The self-regulating brain and neurofeedback: Experimental science and clinical promise

Rapid acquisition of dynamic control over DLPFC using real-time fMRI feedback

It has been postulated that gaining control over activity in the dorsolateral prefrontal cortex (DLPFC), a key region of the working memory brain network, may be beneficial for cognitive performance and treatment of certain psychiatric disorders. Several studies have reported that, with neurofeedback training, subjects can learn to increase DLPFC activity. However, improvement of dynamic control in terms of switching between low and high activity in DLPFC brain states may potentially constitute more effective self-regulation. Here, we report on feasibility of obtaining dynamic control over DLPFC, meaning the ability to both in- and decrease activity at will, within a single functional MRI scan session. Two groups of healthy volunteers (N = 24) were asked to increase and decrease activity in the left DLPFC as often as possible during fMRI scans (at 7 Tesla), while receiving real-time visual feedback. The experimental group practiced with real-time feedback, whereas the control group received sham feedback. The experimental group significantly increased the speed of intentionally alternating DLPFC activity, while performance of the control group did not change. Analysis of the characteristics of the BOLD signal during successful trials revealed that training with neurofeedback predominantly reduced the time for the DLPFC to return to baseline after activation. These results provide a preliminary indication that people may be able to learn to dynamically down-regulate the level of physiological activity in the DLPFC, and may have implications for psychiatric disorders where DLPFC plays a role.

Current state of the use of neuroimaging techniques to understand and alter appetite control in humans

PURPOSE OF REVIEW

It is in the brain where the decision is made what and how much to eat. In the last decades neuroimaging research has contributed extensively to new knowledge about appetite control by revealing the underlying brain processes. Interestingly, there is the fast growing idea of using these methods to develop new treatments for obesity and eating disorders. In this review, we summarize the findings of the importance of the use of neuropharmacology and neuroimaging techniques in understanding and modifying appetite control.

RECENT FINDINGS

Appetite control is a complex interplay between homeostatic, hedonic, and cognitive processes. Administration of the neuropeptides insulin and oxytocin curb food intake and alter brain responses in reward and cognitive control areas. Additionally, these areas can be targeted for neuromodulation or neurofeedback to reduce food cravings and increase self-control to alter food intake.

SUMMARY

The recent findings reveal the potential of intranasal administration of hormones or modifying appetite control brain networks to reduce food consumption in volunteers with overweight and obesity or individuals with an eating disorder. Although long-term clinical studies are still needed.

Design and Validation of an FPGA-Based Configurable Transcranial Doppler Neurofeedback System for Chronic Pain Patients

Neurofeedback is a self-regulation technique that can be applied to learn to voluntarily control cerebral activity in specific brain regions. In this work, a Transcranial Doppler-based configurable neurofeedback system is proposed and described. The hardware configuration is based on the Red Pitaya board, which gives great flexibility and processing power to the system. The parameter to be trained can be selected between several temporal, spectral, or complexity features from the cerebral blood flow velocity signal in different vessels. As previous studies have found alterations in these parameters in chronic pain patients, the system could be applied to help them to voluntarily control these parameters. Two protocols based on different temporal lengths of the training periods have been proposed and tested with six healthy subjects that were randomly assigned to one of the protocols at the beginning of the procedure. For the purposes of the testing, the trained parameter was the mean cerebral blood flow velocity in the aggregated data from the two anterior cerebral arteries. Results show that, using the proposed neurofeedback system, the two groups of healthy volunteers can learn to self-regulate a parameter from their brain activity in a reduced number of training sessions.

Intrinsic connectivity network dynamics in PTSD during amygdala downregulation using real-time fMRI neurofeedback: A preliminary analysis

Posttraumatic stress disorder (PTSD) has been associated with a disturbance in neural intrinsic connectivity networks (ICN), including the central executive network (CEN), default mode network (DMN), and salience network (SN). Here, we conducted a preliminary investigation examining potential changes in ICN recruitment as a function of real-time fMRI neurofeedback (rt-fMRI-NFB) during symptom provocation where we targeted the downregulation of neural response within the amygdala-a key region-of-interest in PTSD neuropathophysiology. Patients with PTSD (n = 14) completed three sessions of rt-fMRI-NFB with the following conditions: (a) regulate: decrease activation in the amygdala while processing personalized trauma words; (b) view: process trauma words while not attempting to regulate the amygdala; and (c) neutral: process neutral words. We found that recruitment of the left CEN increased over neurofeedback runs during the regulate condition, a finding supported by increased dlPFC activation during the regulate as compared to the view condition. In contrast, DMN task-negative recruitment was stable during neurofeedback runs, albeit was the highest during view conditions and increased (normalized) during rest periods. Critically, SN recruitment was high for both the regulate and the view conditions, a finding potentially indicative of CEN modality switching, adaptive learning, and increasing threat/defense processing in PTSD. In conclusion, this study provides provocative, preliminary evidence that downregulation of the amygdala using rt-fMRI-NFB in PTSD is associated with dynamic changes in ICN, an effect similar to those observed using EEG modalities of neurofeedback.

