Differential Activation Patterns in the Same Brain Region Led to Opposite Emotional States

Review of the Brain's Behaviour after Injury and Disease for Its Application in an Agent-Based Model (ABM)

The brain is the most complex organ in the human body and, as such, its study entails great challenges (methodological, theoretical, etc.). Nonetheless, there is a remarkable amount of studies about the consequences of pathological conditions on its development and functioning. This bibliographic review aims to cover mostly findings related to changes in the physical distribution of neurons and their connections-the connectome-both structural and functional, as well as their modelling approaches. It does not intend to offer an extensive description of all conditions affecting the brain; rather, it presents the most common ones. Thus, here, we highlight the need for accurate brain modelling that can subsequently be used to understand brain function and be applied to diagnose, track, and simulate treatments for the most prevalent pathologies affecting the brain.

Unconscious Manipulation of Conceptual Representations with Decoded Neurofeedback Impacts Search Behavior

The necessity of conscious awareness in human learning has been a long-standing topic in psychology and neuroscience. Previous research on non-conscious associative learning is limited by the low signal-to-noise ratio of the subliminal stimulus, and the evidence remains controversial, including failures to replicate. Using functional MRI decoded neurofeedback, we guided participants from both sexes to generate neural patterns akin to those observed when visually perceiving real-world entities (e.g., dogs). Importantly, participants remained unaware of the actual content represented by these patterns. We utilized an associative DecNef approach to imbue perceptual meaning (e.g., dogs) into Japanese hiragana characters that held no inherent meaning for our participants, bypassing a conscious link between the characters and the dogs concept. Despite their lack of awareness regarding the neurofeedback objective, participants successfully learned to activate the target perceptual representations in the bilateral fusiform. The behavioral significance of our training was evaluated in a visual search task. DecNef and control participants searched for dogs or scissors targets that were pre-cued by the hiragana used during DecNef training or by a control hiragana. The DecNef hiragana did not prime search for its associated target but, strikingly, participants were impaired at searching for the targeted perceptual category. Hence, conscious awareness may function to support higher-order associative learning. Meanwhile, lower-level forms of re-learning, modification, or plasticity in existing neural representations can occur unconsciously, with behavioral consequences outside the original training context. The work also provides an account of DecNef effects in terms of neural representational drift.

Protocol description for a randomized controlled trial of fMRI neurofeedback for tics in adolescents with Tourette Syndrome

This article describes the protocol for a randomized, controlled clinical trial of a neurofeedback (NF) intervention for Tourette Syndrome (TS) and chronic tic disorder. The intervention involves using functional magnetic resonance imaging (fMRI) to provide feedback regarding activity in the supplementary motor area: participants practice controlling this brain area while using the feedback as a training signal. The previous version of this NF protocol was tested in a small study (n = 21) training adolescents with TS that yielded clinically promising results. Therefore, we plan a larger trial. Here we describe the background literature that motivated this work, the design of our original neurofeedback study protocol, and adaptations of the research study protocol for the new trial. We focus on those ideas incorporated into our protocol that may be of interest to others designing and running NF studies. For example, we highlight our approach for defining an unrelated brain region to be trained in the control group that is based on identifying a region with low functional connectivity to the target area. Consistent with a desire for transparency and open science, the new protocol is described in detail here prior to conducting the trial.

Upregulation of a Small-World Brain Network Improves Inhibitory Control: An fNIRS Neurofeedback Training Study

The aim of this study was to investigate the inner link between the small-world brain network and inhibitory control. Functional near-infrared spectroscopy (fNIRS) was used to construct a neurofeedback (NF) training system and regulate the frontal small-world brain network. The small-world network downregulation group (DOWN, n = 17) and the small-world network upregulation group (UP, n = 17) received five days of fNIRS-NF training and performed the color-word Stroop task before and after training. The behavioral and functional brain network topology results of both groups were analyzed by a repeated-measures analysis of variance (ANOVA), which showed that the upregulation training helped to improve inhibitory control. The upregulated small-world brain network exhibits an increase in the brain network regularization, links widely dispersed brain resources, and reduces the lateralization of brain functional networks between hemispheres. This suggests an inherent correlation between small-world functional brain networks and inhibitory control; moreover, dynamic optimization under cost efficiency trade-offs provides a neural basis for inhibitory control. Inhibitory control is not a simple function of a single brain region or connectivity but rather an emergent property of a broader network.

