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

Remote Wearable Neuroimaging Devices for Health Monitoring and Neurophenotyping: A Scoping Review

Digital health tracking is a source of valuable insights for public health research and consumer health technology. The brain is the most complex organ, containing information about psychophysical and physiological biomarkers that correlate with health. Specifically, recent developments in electroencephalogram (EEG), functional near-infra-red spectroscopy (fNIRS), and photoplethysmography (PPG) technologies have allowed the development of devices that can remotely monitor changes in brain activity. The inclusion criteria for the papers in this review encompassed studies on self-applied, remote, non-invasive neuroimaging techniques (EEG, fNIRS, or PPG) within healthcare applications. A total of 23 papers were reviewed, comprising 17 on using EEGs for remote monitoring and 6 on neurofeedback interventions, while no papers were found related to fNIRS and PPG. This review reveals that previous studies have leveraged mobile EEG devices for remote monitoring across the mental health, neurological, and sleep domains, as well as for delivering neurofeedback interventions. With headsets and ear-EEG devices being the most common, studies found mobile devices feasible for implementation in study protocols while providing reliable signal quality. Moderate to substantial agreement overall between remote and clinical-grade EEGs was found using statistical tests. The results highlight the promise of portable brain-imaging devices with regard to continuously evaluating patients in natural settings, though further validation and usability enhancements are needed as this technology develops.

Increasing Value and Reducing Waste of Research on Neurofeedback Effects in Post-traumatic Stress Disorder: A State-of-the-Art-Review

Post-Traumatic Stress Disorder (PTSD) is often considered challenging to treat due to factors that contribute to its complexity. In the last decade, more attention has been paid to non-pharmacological or non-psychological therapies for PTSD, including neurofeedback (NFB). NFB is a promising non-invasive technique targeting specific brainwave patterns associated with psychiatric symptomatology. By learning to regulate brain activity in a closed-loop paradigm, individuals can improve their functionality while reducing symptom severity. However, owing to its lax regulation and heterogeneous legal status across different countries, the degree to which it has scientific support as a psychiatric treatment remains controversial. In this state-of-the-art review, we searched PubMed, Cochrane Central, Web of Science, Scopus, and MEDLINE and identified meta-analyses and systematic reviews exploring the efficacy of NFB for PTSD. We included seven systematic reviews, out of which three included meta-analyses (32 studies and 669 participants) that targeted NFB as an intervention while addressing a single condition-PTSD. We used the MeaSurement Tool to Assess systematic Reviews (AMSTAR) 2 and the criteria described by Cristea and Naudet (Behav Res Therapy 123:103479, 2019, https://doi.org/10.1016/j.brat.2019.103479 ) to identify sources of research waste and increasing value in biomedical research. The seven assessed reviews had an overall extremely poor quality score (5 critically low, one low, one moderate, and none high) and multiple sources of waste while opening opportunities for increasing value in the NFB literature. Our research shows that it remains unclear whether NFB training is significantly beneficial in treating PTSD. The quality of the investigated literature is low and maintains a persistent uncertainty over numerous points, which are highly important for deciding whether an intervention has clinical efficacy. Just as importantly, none of the reviews we appraised explored the statistical power, referred to open data of the included studies, or adjusted their pooled effect sizes for publication bias and risk of bias. Based on the obtained results, we identified some recurrent sources of waste (such as a lack of research decisions based on sound questions or using an appropriate methodology in a fully transparent, unbiased, and useable manner) and proposed some directions for increasing value (homogeneity and consensus) in designing and reporting research on NFB interventions in PTSD.

Neurofeedback Effect on Symptoms of Posttraumatic Stress Disorder: A Systematic Review and Meta-Analysis

Posttraumatic stress disorder (PTSD) encompasses various psychological symptoms and a high early dropout rate due to treatment unresponsiveness. In recent years, neurofeedback has been implemented to control PTSD's psychological symptoms through physiological brain regulation. However, a comprehensive analysis concerning its efficacy is lacking. Therefore, we conducted a systematic review and meta-analysis to determine neurofeedback's effect on reducing PTSD symptoms. We analyzed randomized and non-randomized controlled trials (RCTs) from 1990 to July 2020, evaluating neurofeedback treatments for those diagnosed with PTSD and their symptoms. In addition, we calculated the standardized mean difference (SMD)using random-effects models to estimate effect sizes. We assessed ten articles comprising 276 participants, with a - 0.74 SMD (95% confidence interval = - 0.9230, - 0.5567), 42% I2, moderate effect size, and - 1.40 to -0.08 prediction intervals (PI). Neurofeedback was more effective for complex trauma PTSD patients than single trauma. Increasing and lengthening sessions are more effective than fewer, condensed ones. Neurofeedback positively affected arousal, anxiety, depression, and intrusive, numbing, and suicidal thoughts. Therefore, neurofeedback is a promising and effective treatment for complex PTSD.

