditioning. We isolated the perception of the feedback signal from self-regulation of an explicit unskilled visuomotor task, enabling us to quantitatively examine feedback strategy. Our hypothesis was that feedback type, biological variability, and reward threshold affect operant conditioning performance and operant strategy. Healthy individuals (N = 41) were instructed to play a web application game using keyboard inputs to rotate a virtual knob representative of an operant strategy. The goal was to align the knob with a hidden target. Participants were asked to "down-condition" the amplitude of the virtual feedback signal, which was achieved by placing the knob as close as possible to the hidden target. We varied feedback type (knowledge of performance, knowledge of results), biological variability (low, high), and reward threshold (easy, moderate, difficult) in a factorial design. Parameters were extracted from real operant conditioning data. Our main outcomes were the feedback signal amplitude (performance) and the mean change in dial position (operant strategy). We observed that performance was modulated by variability, while operant strategy was modulated by feedback type. These results show complex relations between fundamental feedback parameters and provide the principles for optimizing neural operant conditioning for non-responders.

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.

Preliminary findings on long-term effects of fMRI neurofeedback training on functional networks involved in sustained attention

INTRODUCTION

Neurofeedback based on functional magnetic resonance imaging allows for learning voluntary control over one's own brain activity, aiming to enhance cognition and clinical symptoms. We previously reported improved sustained attention temporarily by training healthy participants to up-regulate the differential activity of the sustained attention network minus the default mode network (DMN). However, the long-term brain and behavioral effects of this training have not yet been studied. In general, despite their relevance, long-term learning effects of neurofeedback training remain under-explored.

METHODS

Here, we complement our previously reported results by evaluating the neurofeedback training effects on functional networks involved in sustained attention and by assessing behavioral and brain measures before, after, and 2 months after training. The behavioral measures include task as well as questionnaire scores, and the brain measures include activity and connectivity during self-regulation runs without feedback (i.e., transfer runs) and during resting-state runs from 15 healthy individuals.

RESULTS

Neurally, we found that participants maintained their ability to control the differential activity during follow-up sessions. Further, exploratory analyses showed that the training increased the functional connectivity between the DMN and the occipital gyrus, which was maintained during follow-up transfer runs but not during follow-up resting-state runs. Behaviorally, we found that enhanced sustained attention right after training returned to baseline level during follow-up.

CONCLUSION

The discrepancy between lasting regulation-related brain changes but transient behavioral and resting-state effects raises the question of how neural changes induced by neurofeedback training translate to potential behavioral improvements. Since neurofeedback directly targets brain measures to indirectly improve behavior in the long term, a better understanding of the brain-behavior associations during and after neurofeedback training is needed to develop its full potential as a promising scientific and clinical tool.

Development and validation of an fMRI-informed EEG model of reward-related ventral striatum activation

Reward processing is essential for our mental-health and well-being. In the current study, we developed and validated a scalable, fMRI-informed EEG model for monitoring reward processing related to activation in the ventral-striatum (VS), a significant node in the brain's reward system. To develop this EEG-based model of VS-related activation, we collected simultaneous EEG/fMRI data from 17 healthy individuals while listening to individually-tailored pleasurable music - a highly rewarding stimulus known to engage the VS. Using these cross-modal data, we constructed a generic regression model for predicting the concurrently acquired Blood-Oxygen-Level-Dependent (BOLD) signal from the VS using spectro-temporal features from the EEG signal (termed hereby VS-related-Electrical Finger Print; VS-EFP). The performance of the extracted model was examined using a series of tests that were applied on the original dataset and, importantly, an external validation dataset collected from a different group of 14 healthy individuals who underwent the same EEG/FMRI procedure. Our results showed that the VS-EFP model, as measured by simultaneous EEG, predicted BOLD activation in the VS and additional functionally relevant regions to a greater extent than an EFP model derived from a different anatomical region. The developed VS-EFP was also modulated by musical pleasure and predictive of the VS-BOLD during a monetary reward task, further indicating its functional relevance. These findings provide compelling evidence for the feasibility of using EEG alone to model neural activation related to the VS, paving the way for future use of this scalable neural probing approach in neural monitoring and self-guided neuromodulation.

OPERANT REFLEX CONDITIONING SIMULATION ENVIRONMENT REVEALS EFFECTS OF FEEDBACK PARAMETERS

Operant conditioning of neural activation has been researched for decades in humans and animals. Many theories suggest two parallel learning processes, implicit and explicit. The degree to which feedback affects these processes individually remains to be fully understood and may contribute to a large percentage of non-learners. Our goal is to determine the explicit decision-making processes in response to feedback representing an operant conditioning environment. We developed a simulated operant conditioning environment based on a feedback model of spinal reflex excitability, one of the simplest forms of neural operant conditioning. We isolated the perception of the feedback signal from self-regulation of an explicit unskilled visuomotor task, enabling us to quantitatively examine feedback strategy. Our hypothesis was that feedback type, signal quality and success threshold affect operant conditioning performance and operant strategy. Healthy individuals (N = 41) were instructed to play a web application game using keyboard inputs to rotate a virtual knob representative of an operant strategy. The goal was to align the knob with a hidden target. Participants were asked to "down-condition" the amplitude of the virtual feedback signal, which was achieved by placing the knob as close as possible to the hidden target. We varied feedback type (knowledge of performance, knowledge of results), success threshold (easy, moderate, difficult), and biological variability (low, high) in a factorial design. Parameters were extracted from real operant conditioning data. Our main outcomes were the feedback signal amplitude (performance) and the mean change in dial position (operant strategy). We observed that performance was modulated by variability, while operant strategy was modulated by feedback type. These results show complex relations between fundamental feedback parameters and provide the principles for optimizing neural operant conditioning for non-responders.

Testing the efficacy of real-time fMRI neurofeedback for training people who smoke daily to upregulate neural responses to nondrug rewards

Although the use of nondrug rewards (e.g., money) to facilitate smoking cessation is widespread, recent research has found that such rewards may be least effective when people who smoke cigarettes are tempted to do so. Specifically, among people who smoke, the neural response to nondrug rewards appears blunted when access to cigarettes is anticipated, and this blunting is linked to a decrease in willingness to refrain from smoking to earn a monetary incentive. Accordingly, methods to enhance the value of nondrug rewards may be theoretically and clinically important. The current proof-of-concept study tested if real-time fMRI neurofeedback training augments the ability to upregulate responses in reward-related brain areas relative to a no-feedback control condition in people who smoke. Adults (n = 44, age range = 20-44) who reported smoking >5 cigarettes per day completed the study. Those in the intervention group (n = 22, 5 females) were trained to upregulate brain responses using feedback of ongoing striatal activity (i.e., a dynamic "thermometer" that reflected ongoing changes of fMRI signal intensity in the striatum) in a single neurofeedback session with three training runs. The control group (n = 22, 5 females) underwent a nearly identical procedure but received no neurofeedback. Those who received neurofeedback training demonstrated significantly greater increases in striatal BOLD activation while attempting to think about something rewarding compared to controls, but this effect was present only during the first training run. Future neurofeedback research with those who smoke should explore how to make neurofeedback training more effective for the self-regulation of reward-related brain activities.