Functional near-infrared spectroscopy-based affective neurofeedback: feedback effect, illiteracy phenomena, and whole-connectivity profiles

Background: Affective neurofeedback constitutes a suitable approach to control abnormal neural activities associated with psychiatric disorders and might consequently relief symptom severity. However, different aspects of neurofeedback remain unclear, such as its neural basis, the performance variation, the feedback effect, among others. Aim: First, we aimed to propose a functional near-infrared spectroscopy (fNIRS)-based affective neurofeedback based on the self-regulation of frontal and occipital networks. Second, we evaluated three different feedback approaches on performance: real, fixed, and random feedback. Third, we investigated different demographic, psychological, and physiological predictors of performance. Approach: Thirty-three healthy participants performed a task whereby an amorphous figure changed its shape according to the elicited affect (positive or neutral). During the task, the participants randomly received three different feedback approaches: real feedback, with no change of the classifier output; fixed feedback, keeping the feedback figure unmodified; and random feedback, where the classifier output was multiplied by an arbitrary value, causing a feedback different than expected by the subject. Then, we applied a multivariate comparison of the whole-connectivity profiles according to the affective states and feedback approaches, as well as during a pretask resting-state block, to predict performance. Results: Participants were able to control this feedback system with 70.00 % ± 24.43 % ( p < 0.01 ) of performance during the real feedback trials. No significant differences were found when comparing the average performances of the feedback approaches. However, the whole functional connectivity profiles presented significant Mahalanobis distances ( p ≪ 0.001 ) when comparing both affective states and all feedback approaches. Finally, task performance was positively correlated to the pretask resting-state whole functional connectivity ( r = 0.512 , p = 0.009 ). Conclusions: Our results suggest that fNIRS might be a feasible tool to develop a neurofeedback system based on the self-regulation of affective networks. This finding enables future investigations using an fNIRS-based affective neurofeedback in psychiatric populations. Furthermore, functional connectivity profiles proved to be a good predictor of performance and suggested an increased effort to maintain task control in the presence of feedback distractors.

Effects of quantitative electroencephalography based neurofeedback training on autonomous regulations in patients with alcohol use disorder

PURPOSE

The study investigated whether neurofeedback training can normalize the excessive high-beta and low alpha waves indicative of hyperarousal, and subsequently improve autonomous regulation based on the self-determination theory in alcohol use disorders.

METHODS

A nonequivalent control group pretest-posttest design was used. Data were collected using self-report questionnaires from 36 Korean inpatients who met the Alcohol Use Disorder Identification Test in Korea criteria. Data were collected from quantitative electroencephalography to assess alpha (8-12 Hz) and high-beta (21-30 Hz) waves for hyperarousal. The questionnaires included Basic Psychological Need Satisfaction scales that assessed autonomy, competence, and relatedness, and the Alcohol Abstinence Self-Efficacy Scale and Treatment Self-Regulation Questionnaire. The experimental group underwent 10 sessions of neurofeedback training over four weeks. Data were analyzed using the chi-squared, Mann-Whitney U, and Wilcoxon signed-rank tests.

RESULTS

In the experimental group, the alpha wave was increased in 15 of 19 sites and high-beta waves were decreased in 15 of 19 sites, but this difference was not significant. However, high-beta waves were increased in 15 of 19 sites in the control group, with seven sites (Fz, Cz, Pz, Fp2, F4, C4, and P4) showing significant increases. The experimental group showed a significant increase in basic psychological need satisfaction, alcohol abstinence self-efficacy, and self-regulation compared with the control group.

CONCLUSIONS

Neurofeedback training is recommended for improving autonomous regulation in alcohol use disorder as a nursing intervention. However, for significantly attenuating hyperarousal through brain wave correction, it may be necessary to increase the number of neurofeedback sessions.

Real-time fMRI neurofeedback training of the amygdala activity with simultaneous EEG in veterans with combat-related PTSD

Posttraumatic stress disorder (PTSD) is a chronic and disabling neuropsychiatric disorder characterized by insufficient top-down modulation of the amygdala activity by the prefrontal cortex. Real-time fMRI neurofeedback (rtfMRI-nf) is an emerging method with potential for modifying the amygdala-prefrontal interactions. We report the first controlled emotion self-regulation study in veterans with combat-related PTSD utilizing rtfMRI-nf of the amygdala activity. PTSD patients in the experimental group (EG, n = 20) learned to upregulate blood‑oxygenation-level-dependent (BOLD) activity of the left amygdala (LA) using the rtfMRI-nf during a happy emotion induction task. PTSD patients in the control group (CG, n = 11) were provided with a sham rtfMRI-nf. The study included three rtfMRI-nf training sessions, and EEG recordings were performed simultaneously with fMRI. PTSD severity was assessed before and after the training using the Clinician-Administered PTSD Scale (CAPS). The EG participants who completed the study showed a significant reduction in total CAPS ratings, including significant reductions in avoidance and hyperarousal symptoms. They also exhibited a significant reduction in comorbid depression severity. Overall, 80% of the EG participants demonstrated clinically meaningful reductions in CAPS ratings, compared to 38% in the CG. No significant difference in the CAPS rating changes was observed between the groups. During the first rtfMRI-nf session, functional connectivity of the LA with the orbitofrontal cortex (OFC) and the dorsolateral prefrontal cortex (DLPFC) was progressively enhanced, and this enhancement significantly and positively correlated with the initial CAPS ratings. Left-lateralized enhancement in upper alpha EEG coherence also exhibited a significant positive correlation with the initial CAPS. Reduction in PTSD severity between the first and last rtfMRI-nf sessions significantly correlated with enhancement in functional connectivity between the LA and the left DLPFC. Our results demonstrate that the rtfMRI-nf of the amygdala activity has the potential to correct the amygdala-prefrontal functional connectivity deficiencies specific to PTSD.