An investigation of the effectiveness of neurofeedback training on motor performance in healthy adults: A systematic review and meta-analysis

Neurofeedback training (NFT) refers to a training where the participants voluntarily aim to manipulate their own brain activity using the sensory feedback abstracted from their brain activity. NFT has attracted attention in the field of motor learning due to its potential as an alternative or additional training method for general physical training. In this study, a systematic review of NFT studies for motor performance improvements in healthy adults and a meta-analysis on the effectiveness of NFT were conducted. A computerized search was performed using the databases Web of Science, Scopus, PubMed, JDreamIII, and Ichushi-Web to identify relevant studies published between January 1st, 1990, and August 3rd, 2021. Thirty-three studies were identified for the qualitative synthesis and 16 randomized controlled trials (374 subjects) for the meta-analysis. The meta-analysis, including all trials found in the search, revealed significant effects of NFT for motor performance improvement examined at the timing after the last NFT session (standardized mean difference = 0.85, 95% CI [0.18-1.51]), but with the existence of publication biases and substantial heterogeneity among the trials. Subsequent meta-regression analysis demonstrated the dose-response gradient between NFTs and motor performance improvements; more than 125 min of cumulative training time may benefit for the subsequent motor performance. For each motor performance measure (e.g., speed, accuracy, and hand dexterity), the effectiveness of NFT remains inconclusive, mainly due to its small sample sizes. More empirical NFT studies for motor performance improvement may be needed to show beneficial effects on motor performance and to safely incorporate NFT into real-world scenarios.

Posterior cingulate cortex targeted real-time fMRI neurofeedback recalibrates functional connectivity with the amygdala, posterior insula, and default-mode network in PTSD

BACKGROUND

Alterations within large-scale brain networks-namely, the default mode (DMN) and salience networks (SN)-are present among individuals with posttraumatic stress disorder (PTSD). Previous real-time functional magnetic resonance imaging (fMRI) and electroencephalography neurofeedback studies suggest that regulating posterior cingulate cortex (PCC; the primary hub of the posterior DMN) activity may reduce PTSD symptoms and recalibrate altered network dynamics. However, PCC connectivity to the DMN and SN during PCC-targeted fMRI neurofeedback remains unexamined and may help to elucidate neurophysiological mechanisms through which these symptom improvements may occur.

METHODS

Using a trauma/emotion provocation paradigm, we investigated psychophysiological interactions over a single session of neurofeedback among PTSD (n = 14) and healthy control (n = 15) participants. We compared PCC functional connectivity between regulate (in which participants downregulated PCC activity) and view (in which participants did not exert regulatory control) conditions across the whole-brain as well as in a priori specified regions-of-interest.

RESULTS

During regulate as compared to view conditions, only the PTSD group showed significant PCC connectivity with anterior DMN (dmPFC, vmPFC) and SN (posterior insula) regions, whereas both groups displayed PCC connectivity with other posterior DMN areas (precuneus/cuneus). Additionally, as compared with controls, the PTSD group showed significantly greater PCC connectivity with the SN (amygdala) during regulate as compared to view conditions. Moreover, linear regression analyses revealed that during regulate as compared to view conditions, PCC connectivity to DMN and SN regions was positively correlated to psychiatric symptoms across all participants.

CONCLUSION

In summary, observations of PCC connectivity to the DMN and SN provide emerging evidence of neural mechanisms underlying PCC-targeted fMRI neurofeedback among individuals with PTSD. This supports the use of PCC-targeted neurofeedback as a means by which to recalibrate PTSD-associated alterations in neural connectivity within the DMN and SN, which together, may help to facilitate improved emotion regulation abilities in PTSD.

Infraslow closed-loop brain training for anxiety and depression (ISAD): a protocol for a randomized, double-blind, sham-controlled pilot trial in adult females with internalizing disorders

Background

The core intrinsic connectivity networks (core-ICNs), encompassing the default-mode network (DMN), salience network (SN) and central executive network (CEN), have been shown to be dysfunctional in individuals with internalizing disorders (IDs, e.g. major depressive disorder, MDD; generalized anxiety disorder, GAD; social anxiety disorder, SOC). As such, source-localized, closed-loop brain training of electrophysiological signals, also known as standardized low-resolution electromagnetic tomography (sLORETA) neurofeedback (NFB), targeting key cortical nodes within these networks has the potential to reduce symptoms associated with IDs and restore normal core ICN function. We intend to conduct a randomized, double-blind (participant and assessor), sham-controlled, parallel-group (3-arm) trial of sLORETA infraslow (<0.1 Hz) fluctuation neurofeedback (sLORETA ISF-NFB) 3 times per week over 4 weeks in participants (n=60) with IDs. Our primary objectives will be to examine patient-reported outcomes (PROs) and neurophysiological measures to (1) compare the potential effects of sham ISF-NFB to either genuine 1-region ISF-NFB or genuine 2-region ISF-NFB, and (2) assess for potential associations between changes in PRO scores and modifications of electroencephalographic (EEG) activity/connectivity within/between the trained regions of interest (ROIs). As part of an exploratory analysis, we will investigate the effects of additional training sessions and the potential for the potentiation of the effects over time.