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.

A new error-monitoring brain-computer interface based on reinforcement learning for people with autism spectrum disorders

Objective.Brain-computer interfaces (BCIs) are emerging as promising cognitive training tools in neurodevelopmental disorders, as they combine the advantages of traditional computerized interventions with real-time tailored feedback. We propose a gamified BCI based on non-volitional neurofeedback for cognitive training, aiming at reaching a neurorehabilitation tool for application in autism spectrum disorders (ASDs).Approach.The BCI consists of an emotional facial expression paradigm controlled by an intelligent agent that makes correct and wrong actions, while the user observes and judges the agent's actions. The agent learns through reinforcement learning (RL) an optimal strategy if the participant generates error-related potentials (ErrPs) upon incorrect agent actions. We hypothesize that this training approach will allow not only the agent to learn but also the BCI user, by participating through implicit error scrutiny in the process of learning through operant conditioning, making it of particular interest for disorders where error monitoring processes are altered/compromised such as in ASD. In this paper, the main goal is to validate the whole methodological BCI approach and assess whether it is feasible enough to move on to clinical experiments. A control group of ten neurotypical participants and one participant with ASD tested the proposed BCI approach.Main results.We achieved an online balanced-accuracy in ErrPs detection of 81.6% and 77.1%, respectively for two different game modes. Additionally, all participants achieved an optimal RL strategy for the agent at least in one of the test sessions.Significance.The ErrP classification results and the possibility of successfully achieving an optimal learning strategy, show the feasibility of the proposed methodology, which allows to move towards clinical experimentation with ASD participants to assess the effectiveness of the approach as hypothesized.

Choice perseverance underlies pursuing a hard-to-get target in an avatar choice task

People sometimes persistently pursue hard-to-get targets. Why people pursue such targets is unclear. Here, we hypothesized that choice perseverance, which is the tendency to repeat the same choice independent of the obtained outcomes, leads individuals to repeatedly choose a hard-to-get target, which consequently increases their preference for the target. To investigate this hypothesis, we conducted an online experiment involving an avatar choice task in which the participants repeatedly selected one avatar, and the selected avatar expressed their valence reactions through facial expressions and voice. We defined “hard-to-get” and “easy-to-get” avatars by manipulating the outcome probability such that the hard-to-get avatars rarely provided a positive reaction when selected, while the easy-to-get avatars frequently did. We found that some participants repeatedly selected hard-to-get avatars (Pursuit group). Based on a simulation, we found that higher choice perseverance accounted for the pursuit of hard-to-get avatars and that the Pursuit group had significantly higher choice perseverance than the No-pursuit group. Model fitting to the choice data also supported that choice perseverance can account for the pursuit of hard-to-get avatars in the Pursuit group. Moreover, we found that although baseline attractiveness was comparable among all avatars used in the choice task, the attractiveness of the hard-to-get avatars was significantly increased only in the Pursuit group. Taken together, we conclude that people with high choice perseverance pursue hard-to-get targets, rendering such targets more attractive. The tolerance for negative outcomes might be an important factor for succeeding in our lives but sometimes triggers problematic behavior, such as stalking. The present findings may contribute to understanding the psychological mechanisms of passion and perseverance for one’s long-term goals, which are more general than the romantic context imitated in avatar choice.

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.

Semantic fMRI neurofeedback: A Multi-Subject Study at 3 Tesla

OBJECTIVE

Real-time fMRI neurofeedback is a non-invasive procedure allowing the self-regulation of brain functions via enhanced self-control of fMRI based neural activation. In semantic real-time fMRI neurofeedback, an estimated relation between multivariate fMRI activation patterns and abstract mental states is exploited for a multi-dimensional feedback stimulus via real-time representational similarity analysis (rt-RSA). Here, we assessed the performances of this framework in a multi-subject multi-session study on a 3T MRI clinical scanner.