Cognitive Neuroscience of Attention Deficit Hyperactivity Disorder (ADHD) and Its Clinical Translation

This review focuses on the cognitive neuroscience of Attention Deficit Hyperactivity Disorder (ADHD) based on functional magnetic resonance imaging (fMRI) studies and on recent clinically relevant applications such as fMRI-based diagnostic classification or neuromodulation therapies targeting fMRI deficits with neurofeedback (NF) or brain stimulation. Meta-analyses of fMRI studies of executive functions (EFs) show that ADHD patients have cognitive-domain dissociated complex multisystem impairments in several right and left hemispheric dorsal, ventral and medial fronto-cingulo-striato-thalamic and fronto-parieto-cerebellar networks that mediate cognitive control, attention, timing and working memory (WM). There is furthermore emerging evidence for abnormalities in orbital and ventromedial prefrontal and limbic areas that mediate motivation and emotion control. In addition, poor deactivation of the default mode network (DMN) suggests an abnormal interrelationship between hypo-engaged task-positive and poorly "switched off" hyper-engaged task-negative networks, both of which are related to impaired cognition. Translational cognitive neuroscience in ADHD is still in its infancy. Pattern recognition analyses have attempted to provide diagnostic classification of ADHD using fMRI data with respectable classification accuracies of over 80%. Necessary replication studies, however, are still outstanding. Brain stimulation has been tested in heterogeneously designed, small numbered proof of concept studies targeting key frontal functional impairments in ADHD. Transcranial direct current stimulation (tDCS) appears to be promising to improve ADHD symptoms and cognitive functions based on some studies, but larger clinical trials of repeated stimulation with and without cognitive training are needed to test clinical efficacy and potential costs on non-targeted functions. Only three studies have piloted NF of fMRI-based frontal dysfunctions in ADHD using fMRI or near-infrared spectroscopy, with the two larger ones finding some improvements in cognition and symptoms, which, however, were not superior to the active control conditions, suggesting potential placebo effects. Neurotherapeutics seems attractive for ADHD due to their safety and potential longer-term neuroplastic effects, which drugs cannot offer. However, they need to be thoroughly tested for short- and longer-term clinical and cognitive efficacy and their potential for individualized treatment.

Freedom of Thought and Mental Integrity: The Moral Requirements for Any Neural Prosthesis

There are many kinds of neural prostheses available or being researched today. In most cases they are intended to cure or improve the condition of patients affected by some cerebral deficiency. In other cases, their goal is to provide new means to maintain or improve an individual's normal performance. In all these circumstances, one of the possible risks is that of violating the privacy of brain contents (which partly coincide with mental contents) or of depriving individuals of full control over their thoughts (mental states), as the latter are at least partly detectable by new prosthetic technologies. Given the (ethical) premise that the absolute privacy and integrity of the most relevant part of one's brain data is (one of) the most valuable and inviolable human right(s), I argue that a (technical) principle should guide the design and regulation of new neural prostheses. The premise is justified by the fact that whatever the coercion, the threat or the violence undergone, the person can generally preserve a "private repository" of thought in which to defend her convictions and identity, her dignity, and autonomy. Without it, the person may end up in a state of complete subjection to other individuals. The following functional principle is that neural prostheses should be technically designed and built so as to prevent such outcomes. They should: (a) incorporate systems that can find and signal the unauthorized detection, alteration, and diffusion of brain data and brain functioning; (b) be able to stop any unauthorized detection, alteration, and diffusion of brain data. This should not only regard individual devices, but act as a general (technical) operating principle shared by all interconnected systems that deal with decoding brain activity and brain functioning.

Neurofeedback for Tinnitus Treatment - Review and Current Concepts

An effective treatment to completely alleviate chronic tinnitus symptoms has not yet been discovered. However, recent developments suggest that neurofeedback (NFB), a method already popular in the treatment of other psychological and neurological disorders, may provide a suitable alternative. NFB is a non-invasive method generally based on electrophysiological recordings and visualizing of certain aspects of brain activity as positive or negative feedback that enables patients to voluntarily control their brain activity and thus triggers them to unlearn typical neural activity patterns related to tinnitus. The purpose of this review is to summarize and discuss previous findings of neurofeedback treatment studies in the field of chronic tinnitus. In doing so, also an overview about the underlying theories of tinnitus emergence is presented and results of resting-state EEG and MEG studies summarized and critically discussed. To date, neurofeedback as well as electrophysiological tinnitus studies lack general guidelines that are crucial to produce more comparable and consistent results. Even though neurofeedback has already shown promising results for chronic tinnitus treatment, further research is needed in order to develop more sophisticated protocols that are able to tackle the individual needs of tinnitus patients more specifically.