Blunted reward responsiveness prospectively predicts emotional distress when exposed to a naturalistic stressor

Both stress and blunted reward responsiveness have been identified as core risk factors of depression. Whether blunted reward responsiveness increases psychological vulnerability to real-life stress from a dynamic perspective (from stress reactivity to recovery) has not been investigated. By utilizing a real-world stressful event (i.e. the final examination), this study aimed to explore the role of reward responsiveness in the stress-emotional distress relationship during stress reactivity and recovery phases. We followed 57 undergraduates with three assessments, from six weeks before examination weeks (T1, baseline), one day before the examinations (T2) to two weeks after the examinations (T3), therefore, covering stress reactivity (T1 to T2) and recovery (T2 to T3) phases. At baseline, reward responsiveness was measured as the Reward Positivity (RewP) in the doors task. Stress and emotional distress (anxiety and depression) were reported at T1, T2 and T3 to capture their dynamic changes. Results showed that self-report stress levels significantly increased from T1 to T2 (stress reactivity phase) and decreased from T2 to T3 (stress recovery phase). Furthermore, blunted reward responsiveness at baseline prospectively predicted emotional distress during the stress reactivity phase but not the recovery phase. Specifically, during the stress reactivity phase, higher perceived stress was associated with greater anxiety and depression only in participants with relatively smaller residual RewP amplitudes but not in participants with relatively larger residual RewP amplitudes. Our study demonstrated that a blunted reward responsiveness is a vulnerable factor of depression, especially when exposed to stress. Our findings provide insights into prevention and intervention for stress-related disturbance.

Neuroanatomical predictors of real‐time fMRI ‐based anterior insula regulation. A supervised machine learning study

Increasing evidence showed that learned control of metabolic activity in selected brain regions can support emotion regulation. Notably, a number of studies demonstrated that neurofeedback-based regulation of fMRI activity in several emotion-related areas leads to modifications of emotional behavior along with changes of neural activity in local and distributed networks, in both healthy individuals and individuals with emotional disorders. However, the current understanding of the neural mechanisms underlying self-regulation of the emotional brain, as well as their relationship with other emotion regulation strategies, is still limited. In this study, we attempted to delineate neuroanatomical regions mediating real-time fMRI-based emotion regulation by exploring whole brain GM and WM features predictive of self-regulation of anterior insula (AI) activity, a neuromodulation procedure that can successfully support emotional brain regulation in healthy individuals and patients. To this aim, we employed a multivariate kernel ridge regression model to assess brain volumetric features, at regional and network level, predictive of real-time fMRI-based AI regulation. Our results showed that several GM regions including fronto-occipital and medial temporal areas and the basal ganglia as well as WM regions including the fronto-occipital fasciculus, tapetum and fornix significantly predicted learned AI regulation. Remarkably, we observed a substantial contribution of the cerebellum in relation to both the most effective regulation run and average neurofeedback performance. Overall, our findings highlighted specific neurostructural features contributing to individual differences of AI-guided emotion regulation. Notably, such neuroanatomical topography partially overlaps with the neurofunctional network associated with cognitive emotion regulation strategies, suggesting common neural mechanisms.

Galvanic Skin Response Features in Psychiatry and Mental Disorders: A Narrative Review

This narrative review is aimed at presenting the galvanic skin response (GSR) Biofeedback method and possibilities for its application in persons with mental disorders as a modern form of neurorehabilitation. In the treatment of mental disorders of various backgrounds and courses, attention is focused on methods that would combine pharmacological treatment with therapies improving functioning. Currently, the focus is on neuronal mechanisms which, being physiological markers, offer opportunities for correction of existing deficits. One such indicator is electrodermal activity (EDA), providing information about emotions, cognitive processes, and behavior, and thus, about the function of various brain regions. Measurement of the galvanic skin response (GSR), both skin conductance level (SCL) and skin conductance responses (SCR), is used in diagnostics and treatment of mental disorders, and the training method itself, based on GSR Biofeedback, allows for modulation of the emotional state depending on needs occurring. Summary: It is relatively probable that neurorehabilitation based on GSR-BF is a method worth noticing, which-in the future-can represent an interesting area of rehabilitation supplementing a comprehensive treatment for people with mental disorders.

Can neurofeedback provide evidence of direct brain-behavior causality?

Neurofeedback is a procedure that measures brain activity in real-time and presents it as feedback to an individual, thus allowing them to self-regulate brain activity with effects on cognitive processes inferred from behavior. One common argument is that neurofeedback studies can reveal how the measured brain activity causes a particular cognitive process. The causal claim is often made regarding the measured brain activity being manipulated as an independent variable, similar to brain stimulation studies. However, this causal inference is vulnerable to the argument that other upstream brain activities change concurrently and cause changes in the brain activity from which feedback is derived. In this paper, we outline the inference that neurofeedback may causally affect cognition by indirect means. We further argue that researchers should remain open to the idea that the trained brain activity could be part of a "causal network" that collectively affects cognition rather than being necessarily causally primary. This particular inference may provide a better translation of evidence from neurofeedback studies to the rest of neuroscience. We argue that the recent advent of multivariate pattern analysis, when combined with implicit neurofeedback, currently comprises the strongest case for causality. Our perspective is that although the burden of inferring direct causality is difficult, it may be triangulated using a collection of various methods in neuroscience. Finally, we argue that the neurofeedback methodology provides unique advantages compared to other methods for revealing changes in the brain and cognitive processes but that researchers should remain mindful of indirect causal effects.