Methods

We will randomly assign participants who meet the criteria for MDD, GAD, and/or SOC per the MINI (Mini International Neuropsychiatric Interview for DSM-5) to one of three groups: (1) 12 sessions of posterior cingulate cortex (PCC) ISF-NFB up-training (n=15), (2) 12 sessions of concurrent PCC ISF up-training and dorsal anterior cingulate cortex (dACC) ISF-NFB down-training (n=15), or (3) 6 sessions of yoked-sham training followed by 6 sessions genuine ISF-NFB (n=30). Transdiagnostic PROs (Hospital Anxiety and Depression Scale, HADS; Inventory of Depression and Anxiety Symptoms – Second Version, IDAS-II; Multidimensional Emotional Disorder Inventory, MEDI; Intolerance of Uncertainty Scale – Short Form, IUS-12; Repetitive Thinking Questionnaire, RTQ-10) as well as resting-state neurophysiological measures (full-band EEG and ECG) will be collected from all subjects during two baseline sessions (approximately 1 week apart) then at post 6 sessions, post 12 sessions, and follow-up (1 month later). We will employ Bayesian methods in R and advanced source-localisation software (i.e. exact low-resolution brain electromagnetic tomography; eLORETA) in our analysis.

Discussion

This protocol will outline the rationale and research methodology for a clinical pilot trial of sLORETA ISF-NFB targeting key nodes within the core-ICNs in a female ID population with the primary aims being to assess its potential efficacy via transdiagnostic PROs and relevant neurophysiological measures.

Trial registration

Our study was prospectively registered with the Australia New Zealand Clinical Trials Registry (ANZCTR; Trial ID: ACTRN12619001428156). Registered on October 15, 2019.

Correction of Stress-Induced States Using Sensory Stimulation Automatically Modulated by Endogenous Human Rhythms

This article considers the dynamics of the development of a potential approach to correcting stress-induced states in humans, i.e., adaptive neurostimulation. The approach consists of presenting sensory stimulation automatically modulated by intrinsic rhythmic human processes such as the respiratory rhythm, the heartbeat rhythm, and electroencephalograph (EEG) rhythms. Many examples have shown that real-time self-adjustment of the stimulation parameters by these rhythms leads to a high level personalization of therapeutic stimulation and increases in its efficacy in suppressing stress-induced states. The publications reviewed here point to the advantages of this approach for developing innovatory technologies using complex feedback from endogenous human rhythms to correct a wide spectrum of functional disorders.

Pattern analysis of neuroimaging data reveals novel insights on threat learning and extinction in humans

Several decades of rodent neurobiology research have identified a network of brain regions that support Pavlovian threat conditioning and extinction, focused predominately on the amygdala, hippocampus, and medial prefrontal cortex (mPFC). Surprisingly, functional magnetic resonance imaging (fMRI) studies have shown inconsistent evidence for these regions while humans undergo threat conditioning and extinction. In this review, we suggest that translational neuroimaging efforts have been hindered by reliance on traditional univariate analysis of fMRI. Whereas univariate analyses average activity across voxels in a given region, multivariate pattern analyses (MVPA) leverage the information present in spatial patterns of activity. MVPA therefore provides a more sensitive analysis tool to translate rodent neurobiology to human neuroimaging. We review human fMRI studies using MVPA that successfully bridge rodent models of amygdala, hippocampus, and mPFC function during Pavlovian learning. We also highlight clinical applications of these information-sensitive multivariate analyses. In sum, we advocate that the field should consider adopting a variety of multivariate approaches to help bridge cutting-edge research on the neuroscience of threat and anxiety.

Efficacy of Neuro-Feedback Training for PTSD Symptoms: A Systematic Review and Meta-Analysis

If the negative emotions experienced in life become trauma, they affect daily life. Neuro-feedback technology has recently been introduced as a treatment, but many different neuro-feedback protocols and methods exits. This study conducted a meta-analysis of neuro-feedback training for post-traumatic stress disorder (PTSD) symptoms to evaluate the effects of functional magnetic resonance imaging (fMRI) and electroencephalogram (EEG)-based neuro-feedback training. A search of PubMed, the Cochrane Library, Web of Science, Science Direct, and ClinicalTrials.gov was conducted from January 2011 to December 2021. The studies' quality was assessed using the Cochrane risk of bias tool and publication bias was assessed by Egger's regression test. Seven studies that met the inclusion criteria were used for the systematic review and meta-analysis. EEG was more effective than fMRI for PTSD symptoms, and the effect on PTSD symptoms was higher than on anxiety and depression. There was no difference in the effectiveness of the training sessions. Our findings showed that EEG-based neuro-feedback training was more helpful for training PTSD symptoms. Additionally, the methods were also shown to be valid for evaluating clinical PTSD diagnoses. Further research is needed to establish a gold standard protocol for the EEG-based neuro-feedback training (EEG-NFT) method for PTSD symptoms.