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

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

Assessing autism at its social and developmental roots: A review of Autism Spectrum Disorder studies using functional near-infrared spectroscopy

We review a relatively new method for studying the developing brain in children and infants with Autism Spectrum Disorder (ASD). Despite advances in behavioral screening and brain imaging, due to paradigms that do not easily allow for testing of awake, very young, and socially-engaged children-i.e., the social and the baby brain-the biological underpinnings of this disorder remain a mystery. We introduce an approach based on functional near-infrared spectroscopy (fNIRS), which offers a noninvasive imaging technique for studying functional activations by measuring changes in the brain's hemodynamic properties. This further enables measurement of brain activation in upright, interactive settings, while maintaining general equivalence to fMRI findings. We review the existing studies that have used fNIRS for ASD, discussing their promise, limitations, and their technical aspects, gearing this study to the researcher who may be new to this technique and highlighting potential targets for future research.

Neurofeedback, sham neurofeedback, and cognitive-behavioural group therapy in adults with attention-deficit hyperactivity disorder: a triple-blind, randomised, controlled trial

BACKGROUND

Many studies suggest that electroencephalographic (EEG) neurofeedback might be beneficial in the treatment of attention-deficit hyperactivity disorder (ADHD). However, numbers of well controlled studies are low and neurofeedback techniques are regarded as highly controversial. The present trial examined the efficacy (compared with sham neurofeedback) and efficiency (compared with meta-cognitive therapy) of a standard EEG neurofeedback protocol in adults with ADHD.

METHODS

We did a concurrent, triple-blind, randomised, controlled trial using authorised deception in adults with ADHD from one centre (University of Tübingen) in Tübingen, Germany. Participants were eligible if they fulfilled the DSM-IV-TR criteria for ADHD, were aged between 18 years and 60 years, and had no or stable use of medication for at least 2 months with no intention to change. We excluded participants who had comorbid schizophrenia or schizoaffective disorder, bipolar disorder, borderline personality disorder, epilepsy, or traumatic brain injury; substance abuse or dependence; or current or planned other psychological treatment. Those eligible were randomly assigned to three groups: a neurofeedback group which received 30 verum θ-to-β neurofeedback sessions over 15 weeks, a sham neurofeedback group which received 15 sham followed by 15 verum θ-to-β neurofeedback sessions over 15 weeks, or a meta-cognitive group therapy group which received 12 sessions over 12 weeks. Participants were assigned equally to one of the three interventions through a computerised minimisation randomisation procedure stratified by sex, age, and baseline symptom severity of ADHD. Participants were masked as to whether they were receiving neurofeedback or sham neurofeedback, but those receiving meta-cognitive therapy were aware of their treatment. Clinical assessors (ie, those assessing outcomes) and research staff who did the neurofeedback training were masked to participants' randomisation status only for neurofeedback and sham neurofeedback. The primary outcome was symptom score on the Conners' adult ADHD rating scale, assessed before treatment, at midtreatment (after 8 weeks), after treatment (after 16 weeks), and 6 months later. All individuals with at least one observation after randomisation were included in the analyses. This trial is registered with ClinicalTrials.gov, number NCT01883765.

FINDINGS

Between Feb 1, 2013, and Dec 1, 2015, 761 people were assessed for eligibility. 656 (86%) were excluded and 118 (15%) were eligible for participation in this study. Eligible participants were randomly assigned to neurofeedback (38 [32%]), sham neurofeedback (39 [33%]), or meta-cognitive therapy (41 [35%]). 37 (97%) individuals for neurofeedback, 38 (97%) for sham neurofeedback, and 38 (93%) for meta-cognitive therapy were included in analyses. Self-reported ADHD symptoms decreased substantially for all treatment groups (B=-2·58 [95% CI -3·48 to -1·68]; p<0·0001) between pretreatment and the end of 6 month follow-up, independent of treatment condition (neurofeedback vs sham neurofeedback B=-0·89 [95% CI -2·14 to 0·37], p=0·168; neurofeedback vs meta-cognitive therapy -0·30 [-1·55 to 0·95], p=0·639). No treatment-related or trial-related serious adverse events were reported.

INTERPRETATION

Our findings suggest that neurofeedback training is not superior to a sham condition or group psychotherapy. All three treatments were equivalently effective in reducing ADHD symptoms. This first randomised, sham-controlled trial did not show any specific effects of neurofeedback on ADHD symptoms in adults.

FUNDING

German Research Foundation.