APPROACH

Eighteen healthy volunteers underwent two semantic real-time fMRI neurofeedback sessions on two different days. In each session, participants were first requested to engage in specific mental states while local fMRI patterns of brain activity were recorded during stimulated mental imagery of concrete objects (pattern generation). The obtained neural representations were to be replicated and modulated by the participants in subsequent runs of the same session under the guidance of a rt-RSA generated visual feedback (pattern modulation). Performance indicators were derived from the rt-RSA output to assess individual abilities in replicating (and maintaining over time) a target pattern. Simulations were carried out to assess the impact of the geometric distortions implied by the low-dimensional representation of patterns' dissimilarities in the visual feedback.

MAIN RESULTS

Sixteen subjects successfully completed both semantic real-time fMRI neurofeedback sessions. Considering some performance indicators, a significant improvement between the first and the second runs, and within run increasing modulation performances were observed, whereas no improvements were found between sessions. Simulations confirmed that in a small percentage of cases visual feedback could be affected by metric distortions due to dimensionality reduction implicit to the rt-RSA approach.

SIGNIFICANCE

Our results proved the feasibility of the semantic real-time fMRI neurofeedback at 3T, showing that subjects can successfully modulate and maintain a target mental state, guided by rt-RSA derived feedback. Further development is needed to encourage future clinical applications.

Neuroimaging Examination of Driving Mode Switching Corresponding to Changes in the Driving Environment

Car driving is supported by perceptual, cognitive, and motor skills trained through continuous daily practice. One of the skills that characterize experienced drivers is to detect changes in the driving environment and then flexibly switch their driving modes in response to the changes. Previous functional neuroimaging studies on motor control investigated the mechanisms underlying behaviors adaptive to changes in control properties or parameters of experimental devices such as a computer mouse or a joystick. The switching of multiple internal models mainly engages adaptive behaviors and underlies the interplay between the cerebellum and frontoparietal network (FPN) regions as the neural process. However, it remains unclear whether the neural mechanisms identified in previous motor control studies also underlie practical driving behaviors. In the current study, we measure functional magnetic resonance imaging (fMRI) activities while participants control a realistic driving simulator inside the MRI scanner. Here, the accelerator sensitivity of a virtual car is abruptly changed, requiring participants to respond to this change flexibly to maintain stable driving. We first compare brain activities before and after the sensitivity change. As a result, sensorimotor areas, including the left cerebellum, increase their activities after the sensitivity change. Moreover, after the change, activity significantly increases in the inferior parietal lobe (IPL) and dorsolateral prefrontal cortex (DLPFC), parts of the FPN regions. By contrast, the posterior cingulate cortex, a part of the default mode network, deactivates after the sensitivity change. Our results suggest that the neural bases found in previous experimental studies can serve as the foundation of adaptive driving behaviors. At the same time, this study also highlights the unique contribution of non-motor regions to addressing the high cognitive demands of driving.

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.

Conducting decoded neurofeedback studies

Closed-loop neurofeedback has sparked great interest since its inception in the late 1960s. However, the field has historically faced various methodological challenges. Decoded fMRI neurofeedback may provide solutions to some of these problems. Notably, thanks to the recent advancements of machine learning approaches, it is now possible to target unconscious occurrences of specific multivoxel representations. In this tools of the trade paper, we discuss how to implement these interventions in rigorous double-blind placebo-controlled experiments. We aim to provide a step-by-step guide to address some of the most common methodological and analytical considerations. We also discuss tools that can be used to facilitate the implementation of new experiments. We hope that this will encourage more researchers to try out this powerful new intervention method.