RT-Cloud: A cloud-based software framework to simplify and standardize real-time fMRI

Real-time fMRI (RT-fMRI) neurofeedback has been shown to be effective in treating neuropsychiatric disorders and holds tremendous promise for future breakthroughs, both with regard to basic science and clinical applications. However, the prevalence of its use has been hampered by computing hardware requirements, the complexity of setting up and running an experiment, and a lack of standards that would foster collaboration. To address these issues, we have developed RT-Cloud (https://github.com/brainiak/rt-cloud), a flexible, cloud-based, open-source Python software package for the execution of RT-fMRI experiments. RT-Cloud uses standardized data formats and adaptable processing streams to support and expand open science in RT-fMRI research and applications. Cloud computing is a key enabling technology for advancing RT-fMRI because it eliminates the need for on-premise technical expertise and high-performance computing; this allows installation, configuration, and maintenance to be automated and done remotely. Furthermore, the scalability of cloud computing makes it easier to deploy computationally-demanding multivariate analyses in real time. In this paper, we describe how RT-Cloud has been integrated with open standards, including the Brain Imaging Data Structure (BIDS) standard and the OpenNeuro database, how it has been applied thus far, and our plans for further development and deployment of RT-Cloud in the coming years.

A predictive coding account of value-based learning in PTSD: Implications for precision treatments

While there are a number of recommended first-line interventions for posttraumatic stress disorder (PTSD), treatment efficacy has been less than ideal. Generally, PTSD treatment models explain symptom manifestation via associative learning, treating the individual as a passive organism - acted upon - rather than self as agent. At their core, predictive coding (PC) models introduce the fundamental role of self-conceptualisation and hierarchical processing of one's sensory context in safety learning. This theoretical article outlines how predictive coding models of emotion offer a parsimonious framework to explain PTSD treatment response within a value-based decision-making framework. Our model integrates the predictive coding elements of the perceived: self, world and self-in the world and how they impact upon one or more discrete stages of value-based decision-making: (1) mental representation; (2) emotional valuation; (3) action selection and (4) outcome valuation. We discuss treatment and research implications stemming from our hypotheses.

Is There Evidence for the Specificity of Closed-Loop Brain Training in the Treatment of Internalizing Disorders? A Systematic Review

Introduction

Internalizing disorders (IDs), e.g., major depressive disorder (MDD), posttraumatic stress disorder (PTSD), obsessive-compulsive disorder (OCD) are the most prevalent psychopathologies experienced worldwide. Current first-line therapies (i.e., pharmacotherapy and/or psychotherapy) offer high failure rates, limited accessibility, and substantial side-effects. Electroencephalography (EEG) guided closed-loop brain training, also known as EEG-neurofeedback (EEG-NFB), is believed to be a safe and effective alternative, however, there is much debate in the field regarding the existence of specificity [i.e., clinical effects specific to the modulation of the targeted EEG variable(s)]. This review was undertaken to determine if there is evidence for EEG-NFB specificity in the treatment of IDs.

Methods

We considered only randomized, double-blind, sham-controlled trials. Outcomes of interest included self/parent/teacher reports and clinician ratings of ID-related symptomatology.

Results

Of the four reports (total participant number = 152) meeting our eligibility criteria, three had point estimates suggesting small to moderate effect sizes favoring genuine therapy over sham, however, due to small sample sizes, all 95% confidence intervals (CIs) were wide and spanned the null. The fourth trial had yet to post results as of the submission date of this review. The limited overall number of eligible reports (and participants), large degree of inter-trial heterogeneity, and restricted span of ID populations with published/posted outcome data (i.e., PTSD and OCD) precluded a quantitative synthesis.

Discussion

The current literature suggests that EEG-NFB may induce specific effects in the treatment of some forms of IDs, however, the evidence is very limited. Ultimately, more randomized, double-blind, sham-controlled trials encompassing a wider array of ID populations are needed to determine the existence and, if present, degree of EEG-NFB specificity in the treatment of IDs.

Systematic Review Registration

[https://www.crd.york.ac.uk/prospero], identifier [CRD42020159702].

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.

Differential mechanisms of posterior cingulate cortex downregulation and symptom decreases in posttraumatic stress disorder and healthy individuals using real-time fMRI neurofeedback

BACKGROUND

Intrinsic connectivity networks, including the default mode network (DMN), are frequently disrupted in individuals with posttraumatic stress disorder (PTSD). The posterior cingulate cortex (PCC) is the main hub of the posterior DMN, where the therapeutic regulation of this region with real-time fMRI neurofeedback (NFB) has yet to be explored.