BrainIAK: The Brain Imaging Analysis Kit

Functional magnetic resonance imaging (fMRI) offers a rich source of data for studying the neural basis of cognition. Here, we describe the Brain Imaging Analysis Kit (BrainIAK), an open-source, free Python package that provides computationally optimized solutions to key problems in advanced fMRI analysis. A variety of techniques are presently included in BrainIAK: intersubject correlation (ISC) and intersubject functional connectivity (ISFC), functional alignment via the shared response model (SRM), full correlation matrix analysis (FCMA), a Bayesian version of representational similarity analysis (BRSA), event segmentation using hidden Markov models, topographic factor analysis (TFA), inverted encoding models (IEMs), an fMRI data simulator that uses noise characteristics from real data (fmrisim), and some emerging methods. These techniques have been optimized to leverage the efficiencies of high-performance compute (HPC) clusters, and the same code can be seamlessly transferred from a laptop to a cluster. For each of the aforementioned techniques, we describe the data analysis problem that the technique is meant to solve and how it solves that problem; we also include an example Jupyter notebook for each technique and an annotated bibliography of papers that have used and/or described that technique. In addition to the sections describing various analysis techniques in BrainIAK, we have included sections describing the future applications of BrainIAK to real-time fMRI, tutorials that we have developed and shared online to facilitate learning the techniques in BrainIAK, computational innovations in BrainIAK, and how to contribute to BrainIAK. We hope that this manuscript helps readers to understand how BrainIAK might be useful in their research.

Neurofeedback and neural self-regulation: a new perspective based on allostasis

The field of neurofeedback training (NFT) has seen growing interest and an expansion of scope, resulting in a steadily increasing number of publications addressing different aspects of NFT. This development has been accompanied by a debate about the underlying mechanisms and expected outcomes. Recent developments in the understanding of psychophysiological regulation have cast doubt on the validity of control systems theory, the principal framework traditionally used to characterize NFT. The present article reviews the theoretical and empirical aspects of NFT and proposes a predictive framework based on the concept of allostasis. Specifically, we conceptualize NFT as an adaptation to changing contingencies. In an allostasis four-stage model, NFT involves (a) perceiving relations between demands and set-points, (b) learning to apply collected patterns (experience) to predict future output, (c) determining efficient set-points, and (d) adapting brain activity to the desired ("set") state. This model also identifies boundaries for what changes can be expected from a neurofeedback intervention and outlines a time frame for such changes to occur.

Closed-loop neuromodulation for studying spontaneous activity and causality

Having established that spontaneous brain activity follows meaningful coactivation patterns and correlates with behavior, researchers have turned their attention to understanding its function and behavioral significance. We suggest closed-loop neuromodulation as a neural perturbation tool uniquely well suited for this task. Closed-loop neuromodulation has primarily been viewed as an interventionist tool to teach subjects to directly control their own brain activity. We examine an alternative operant conditioning model of closed-loop neuromodulation which, through implicit feedback, can manipulate spontaneous activity at the network level, without violating the spontaneous or endogenous nature of the signal, thereby providing a direct test of network causality.

Examination of the importance of anger/irritability and limited prosocial emotion/callous-unemotional traits to understand externalizing symptoms and adjustment problems in adolescence: A 10-year longitudinal study

Objective

Within a longitudinal study (10-year follow-up), we aim to examine the role of anger/irritability and limited prosocial emotion/callous-unemotional traits in predicting externalizing symptoms and adjustment problems in individuals formerly in youth residential care institutions.

Method

These dimensions were assessed in 203 young adults, with baseline assessments during youth residential care and a follow-up 10 years later.

Results

In general, emotional problems and psychopathological symptoms did not reduce over time. Analyses of regression revealed that a younger age at baseline, anger/irritability both at baseline assessment, and regarding their aggravation over time refer to significant predictors of the level of externalizing symptoms at 10-year follow-up (R ² = 0.431) and the worsening of externalizing symptoms over time (R ² = 0.638). Anger/irritability has been observed to be a significant predictors of both the level of adjustment problems at 10-year follow-up (R ² = 0.471) and its worsening over time (R ² = 0.656).

Discussion

Our results suggest that dysregulation of anger/irritability is a key factor in the prediction of long-term externalizing symptoms and adjustment problems as well as its worsening over time. Possible implications for intervention and prevention are discussed.

Deep brain electrical neurofeedback allows Parkinson patients to control pathological oscillations and quicken movements

Parkinsonian motor symptoms are linked to pathologically increased beta-oscillations in the basal ganglia. While pharmacological treatment and deep brain stimulation (DBS) reduce these pathological oscillations concomitantly with improving motor performance, we set out to explore neurofeedback as an endogenous modulatory method. We implemented real-time processing of pathological subthalamic beta oscillations through implanted DBS electrodes to provide deep brain electrical neurofeedback. Patients volitionally controlled ongoing beta-oscillatory activity by visual neurofeedback within minutes of training. During a single one-hour training session, the reduction of beta-oscillatory activity became gradually stronger and we observed improved motor performance. Lastly, endogenous control over deep brain activity was possible even after removing visual neurofeedback, suggesting that neurofeedback-acquired strategies were retained in the short-term. Moreover, we observed motor improvement when the learnt mental strategies were applied 2 days later without neurofeedback. Further training of deep brain neurofeedback might provide therapeutic benefits for Parkinson patients by improving symptom control using strategies optimized through neurofeedback.

Pyneal: Open Source Real-Time fMRI Software

Increasingly, neuroimaging researchers are exploring the use of real-time functional magnetic resonance imaging (rt-fMRI) as a way to access a participant's ongoing brain function throughout a scan. This approach presents novel and exciting experimental applications ranging from monitoring data quality in real time, to delivering neurofeedback from a region of interest, to dynamically controlling experimental flow, or interfacing with remote devices. Yet, for those interested in adopting this method, the existing software options are few and limited in application. This presents a barrier for new users, as well as hinders existing users from refining techniques and methods. Here we introduce a free, open-source rt-fMRI package, the Pyneal toolkit, designed to address this limitation. The Pyneal toolkit is python-based software that offers a flexible and user friendly framework for rt-fMRI, is compatible with all three major scanner manufacturers (GE, Siemens, Phillips), and, critically, allows fully customized analysis pipelines. In this article, we provide a detailed overview of the architecture, describe how to set up and run the Pyneal toolkit during an experimental session, offer tutorials with scan data that demonstrate how data flows through the Pyneal toolkit with example analyses, and highlight the advantages that the Pyneal toolkit offers to the neuroimaging community.