Visual perceptual learning of a primitive feature in human V1/V2 as a result of unconscious processing, revealed by decoded functional MRI neurofeedback (DecNef)

Although numerous studies have shown that visual perceptual learning (VPL) occurs as a result of exposure to a visual feature in a task-irrelevant manner, the underlying neural mechanism is poorly understood. In a previous psychophysical study (Watanabe et al., 2002), subjects were repeatedly exposed to a task-irrelevant Sekuler motion display that induced the perception of not only the local motions, but also a global motionmoving in the direction of the spatiotemporal average of the local motion vectors. As a result of this exposure, subjects enhanced their sensitivity only to the local moving directions, suggesting that early visual areas (V1/V2) that process local motions are involved in task-irrelevant VPL. However, this hypothesis has never been tested directly using neuronal recordings. Here, we employed a decoded neurofeedback technique (DecNef) using functional magnetic resonance imaging in human subjects to examine the involvement of early visual areas (V1/V2) in task-irrelevant VPL of local motion within a Sekuler motion display. During the DecNef training, subjects were trained to induce the activity patterns in V1/V2 that were similar to those evoked by the actual presentation of the Sekuler motion display. The DecNef training was conducted with neither the actual presentation of the display nor the subjects’ awareness of the purpose of the experiment. After the experiment, subjects reported that they neither perceived nor imagined the trained motion during the DecNef training. As a result of DecNef training, subjects increased their sensitivity to the local motion directions, but not specifically to the global motion direction. Neuronal changes related to DecNef training were confined to V1/V2. These results suggest that V1/V2 are involved in exposure-based task-irrelevant VPL of local motion.

Neural processing of personal, relational, and collective self-worth reflected individual differences of self-esteem

INTRODUCTION

Self-esteem stems from an individual's attributes (PSE), relationships with important others (RSE), and collective membership (CSE). Our study aimed to identify neurological indicators in the processing of personal, relational, and collective self-worth, and to investigate whether these neural indicators could reflect individual differences of self-esteem.

METHODS

Fifty students underwent the evaluation of personal, relational, and collective self-worth using a self-referential paradigm while brain activities were recorded using functional-magnetic-resonance-imaging. Meanwhile, their PSE, RSE, and CSE were measured through questionnaires.

RESULTS

Conjunction analysis found self-worth processing recruited the precuneus, posterior cingulate cortex, and posterior insula. Multivariate pattern analysis showed compared to relational and collective self-worth, personal self-worth processing was distinguished by cortical-midline-structures and affective-related regions, including caudate and putamen, and that these neural patterns could reflect individual differences of PSE. Compared to personal self-worth, relational self-worth was distinguished by the neural activity of temporoparietal-junction, and this neural pattern reflected individual differences of RSE. Compared to relational self-worth, collective self-worth was distinguished by neural activity of the anterior insula, and this neural pattern reflected individual differences of CSE.

DISCUSSION

These results suggested the neurological indicators of self-worth can be recognized as an alternative way to reflect individual differences of self-esteem.

Computational methods in social neuroscience: recent advances, new tools and future directions

Recent years have seen a surge of exciting developments in the computational tools available to social neuroscientists. This paper highlights and synthesizes recent advances that have been enabled by the application of such tools, as well as methodological innovations likely to be of interest and utility to social neuroscientists, but that have been concentrated in other sub-fields. Papers in this special issue are emphasized—many of which contain instructive materials (e.g. tutorials and code) for researchers new to the highlighted methods. These include approaches for modeling social decisions, characterizing multivariate neural response patterns at varying spatial scales, using decoded neurofeedback to draw causal links between specific neural response patterns and psychological and behavioral phenomena, examining time-varying patterns of connectivity between brain regions, and characterizing the social networks in which social thought and behavior unfold in everyday life. By combining computational methods for characterizing participants’ rich social environments—at the levels of stimuli, paradigms and the webs of social relationships that surround people—with those for capturing the psychological processes that undergird social behavior and the wealth of information contained in neuroimaging datasets, social neuroscientists can gain new insights into how people create, understand and navigate their complex social worlds.