METHODS

We investigated PCC downregulation while processing trauma/stressful words over 3 NFB training runs and a transfer run without NFB (total n = 29, PTSD n = 14, healthy controls n = 15). We also examined the predictive accuracy of machine learning models in classifying PTSD versus healthy controls during NFB training.

RESULTS

Both the PTSD and healthy control groups demonstrated reduced reliving symptoms in response to trauma/stressful stimuli, where the PTSD group additionally showed reduced symptoms of distress. We found that both groups were able to downregulate the PCC with similar success over NFB training and in the transfer run, although downregulation was associated with unique within-group decreases in activation within the bilateral dmPFC, bilateral postcentral gyrus, right amygdala/hippocampus, cingulate cortex, and bilateral temporal pole/gyri. By contrast, downregulation was associated with increased activation in the right dlPFC among healthy controls as compared to PTSD. During PCC downregulation, right dlPFC activation was negatively correlated to PTSD symptom severity scores and difficulties in emotion regulation. Finally, machine learning algorithms were able to classify PTSD versus healthy participants based on brain activation during NFB training with 80% accuracy.

CONCLUSIONS

This is the first study to investigate PCC downregulation with real-time fMRI NFB in both PTSD and healthy controls. Our results reveal acute decreases in symptoms over training and provide converging evidence for EEG-NFB targeting brain networks linked to the PCC.

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

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

EEG Neurofeedback for Anxiety Disorders and Post-Traumatic Stress Disorders: A Blueprint for a Promising Brain-Based Therapy

PURPOSE OF REVIEW

This review provides an overview of current knowledge and understanding of EEG neurofeedback for anxiety disorders and post-traumatic stress disorders.

RECENT FINDINGS

The manifestations of anxiety disorders and post-traumatic stress disorders (PTSD) are associated with dysfunctions of neurophysiological stress axes and brain arousal circuits, which are important dimensions of the research domain criteria (RDoC). Even if the pathophysiology of these disorders is complex, one of its defining signatures is behavioral and physiological over-arousal. Interestingly, arousal-related brain activity can be modulated by electroencephalogram-based neurofeedback (EEG NF), a non-pharmacological and non-invasive method that involves neurocognitive training through a brain-computer interface (BCI). EEG NF is characterized by a simultaneous learning process where both patient and computer are involved in modifying neuronal activity or connectivity, thereby improving associated symptoms of anxiety and/or over-arousal. Positive effects of EEG NF have been described for both anxiety disorders and PTSD, yet due to a number of methodological issues, it remains unclear whether symptom improvement is the direct result of neurophysiological changes targeted by EEG NF. Thus, in this work we sought to bridge current knowledge on brain mechanisms of arousal with past and present EEG NF therapies for anxiety and PTSD. In a nutshell, we discuss the neurophysiological mechanisms underlying the effects of EEG NF in anxiety disorder and PTSD, the methodological strengths/weaknesses of existing EEG NF randomized controlled trials for these disorders, and the neuropsychological factors that may impact NF training success.

Neurofeedback for cognitive enhancement and intervention and brain plasticity

In recent years, neurofeedback has been used as a cognitive training tool to improve brain functions for clinical or recreational purposes. It is based on providing participants with feedback about their brain activity and training them to control it, initiating directional changes. The overarching hypothesis behind this method is that this control results in an enhancement of the cognitive abilities associated with this brain activity, and triggers specific structural and functional changes in the brain, promoted by learning and neuronal plasticity effects. Here, we review the general methodological principles behind neurofeedback and we describe its behavioural benefits in clinical and experimental contexts. We review the non-specific effects of neurofeedback on the reinforcement learning striato-frontal networks as well as the more specific changes in the cortical networks on which the neurofeedback control is exerted. Last, we analyse the current challenges faces by neurofeedback studies, including the quantification of the temporal dynamics of neurofeedback effects, the generalisation of its behavioural outcomes to everyday life situations, the design of appropriate controls to disambiguate placebo from true neurofeedback effects and the development of more advanced cortical signal processing to achieve a finer-grained real-time modelling of cognitive functions.