Assessing the Effectiveness of Neurofeedback Training in the Context of Clinical and Social Neuroscience

Social neuroscience benefits from the experimental manipulation of neuronal activity. One possible manipulation, neurofeedback, is an operant conditioning-based technique in which individuals sense, interact with, and manage their own physiological and mental states. Neurofeedback has been applied to a wide variety of psychiatric illnesses, as well as to treat sub-clinical symptoms, and even to enhance performance in healthy populations. Despite growing interest, there persists a level of distrust and/or bias in the medical and research communities in the USA toward neurofeedback and other functional interventions. As a result, neurofeedback has been largely ignored, or disregarded within social neuroscience. We propose a systematic, empirically-based approach for assessing the effectiveness, and utility of neurofeedback. To that end, we use the term perturbative physiologic plasticity to suggest that biological systems function as an integrated whole that can be perturbed and guided, either directly or indirectly, into different physiological states. When the intention is to normalize the system, e.g., via neurofeedback, we describe it as self-directed neuroplasticity, whose outcome is persistent functional, structural, and behavioral changes. We argue that changes in physiological, neuropsychological, behavioral, interpersonal, and societal functioning following neurofeedback can serve as objective indices and as the metrics necessary for assessing levels of efficacy. In this chapter, we examine the effects of neurofeedback on functional connectivity in a few clinical disorders as case studies for this approach. We believe this broader perspective will open new avenues of investigation, especially within social neuroscience, to further elucidate the mechanisms and effectiveness of these types of interventions, and their relevance to basic research.

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.

Real-time functional magnetic resonance imaging in obsessive-compulsive disorder

The current literature provides substantial evidence of brain alterations associated with obsessive-compulsive disorder (OCD) symptoms (eg, checking, cleaning/decontamination, counting compulsions; harm or sexual, symmetry/exactness obsessions), and emotional problems (eg, defensive/appetitive emotional imbalance, disgust, guilt, shame, and fear learning/extinction) and cognitive impairments associated with this disorder (eg, inhibitory control, working memory, cognitive flexibility). Building on this evidence, new clinical trials can now target specific brain regions/networks. Real-time functional magnetic resonance imaging (rtfMRI) was introduced as a new therapeutic tool for the self-regulation of brain-mind. In this review, we describe initial trials testing the use of rtfMRI to target brain regions associated with specific OCD symptoms (eg, contamination), and other mind-brain processes (eg, cognitive - working memory, inhibitory control, emotional - defensive, appetitive systems, fear reduction through counter-conditioning) found impaired in OCD patients. While this is a novel topic of research, initial evidence shows the promise of using rtfMRI in training the self-regulation of brain regions and mental processes associated with OCD. Additionally, studies with healthy populations have shown that individuals can regulate brain regions associated with cognitive and emotional processes found impaired in OCD. After the initial "proof-of-concept" stage, there is a need to follow up with controlled clinical trials that could test rtfMRI innovative treatments targeting brain regions and networks associated with different OCD symptoms and cognitive-emotional impairments.

Specific or nonspecific? Evaluation of band, baseline, and cognitive specificity of sensorimotor rhythm- and gamma-based neurofeedback

Neurofeedback (NF) is often criticized because of the lack of empirical evidence of its specificity. Our present study thus focused on the specificity of NF on three levels: band specificity, cognitive specificity, and baseline specificity. Ten healthy middle-aged individuals performed ten sessions of SMR (sensorimotor rhythm, 12-15Hz) NF training. A second group (N=10) received feedback of a narrow gamma band (40-43Hz). Effects of NF on EEG resting measurements (tonic EEG) and cognitive functions (memory, intelligence) were evaluated using a pre-post design. Both training groups were able to linearly increase the target training frequencies (either SMR or gamma), indicating the trainability of these EEG frequencies. Both NF training protocols led to nonspecific changes in other frequency bands during NF training. While SMR NF only led to concomitant changes in slower frequencies, gamma training affected nearly the whole power spectrum. SMR NF specifically improved memory functions. Gamma training showed only marginal effects on cognitive functions. SMR power assessed during resting measurements significantly increased after SMR NF training compared to a pre-assessment, indicating specific effects of SMR NF on baseline/tonic EEG. The gamma group did not show any pre-post changes in their EEG resting activity. In conclusion, SMR NF specifically affects cognitive functions (cognitive specificity) and tonic EEG (baseline specificity), while increasing SMR during NF training nonspecifically affects slower EEG frequencies as well (band non-specificity). Gamma NF was associated with nonspecific effects on the EEG power spectrum during training, which did not lead to considerable changes in cognitive functions or baseline EEG activity.

Neurofeedback learning modifies the incidence rate of alpha spindles, but not their duration and amplitude

Although the first experiments on alpha-neurofeedback date back nearly six decades ago, when Joseph Kamiya reported successful operant conditioning of alpha-rhythm in humans, the effectiveness of this paradigm in various experimental and clinical settings is still a matter of debate. Here, we investigated the changes in EEG patterns during a continuously administered neurofeedback of P4 alpha activity. Two days of neurofeedback training were sufficient for a significant increase in the alpha power to occur. A detailed analysis of these EEG changes showed that the alpha power rose because of an increase in the incidence rate of alpha episodes, whereas the amplitude and the duration of alpha oscillations remained unchanged. These findings suggest that neurofeedback facilitates volitional control of alpha activity onset, but alpha episodes themselves appear to be maintained automatically with no volitional control – a property overlooked by previous studies that employed continuous alpha-power neurofeedback. We propose that future research on alpha neurofeedback should explore reinforcement schedules based on detection of onsets and offsets of alpha waves, and employ these statistics for exploration and quantification of neurofeedback induced effects.