The DecNef collection, fMRI data from closed-loop decoded neurofeedback experiments

Decoded neurofeedback (DecNef) is a form of closed-loop functional magnetic resonance imaging (fMRI) combined with machine learning approaches, which holds some promises for clinical applications. Yet, currently only a few research groups have had the opportunity to run such experiments; furthermore, there is no existing public dataset for scientists to analyse and investigate some of the factors enabling the manipulation of brain dynamics. We release here the data from published DecNef studies, consisting of 5 separate fMRI datasets, each with multiple sessions recorded per participant. For each participant the data consists of a session that was used in the main experiment to train the machine learning decoder, and several (from 3 to 10) closed-loop fMRI neural reinforcement sessions. The large dataset, currently comprising more than 60 participants, will be useful to the fMRI community at large and to researchers trying to understand the mechanisms underlying non-invasive modulation of brain dynamics. Finally, the data collection size will increase over time as data from newly run DecNef studies will be added.

Measurement(s)	functional brain measurement
Technology Type(s)	3 T MRI scanner • functional magnetic resonance imaging • Neurofeedback • machine learning
Factor Type(s)	type of task performed: visual, preference, perceptual, or memory task
Sample Characteristic - Organism	Homo sapiens

Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.13578008

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

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

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.

A computational paradigm for real-time MEG neurofeedback for dynamic allocation of spatial attention

Background

Neurofeedback aids volitional control of one’s own brain activity using non-invasive recordings of brain activity. The applications of neurofeedback include improvement of cognitive performance and treatment of various psychiatric and neurological disorders. During real-time magnetoencephalography (rt-MEG), sensor-level or source-localized brain activity is measured and transformed into a visual feedback cue to the subject. Recent real-time fMRI (rt-fMRI) neurofeedback studies have used pattern recognition techniques to decode and train a brain state to link brain activities and cognitive behaviors. Here, we utilize the real-time decoding technique similar to ones employed in rt-fMRI to analyze time-varying rt-MEG signals.

Results

We developed a novel rt-MEG method, state-based neurofeedback (sb-NFB), to decode a time-varying brain state, a state signal, from which timings are extracted for neurofeedback training. The approach is entirely data-driven: it uses sensor-level oscillatory activity to find relevant features that best separate the targeted brain states. In a psychophysical task of spatial attention switching, we trained five young, healthy subjects using the sb-NFB method to decrease the time necessary for switch spatial attention from one visual hemifield to the other (referred to as switch time). Training resulted in a decrease in switch time with training. We saw that the activity targeted by the training involved proportional changes in alpha and beta-band oscillations, in sensors at the occipital and parietal regions. We also found that the state signal that encodes whether subjects attend to the left or right visual field effectively switches consistently with the task.

Conclusion

We demonstrated the use of the sb-NFB method when the subject learns to increase the speed of shifting covert spatial attention from one visual field to the other. The sb-NFB method can target timing features that would otherwise also include extraneous features such as visual detection and motor response in a simple reaction time task.

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.

Sense of Agency Beyond Sensorimotor Process: Decoding Self-Other Action Attribution in the Human Brain

The sense of agency is defined as the subjective experience that "I" am the one who is causing the action. Theoretical studies postulate that this subjective experience is developed through multistep processes extending from the sensorimotor to the cognitive level. However, it remains unclear how the brain processes such different levels of information and constitutes the neural substrates for the sense of agency. To answer this question, we combined two strategies: an experimental paradigm, in which self-agency gradually evolves according to sensorimotor experience, and a multivoxel pattern analysis. The combined strategies revealed that the sensorimotor, posterior parietal, anterior insula, and higher visual cortices contained information on self-other attribution during movement. In addition, we investigated whether the found regions showed a preference for self-other attribution or for sensorimotor information. As a result, the right supramarginal gyrus, a portion of the inferior parietal lobe (IPL), was found to be the most sensitive to self-other attribution among the found regions, while the bilateral precentral gyri and left IPL dominantly reflected sensorimotor information. Our results demonstrate that multiple brain regions are involved in the development of the sense of agency and that these show specific preferences for different levels of information.

