Covert neurofeedback without awareness shapes cortical network spontaneous connectivity

Modulation of human frontal midline theta by neurofeedback: A systematic review and quantitative meta-analysis

Human brain activity consists of different frequency bands associated with varying functions. Oscillatory activity of frontal brain regions in the theta range (4-8 Hz) is linked to cognitive processing and can be modulated by neurofeedback - a technique where participants receive real-time feedback about their brain activity and learn to modulate it. However, criticism of this technique evolved, and high heterogeneity of study designs complicates a valid evaluation of its effectiveness. This meta-analysis provides the first systematic overview over studies attempting to modulate frontal midline theta with neurofeedback in healthy human participants. Out of 1261 articles screened, 14 studies were eligible for systematic review and 11 for quantitative meta-analyses. Studies were evaluated following the DIAD model and the PRISMA guidelines. A significant across-study effect of medium size (Hedges' g = .66; 95%-CI [-0.62, 1.73]) with substantial between-study heterogeneity (Q(16) = 167.43, p < .001) was observed and subanalysis revealed effective frontal midline theta upregulation. We discuss moderators of effect sizes and provide guidelines for future research in this dynamic field.

Functionality of arousal-regulating brain circuitry at rest predicts human cognitive abilities

Arousal state is regulated by subcortical neuromodulatory nuclei, such as locus coeruleus, which send wide-reaching projections to cortex. Whether higher-order cortical regions have the capacity to recruit neuromodulatory systems to aid cognition is unclear. Here, we hypothesized that select cortical regions activate the arousal system, which, in turn, modulates large-scale brain activity, creating a functional circuit predicting cognitive ability. We utilized the Human Connectome Project 7T functional magnetic resonance imaging dataset (n = 149), acquired at rest with simultaneous eye tracking, along with extensive cognitive assessment for each subject. First, we discovered select frontoparietal cortical regions that drive large-scale spontaneous brain activity specifically via engaging the arousal system. Second, we show that the functionality of the arousal circuit driven by bilateral posterior cingulate cortex (associated with the default mode network) predicts subjects' cognitive abilities. This suggests that a cortical region that is typically associated with self-referential processing supports cognition by regulating the arousal system.

Functionality of arousal-regulating brain circuitry at rest predicts human cognitive abilities

Arousal state is regulated by subcortical neuromodulatory nuclei, such as locus coeruleus, which send wide-reaching projections to cortex. Whether higher-order cortical regions have the capacity to recruit neuromodulatory systems to aid cognition is unclear. Here, we hypothesized that select cortical regions activate the arousal system, which in turn modulates large-scale brain activity, creating a functional circuit predicting cognitive ability. We utilized the Human Connectome Project 7T functional magnetic resonance imaging dataset (N=149), acquired at rest with simultaneous eye tracking, along with extensive cognitive assessment for each subject. First, we discovered select frontoparietal cortical regions that drive large-scale spontaneous brain activity specifically via engaging the arousal system. Second, we show that the functionality of the arousal circuit driven by bilateral posterior cingulate cortex (associated with the default mode network) predicts subjects' cognitive abilities. This suggests that a cortical region that is typically associated with self-referential processing supports cognition by regulating the arousal system.

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.

The neuronal basis of human creativity

Human creativity is a powerful cognitive ability underlying all uniquely human cultural and scientific advancement. However, the neuronal basis of this creative ability is unknown. Here, I propose that slow, spontaneous fluctuations in neuronal activity, also known as "resting state" fluctuations, constitute a universal mechanism underlying all facets of human creativity. Support for this hypothesis is derived from experiments that directly link spontaneous fluctuations and verbal creativity. Recent experimental and modeling advances in our understanding of the spontaneous fluctuations offer an explanation for the diversity and innovative nature of creativity, which is derived from a unique integration of random, neuronal noise on the one hand with individually specified, deterministic information acquired through learning, expertise training, and hereditary traits. This integration between stochasticity and order leads to a process that offers, on the one hand, original, unexpected outcomes but, on the other hand, endows these outcomes with knowledge-based meaning and significance.

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.

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.

Doubting the double-blind: Introducing a questionnaire for awareness of experimental purposes in neurofeedback studies

Double-blinding subjects to the experiment's purpose is an important standard in neurofeedback studies. However, it is difficult to provide evidence that humans are entirely unaware of certain information. This study used insights from consciousness studies and neurophenomenology to develop a contingency awareness questionnaire for neurofeedback. We assessed whether participants had an awareness of experimental purposes to manipulate their attention and multisensory perception. A subset of subjects (5 out of 20) gained a degree of awareness of experimental purposes as evidenced by their correct guess about the purposes of the experiment to affect their attention and multisensory perceptions specific to their double-blinded group assignment. The results warrant replication before they are applied to clinical neurofeedback studies, given the considerable time taken to perform the questionnaire (∼25 min). We discuss the strengths and limitations of our contingency awareness questionnaire and the growing appeal of the double-blinded standard in clinical neurofeedback studies.