Real-time fMRI neurofeedback in adolescents with attention deficit hyperactivity disorder

Attention Deficit Hyperactivity Disorder (ADHD) is associated with poor self-control, underpinned by inferior fronto-striatal deficits. Real-time functional magnetic resonance neurofeedback (rtfMRI-NF) allows participants to gain self-control over dysregulated brain regions. Despite evidence for beneficial effects of electrophysiological-NF on ADHD symptoms, no study has applied the spatially superior rtfMRI-NF neurotherapy to ADHD. A randomized controlled trial tested the efficacy of rtfMRI-NF of right inferior prefrontal cortex (rIFG), a key region that is compromised in ADHD and upregulated with psychostimulants, on improvement of ADHD symptoms, cognition, and inhibitory fMRI activation. To control for region-specificity, an active control group received rtfMRI-NF of the left parahippocampal gyrus (lPHG). Thirty-one ADHD boys were randomly allocated and had to learn to upregulate their target brain region in an average of 11 rtfMRI-NF runs over 2 weeks. Feedback was provided through a video-clip of a rocket that had to be moved up into space. A transfer session without feedback tested learning retention as a proximal measure of transfer to everyday life. Both NF groups showed significant linear activation increases with increasing number of runs in their respective target regions and significant reduction in ADHD symptoms after neurotherapy and at 11-month follow-up. Only the group targeting rIFG, however, showed a transfer effect, which correlated with ADHD symptom reductions, improved at trend level in sustained attention, and showed increased IFG activation during an inhibitory fMRI task. This proof-of-concept study demonstrates for the first time feasibility, safety, and shorter- and longer-term efficacy of rtfMRI-NF of rIFG in adolescents with ADHD. Hum Brain Mapp 38:3190-3209, 2017. © 2017 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

Assessment of mental stress effects on prefrontal cortical activities using canonical correlation analysis: an fNIRS-EEG study

This paper presents an investigation about the effects of mental stress on prefrontal cortex (PFC) subregions using simultaneous measurement of functional Near-Infrared Spectroscopy (fNIRS) and Electroencephalography (EEG) signals. The aim is to explore canonical correlation analysis (CCA) technique to study the relationship among the bi-modality signals in mental stress assessment, and how we could fuse the signals for better accuracy in stress detection. Twenty-five male healthy subjects participated in the study while performing mental arithmetic task under control and stress (under time pressure with negative feedback) conditions. The fusion of brain signals acquired by fNIRS-EEG was performed at feature-level using CCA by maximizing the inter-subject covariance across modalities. The CCA result discovered the associations across the modalities and estimated the components responsible for these associations. The experiment results showed that mental stress experienced by this cohort of subjects is subregion specific and localized to the right ventrolateral PFC subregion. These suggest the right ventrolateral PFC as a suitable candidate region to extract biomarkers as performance indicators of neurofeedback training in stress coping.

Unimodal Versus Bimodal EEG-fMRI Neurofeedback of a Motor Imagery Task

Neurofeedback is a promising tool for brain rehabilitation and peak performance training. Neurofeedback approaches usually rely on a single brain imaging modality such as EEG or fMRI. Combining these modalities for neurofeedback training could allow to provide richer information to the subject and could thus enable him/her to achieve faster and more specific self-regulation. Yet unimodal and multimodal neurofeedback have never been compared before. In the present work, we introduce a simultaneous EEG-fMRI experimental protocol in which participants performed a motor-imagery task in unimodal and bimodal NF conditions. With this protocol we were able to compare for the first time the effects of unimodal EEG-neurofeedback and fMRI-neurofeedback versus bimodal EEG-fMRI-neurofeedback by looking both at EEG and fMRI activations. We also propose a new feedback metaphor for bimodal EEG-fMRI-neurofeedback that integrates both EEG and fMRI signal in a single bi-dimensional feedback (a ball moving in 2D). Such a feedback is intended to relieve the cognitive load of the subject by presenting the bimodal neurofeedback task as a single regulation task instead of two. Additionally, this integrated feedback metaphor gives flexibility on defining a bimodal neurofeedback target. Participants were able to regulate activity in their motor regions in all NF conditions. Moreover, motor activations as revealed by offline fMRI analysis were stronger during EEG-fMRI-neurofeedback than during EEG-neurofeedback. This result suggests that EEG-fMRI-neurofeedback could be more specific or more engaging than EEG-neurofeedback. Our results also suggest that during EEG-fMRI-neurofeedback, participants tended to regulate more the modality that was harder to control. Taken together our results shed first light on the specific mechanisms of bimodal EEG-fMRI-neurofeedback and on its added-value as compared to unimodal EEG-neurofeedback and fMRI-neurofeedback.