Effectiveness, Cost-Utility, and Safety of Neurofeedback Self-Regulating Training in Patients with Post-Traumatic Stress Disorder: A Randomized Controlled Trial

Post-traumatic stress disorder (PTSD) is characterized by neurophysiological and psycho-emotional problems after exposure to trauma. Several pharmacological and psychotherapy limitations, such as adverse events and low adherence, increase the need for alternative therapeutic options. Neurofeedback is widely used for PTSD management. However, evidence of its clinical efficacy is lacking. We conducted a randomized, waitlist-controlled, assessor-blinded clinical trial to assess the effectiveness, cost-utility, and safety of 16 sessions of neurofeedback on people with PTSD for eight weeks. Eleven participants were allocated to each group. One and two subjects dropped out from the neurofeedback and control groups, respectively. The primary outcome was PTSD symptom change evaluated using the PTSD Checklist-5 (PCL-5-K). The PCL-5-K levels improved more in the neurofeedback group (44.3 ± 10.8 to 19.4 ± 7.75) than in the control group (35.1 ± 18.5 to 31.0 ± 14.92). The change value was significantly improved in the neurofeedback group (24.90 ± 13.13 vs. 4.11 ± 9.03). Secondary outcomes such as anxiety, depression, insomnia, and quality of life were also improved. In an economic analysis using EuroQol-5D, the incremental cost-per-quality-adjusted life-year was approximately $15,600, indicating acceptable cost-utility. There were no adverse events in either group. In conclusion, neurofeedback might be a useful, cost-effective, and safe intervention for PTSD management.

Mind the gap: State-of-the-art technologies and applications for EEG-based brain-computer interfaces

Brain-computer interfaces (BCIs) provide bidirectional communication between the brain and output devices that translate user intent into function. Among the different brain imaging techniques used to operate BCIs, electroencephalography (EEG) constitutes the preferred method of choice, owing to its relative low cost, ease of use, high temporal resolution, and noninvasiveness. In recent years, significant progress in wearable technologies and computational intelligence has greatly enhanced the performance and capabilities of EEG-based BCIs (eBCIs) and propelled their migration out of the laboratory and into real-world environments. This rapid translation constitutes a paradigm shift in human-machine interaction that will deeply transform different industries in the near future, including healthcare and wellbeing, entertainment, security, education, and marketing. In this contribution, the state-of-the-art in wearable biosensing is reviewed, focusing on the development of novel electrode interfaces for long term and noninvasive EEG monitoring. Commercially available EEG platforms are surveyed, and a comparative analysis is presented based on the benefits and limitations they provide for eBCI development. Emerging applications in neuroscientific research and future trends related to the widespread implementation of eBCIs for medical and nonmedical uses are discussed. Finally, a commentary on the ethical, social, and legal concerns associated with this increasingly ubiquitous technology is provided, as well as general recommendations to address key issues related to mainstream consumer adoption.

Teasing apart trauma: neural oscillations differentiate individual cases of mild traumatic brain injury from post-traumatic stress disorder even when symptoms overlap

Post-traumatic stress disorder (PTSD) and mild traumatic brain injury (mTBI) are highly prevalent and closely related disorders. Affected individuals often exhibit substantially overlapping symptomatology - a major challenge for differential diagnosis in both military and civilian contexts. According to our symptom assessment, the PTSD group exhibited comparable levels of concussion symptoms and severity to the mTBI group. An objective and reliable system to uncover the key neural signatures differentiating these disorders would be an important step towards translational and applied clinical use. Here we explore use of MEG (magnetoencephalography)-multivariate statistical learning analysis in identifying the neural features for differential PTSD/mTBI characterisation. Resting state MEG-derived regional neural activity and coherence (or functional connectivity) across seven canonical neural oscillation frequencies (delta to high gamma) were used. The selected features were consistent and largely confirmatory with previously established neurophysiological markers for the two disorders. For regional power from theta, alpha and high gamma bands, the amygdala, hippocampus and temporal areas were identified. In line with regional activity, additional connections within the occipital, parietal and temporal regions were selected across a number of frequency bands. This study is the first to employ MEG-derived neural features to reliably and differentially stratify the two disorders in a multi-group context. The features from alpha and beta bands exhibited the best classification performance, even in cases where distinction by concussion symptom profiles alone were extremely difficult. We demonstrate the potential of using 'invisible' neural indices of brain functioning to understand and differentiate these debilitating conditions.

Cannabis in the management of PTSD: a systematic review

Introduction

Existing reviews exploring cannabis effectiveness have numerous limitations including narrow search strategies. We systematically explored cannabis effects on PTSD symptoms, quality of life (QOL), and return to work (RTW). We also investigated harm outcomes such as adverse effects and dropouts due to adverse effects, inefficacy, and all-cause dropout rates.

Methods

Our search in MEDLINE, EMBASE, PsycInfo, CINAHL, Web of Science, CENTRAL, and PubMed databases, yielded 1 eligible RCT and 10 observational studies (n = 4672). Risk of bias (RoB) was assessed with the Cochrane risk of bias tool and ROBINS-I.

Results

Evidence from the included studies was mainly based on non-randomized studies with no comparators. Results from unpooled, high RoB studies showed that cannabis was associated with a reduction in overall PTSD symptoms and improved QOL. Dry mouth, headaches, and psychoactive effects such as agitation and euphoria were the commonly reported adverse effects. In most studies, cannabis was well tolerated, but small proportions of patients experienced a worsening of PTSD symptoms.