Feasibility and utility of amygdala neurofeedback

Amygdala NeuroFeedback (NF) have the potential of being a valuable non-invasive intervention tool in many psychiatric disporders. However, the feasibility and best practices of this method have not been systematically examined. The current article presents a review of amygdala-NF studies, an analytic summary of study design parameters, and examination of brain mechanisms related to successful amygdala-NF performance. A meta-analysis of 33 publications showed that real amygdala-NF facilitates learned modulation compared to control conditions. In addition, while variability in study dsign parameters is high, these design choices are implicitly organized by the targeted valence domain (positive or negative). However, in most cases the neuro-behavioral effects of targeting such domains were not directly assessed. Lastly, re-analyzing six data sets of amygdala-fMRI-NF revealed that successful amygdala down-modulation is coupled with deactivation of the posterior insula and nodes in the Default-Mode-Network. Our findings suggest that amygdala self-modulation can be acquired using NF. Yet, additional controlled studies, relevant behavioral tasks before and after NF intervention, and neural 'target engagement' measures are critically needed to establish efficacy and specificity. In addition, the fMRI analysis presented here suggest that common accounts regarding the brain network involved in amygdala NF might reflect unsuccessful modulation attempts rather than successful modulation.

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.

Neurofeedback through the lens of reinforcement learning

Despite decades of experimental and clinical practice, the neuropsychological mechanisms underlying neurofeedback (NF) training remain obscure. NF is a unique form of reinforcement learning (RL) task, during which participants are provided with rewarding feedback regarding desired changes in neural patterns. However, key RL considerations - including choices during practice, prediction errors, credit-assignment problems, or the exploration-exploitation tradeoff - have infrequently been considered in the context of NF. We offer an RL-based framework for NF, describing different internal states, actions, and rewards in common NF protocols, thus fashioning new proposals for characterizing, predicting, and hastening the course of learning. In this way we hope to advance current understanding of neural regulation via NF, and ultimately to promote its effectiveness, personalization, and clinical utility.

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.

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.

Closed-loop Modulation of the Self-regulating Brain: A Review on Approaches, Emerging Paradigms, and Experimental Designs

Closed-loop approaches, setups, and experimental designs have been applied within the field of neuroscience to enhance the understanding of basic neurophysiology principles (closed-loop neuroscience; CLNS) and to develop improved procedures for modulating brain circuits and networks for clinical purposes (closed-loop neuromodulation; CLNM). The contents of this review are thus arranged into the following sections. First, we describe basic research findings that have been made using CLNS. Next, we provide an overview of the application, rationale, and therapeutic aspects of CLNM for clinical purposes. Finally, we summarize methodological concerns and critics in clinical practice of neurofeedback and novel applications of closed-loop perspective and techniques to improve and optimize its experiments. Moreover, we outline the theoretical explanations and experimental ideas to test animal models of neurofeedback and discuss technical issues and challenges associated with implementing closed-loop systems. We hope this review is helpful for both basic neuroscientists and clinical/ translationally-oriented scientists interested in applying closed-loop methods to improve mental health and well-being.

fMRI neurofeedback in the motor system elicits bidirectional changes in activity and in white matter structure in the adult human brain

White matter (WM) plasticity supports skill learning and memory. Up- and downregulation of brain activity in animal models lead to WM alterations. But can bidirectional brain-activity manipulation change WM structure in the adult human brain? We employ fMRI neurofeedback to endogenously and directionally modulate activity in the sensorimotor cortices. Diffusion tensor imaging is acquired before and after two separate conditions, involving regulating sensorimotor activity either up or down using real or sham neurofeedback (n = 20 participants × 4 scans). We report rapid opposing changes in corpus callosum microstructure that depend on the direction of activity modulation. Our findings show that fMRI neurofeedback can be used to endogenously and directionally alter not only brain-activity patterns but also WM pathways connecting the targeted brain areas. The level of associated brain activity in connected areas is therefore a possible mediator of previously described learning-related changes in WM.

Local neuronal relational structures underlying the contents of human conscious experience

While most theories of consciousness posit some kind of dependence on global network activities, I consider here an alternative, localist perspective-in which localized cortical regions each underlie the emergence of a unique category of conscious experience. Under this perspective, the large-scale activation often found in the cortex is a consequence of the complexity of typical conscious experiences rather than an obligatory condition for the emergence of conscious awareness-which can flexibly shift, depending on the richness of its contents, from local to more global activation patterns. This perspective fits a massive body of human imaging, recordings, lesions and stimulation data but opens a fundamental problem: how can the information, defining each content, be derived locally in each cortical region. Here, I will discuss a solution echoing pioneering structuralist ideas in which the content of a conscious experience is defined by its relationship to all other contents within an experiential category. In neuronal terms, this relationship structure between contents is embodied by the local geometry of similarity distances between cortical activation patterns generated during each conscious experience, likely mediated via networks of local neuronal connections. Thus, in order for any conscious experience to appear in an individual's mind, two central conditions must be met. First, a specific configural pattern ("bar-code") of neuronal activity must appear within a local relational geometry, i.e. a cortical area. Second, the individual neurons underlying the activated pattern must be bound into a unified functional ensemble through a burst of recurrent neuronal firing: local "ignitions".