Quality and denoising in real-time functional magnetic resonance imaging neurofeedback: A methods review

Neurofeedback training using real-time functional magnetic resonance imaging (rtfMRI-NF) allows subjects voluntary control of localised and distributed brain activity. It has sparked increased interest as a promising non-invasive treatment option in neuropsychiatric and neurocognitive disorders, although its efficacy and clinical significance are yet to be determined. In this work, we present the first extensive review of acquisition, processing and quality control methods available to improve the quality of the neurofeedback signal. Furthermore, we investigate the state of denoising and quality control practices in 128 recently published rtfMRI-NF studies. We found: (a) that less than a third of the studies reported implementing standard real-time fMRI denoising steps, (b) significant room for improvement with regards to methods reporting and (c) the need for methodological studies quantifying and comparing the contribution of denoising steps to the neurofeedback signal quality. Advances in rtfMRI-NF research depend on reproducibility of methods and results. Notably, a systematic effort is needed to build up evidence that disentangles the various mechanisms influencing neurofeedback effects. To this end, we recommend that future rtfMRI-NF studies: (a) report implementation of a set of standard real-time fMRI denoising steps according to a proposed COBIDAS-style checklist (https://osf.io/kjwhf/), (b) ensure the quality of the neurofeedback signal by calculating and reporting community-informed quality metrics and applying offline control checks and (c) strive to adopt transparent principles in the form of methods and data sharing and support of open-source rtfMRI-NF software. Code and data for reproducibility, as well as an interactive environment to explore the study data, can be accessed at https://github.com/jsheunis/quality-and-denoising-in-rtfmri-nf.

Neuromodulation of brain activation associated with addiction: A review of real-time fMRI neurofeedback studies

Real-time functional magnetic resonance imaging neurofeedback (rtfMRI-nf) has emerged in recent years as an imaging modality used to examine volitional control over targeted brain activity. rtfMRI-nf has also been applied clinically as a way to train individuals to self-regulate areas of the brain, or circuitry, involved in various disorders. One such application of rtfMRI-nf has been in the domain of addictive behaviors, including substance use. Given the pervasiveness of substance use and the challenges of existing treatments to sustain abstinence, rtfMRI-nf has been identified as a promising treatment tool. rtfMRI-nf has also been used in basic science research in order to test the ability to modulate brain function involved in addiction. This review focuses first on providing an overview of recent rtfMRI-nf studies in substance-using populations, specifically nicotine, alcohol, and cocaine users, aimed at reducing craving-related brain activation. Next, rtfMRI-nf studies targeting reward responsivity and emotion regulation in healthy samples are reviewed in order to examine the extent to which areas of the brain involved in addiction can be self-regulated using neurofeedback. We propose that future rtfMRI-nf studies could be strengthened by improvements to study design, sample selection, and more robust strategies in the development and assessment of rtfMRI-nf as a clinical treatment. Recommendations for ways to accomplish these improvements are provided. rtfMRI-nf holds much promise as an imaging modality that can directly target key brain regions involved in addiction, however additional studies are needed in order to establish rtfMRI-nf as an effective, and practical, treatment for addiction.

Mesocorticolimbic Interactions Mediate fMRI-Guided Regulation of Self-Generated Affective States

Increasing evidence shows that the generation and regulation of affective responses is associated with activity of large brain networks that also include phylogenetically older regions in the brainstem. Mesencephalic regions not only control autonomic responses but also participate in the modulation of autonomic, emotional, and motivational responses. The specific contribution of the midbrain to emotion regulation in humans remains elusive. Neuroimaging studies grounding on appraisal models of emotion emphasize a major role of prefrontal cortex in modulating emotion-related cortical and subcortical regions but usually neglect the contribution of the midbrain and other brainstem regions. Here, the role of mesolimbic and mesocortical networks in core affect generation and regulation was explored during emotion regulation guided by real-time fMRI feedback of the anterior insula activity. The fMRI and functional connectivity analysis revealed that the upper midbrain significantly contributes to emotion regulation in humans. Moreover, differential functional interactions between the dopaminergic mesocorticolimbic system and frontoparietal networks mediate up and down emotion regulatory processes. Finally, these findings further indicate the potential of real-time fMRI feedback approach in guiding core affect regulation.

Endogenous fluctuations in the dopaminergic midbrain drive behavioral choice variability

Humans are surprisingly inconsistent in their behavior, often making different choices under identical conditions. Previous research suggests that intrinsic fluctuations in brain activity can influence low-level processes, such as the amount of force applied in a motor response. Here, we show that intrinsic prestimulus brain activity in the dopaminergic midbrain influences how we choose between risky and safe options. Using computational modeling, we demonstrate that endogenous fluctuations alter phasic responses in a decision network and thereby modulate risk taking. Our findings demonstrate that higher-order cognition is influenced by fluctuations in internal brain states, providing a physiological basis for variability in complex human behavior.

Human behavior is surprisingly variable, even when facing the same problem under identical circumstances. A prominent example is risky decision making. Economic theories struggle to explain why humans are so inconsistent. Resting-state studies suggest that ongoing endogenous fluctuations in brain activity can influence low-level perceptual and motor processes, but it remains unknown whether endogenous fluctuations also influence high-level cognitive processes including decision making. Here, using real-time functional magnetic resonance imaging, we tested whether risky decision making is influenced by endogenous fluctuations in blood oxygenation level-dependent (BOLD) activity in the dopaminergic midbrain, encompassing ventral tegmental area and substantia nigra. We show that low prestimulus brain activity leads to increased risky choice in humans. Using computational modeling, we show that increased risk taking is explained by enhanced phasic responses to offers in a decision network. Our findings demonstrate that endogenous brain activity provides a physiological basis for variability in complex human behavior.

Emotion Regulation of Hippocampus Using Real-Time fMRI Neurofeedback in Healthy Human