Conclusion

Evidence in the current study primarily stems from low quality and high RoB observational studies. Further RCTs investigating cannabis effects on PTSD treatment should be conducted with larger sample sizes and explore a broader range of patient-important outcomes.

Real-Time Decoding of Attentional States Using Closed-Loop EEG Neurofeedback

Sustained attention is a cognitive ability to maintain task focus over extended periods of time (Mackworth, 1948; Chun, Golomb, & Turk-Browne, 2011). In this study, scalp electroencephalography (EEG) signals were processed in real time using a 32 dry-electrode system during a sustained visual attention task. An attention training paradigm was implemented, as designed in DeBettencourt, Cohen, Lee, Norman, and Turk-Browne (2015) in which the composition of a sequence of blended images is updated based on the participant's decoded attentional level to a primed image category. It was hypothesized that a single neurofeedback training session would improve sustained attention abilities. Twenty-two participants were trained on a single neurofeedback session with behavioral pretraining and posttraining sessions within three consecutive days. Half of the participants functioned as controls in a double-blinded design and received sham neurofeedback. During the neurofeedback session, attentional states to primed categories were decoded in real time and used to provide a continuous feedback signal customized to each participant in a closed-loop approach. We report a mean classifier decoding error rate of 34.3% (chance = 50%). Within the neurofeedback group, there was a greater level of task-relevant attentional information decoded in the participant's brain before making a correct behavioral response than before an incorrect response. This effect was not visible in the control group (interaction p=7.23e-4), which strongly indicates that we were able to achieve a meaningful measure of subjective attentional state in real time and control participants' behavior during the neurofeedback session. We do not provide conclusive evidence whether the single neurofeedback session per se provided lasting effects in sustained attention abilities. We developed a portable EEG neurofeedback system capable of decoding attentional states and predicting behavioral choices in the attention task at hand. The neurofeedback code framework is Python based and open source, and it allows users to actively engage in the development of neurofeedback tools for scientific and translational use.

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

A systematic review of fMRI neurofeedback reporting and effects in clinical populations

Real-time fMRI-based neurofeedback is a relatively young field with a potential to impact the currently available treatments of various disorders. In order to evaluate the evidence of clinical benefits and investigate how consistently studies report their methods and results, an exhaustive search of fMRI neurofeedback studies in clinical populations was performed. Reporting was evaluated using a limited number of Consensus on the reporting and experimental design of clinical and cognitive-behavioral neurofeedback studies (CRED-NF checklist) items, which was, together with a statistical power and sensitivity calculation, used to also evaluate the existing evidence of the neurofeedback benefits on clinical measures. The 62 found studies investigated regulation abilities and/or clinical benefits in a wide range of disorders, but with small sample sizes and were therefore unable to detect small effects. Most points from the CRED-NF checklist were adequately reported by the majority of the studies, but some improvements are suggested for the reporting of group comparisons and relations between regulation success and clinical benefits. To establish fMRI neurofeedback as a clinical tool, more emphasis should be placed in the future on using larger sample sizes determined through a priori power calculations and standardization of procedures and reporting.

Predicting PTSD severity using longitudinal magnetoencephalography with a multi-step learning framework

Objective The present study explores the effectiveness of incorporating temporal information in predicting Post-Traumatic Stress Disorder (PTSD) severity using magnetoencephalography (MEG) imaging data. The main objective was to assess the relationship between longitudinal MEG functional connectome data, measured across a variety of neural oscillatory frequencies and collected at two-timepoints (Phase I & II), against PTSD severity captured at the later time point. Approach We used an in-house developed informatics solution, featuring a two-step process featuring pre-learn feature selection (CV-SVR-rRF-FS, cross-validation with support vector regression and recursive random forest feature selection) and deep learning (long-short term memory recurrent neural network, LSTM-RNN) techniques. Main results The pre-learn step selected a small number of functional connections (or edges) from Phase I MEG data associated with Phase II PTSD severity, indexed using the PTSD CheckList (PCL) score. This strategy identified the functional edges affected by traumatic exposure and indexed disease severity, either permanently or evolving dynamically over time, for optimal predictive performance. Using the selected functional edges, LSTM modelling was used to incorporate the Phase II MEG data into longitudinal regression models. Single timepoint (Phase I and Phase II MEG data) SVR models were generated for comparison. Assessed with holdout test data, alpha and high gamma bands showed enhanced predictive performance with the longitudinal models comparing to the Phase I single timepoint models. The best predictive performance was observed for lower frequency ranges compared to the higher frequencies (low gamma), for both model types. Significance This study identified the neural oscillatory signatures that benefited from additional temporal information when estimating the outcome of PTSD severity using MEG functional connectome data. Crucially, this approach can similarly be applied to any other mental health challenge, using this effective informatics foundation for longitudinal tracking of pathological brain states and predicting outcome with a MEG-based neurophysiology imaging system.