The impact of real-time fMRI denoising on online evaluation of brain activity and functional connectivity

Objective. Comprehensive denoising is imperative in functional magnetic resonance imaging (fMRI) analysis to reliably evaluate neural activity from the blood oxygenation level dependent signal. In real-time fMRI, however, only a minimal denoising process has been applied and the impact of insufficient denoising on online brain activity estimation has not been assessed comprehensively. This study evaluated the noise reduction performance of online fMRI processes in a real-time estimation of regional brain activity and functional connectivity.Approach.We performed a series of real-time processing simulations of online fMRI processing, including slice-timing correction, motion correction, spatial smoothing, signal scaling, and noise regression with high-pass filtering, motion parameters, motion derivatives, global signal, white matter/ventricle average signals, and physiological noise models with image-based retrospective correction of physiological motion effects (RETROICOR) and respiration volume per time (RVT).Main results.All the processing was completed in less than 400 ms for whole-brain voxels. Most processing had a benefit for noise reduction except for RVT that did not work due to the limitation of the online peak detection. The global signal regression, white matter/ventricle signal regression, and RETROICOR had a distinctive noise reduction effect, depending on the target signal, and could not substitute for each other. Global signal regression could eliminate the noise-associated bias in the mean dynamic functional connectivity across time.Significance.The results indicate that extensive real-time denoising is possible and highly recommended for real-time fMRI applications.

Predictors of real-time fMRI neurofeedback performance and improvement - A machine learning mega-analysis

Real-time fMRI neurofeedback is an increasingly popular neuroimaging technique that allows an individual to gain control over his/her own brain signals, which can lead to improvements in behavior in healthy participants as well as to improvements of clinical symptoms in patient populations. However, a considerably large ratio of participants undergoing neurofeedback training do not learn to control their own brain signals and, consequently, do not benefit from neurofeedback interventions, which limits clinical efficacy of neurofeedback interventions. As neurofeedback success varies between studies and participants, it is important to identify factors that might influence neurofeedback success. Here, for the first time, we employed a big data machine learning approach to investigate the influence of 20 different design-specific (e.g. activity vs. connectivity feedback), region of interest-specific (e.g. cortical vs. subcortical) and subject-specific factors (e.g. age) on neurofeedback performance and improvement in 608 participants from 28 independent experiments. With a classification accuracy of 60% (considerably different from chance level), we identified two factors that significantly influenced neurofeedback performance: Both the inclusion of a pre-training no-feedback run before neurofeedback training and neurofeedback training of patients as compared to healthy participants were associated with better neurofeedback performance. The positive effect of pre-training no-feedback runs on neurofeedback performance might be due to the familiarization of participants with the neurofeedback setup and the mental imagery task before neurofeedback training runs. Better performance of patients as compared to healthy participants might be driven by higher motivation of patients, higher ranges for the regulation of dysfunctional brain signals, or a more extensive piloting of clinical experimental paradigms. Due to the large heterogeneity of our dataset, these findings likely generalize across neurofeedback studies, thus providing guidance for designing more efficient neurofeedback studies specifically for improving clinical neurofeedback-based interventions. To facilitate the development of data-driven recommendations for specific design details and subpopulations the field would benefit from stronger engagement in open science research practices and data sharing.

Towards semantic fMRI neurofeedback: navigating among mental states using real-time representational similarity analysis

Objective. Real-time functional magnetic resonance imaging neurofeedback (rt-fMRI-NF) is a non-invasive MRI procedure allowing examined participants to learn to self-regulate brain activity by performing mental tasks. A novel two-step rt-fMRI-NF procedure is proposed whereby the feedback display is updated in real-time based on high-level representations of experimental stimuli (e.g. objects to imagine) via real-time representational similarity analysis of multi-voxel patterns of brain activity.Approach. In a localizer session, the stimuli become associated with anchored points on a two-dimensional representational space where distances approximate between-pattern (dis)similarities. In the NF session, participants modulate their brain response, displayed as a movable point, to engage in a specific neural representation. The developed method pipeline is verified in a proof-of-concept rt-fMRI-NF study at 7 T involving a single healthy participant imagining concrete objects. Based on this data and artificial data sets with similar (simulated) spatio-temporal structure and variable (injected) signal and noise, the dependence on noise is systematically assessed.Main results. The participant in the proof-of-concept study exhibited robust activation patterns in the localizer session and managed to control the neural representation of a stimulus towards the selected target in the NF session. The offline analyses validated the rt-fMRI-NF results, showing that the rapid convergence to the target representation is noise-dependent.Significance. Our proof-of-concept study introduces a new NF method allowing the participant to navigate among different mental states. Compared to traditional NF designs (e.g. using a thermometer display to set the level of the neural signal), the proposed approach provides content-specific feedback to the participant and extra degrees of freedom to the experimenter enabling real-time control of the neural activity towards a target brain state without suggesting a specific mental strategy to the subject.