Real-time functional magnetic resonance imaging neurofeedback (rtfMRI-NF) is a prospective tool to enhance the emotion regulation capability of participants and to alleviate their emotional disorders. The hippocampus is a key brain region in the emotional brain network and plays a significant role in social cognition and emotion processing in the brain. However, few studies have focused on the emotion NF of the hippocampus. This study investigated the feasibility of NF training of healthy participants to self-regulate the activation of the hippocampus and assessed the effect of rtfMRI-NF on the hippocampus before and after training. Twenty-six right-handed healthy volunteers were randomly assigned to the experimental group receiving hippocampal rtfMRI-NF (n = 13) and the control group (CG) receiving rtfMRI-NF from the intraparietal sulcus rtfMRI-NF (n = 13) and completed a total of four NF runs. The hippocampus and the intraparietal sulcus were defined based on the Montreal Neurological Institute (MNI) standard template, and NF signal was measured as a percent signal change relative to the baseline obtained by averaging the fMRI signal for the preceding 20 s long rest block. NF signal (percent signal change) was updated every 2 s and was displayed on the screen. The amplitude of low-frequency fluctuation and regional homogeneity values was calculated to evaluate the effects of NF on spontaneous neural activity in resting-state fMRI. A standard general linear model (GLM) analysis was separately conducted for each fMRI NF run. Results showed that the activation of hippocampus increased after four NF training runs. The hippocampal activity of the experiment group participants was higher than that of the CG. They also showed elevated hippocampal activity and the greater amygdala–hippocampus connectivity. The anterior temporal lobe, parahippocampal gyrus, hippocampus, and amygdala of brain regions associated with emotional processing were activated during training. We presented a proof-of-concept study using rtfMRI-NF for hippocampus up-regulation in the recall of positive autobiographical memories. The current study may provide a new method to regulate our emotions and can potentially be applied to the clinical treatment of emotional disorders.

A Unifying Pathophysiological Account for Post-stroke Spasticity and Disordered Motor Control

Cortical and subcortical plastic reorganization occurs in the course of motor recovery after stroke. It is largely accepted that plasticity of ipsilesional motor cortex primarily contributes to recovery of motor function, while the contributions of contralesional motor cortex are not completely understood. As a result of damages to motor cortex and its descending pathways and subsequent unmasking of inhibition, there is evidence of upregulation of reticulospinal tract (RST) excitability in the contralesional side. Both animal studies and human studies with stroke survivors suggest and support the role of RST hyperexcitability in post-stroke spasticity. Findings from animal studies demonstrate the compensatory role of RST hyperexcitability in recovery of motor function. In contrast, RST hyperexcitability appears to be related more to abnormal motor synergy and disordered motor control in stroke survivors. It does not contribute to recovery of normal motor function. Recent animal studies highlight laterality dominance of corticoreticular projections. In particular, there exists upregulation of ipsilateral corticoreticular projections from contralesional premotor cortex (PM) and supplementary motor area (SMA) to medial reticular nuclei. We revisit and revise the previous theoretical framework and propose a unifying account. This account highlights the importance of ipsilateral PM/SMA-cortico-reticulospinal tract hyperexcitability from the contralesional motor cortex as a result of disinhibition after stroke. This account provides a pathophysiological basis for post-stroke spasticity and related movement impairments, such as abnormal motor synergy and disordered motor control. However, further research is needed to examine this pathway in stroke survivors to better understand its potential roles, especially in muscle strength and motor recovery. This account could provide a pathophysiological target for developing neuromodulatory interventions to manage spasticity and thus possibly to facilitate motor recovery.

Real-Time Functional Connectivity-Informed Neurofeedback of Amygdala-Frontal Pathways Reduces Anxiety

BACKGROUND

Deficient emotion regulation and exaggerated anxiety represent a major transdiagnostic psychopathological marker. On the neural level these deficits have been closely linked to impaired, yet treatment-sensitive, prefrontal regulatory control over the amygdala. Gaining direct control over these pathways could therefore provide an innovative and promising intervention to regulate exaggerated anxiety. To this end the current proof-of-concept study evaluated the feasibility, functional relevance and maintenance of a novel connectivity-informed real-time fMRI neurofeedback training.

METHODS

In a randomized crossover sham-controlled design, 26 healthy subjects with high anxiety underwent real-time fMRI-guided neurofeedback training to enhance connectivity between the ventrolateral prefrontal cortex (vlPFC) and the amygdala (target pathway) during threat exposure. Maintenance of regulatory control was assessed after 3 days and in the absence of feedback. Training-induced changes in functional connectivity of the target pathway and anxiety ratings served as primary outcomes.

RESULTS

Training of the target, yet not the sham control, pathway significantly increased amygdala-vlPFC connectivity and decreased levels of anxiety. Stronger connectivity increases were significantly associated with higher anxiety reduction on the group level. At the follow-up, volitional control over the target pathway was maintained in the absence of feedback.

CONCLUSIONS

The present results demonstrate for the first time that successful self-regulation of amygdala-prefrontal top-down regulatory circuits may represent a novel intervention to control anxiety. As such, the present findings underscore both the critical contribution of amygdala-prefrontal circuits to emotion regulation and the therapeutic potential of connectivity-informed real-time neurofeedback.

A startling acoustic stimulation (SAS)-TMS approach to assess the reticulospinal system in healthy and stroke subjects

Reticulospinal (RS) hyperexcitability is observed in stroke survivors with spastic hemiparesis. Habituated startle acoustic stimuli (SAS) can be used to stimulate the RS pathways non-reflexively. However, the role of RS pathways in motor function and its interactions with the corticospinal system after stroke still remain unclear. Therefore, the purpose of this study was to investigate the effects of conditioning SAS on the corticospinal system in healthy subjects and in stroke subjects with spastic hemiparesis. An established conditioning SAS- transcranial magnetic stimulation (TMS) paradigm was used to test the interactions between the RS pathways and the corticospinal system. TMS was delivered to the right hemisphere of eleven healthy subjects and the contralesional hemisphere of eleven stroke subjects during isometric elbow flexor contraction on the non-impaired (or left) side. Conditioning SAS had similar effects on the corticospinal motor system in both healthy and stroke subjects, including similar SAS-induced motor evoked potential (MEP) reduction at rest, but not during voluntary contraction tasks; similar magnitudes of TMS-induced MEP and force increment and shortening of the silent period during voluntary elbow flexor contraction. This study provides evidence that RS excitability on the contralesional side in stroke subjects with spastic hemiparesis is not abnormal, and suggests that RS projections are likely to be primarily unilateral in humans.

Control freaks: Towards optimal selection of control conditions for fMRI neurofeedback studies

fMRI Neurofeedback research employs many different control conditions. Currently, there is no consensus as to which control condition is best, and the answer depends on what aspects of the neurofeedback-training design one is trying to control for. These aspects can range from determining whether participants can learn to control brain activity via neurofeedback to determining whether there are clinically significant effects of the neurofeedback intervention. Lack of consensus over criteria for control conditions has hampered the design and interpretation of studies employing neurofeedback protocols. This paper presents an overview of the most commonly employed control conditions currently used in neurofeedback studies and discusses their advantages and disadvantages. Control conditions covered include no control, treatment-as-usual, bidirectional-regulation control, feedback of an alternative brain signal, sham feedback, and mental-rehearsal control. We conclude that the selection of the control condition(s) should be determined by the specific research goal of the study and best procedures that effectively control for relevant confounding factors.