Computational neuroscience approach to biomarkers and treatments for mental disorders

Psychiatry research has long experienced a stagnation stemming from a lack of understanding of the neurobiological underpinnings of phenomenologically defined mental disorders. Recently, the application of computational neuroscience to psychiatry research has shown great promise in establishing a link between phenomenological and pathophysiological aspects of mental disorders, thereby recasting current nosology in more biologically meaningful dimensions. In this review, we highlight recent investigations into computational neuroscience that have undertaken either theory- or data-driven approaches to quantitatively delineate the mechanisms of mental disorders. The theory-driven approach, including reinforcement learning models, plays an integrative role in this process by enabling correspondence between behavior and disorder-specific alterations at multiple levels of brain organization, ranging from molecules to cells to circuits. Previous studies have explicated a plethora of defining symptoms of mental disorders, including anhedonia, inattention, and poor executive function. The data-driven approach, on the other hand, is an emerging field in computational neuroscience seeking to identify disorder-specific features among high-dimensional big data. Remarkably, various machine-learning techniques have been applied to neuroimaging data, and the extracted disorder-specific features have been used for automatic case-control classification. For many disorders, the reported accuracies have reached 90% or more. However, we note that rigorous tests on independent cohorts are critically required to translate this research into clinical applications. Finally, we discuss the utility of the disorder-specific features found by the data-driven approach to psychiatric therapies, including neurofeedback. Such developments will allow simultaneous diagnosis and treatment of mental disorders using neuroimaging, thereby establishing 'theranostics' for the first time in clinical psychiatry.

Neurofeedback-based functional near-infrared spectroscopy upregulates motor cortex activity in imagined motor tasks

Neurofeedback is a method for using neural activity displayed on a computer to regulate one's own brain function and has been shown to be a promising technique for training individuals to interact with brain-machine interface applications such as neuroprosthetic limbs. The goal of this study was to develop a user-friendly functional near-infrared spectroscopy (fNIRS)-based neurofeedback system to upregulate neural activity associated with motor imagery, which is frequently used in neuroprosthetic applications. We hypothesized that fNIRS neurofeedback would enhance activity in motor cortex during a motor imagery task. Twenty-two participants performed active and imaginary right-handed squeezing movements using an elastic ball while wearing a 98-channel fNIRS device. Neurofeedback traces representing localized cortical hemodynamic responses were graphically presented to participants in real time. Participants were instructed to observe this graphical representation and use the information to increase signal amplitude. Neural activity was compared during active and imaginary squeezing with and without neurofeedback. Active squeezing resulted in activity localized to the left premotor and supplementary motor cortex, and activity in the motor cortex was found to be modulated by neurofeedback. Activity in the motor cortex was also shown in the imaginary squeezing condition only in the presence of neurofeedback. These findings demonstrate that real-time fNIRS neurofeedback is a viable platform for brain-machine interface applications.

Positive effects of neurofeedback on autism symptoms correlate with brain activation during imitation and observation

Autism has been characterized by atypical task-related brain activation and functional connections, coinciding with deficits in sociocommunicative abilities. However, evidence of the brain's experience-dependent plasticity suggests that abnormal activity patterns may be reversed with treatment. In particular, neurofeedback training (NFT), an intervention based on operant conditioning resulting in self-regulation of brain electrical oscillations, has shown increasing promise in addressing abnormalities in brain function and behavior. We examined the effects of ≥ 20 h of sensorimotor mu-rhythm-based NFT in children with high-functioning autism spectrum disorders (ASD) and a matched control group of typically developing children (ages 8-17). During a functional magnetic resonance imaging imitation and observation task, the ASD group showed increased activation in regions of the human mirror neuron system following the NFT, as part of a significant interaction between group (ASD vs. controls) and training (pre- vs. post-training). These changes were positively correlated with behavioral improvements in the ASD participants, indicating that mu-rhythm NFT may be beneficial to individuals with ASD.

Assessing motor imagery in brain-computer interface training: Psychological and neurophysiological correlates

Motor imagery (MI) is considered to be a promising cognitive tool for improving motor skills as well as for rehabilitation therapy of movement disorders. It is believed that MI training efficiency could be improved by using the brain-computer interface (BCI) technology providing real-time feedback on person's mental attempts. While BCI is indeed a convenient and motivating tool for practicing MI, it is not clear whether it could be used for predicting or measuring potential positive impact of the training. In this study, we are trying to establish whether the proficiency in BCI control is associated with any of the neurophysiological or psychological correlates of motor imagery, as well as to determine possible interrelations among them. For that purpose, we studied motor imagery in a group of 19 healthy BCI-trained volunteers and performed a correlation analysis across various quantitative assessment metrics. We examined subjects' sensorimotor event-related EEG events, corticospinal excitability changes estimated with single-pulse transcranial magnetic stimulation (TMS), BCI accuracy and self-assessment reports obtained with specially designed questionnaires and interview routine. Our results showed, expectedly, that BCI performance is dependent on the subject's capability to suppress EEG sensorimotor rhythms, which in turn is correlated with the idle state amplitude of those oscillations. Neither BCI accuracy nor the EEG features associated with MI were found to correlate with the level of corticospinal excitability increase during motor imagery, and with assessed imagery vividness. Finally, a significant correlation was found between the level of corticospinal excitability increase and kinesthetic vividness of imagery (KVIQ-20 questionnaire). Our results suggest that two distinct neurophysiological mechanisms might mediate possible effects of motor imagery: the non-specific cortical sensorimotor disinhibition and the focal corticospinal excitability increase. Acquired data suggests that BCI-based approach is unreliable in assessing motor imagery due to its high dependence on subject's innate EEG features (e.g. resting mu-rhythm amplitude). Therefore, employment of additional assessment protocols, such as TMS and psychological testing, is required for more comprehensive evaluation of the subject's motor imagery training efficiency.