Enhancing Working Memory Based on Mismatch Negativity Neurofeedback in Subjective Cognitive Decline Patients: A Preliminary Study

Mismatch negativity (MMN) is suitable for studies of preattentive auditory discriminability and the auditory memory trace. Subjective cognitive decline (SCD) is an ideal target for early therapeutic intervention because SCD occurs at preclinical stages many years before the onset of Alzheimer’s disease (AD). According to a novel lifespan-based model of dementia risk, hearing loss is considered the greatest potentially modifiable risk factor of dementia among nine health and lifestyle factors, and hearing impairment is associated with cognitive decline. Therefore, we propose a neurofeedback training based on MMN, which is an objective index of auditory discriminability, to regulate sensory ability and memory as a non-pharmacological intervention (NPI) in SCD patients. Seventeen subjects meeting the standardized clinical evaluations for SCD received neurofeedback training. The auditory frequency discrimination test, the visual digital N-back (1-, 2-, and 3-back), auditory digital N-back (1-, 2-, and 3-back), and auditory tone N-back (1-, 2-, and 3-back) tasks were used pre- and post-training in all SCD patients. The intervention schedule comprised five 60-min training sessions over 2 weeks. The results indicate that the subjects who received neurofeedback training had successfully improved the amplitude of MMN at the parietal electrode (Pz). A slight decrease in the threshold of auditory frequency discrimination was observed after neurofeedback training. Notably, after neurofeedback training, the working memory (WM) performance was significantly enhanced in the auditory tone 3-back test. Moreover, improvements in the accuracy of all WM tests relative to the baseline were observed, although the changes were not significant. To the best of our knowledge, our preliminary study is the first to investigate the effects of MMN neurofeedback training on WM in SCD patients, and our results suggest that MMN neurofeedback may represent an effective treatment for intervention in SCD patients and the elderly with aging memory decline.

Delineating implicit and explicit processes in neurofeedback learning

Neurofeedback allows humans to self-regulate neural activity in specific brain regions and is considered a promising tool for psychiatric interventions. Recently, methods have been developed to use neurofeedback implicitly, prompting a theoretical debate on the role of awareness in neurofeedback learning. We offer a critical review of the role of awareness in neurofeedback learning, with a special focus on recently developed neurofeedback paradigms. We detail differences in instructions and propose a fine-grained categorization of tasks based on the degree of involvement of explicit and implicit processes. Finally, we review the methods used to measure awareness in neurofeedback and propose new candidate measures. We conclude that explicit processes cannot be eschewed in most current implicit tasks that have explicit goals, and suggest ways in which awareness could be better measured in the future. Investigating awareness during learning will help understand the learning mechanisms underlying neurofeedback learning and will help shape future tasks.

Can we predict real-time fMRI neurofeedback learning success from pretraining brain activity?

Neurofeedback training has been shown to influence behavior in healthy participants as well as to alleviate clinical symptoms in neurological, psychosomatic, and psychiatric patient populations. However, many real-time fMRI neurofeedback studies report large inter-individual differences in learning success. The factors that cause this vast variability between participants remain unknown and their identification could enhance treatment success. Thus, here we employed a meta-analytic approach including data from 24 different neurofeedback studies with a total of 401 participants, including 140 patients, to determine whether levels of activity in target brain regions during pretraining functional localizer or no-feedback runs (i.e., self-regulation in the absence of neurofeedback) could predict neurofeedback learning success. We observed a slightly positive correlation between pretraining activity levels during a functional localizer run and neurofeedback learning success, but we were not able to identify common brain-based success predictors across our diverse cohort of studies. Therefore, advances need to be made in finding robust models and measures of general neurofeedback learning, and in increasing the current study database to allow for investigating further factors that might influence neurofeedback learning.

Mindfulness Improves Brain-Computer Interface Performance by Increasing Control Over Neural Activity in the Alpha Band

Brain-computer interfaces (BCIs) are promising tools for assisting patients with paralysis, but suffer from long training times and variable user proficiency. Mind-body awareness training (MBAT) can improve BCI learning, but how it does so remains unknown. Here, we show that MBAT allows participants to learn to volitionally increase alpha band neural activity during BCI tasks that incorporate intentional rest. We trained individuals in mindfulness-based stress reduction (MBSR; a standardized MBAT intervention) and compared performance and brain activity before and after training between randomly assigned trained and untrained control groups. The MBAT group showed reliably faster learning of BCI than the control group throughout training. Alpha-band activity in electroencephalogram signals, recorded in the volitional resting state during task performance, showed a parallel increase over sessions, and predicted final BCI performance. The level of alpha-band activity during the intentional resting state correlated reliably with individuals' mindfulness practice as well as performance on a breath counting task. Collectively, these results show that MBAT modifies a specific neural signal used by BCI. MBAT, by increasing patients' control over their brain activity during rest, may increase the effectiveness of BCI in the large population who could benefit from alternatives to direct motor control.