Current Status of Neurofeedback for Post-traumatic Stress Disorder: A Systematic Review and the Possibility of Decoded Neurofeedback

Background: Post-traumatic stress disorder (PTSD) is a neuropsychiatric affective disorder that can develop after traumatic life-events. Exposure-based therapy is currently one of the most effective treatments for PTSD. However, exposure to traumatic stimuli is so aversive that a significant number of patients drop-out of therapy during the course of treatment. Among various attempts to develop novel therapies that bypass such aversiveness, neurofeedback appears promising. With neurofeedback, patients can unconsciously self-regulate brain activity via real-time monitoring and feedback of the EEG or fMRI signals. With conventional neurofeedback methods, however, it is difficult to induce neural representation related to specific trauma because the feedback is based on the neural signals averaged within specific brain areas. To overcome this difficulty, novel neurofeedback approaches such as Decoded Neurofeedback (DecNef) might prove helpful. Instead of the average BOLD signals, DecNef allows patients to implicitly regulate multivariate voxel patterns of the BOLD signals related with feared stimuli. As such, DecNef effects are postulated to derive either from exposure or counter-conditioning, or some combination of both. Although the exact mechanism is not yet fully understood. DecNef has been successfully applied to reduce fear responses induced either by fear-conditioned or phobic stimuli among non-clinical participants.

Methods: Follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a systematic review was conducted to compare DecNef effect with those of conventional EEG/fMRI-based neurofeedback on PTSD amelioration. To elucidate the possible mechanisms of DecNef on fear reduction, we mathematically modeled the effects of exposure-based and counter conditioning separately and applied it to the data obtained from past DecNef studies. Finally, we conducted DecNef on four PTSD patients. Here, we review recent advances in application of neurofeedback to PTSD treatments, including the DecNef. This review is intended to be informative for neuroscientists in general as well as practitioners planning to use neurofeedback as a therapeutic strategy for PTSD.

Results: Our mathematical model suggested that exposure is the key component for DecNef effects in the past studies. Following DecNef a significant reduction of PTSD severity was observed. This effect was comparable to those reported for conventional neurofeedback approach.

Conclusions: Although a much larger number of participants will be needed in future, DecNef could be a promising therapy that bypasses the unpleasantness of conscious exposure associated with conventional therapies for fear related disorders, including PTSD.

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.

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.

Training in Use of Brain-Machine Interface-Controlled Robotic Hand Improves Accuracy Decoding Two Types of Hand Movements

Objective: Brain-machine interfaces (BMIs) are useful for inducing plastic changes in cortical representation. A BMI first decodes hand movements using cortical signals and then converts the decoded information into movements of a robotic hand. By using the BMI robotic hand, the cortical representation decoded by the BMI is modulated to improve decoding accuracy. We developed a BMI based on real-time magnetoencephalography (MEG) signals to control a robotic hand using decoded hand movements. Subjects were trained to use the BMI robotic hand freely for 10 min to evaluate plastic changes in the cortical representation due to the training.

Method: We trained nine young healthy subjects with normal motor function. In open-loop conditions, they were instructed to grasp or open their right hands during MEG recording. Time-averaged MEG signals were then used to train a real decoder to control the robotic arm in real time. Then, subjects were instructed to control the BMI-controlled robotic hand by moving their right hands for 10 min while watching the robot's movement. During this closed-loop session, subjects tried to improve their ability to control the robot. Finally, subjects performed the same offline task to compare cortical activities related to the hand movements. As a control, we used a random decoder trained by the MEG signals with shuffled movement labels. We performed the same experiments with the random decoder as a crossover trial. To evaluate the cortical representation, cortical currents were estimated using a source localization technique. Hand movements were also decoded by a support vector machine using the MEG signals during the offline task. The classification accuracy of the movements was compared among offline tasks.

Results: During the BMI training with the real decoder, the subjects succeeded in improving their accuracy in controlling the BMI robotic hand with correct rates of 0.28 ± 0.13 to 0.50 ± 0.11 (p = 0.017, n = 8, paired Student's t-test). Moreover, the classification accuracy of hand movements during the offline task was significantly increased after BMI training with the real decoder from 62.7 ± 6.5 to 70.0 ± 11.1% (p = 0.022, n = 8, t₍₇₎ = 2.93, paired Student's t-test), whereas accuracy did not significantly change after BMI training with the random decoder from 63.0 ± 8.8 to 66.4 ± 9.0% (p = 0.225, n = 8, t₍₇₎ = 1.33).