Self-regulation of the dopaminergic reward circuit in cocaine users with mental imagery and neurofeedback

BACKGROUND

Enhanced drug-related reward sensitivity accompanied by impaired sensitivity to non-drug related rewards in the mesolimbic dopamine system are thought to underlie the broad motivational deficits and dysfunctional decision-making frequently observed in cocaine use disorder (CUD). Effective approaches to modify this imbalance and reinstate non-drug reward responsiveness are urgently needed. Here, we examined whether cocaine users (CU) can use mental imagery of non-drug rewards to self-regulate the ventral tegmental area and substantia nigra (VTA/SN). We expected that obsessive and compulsive thoughts about cocaine consumption would hamper the ability to self-regulate the VTA/SN activity and tested if real-time fMRI (rtfMRI) neurofeedback (NFB) can improve self-regulation of the VTA/SN.

METHODS

Twenty-two CU and 28 healthy controls (HC) were asked to voluntarily up-regulate VTA/SN activity with non-drug reward imagery alone, or combined with rtfMRI NFB.

RESULTS

On a group level, HC and CU were able to activate the dopaminergic midbrain and other reward regions with reward imagery. In CU, the individual ability to self-regulate the VTA/SN was reduced in those with more severe obsessive-compulsive drug use. NFB enhanced the effect of reward imagery but did not result in transfer effects at the end of the session.

CONCLUSION

CU can voluntary activate their reward system with non-drug reward imagery and improve this ability with rtfMRI NFB. Combining mental imagery and rtFMRI NFB has great potential for modifying the maladapted reward sensitivity and reinstating non-drug reward responsiveness. This motivates further work to examine the use of rtfMRI NFB in the treatment of CUD.

Mapping the Cortical Network Arising From Up-Regulated Amygdaloidal Activation Using -Louvain Algorithm

The amygdala plays an important role in emotion processing. Several studies have proved that its activation can be regulated by real-time functional magnetic resonance imaging (rtfMRI)-based neurofeedback training. However, although studies have found brain regions that are functionally closely connected to the amygdala in the cortex, it is not clear whether these brain regions and the amygdala are structurally closely connected, and if they show the same training effect as the amygdala in the process of emotional regulation. In this paper, we instructed subjects to up-regulate the activation of the left amygdala (LA) through rtfMRI-based neurofeedback training. In order to fuse multimodal imaging data, we introduced a network analysis method called the -Louvain clustering algorithm. This method was used to integrate multimodal data from the training experiment and construct an LA-cortical network. Correlation analysis and main-effect analysis were conducted to determine the signal covariance associated with the activation of the target area; ultimately, we identified the left temporal pole superior as the amygdaloidal-cortical network region. As a deep nucleus in the brain, the treatment and stimulation of the amygdala remains challenging. Our results provide new insights for the regulation of activation in a deep nucleus using more neurofeedback techniques.

Improved Volitional Recall of Motor-Imagery-Related Brain Activation Patterns Using Real-Time Functional MRI-Based Neurofeedback

Motor imagery (MI), a covert cognitive process where an action is mentally simulated but not actually performed, could be used as an effective neurorehabilitation tool for motor function improvement or recovery. Recent approaches employing brain–computer/brain–machine interfaces to provide online feedback of the MI during rehabilitation training have promising rehabilitation outcomes. In this study, we examined whether participants could volitionally recall MI-related brain activation patterns when guided using neurofeedback (NF) during training. The participants’ performance was compared to that without NF. We hypothesized that participants would be able to consistently generate the relevant activation pattern associated with the MI task during training with NF compared to that without NF. To assess activation consistency, we used the performance of classifiers trained to discriminate MI-related brain activation patterns. Our results showed significantly higher predictive values of MI-related activation patterns during training with NF. Additionally, this improvement in the classification performance tends to be associated with the activation of middle temporal gyrus/inferior occipital gyrus, a region associated with visual motion processing, suggesting the importance of performance monitoring during MI task training. Taken together, these findings suggest that the efficacy of MI training, in terms of generating consistent brain activation patterns relevant to the task, can be enhanced by using NF as a mechanism to enable participants to volitionally recall task-related brain activation patterns.

Real-time fMRI neurofeedback of the mediodorsal and anterior thalamus enhances correlation between thalamic BOLD activity and alpha EEG rhythm

Real-time fMRI neurofeedback (rtfMRI-nf) with simultaneous EEG allows volitional modulation of BOLD activity of target brain regions and investigation of related electrophysiological activity. We applied this approach to study correlations between thalamic BOLD activity and alpha EEG rhythm. Healthy volunteers in the experimental group (EG, n = 15) learned to upregulate BOLD activity of the target region consisting of the mediodorsal (MD) and anterior (AN) thalamic nuclei using rtfMRI-nf during retrieval of happy autobiographical memories. Healthy subjects in the control group (CG, n = 14) were provided with a sham feedback. The EG participants were able to significantly increase BOLD activities of the MD and AN. Functional connectivity between the MD and the inferior precuneus was significantly enhanced during the rtfMRI-nf task. Average individual changes in the occipital alpha EEG power significantly correlated with the average MD BOLD activity levels for the EG. Temporal correlations between the occipital alpha EEG power and BOLD activities of the MD and AN were significantly enhanced, during the rtfMRI-nf task, for the EG compared to the CG. Temporal correlations with the alpha power were also significantly enhanced for the posterior nodes of the default mode network, including the precuneus/posterior cingulate, and for the dorsal striatum. Our findings suggest that the temporal correlation between the MD BOLD activity and posterior alpha EEG power is modulated by the interaction between the MD and the inferior precuneus, reflected in their functional connectivity. Our results demonstrate the potential of the rtfMRI-nf with simultaneous EEG for noninvasive neuromodulation studies of human brain function.

Intermittent compared to continuous real-time fMRI neurofeedback boosts control over amygdala activation

Real-time fMRI neurofeedback is a feasible tool to learn the volitional regulation of brain activity. So far, most studies provide continuous feedback information that is presented upon every volume acquisition. Although this maximizes the temporal resolution of feedback information, it may be accompanied by some disadvantages. Participants can be distracted from the regulation task due to (1) the intrinsic delay of the hemodynamic response and associated feedback and (2) limited cognitive resources available to simultaneously evaluate feedback information and stay engaged with the task. Here, we systematically investigate differences between groups presented with different variants of feedback (continuous vs. intermittent) and a control group receiving no feedback on their ability to regulate amygdala activity using positive memories and feelings. In contrast to the feedback groups, no learning effect was observed in the group without any feedback presentation. The group receiving intermittent feedback exhibited better amygdala regulation performance when compared with the group receiving continuous feedback. Behavioural measurements show that these effects were reflected in differences in task engagement. Overall, we not only demonstrate that the presentation of feedback is a prerequisite to learn volitional control of amygdala activity but also that intermittent feedback is superior to continuous feedback presentation.