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.

Modulation of Intrinsic Brain Connectivity by Implicit Electroencephalographic Neurofeedback

Despite the increasing popularity of neurofeedback, its mechanisms of action are still poorly understood. This study aims to describe the processes underlying implicit electroencephalographic neurofeedback. Fifty-two healthy volunteers were randomly assigned to a single session of infra-low frequency neurofeedback or sham neurofeedback, with electrodes over the right middle temporal gyrus and the right inferior parietal lobule. They observed a moving rocket, the speed of which was modulated by the waveform derived from a band-limited infra-low frequency filter. Immediately before and after the session, the participants underwent a resting-state fMRI. Network-based statistical analysis was applied, comparing post- vs. pre-session and real vs. sham neurofeedback conditions. As a result, two phenomena were observed. First, we described a brain circuit related to the implicit neurofeedback process itself, consisting of the lateral occipital cortex, right dorsolateral prefrontal cortex, left orbitofrontal cortex, right ventral striatum, and bilateral dorsal striatum. Second, we found increased connectivity between key regions of the salience, language, and visual networks, which is indicative of integration in sensory processing. Thus, it appears that a single session of implicit infra-low frequency electroencephalographic neurofeedback leads to significant changes in intrinsic brain connectivity.

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.

What would a synthetic connectome look like?

A major challenge of contemporary neuroscience is to unravel the structure of the connectome, the ensemble of neural connections that link between different functional units of the brain, and to reveal how this structure relates to brain function. This thriving area of research largely follows the general tradition in biology of reverse-engineering, which consists of first observing and characterizing a biological system or process, and then deconstructing it into its fundamental building blocks in order to infer its modes of operation. However, a complementary form of biology has emerged, synthetic biology, which emphasizes construction-based forward-engineering. The synthetic biology approach comprises the assembly of new biological systems out of elementary biological parts. The rationale is that the act of building a system can be a powerful method for gaining deep understanding of how that system works. As the fields of connectomics and synthetic biology are independently growing, I propose to consider the benefits of combining the two, to create synthetic connectomics, a new form of neuroscience and a new form of synthetic biology. The goal of synthetic connectomics would be to artificially design and construct the connectomes of live behaving organisms. Synthetic connectomics could serve as a unifying platform for unraveling the complexities of brain operation and perhaps also for generating new forms of artificial life, and, in general, could provide a valuable opportunity for empirically exploring theoretical predictions about network function. What would a synthetic connectome look like? What purposes would it serve? How could it be constructed? This review delineates the novel notion of a synthetic connectome and aims to lay out the initial steps towards its implementation, contemplating its impact on science and society.

Altered resting-state dynamics in autism spectrum disorder: Causal to the social impairment?

Autism spectrum disorder (ASD) is characterized by profound impairments in social abilities and by restricted interests and repetitive behaviors. Much work in the past decade has been dedicated to understanding the brain-bases of ASD, and in the context of resting-state functional connectivity fMRI in high-functioning adolescents and adults, the field has established a set of reliable findings: decreased cortico-cortical interactions among brain regions thought to be engaged in social processing, along with a simultaneous increase in thalamo-cortical and striato-cortical interactions. However, few studies have attempted to manipulate these altered patterns, leading to the question of whether such patterns are actually causally involved in producing the corresponding behavioral impairments. We discuss a few such recent attempts in the domains of fMRI neurofeedback and overt social interaction during scanning, and we conclude that the evidence of causal involvement is somewhat mixed. We highlight the potential role of the thalamus and striatum in ASD and emphasize the need for studies that directly compare scanning during multiple cognitive states in addition to the resting-state.

Success and failure of controlling the real-time functional magnetic resonance imaging neurofeedback signal are reflected in the striatum

INTRODUCTION

Over the last decades, neurofeedback has been applied in variety of research contexts and therapeutic interventions. Despite this extensive use, its neural mechanisms are still under debate. Several scientific advances have suggested that different networks become jointly active during neurofeedback, including regions generally involved in self-regulation, regions related to the specific mental task driving the neurofeedback and regions generally involved in feedback learning (Sitaram et al., 2017, Nature Reviews Neuroscience, 18, 86).

METHODS

To investigate the neural mechanisms specific to neurofeedback but independent from general effects of self-regulation, we compared brain activation as measured with functional magnetic resonance imaging (fMRI) across different mental tasks involving gradual self-regulation with and without providing neurofeedback. Ten participants freely chose one self-regulation task and underwent two training sessions during fMRI scanning, one with and one without receiving neurofeedback. During neurofeedback sessions, feedback signals were provided in real-time based on activity in task-related, individually defined target regions. In both sessions, participants aimed at reaching and holding low, medium, or high brain-activation levels in the target region.

RESULTS

During gradual self-regulation with neurofeedback, a network of cortical control regions as well as regions implicated in reward and feedback processing were activated. Self-regulation with feedback was accompanied by stronger activation within the striatum across different mental tasks. Additional time-resolved single-trial analysis revealed that neurofeedback performance was positively correlated with a delayed brain response in the striatum that reflected the accuracy of self-regulation.