Conclusion: BMI training is a useful tool to train the cortical activity necessary for BMI control and to induce some plastic changes in the activity.

Dissociating frontoparietal brain networks with neuroadaptive Bayesian optimization

Understanding the unique contributions of frontoparietal networks (FPN) in cognition is challenging because they overlap spatially and are co-activated by diverse tasks. Characterizing these networks therefore involves studying their activation across many different cognitive tasks, which previously was only possible with meta-analyses. Here, we use neuroadaptive Bayesian optimization, an approach combining real-time analysis of functional neuroimaging data with machine-learning, to discover cognitive tasks that segregate ventral and dorsal FPN activity. We identify and subsequently refine two cognitive tasks, Deductive Reasoning and Tower of London, which maximally dissociate the dorsal from ventral FPN. We subsequently investigate these two FPNs in the context of a wider range of FPNs and demonstrate the importance of studying the whole activity profile across tasks to uniquely differentiate any FPN. Our findings deviate from previous meta-analyses and hypothesized functional labels for these FPNs. Taken together the results form the starting point for a neurobiologically-derived cognitive taxonomy.

Towards an unconscious neural reinforcement intervention for common fears

Conventional therapies for the treatment of anxiety disorders are aversive, and as a result, many patients terminate treatment prematurely. We have developed an unconscious method to bypass the unpleasantness in conscious exposure using functional magnetic resonance imaging neural reinforcement. Using this method, participants learn to generate brain patterns similar to the multivariate brain pattern of a feared animal. We demonstrate in a double-blind placebo-controlled experiment that neural reinforcement can lead to reliable reductions in physiological fear responses. Crucially, this intervention can be achieved completely unconsciously and without any aversive reaction. Extending our approach to other forms of psychopathologies, such as posttraumatic stress disorders, might eventually provide another means of intervention for patients currently receiving insufficient exposure treatments.

Can “hardwired” physiological fear responses (e.g., for spiders and snakes) be reprogramed unconsciously in the human brain? Currently, exposure therapy is among the most effective treatments for anxiety disorders, but this intervention is subjectively aversive to patients, causing many to drop out of treatment prematurely. Here we introduce a method to bypass the subjective unpleasantness in conscious exposure, by directly pairing monetary reward with unconscious occurrences of decoded representations of naturally feared animals in the brain. To decode physiological fear representations without triggering excessively aversive reactions, we capitalize on recent advancements in functional magnetic resonance imaging decoding techniques, and use a method called hyperalignment to infer the relevant representations of feared animals for a designated participant based on data from other “surrogate” participants. In this way, the procedure completely bypasses the need for a conscious encounter with feared animals. We demonstrate that our method can lead to reliable reductions in physiological fear responses, as measured by skin conductance as well as amygdala hemodynamic activity. Not only do these results raise the intriguing possibility that naturally occurring fear responses can be “reprogrammed” outside of conscious awareness, importantly, they also create the rare opportunity to rigorously test a psychological intervention of this nature in a double-blind, placebo-controlled fashion. This may pave the way for a new approach combining the appealing rationale and proven efficacy of conventional psychotherapy with the rigor and leverage of clinical neuroscience.

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.

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.

Resting-State Functional Connectivity-Based Biomarkers and Functional MRI-Based Neurofeedback for Psychiatric Disorders: A Challenge for Developing Theranostic Biomarkers