A Protocol for the Administration of Real-Time fMRI Neurofeedback Training

Neurologic disorders are characterized by abnormal cellular-, molecular-, and circuit-level functions in the brain. New methods to induce and control neuroplastic processes and correct abnormal function, or even shift functions from damaged tissue to physiologically healthy brain regions, hold the potential to dramatically improve overall health. Of the current neuroplastic interventions in development, neurofeedback training (NFT) from functional Magnetic Resonance Imaging (fMRI) has the advantages of being completely non-invasive, non-pharmacologic, and spatially localized to target brain regions, as well as having no known side effects. Furthermore, NFT techniques, initially developed using fMRI, can often be translated to exercises that can be performed outside of the scanner without the aid of medical professionals or sophisticated medical equipment. In fMRI NFT, the fMRI signal is measured from specific regions of the brain, processed, and presented to the participant in real-time. Through training, self-directed mental processing techniques, that regulate this signal and its underlying neurophysiologic correlates, are developed. FMRI NFT has been used to train volitional control over a wide range of brain regions with implications for several different cognitive, behavioral, and motor systems. Additionally, fMRI NFT has shown promise in a broad range of applications such as the treatment of neurologic disorders and the augmentation of baseline human performance. In this article, we present an fMRI NFT protocol developed at our institution for modulation of both healthy and abnormal brain function, as well as examples of using the method to target both cognitive and auditory regions of the brain.

Neural Correlates of Deficits in Humor Appreciation in Gelotophobics

Gelotophobics have social deficits in the form of relative humorlessness and heightened sensitivity to aggressive humor; however, little is known about the neural reward mechanisms for this group. The present study attempted to identify the neural substrates of responses to hostile and non-hostile jokes in gelotophobics and non-gelotophobics. Gelotophobics showed greater activation than did non-gelotophobics in the dorsal corticostriatal system, which comprises the dorsolateral prefrontal cortex and dorsal striatum, suggesting a higher degree of voluntary top-down cognitive control of emotion. As expected, gelotophobics showed less activation in the ventral mesocorticolimbic system (MCL) in response to both hostile and non-hostile jokes, suggesting a relative deficit in the reward system. Conversely, non-gelotophobics displayed greater activation than gelotophobics did in the MCL system, particularly for non-hostile jokes, which suggests a more robust bottom-up emotional response. In response to non-hostile jokes, non-gelotophobics showed greater activation in the ventral MCL reward system, which comprises the midbrain, amygdalae, nucleus accumbens, ventral anterior cingulate cortex, and insula. Psychophysiological interaction analyses further showed that gelotophobics exhibited diminished MCL activation in response to hostile jokes. These group differences may have important implications for our understanding of the neural correlates of social motivation and humor appreciation.

When the Brain Takes 'BOLD' Steps: Real-Time fMRI Neurofeedback Can Further Enhance the Ability to Gradually Self-regulate Regional Brain Activation

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Humans are able to gradually self-regulate regional brain activation by applying cognitive strategies.

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Providing rtfMRI neurofeedback can enhance the gradual self-regulation ability.

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Findings are generalizable to various mental tasks and clinical MR field strengths.

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Novel parametric activation paradigm enriches spectrum of rtfMRI-neurofeedback and BCI methodology.

Brain-computer interfaces (BCIs) based on real-time functional magnetic resonance imaging (rtfMRI) are currently explored in the context of developing alternative (motor-independent) communication and control means for the severely disabled. In such BCI systems, the user encodes a particular intention (e.g., an answer to a question or an intended action) by evoking specific mental activity resulting in a distinct brain state that can be decoded from fMRI activation. One goal in this context is to increase the degrees of freedom in encoding different intentions, i.e., to allow the BCI user to choose from as many options as possible. Recently, the ability to voluntarily modulate spatial and/or temporal blood oxygenation level-dependent (BOLD)-signal features has been explored implementing different mental tasks and/or different encoding time intervals, respectively. Our two-session fMRI feasibility study systematically investigated for the first time the possibility of using magnitudinal BOLD-signal features for intention encoding. Particularly, in our novel paradigm, participants (n = 10) were asked to alternately self-regulate their regional brain-activation level to 30%, 60% or 90% of their maximal capacity by applying a selected activation strategy (i.e., performing a mental task, e.g., inner speech) and modulation strategies (e.g., using different speech rates) suggested by the experimenters. In a second step, we tested the hypothesis that the additional availability of feedback information on the current BOLD-signal level within a region of interest improves the gradual-self regulation performance. Therefore, participants were provided with neurofeedback in one of the two fMRI sessions. Our results show that the majority of the participants were able to gradually self-regulate regional brain activation to at least two different target levels even in the absence of neurofeedback. When provided with continuous feedback on their current BOLD-signal level, most participants further enhanced their gradual self-regulation ability. Our findings were observed across a wide variety of mental tasks and across clinical MR field strengths (i.e., at 1.5 T and 3 T), indicating that these findings are robust and can be generalized across mental tasks and scanner types. The suggested novel parametric activation paradigm enriches the spectrum of current rtfMRI-neurofeedback and BCI methodology and has considerable potential for fundamental and clinical neuroscience applications.

To work or not to work: Neural representation of cost and benefit of instrumental action

By definition, instrumental actions are performed in order to obtain certain goals. Nevertheless, the attainment of goals typically implies obstacles, and response vigor is known to reflect an integration of subjective benefit and cost. Whereas several brain regions have been associated with cost/benefit ratio decision-making, trial-by-trial fluctuations in motivation are not well understood. We review recent evidence supporting the motivational implications of signal fluctuations in the mesocorticolimbic system. As an extension of "set-point" theories of instrumental action, we propose that response vigor is determined by a rapid integration of brain signals that reflect value and cost on a trial-by-trial basis giving rise to an online estimate of utility. Critically, we posit that fluctuations in key nodes of the network can predict deviations in response vigor and that variability in instrumental behavior can be accounted for by models devised from optimal control theory, which incorporate the effortful control of noise. Notwithstanding, the post hoc analysis of signaling dynamics has caveats that can effectively be addressed in future research with the help of two novel fMRI imaging techniques. First, adaptive fMRI paradigms can be used to establish a time-order relationship, which is a prerequisite for causality, by using observed signal fluctuations as triggers for stimulus presentation. Second, real-time fMRI neurofeedback can be employed to induce predefined brain states that may facilitate benefit or cost aspects of instrumental actions. Ultimately, understanding temporal dynamics in brain networks subserving response vigor holds the promise for targeted interventions that could help to readjust the motivational balance of behavior.