CONCLUSION

Overall, these findings support that neurofeedback contributes to self-regulation through task-general regions involved in feedback and reward processing.

Toward a comprehensive understanding of the neural mechanisms of decoded neurofeedback

Real-time functional magnetic resonance imaging (fMRI) neurofeedback is an experimental framework in which fMRI signals are presented to participants in a real-time manner to change their behaviors. Changes in behaviors after real-time fMRI neurofeedback are postulated to be caused by neural plasticity driven by the induction of specific targeted activities at the neuronal level (targeted neural plasticity model). However, some research groups argued that behavioral changes in conventional real-time fMRI neurofeedback studies are explained by alternative accounts, including the placebo effect and physiological artifacts. Recently, decoded neurofeedback (DecNef) has been developed as a result of adapting new technological advancements, including implicit neurofeedback and fMRI multivariate analyses. DecNef provides strong evidence for the targeted neural plasticity model while refuting the abovementioned alternative accounts. In this review, we first discuss how DecNef refutes the alternative accounts. Second, we propose a model that shows how targeted neural plasticity occurs at the neuronal level during DecNef training. Finally, we discuss computational and empirical evidence that supports the model. Clarification of the neural mechanisms of DecNef would lead to the development of more advanced fMRI neurofeedback methods that may serve as powerful tools for both basic and clinical research.

Neurofeedback: Principles, appraisal, and outstanding issues

Neurofeedback is a form of brain training in which subjects are fed back information about some measure of their brain activity which they are instructed to modify in a way thought to be functionally advantageous. Over the last 20 years, neurofeedback has been used to treat various neurological and psychiatric conditions, and to improve cognitive function in various contexts. However, in spite of a growing popularity, neurofeedback protocols typically make (often covert) assumptions on what aspects of brain activity to target, where in the brain to act and how, which have far-reaching implications for the assessment of its potential and efficacy. Here we critically examine some conceptual and methodological issues associated with the way neurofeedback's general objectives and neural targets are defined. The neural mechanisms through which neurofeedback may act at various spatial and temporal scales, and the way its efficacy is appraised are reviewed, and the extent to which neurofeedback may be used to control functional brain activity discussed. Finally, it is proposed that gauging neurofeedback's potential, as well as assessing and improving its efficacy will require better understanding of various fundamental aspects of brain dynamics and a more precise definition of functional brain activity and brain-behaviour relationships.

Unconscious Psychological Treatments for Physiological Survival Circuits

The idea of targeting unconscious or implicit processes in psychological treatments is not new, but until recently it has not been easy to manipulate these processes without also engaging consciousness. Here we review how this is possible, using various modern cognitive neuroscience methods including a technique known as Decoded Neural-Reinforcement. We discuss the general advantages of this approach, such as how it can facilitate double-blind placebo-controlled studies, and minimize premature patient dropouts in the treatment of fear. We also speculate how this may generalize to other similar physiological survival processes.

Monitoring and control of amygdala neurofeedback involves distributed information processing in the human brain

Brain-computer interfaces provide conscious access to neural activity by means of brain-derived feedback ("neurofeedback"). An individual's abilities to monitor and control feedback are two necessary processes for effective neurofeedback therapy, yet their underlying functional neuroanatomy is still being debated. In this study, healthy subjects received visual feedback from their amygdala response to negative pictures. Activation and functional connectivity were analyzed to disentangle the role of brain regions in different processes. Feedback monitoring was mapped to the thalamus, ventromedial prefrontal cortex (vmPFC), ventral striatum (VS), and rostral PFC. The VS responded to feedback corresponding to instructions while rPFC activity differentiated between conditions and predicted amygdala regulation. Control involved the lateral PFC, anterior cingulate, and insula. Monitoring and control activity overlapped in the VS and thalamus. Extending current neural models of neurofeedback, this study introduces monitoring and control of feedback as anatomically dissociated processes, and suggests their important role in voluntary neuromodulation.

Advances in fMRI Real-Time Neurofeedback

Functional magnetic resonance imaging (fMRI) neurofeedback is a type of biofeedback in which real-time online fMRI signals are used to self-regulate brain function. Since its advent in 2003 significant progress has been made in fMRI neurofeedback techniques. Specifically, the use of implicit protocols, external rewards, multivariate analysis, and connectivity analysis has allowed neuroscientists to explore a possible causal involvement of modified brain activity in modified behavior. These techniques have also been integrated into groundbreaking new neurofeedback technologies, specifically decoded neurofeedback (DecNef) and functional connectivity-based neurofeedback (FCNef). By modulating neural activity and behavior, DecNef and FCNef have substantially advanced both basic and clinical research.