Psychiatric research has been hampered by an explanatory gap between psychiatric symptoms and their neural underpinnings, which has resulted in poor treatment outcomes. This situation has prompted us to shift from symptom-based diagnosis to data-driven diagnosis, aiming to redefine psychiatric disorders as disorders of neural circuitry. Promising candidates for data-driven diagnosis include resting-state functional connectivity MRI (rs-fcMRI)-based biomarkers. Although biomarkers have been developed with the aim of diagnosing patients and predicting the efficacy of therapy, the focus has shifted to the identification of biomarkers that represent therapeutic targets, which would allow for more personalized treatment approaches. This type of biomarker (i.e., "theranostic biomarker") is expected to elucidate the disease mechanism of psychiatric conditions and to offer an individualized neural circuit-based therapeutic target based on the neural cause of a condition. To this end, researchers have developed rs-fcMRI-based biomarkers and investigated a causal relationship between potential biomarkers and disease-specific behavior using functional MRI (fMRI)-based neurofeedback on functional connectivity. In this review, we introduce a recent approach for creating a theranostic biomarker, which consists mainly of 2 parts: (1) developing an rs-fcMRI-based biomarker that can predict diagnosis and/or symptoms with high accuracy, and (2) the introduction of a proof-of-concept study investigating the relationship between normalizing the biomarker and symptom changes using fMRI-based neurofeedback. In parallel with the introduction of recent studies, we review rs-fcMRI-based biomarker and fMRI-based neurofeedback, focusing on the technological improvements and limitations associated with clinical use.

OpenNFT: An open-source Python/Matlab framework for real-time fMRI neurofeedback training based on activity, connectivity and multivariate pattern analysis

Neurofeedback based on real-time functional magnetic resonance imaging (rt-fMRI) is a novel and rapidly developing research field. It allows for training of voluntary control over localized brain activity and connectivity and has demonstrated promising clinical applications. Because of the rapid technical developments of MRI techniques and the availability of high-performance computing, new methodological advances in rt-fMRI neurofeedback become possible. Here we outline the core components of a novel open-source neurofeedback framework, termed Open NeuroFeedback Training (OpenNFT), which efficiently integrates these new developments. This framework is implemented using Python and Matlab source code to allow for diverse functionality, high modularity, and rapid extendibility of the software depending on the user's needs. In addition, it provides an easy interface to the functionality of Statistical Parametric Mapping (SPM) that is also open-source and one of the most widely used fMRI data analysis software. We demonstrate the functionality of our new framework by describing case studies that include neurofeedback protocols based on brain activity levels, effective connectivity models, and pattern classification approaches. This open-source initiative provides a suitable framework to actively engage in the development of novel neurofeedback approaches, so that local methodological developments can be easily made accessible to a wider range of users.

Neuroadaptive Bayesian Optimization and Hypothesis Testing

Cognitive neuroscientists are often interested in broad research questions, yet use overly narrow experimental designs by considering only a small subset of possible experimental conditions. This limits the generalizability and reproducibility of many research findings. Here, we propose an alternative approach that resolves these problems by taking advantage of recent developments in real-time data analysis and machine learning. Neuroadaptive Bayesian optimization is a powerful strategy to efficiently explore more experimental conditions than is currently possible with standard methodology. We argue that such an approach could broaden the hypotheses considered in cognitive science, improving the generalizability of findings. In addition, Bayesian optimization can be combined with preregistration to cover exploration, mitigating researcher bias more broadly and improving reproducibility.

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.

Multivoxel neurofeedback selectively modulates confidence without changing perceptual performance

A central controversy in metacognition studies concerns whether subjective confidence directly reflects the reliability of perceptual or cognitive processes, as suggested by normative models based on the assumption that neural computations are generally optimal. This view enjoys popularity in the computational and animal literatures, but it has also been suggested that confidence may depend on a late-stage estimation dissociable from perceptual processes. Yet, at least in humans, experimental tools have lacked the power to resolve these issues convincingly. Here, we overcome this difficulty by using the recently developed method of decoded neurofeedback (DecNef) to systematically manipulate multivoxel correlates of confidence in a frontoparietal network. Here we report that bi-directional changes in confidence do not affect perceptual accuracy. Further psychophysical analyses rule out accounts based on simple shifts in reporting strategy. Our results provide clear neuroscientific evidence for the systematic dissociation between confidence and perceptual performance, and thereby challenge current theoretical thinking.

Confidence associated with perceptual judgements is generally seen as directly reflecting the reliability of perceptual processes. Here the authors use fMRI-based decoded neurofeedback to manipulate confidence and show that it does not affect perceptual performance.