Could Brain Decoding Machines Change Our Minds?

In a recent experiment, Zhang and colleagues designed a closed-loop brain-machine interface that learned to reduce participants' pain by decoding pain-related brain activity. In doing so, they also highlighted some of the challenges associated with coadaptive processes in brain-machine communication.

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.

Real-time fMRI feedback impacts brain activation, results in auditory hallucinations reduction: Part 1: Superior temporal gyrus -Preliminary evidence

Auditory hallucinations (AH) are one of the core symptoms of schizophrenia (SZ) and constitute a significant source of suffering and disability. One third of SZ patients experience pharmacology-resistant AH, so an alternative/complementary treatment strategy is needed to alleviate this debilitating condition. In this study, real-time functional Magnetic Resonance Imaging neurofeedback (rt-fMRI NFB), a non-invasive technique, was used to teach 10 SZ patients with pharmacology-resistant AH to modulate their brain activity in the superior temporal gyrus (STG), a key area in the neurophysiology of AH. A functional task was designed in order to provide patients with a specific strategy to help them modify their brain activity in the desired direction. Specifically, they received neurofeedback from their own STG and were trained to upregulate it while listening to their own voice recording and downregulate it while ignoring a stranger's voice recording. This guided performance neurofeedback training resulted in a) a significant reduction in STG activation while ignoring a stranger's voice, and b) reductions in AH scores after the neurofeedback session. A single, 21-minute session of rt-fMRI NFB was enough to produce these effects, suggesting that this approach may be an efficient and clinically viable alternative for the treatment of pharmacology-resistant AH.

Neural Decoding of Multi-Modal Imagery Behavior Focusing on Temporal Complexity

Mental imagery behaviors of various modalities include visual, auditory, and motor behaviors. Their alterations are pathologically involved in various psychiatric disorders. Results of earlier studies suggest that imagery behaviors are correlated with the modulated activities of the respective modality-specific regions and the additional activities of supramodal imagery-related regions. Additionally, despite the availability of complexity analysis in the neuroimaging field, it has not been used for neural decoding approaches. Therefore, we sought to characterize neural oscillation related to multimodal imagery through complexity-based neural decoding. For this study, we modified existing complexity measures to characterize the time evolution of temporal complexity. We took magnetoencephalography (MEG) data of eight healthy subjects as they performed multimodal imagery and non-imagery tasks. The MEG data were decomposed into amplitude and phase of sub-band frequencies by Hilbert-Huang transform. Subsequently, we calculated the complexity values of each reconstructed time series, along with raw data and band power for comparison, and applied these results as inputs to decode visual perception (VP), visual imagery (VI), motor execution (ME), and motor imagery (MI) functions. Consequently, intra-subject decoding with the complexity yielded a characteristic sensitivity map for each task with high decoding accuracy. The map is inverted in the occipital regions between VP and VI and in the central regions between ME and MI. Additionally, replacement of the labels into two classes as imagery and non-imagery also yielded better classification performance and characteristic sensitivity with the complexity. It is particularly interesting that some subjects showed characteristic sensitivities not only in modality-specific regions, but also in supramodal regions. These analyses indicate that two-class and four-class classifications each provided better performance when using complexity than when using raw data or band power as input. When inter-subject decoding was used with the same model, characteristic sensitivity maps were also obtained, although their decoding performance was lower. Results of this study underscore the availability of complexity measures in neural decoding approaches and suggest the possibility of a modality-independent imagery-related mechanism. The use of time evolution of temporal complexity in neural decoding might extend our knowledge of the neural bases of hierarchical functions in the human brain.

A Systematic Review of the Potential Use of Neurofeedback in Patients With Schizophrenia

Schizophrenia (SCZ) is a neurodevelopmental disorder characterized by positive symptoms (hallucinations and delusions), negative symptoms (anhedonia, social withdrawal) and marked cognitive deficits (memory, executive function, and attention). Current mainstays of treatment, including medications and psychotherapy, do not adequately address cognitive symptoms, which are essential for everyday functioning. However, recent advances in computational neurobiology have rekindled interest in neurofeedback (NF), a form of self-regulation or neuromodulation, in potentially alleviating cognitive symptoms in patients with SCZ. Therefore, we conducted a systematic review of the literature for NF studies in SCZ to identify lessons learned and to identify steps to move the field forward. Our findings reveal that NF studies to date consist mostly of case studies and small sample, single-group studies. Despite few randomized clinical trials, the results suggest that NF is feasible and that it leads to measurable changes in brain function. These findings indicate early proof-of-concept data that needs to be followed up by larger, randomized clinical trials, testing the efficacy of NF compared to well thought out placebos. We hope that such an undertaking by the field will lead to innovative solutions that address refractory symptoms and improve everyday functioning in patients with SCZ.