The potential of real-time fMRI neurofeedback for stroke rehabilitation: A systematic review

Real-time functional magnetic resonance imaging (rt-fMRI) neurofeedback aids the modulation of neural functions by training self-regulation of brain activity through operant conditioning. This technique has been applied to treat several neurodevelopmental and neuropsychiatric disorders, but its effectiveness for stroke rehabilitation has not been examined yet. Here, we systematically review the effectiveness of rt-fMRI neurofeedback training in modulating motor and cognitive processes that are often impaired after stroke. Based on predefined search criteria, we selected and examined 33 rt-fMRI neurofeedback studies, including 651 healthy individuals and 15 stroke patients in total. The results of our systematic review suggest that rt-fMRI neurofeedback training can lead to a learned modulation of brain signals, with associated changes at both the neural and the behavioural level. However, more research is needed to establish how its use can be optimized in the context of stroke rehabilitation.

Direct modulation of aberrant brain network connectivity through real-time NeuroFeedback

The existence of abnormal connectivity patterns between resting state networks in neuropsychiatric disorders, including Autism Spectrum Disorder (ASD), has been well established. Traditional treatment methods in ASD are limited, and do not address the aberrant network structure. Using real-time fMRI neurofeedback, we directly trained three brain nodes in participants with ASD, in which the aberrant connectivity has been shown to correlate with symptom severity. Desired network connectivity patterns were reinforced in real-time, without participants’ awareness of the training taking place. This training regimen produced large, significant long-term changes in correlations at the network level, and whole brain analysis revealed that the greatest changes were focused on the areas being trained. These changes were not found in the control group. Moreover, changes in ASD resting state connectivity following the training were correlated to changes in behavior, suggesting that neurofeedback can be used to directly alter complex, clinically relevant network connectivity patterns.

Even when we are at rest, our brains are always active. For example, areas of the brain involved in vision remain active in complete darkness. Different brain regions that connect together to perform a given task often show coordinated activity at rest. Past studies have shown that these resting connections are different in people with conditions such as autism. Some brain regions are more weakly connected while others are more strongly connected in people with autism spectrum disorder compared to those without. Furthermore, people with more severe symptoms seem to have more abnormal connections.

“Neurofeedback training” is a method of changing the resting connections between different brain regions. Scientists measure a brain signal – the connection between different brain regions – from a person in real time. They then provide positive feedback to the person if this signal improves. For example, if a connection that is too weak becomes stronger, the scientists might reinforce this by providing feedback on the success. Previous work has shown that neurofeedback training may even change people’s behaviour. However, it has not yet been explored as a method of treating the abnormal connections seen in people with autism when their brains are at rest.

To address this, Ramot et al. used a technique known as “functional magnetic resonance imaging” (or fMRI for short) to measure brain activity in young men with autism. First, certain brain regions were identified as having abnormal resting connections with each other. The participants were then asked to look at a blank screen and to try to reveal a picture hidden underneath. Whenever the connections between the chosen brain regions improved, part of the picture was revealed on the screen, accompanied by an upbeat sound. The participants were unaware that it was their brain signals causing this positive feedback.

This form of neurofeedback training successfully changed the abnormal brain connections in most of the participants with autism, making their connections more similar to those seen in the wider population. These effects lasted up to a year after training. Early results also suggest that these changes were related to improvements in symptoms, although further work is needed to see if doctors could reliably use this method as a therapy. These findings show that neurofeedback training could potentially help treat not only autism spectrum disorder, but a range of other disorders that involve abnormal brain connections, including depression and schizophrenia.

Computational approaches to fMRI analysis

Analysis methods in cognitive neuroscience have not always matched the richness of fMRI data. Early methods focused on estimating neural activity within individual voxels or regions, averaged over trials or blocks and modeled separately in each participant. This approach mostly neglected the distributed nature of neural representations over voxels, the continuous dynamics of neural activity during tasks, the statistical benefits of performing joint inference over multiple participants and the value of using predictive models to constrain analysis. Several recent exploratory and theory-driven methods have begun to pursue these opportunities. These methods highlight the importance of computational techniques in fMRI analysis, especially machine learning, algorithmic optimization and parallel computing. Adoption of these techniques is enabling a new generation of experiments and analyses that could transform our understanding of some of the most complex-and distinctly human-signals in the brain: acts of cognition such as thoughts, intentions and memories.

Self-regulation strategy, feedback timing and hemodynamic properties modulate learning in a simulated fMRI neurofeedback environment