Real-time functional magnetic resonance imaging neurofeedback in motor neurorehabilitation

PURPOSE OF REVIEW

Recent developments in functional magnetic resonance imaging (fMRI) have catalyzed a new field of translational neuroscience. Using fMRI to monitor the aspects of task-related changes in neural activation or brain connectivity, investigators can offer feedback of simple or complex neural signals/patterns back to the participant on a quasireal-time basis [real-time-fMRI-based neurofeedback (rt-fMRI-NF)]. Here, we introduce some background methodology of the new developments in this field and give a perspective on how they may be used in neurorehabilitation in the future.

RECENT FINDINGS

The development of rt-fMRI-NF has been used to promote self-regulation of activity in several brain regions and networks. In addition, and unlike other noninvasive techniques, rt-fMRI-NF can access specific subcortical regions and in principle any region that can be monitored using fMRI including the cerebellum, brainstem and spinal cord. In Parkinson's disease and stroke, rt-fMRI-NF has been demonstrated to alter neural activity after the self-regulation training was completed and to modify specific behaviours.

SUMMARY

Future exploitation of rt-fMRI-NF could be used to induce neuroplasticity in brain networks that are involved in certain neurological conditions. However, currently, the use of rt-fMRI-NF in randomized, controlled clinical trials is in its infancy.

Pathophysiological targets for non-pharmacological treatment of migraine

Background Migraine is the most prevalent neurological disorder worldwide and ranked sixth among all diseases in years lived with disability. Overall preventive anti-migraine therapies have an effect in one patient out of two at the most, many of them being endowed with disabling adverse effects. No new disease-modifying drugs have come into clinical practice since the application to migraine of topiramate and botulinum toxin, the latter for its chronic form. There is thus clearly a need for more effective treatments that are devoid of, or have acceptable side effects. In recent years, scientific progress in migraine research has led to substantial changes in our understanding of the pathophysiology of migraine and paved the way for novel non-drug pathophysiological-targeted treatment strategies. Overview Several such non-drug therapies have been tested in migraine, such as oxidative phosphorylation enhancers, diets and non-invasive central or peripheral neurostimulation. All of them are promising for preventive migraine treatment and are quasi-devoid of side effects. Their advantage is that they can in theory be selected for individual patients according to their pathophysiological profile and they can (and probably should) be combined with the classical pharmacological armamentarium. Conclusion We will review here how knowledge of the functional anatomy and physiology of migraine mechanisms holds the key for more specific and effective non-pharmacological treatments.

How feedback, motor imagery, and reward influence brain self-regulation using real-time fMRI

The learning process involved in achieving brain self-regulation is presumed to be related to several factors, such as type of feedback, reward, mental imagery, duration of training, among others. Explicitly instructing participants to use mental imagery and monetary reward are common practices in real-time fMRI (rtfMRI) neurofeedback (NF), under the assumption that they will enhance and accelerate the learning process. However, it is still not clear what the optimal strategy is for improving volitional control. We investigated the differential effect of feedback, explicit instructions and monetary reward while training healthy individuals to up-regulate the blood-oxygen-level dependent (BOLD) signal in the supplementary motor area (SMA). Four groups were trained in a two-day rtfMRI-NF protocol: GF with NF only, GF,I with NF + explicit instructions (motor imagery), GF,R with NF + monetary reward, and GF,I,R with NF + explicit instructions (motor imagery) + monetary reward. Our results showed that GF increased significantly their BOLD self-regulation from day-1 to day-2 and GF,R showed the highest BOLD signal amplitude in SMA during the training. The two groups who were instructed to use motor imagery did not show a significant learning effect over the 2 days. The additional factors, namely motor imagery and reward, tended to increase the intersubject variability in the SMA during the course of training. Whole brain univariate and functional connectivity analyses showed common as well as distinct patterns in the four groups, representing the varied influences of feedback, reward, and instructions on the brain. Hum Brain Mapp 37:3153-3171, 2016. © 2016 Wiley Periodicals, Inc.

Cognitive Neurostimulation: Learning to Volitionally Sustain Ventral Tegmental Area Activation

Activation of the ventral tegmental area (VTA) and mesolimbic networks is essential to motivation, performance, and learning. Humans routinely attempt to motivate themselves, with unclear efficacy or impact on VTA networks. Using fMRI, we found untrained participants' motivational strategies failed to consistently activate VTA. After real-time VTA neurofeedback training, however, participants volitionally induced VTA activation without external aids, relative to baseline, Pre-test, and control groups. VTA self-activation was accompanied by increased mesolimbic network connectivity. Among two comparison groups (no neurofeedback, false neurofeedback) and an alternate neurofeedback group (nucleus accumbens), none sustained activation in target regions of interest nor increased VTA functional connectivity. The results comprise two novel demonstrations: learning and generalization after VTA neurofeedback training and the ability to sustain VTA activation without external reward or reward cues. These findings suggest theoretical alignment of ideas about motivation and midbrain physiology and the potential for generalizable interventions to improve performance and learning.

Modeling event-related heart period responses

Cardiac rhythm is generated locally in the sinoatrial node, but modulated by central neural input. This may provide a possibility to infer central processes from observed phasic heart period responses (HPR). Currently, operational methods are used for HPR analysis. These methods embody implicit assumptions on how central states influence heart period. Here, we build an explicit psychophysiological model (PsPM) for event‐related HPR. This phenomenological PsPM is based on three experiments involving white noise sounds, an auditory oddball task, and emotional picture viewing. The model is optimized with respect to predictive validity—the ability to separate experimental conditions from each other. To validate the PsPM, an independent sample of participants is presented with auditory stimuli of varying intensity and emotional pictures of negative and positive valence, at short intertrial intervals. Our model discriminates these experimental conditions from each other better than operational approaches. We conclude that our PsPM is more sensitive to distinguish experimental manipulations based on heart period data than operational methods, and furnishes a principled approach to analysis of HPR.