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

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

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

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

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

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

Real-time functional magnetic resonance imaging neurofeedback in motor neurorehabilitation

PURPOSE OF REVIEW

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

RECENT FINDINGS

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

SUMMARY

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

Smartphone-Based Psychotherapeutic Micro-Interventions to Improve Mood in a Real-World Setting

BACKGROUND

Using mobile communication technology as new personalized approach to treat mental disorders or to more generally improve quality of life is highly promising. Knowledge about intervention components that target key psychopathological processes in terms of transdiagnostic psychotherapy approaches is urgently needed. We explored the use of smartphone-based micro-interventions based on psychotherapeutic techniques, guided by short video-clips, to elicit mood changes.

METHOD

As part of a larger neurofeedback study, all subjects-after being randomly assigned to an experimental or control neurofeedback condition-underwent daily smartphone-based micro-interventions for 13 consecutive days. They were free to choose out of provided techniques, including viscerosensory attention, emotional imagery, facial expression, and contemplative repetition. Changes in mood were assessed in real world using the Multidimensional Mood State Questionnaire (scales: good-bad, GB; awake-tired, AT; and calm-nervous, CN).

RESULTS

Twenty-seven men participated on at least 11 days and were thus included in the analyses. Altogether, they underwent 335, generally well-tolerated, micro-intervention sessions, with viscerosensory attention (178 sessions, 53.13%) and contemplative repetition (68 sessions, 20.30%) being the most frequently applied techniques. Mixed models indicated that subjects showed better mood [GB: b = 0.464, 95%confidence interval (CI) [0.068, 0.860], t (613.3) = 2.298, p = 0.022] and became more awake [AT: b = 0.514, 95%CI [0.103, 0.925], t (612.4) = 2.456, p = 0.014] and calmer [CN: b = 0.685, 95%CI [0.360, 1.010], t (612.3) = 4.137, p < 0.001] from pre- to post-micro-intervention. These mood improvements from pre- to post-micro-intervention were associated with changes in mood from the 1st day until the last day with regard to GB mood (r = 0.614, 95%CI [0.297, 0.809], p < 0.001), but not AT mood (r = 0.279, 95%CI [-0.122, 0.602], p = 0.167) and CN mood (r = 0.277, 95%CI [0.124, 0.601], p = 0.170).

DISCUSSION

Our findings provide evidence for the applicability of smartphone-based micro-interventions eliciting short-term mood changes, based on techniques used in psychotherapeutic approaches, such as mindfulness-based psychotherapy, transcendental meditation, and other contemplative therapies. The results encourage exploring these techniques' capability to improve mood in randomized controlled studies and patients. Smartphone-based micro-interventions are promising to modify mood in real-world settings, complementing other psychotherapeutic interventions, in line with the precision medicine approach. The here presented data were collected within a randomized trial, registered at ClinicalTrials.gov (Identifier: NCT01921088) https://clinicaltrials.gov/ct2/show/NCT01921088.

Translating Neurocognitive Models of Auditory-Verbal Hallucinations into Therapy: Using Real-time fMRI-Neurofeedback to Treat Voices

Auditory-verbal hallucinations (AVHs) are frequent and disabling symptoms, which can be refractory to conventional psychopharmacological treatment in more than 25% of the cases. Recent advances in brain imaging allow for a better understanding of the neural underpinnings of AVHs. These findings strengthened transdiagnostic neurocognitive models that characterize these frequent and disabling experiences. At the same time, technical improvements in real-time functional magnetic resonance imaging (fMRI) enabled the development of innovative and non-invasive methods with the potential to relieve psychiatric symptoms, such as fMRI-based neurofeedback (fMRI-NF). During fMRI-NF, brain activity is measured and fed back in real time to the participant in order to help subjects to progressively achieve voluntary control over their own neural activity. Precisely defining the target brain area/network(s) appears critical in fMRI-NF protocols. After reviewing the available neurocognitive models for AVHs, we elaborate on how recent findings in the field may help to develop strong a priori strategies for fMRI-NF target localization. The first approach relies on imaging-based "trait markers" (i.e., persistent traits or vulnerability markers that can also be detected in the presymptomatic and remitted phases of AVHs). The goal of such strategies is to target areas that show aberrant activations during AVHs or are known to be involved in compensatory activation (or resilience processes). Brain regions, from which the NF signal is derived, can be based on structural MRI and neurocognitive knowledge, or functional MRI information collected during specific cognitive tasks. Because hallucinations are acute and intrusive symptoms, a second strategy focuses more on "state markers." In this case, the signal of interest relies on fMRI capture of the neural networks exhibiting increased activity during AVHs occurrences, by means of multivariate pattern recognition methods. The fine-grained activity patterns concomitant to hallucinations can then be fed back to the patients for therapeutic purpose. Considering the potential cost necessary to implement fMRI-NF, proof-of-concept studies are urgently required to define the optimal strategy for application in patients with AVHs. This technique has the potential to establish a new brain imaging-guided psychotherapy for patients that do not respond to conventional treatments and take functional neuroimaging to therapeutic applications.