Direct manipulation of brain activity can be used to investigate causal brain-behavior relationships. Current noninvasive neural stimulation techniques are too coarse to manipulate behaviors that correlate with fine-grained spatial patterns recorded by fMRI. However, these activity patterns can be manipulated by having people learn to self-regulate their own recorded neural activity. This technique, known as fMRI neurofeedback, faces challenges as many participants are unable to self-regulate. The causes of this non-responder effect are not well understood due to the cost and complexity of such investigation in the MRI scanner. Here, we investigated the temporal dynamics of the hemodynamic response measured by fMRI as a potential cause of the non-responder effect. Learning to self-regulate the hemodynamic response involves a difficult temporal credit-assignment problem because this signal is both delayed and blurred over time. Two factors critical to this problem are the prescribed self-regulation strategy (cognitive or automatic) and feedback timing (continuous or intermittent). Here, we sought to evaluate how these factors interact with the temporal dynamics of fMRI without using the MRI scanner. We first examined the role of cognitive strategies by having participants learn to regulate a simulated neurofeedback signal using a unidimensional strategy: pressing one of two buttons to rotate a visual grating that stimulates a model of visual cortex. Under these conditions, continuous feedback led to faster regulation compared to intermittent feedback. Yet, since many neurofeedback studies prescribe implicit self-regulation strategies, we created a computational model of automatic reward-based learning to examine whether this result held true for automatic processing. When feedback was delayed and blurred based on the hemodynamics of fMRI, this model learned more reliably from intermittent feedback compared to continuous feedback. These results suggest that different self-regulation mechanisms prefer different feedback timings, and that these factors can be effectively explored and optimized via simulation prior to deployment in the MRI scanner.

By repeatedly stimulating fine-grained patterns of neural activity, it is possible to manipulate behaviors associated with these patterns. While millimeter-scale patterns cannot yet be targeted with noninvasive brain stimulation, some people can learn to self-stimulate these activity patterns if they receive real-time feedback of their own recorded brain activity through a procedure known as fMRI neurofeedback. Other ‘non-responders’ are, for reasons unknown, unable to learn how to self-regulate these patterns. Here, we investigate how the properties of the fMRI signal, feedback timing, and self-regulation strategies may lead to this non-responder effect. The signal recorded by fMRI is related to blood flow in the brain and can be delayed by up to six seconds relative to underlying neural activity, which makes it difficult to learn. Because experiments in the MRI scanner are costly and time-consuming, we created a simulated neurofeedback environment to compare continuous versus intermittent feedback timing and cognitive versus automatic self-regulation strategies. In a cognitive experiment with human participants playing a simple game with the simulated neurofeedback signal, we found continuous feedback led to faster learning. However, in a computer model of automatic reward-based learning, we found that intermittent feedback was more reliable. These results will help improve future fMRI neurofeedback experiments and treatments by improving the efficacy of neurofeedback training procedures.

Decoded fMRI neurofeedback can induce bidirectional confidence changes within single participants

Neurofeedback studies using real-time functional magnetic resonance imaging (rt-fMRI) have recently incorporated the multi-voxel pattern decoding approach, allowing for fMRI to serve as a tool to manipulate fine-grained neural activity embedded in voxel patterns. Because of its tremendous potential for clinical applications, certain questions regarding decoded neurofeedback (DecNef) must be addressed. Specifically, can the same participants learn to induce neural patterns in opposite directions in different sessions? If so, how does previous learning affect subsequent induction effectiveness? These questions are critical because neurofeedback effects can last for months, but the short- to mid-term dynamics of such effects are unknown. Here we employed a within-subjects design, where participants underwent two DecNef training sessions to induce behavioural changes of opposing directionality (up or down regulation of perceptual confidence in a visual discrimination task), with the order of training counterbalanced across participants. Behavioral results indicated that the manipulation was strongly influenced by the order and the directionality of neurofeedback training. We applied nonlinear mathematical modeling to parametrize four main consequences of DecNef: main effect of change in confidence, strength of down-regulation of confidence relative to up-regulation, maintenance of learning effects, and anterograde learning interference. Modeling results revealed that DecNef successfully induced bidirectional confidence changes in different sessions within single participants. Furthermore, the effect of up- compared to down-regulation was more prominent, and confidence changes (regardless of the direction) were largely preserved even after a week-long interval. Lastly, the effect of the second session was markedly diminished as compared to the effect of the first session, indicating strong anterograde learning interference. These results are interpreted in the framework of reinforcement learning and provide important implications for its application to basic neuroscience, to occupational and sports training, and to therapy.

Optical-imaging-based neurofeedback to enhance therapeutic intervention in adolescents with autism: methodology and initial data

Impaired facial processing may contribute to social dysfunction in certain individuals with autism spectrum disorder (ASD). Prior studies show that electroencephalogram-based and functional magnetic resonance imaging-based neurofeedback might help some individuals with ASD learn to modulate regional brain activity and thus reduce symptoms. Here, we report for the first time the feasibility of employing functional near-infrared spectroscopy (fNIRS)-based neurofeedback training in children with ASD. We developed a method to study physiological self-regulation of oxy-hemoglobin using real-time feedback. The paradigm is illustrated with initial data from four subjects who engaged in a facial-identity recognition training program during which an implicit reinforcement was given based on the participant's brain activity and behavioral performance. Two participants had a confirmed diagnosis of ASD, and the other two were typically developing (TD). One participant with ASD and one TD participant received real-feedback (real-FB) during the training, whereas the other two received sham-feedback (sham-FB). After five training sessions, the subjects who received real-FB showed more improvement in facial recognition performance compared with those receiving sham-FB, particularly in the participant with ASD. These results suggest fNIRS-based neurofeedback could enhance therapeutic intervention in children with ASD.