Real-time fMRI and its application to neurofeedback

A systematic review on the role of EEG and fMRI-Neurofeedback training in the treatment of substance use disorders and behavioral addiction

Neurofeedback (NF), a form of biofeedback, is used to enhance the self-regulation of brain functions by assessing brain activity and delivering feedback signals to ameliorate emotional attributes, cognitive functions, and behaviors. Despite the potential role of NF in substance use disorder (SUD) treatment, a number of gaps such as variations in NF methods persist. This study aims to address such gaps and present comprehensive insights into EEG and fMRI-NF applications in SUD management. This study has been conducted according to the PRISMA guidelines. The search spanned four major databases: Web of Science, Scopus, PubMed, and Embase. The search terms encompassed "Neurofeedback" OR "EEG biofeedback" OR "neurotherapy" OR "Functional near-infrared spectroscopy Neurofeedback" OR "fNIRS-Neurofeedback" AND addiction OR Drug OR "substance dependence" OR "substance abuse" OR Heroin OR Opioid OR Cannabis OR Marihuana OR Cocaine OR Crack OR Amphetamine OR Methamphetamine OR Hallucinogen. Our systematic review yielded 32 articles, including 18 EEG-, 11 fMRI-neurofeedback, and 3 fNIRS-neurofeedback studies. The primary outcome was reduced drug craving and some aspects of mental health and EEG-NF studies consistently indicated a preference for the alpha-theta protocol, whereas the high heterogeneity among fMRI-NF protocols limited direct comparisons. In conclusion, the results of this systematic review indicate that NF shows promise as an adjunctive intervention for treating SUD.

Self-navigating the "Island of Reil": a systematic review of real-time fMRI neurofeedback training of insula activity

Real-time fMRI (rtfMRI) neurofeedback (NF) is a novel noninvasive technique that permits individuals to voluntarily control brain activity. The crucial role of the insula in emotional and salience processing makes it one of the most commonly targeted regions in previous rtfMRI studies. To provide an overview of progress in the field, the present review identified 25 rtfMRI insula studies and systematically reviewed key characteristics and findings in these studies. We found that rtfMRI-based NF training is efficient for modulating insula activity and its associated behavioral/symptom-related and neural changes. Furthermore, we also observed a maintenance effect of self-regulation ability and sustained symptom improvement, which is of importance for clinical application. However, training success of insula regulation was not consistently paralleled by behavioral/symptom-related changes, suggesting a need for optimizing the NF training protocol enabling more robust training effects. Principles including inclusion of a well-designed control group/condition, statistical analyses and reporting results following common criteria and a priori determination of sample and effect sizes as well as pre-registration are also highly recommended. In summary, we believe our review will inspire and inform both basic research and therapeutic translation of rtfMRI NF training as an intervention in mental disorders particularly those with insula dysfunction.

Signal acquisition of brain-computer interfaces: A medical-engineering crossover perspective review

Brain-computer interface (BCI) technology represents a burgeoning interdisciplinary domain that facilitates direct communication between individuals and external devices. The efficacy of BCI systems is largely contingent upon the progress in signal acquisition methodologies. This paper endeavors to provide an exhaustive synopsis of signal acquisition technologies within the realm of BCI by scrutinizing research publications from the last ten years. Our review synthesizes insights from both clinical and engineering viewpoints, delineating a comprehensive two-dimensional framework for understanding signal acquisition in BCIs. We delineate nine discrete categories of technologies, furnishing exemplars for each and delineating the salient challenges pertinent to these modalities. This review furnishes researchers and practitioners with a broad-spectrum comprehension of the signal acquisition landscape in BCI, and deliberates on the paramount issues presently confronting the field. Prospective enhancements in BCI signal acquisition should focus on harmonizing a multitude of disciplinary perspectives. Achieving equilibrium between signal fidelity, invasiveness, biocompatibility, and other pivotal considerations is imperative. By doing so, we can propel BCI technology forward, bolstering its effectiveness, safety, and dependability, thereby contributing to an auspicious future for human-technology integration.

From lab to life: challenges and perspectives of fNIRS for haemodynamic-based neurofeedback in real-world environments

Neurofeedback allows individuals to monitor and self-regulate their brain activity, potentially improving human brain function. Beyond the traditional electrophysiological approach using primarily electroencephalography, brain haemodynamics measured with functional magnetic resonance imaging (fMRI) and more recently, functional near-infrared spectroscopy (fNIRS) have been used (haemodynamic-based neurofeedback), particularly to improve the spatial specificity of neurofeedback. Over recent years, especially fNIRS has attracted great attention because it offers several advantages over fMRI such as increased user accessibility, cost-effectiveness and mobility-the latter being the most distinct feature of fNIRS. The next logical step would be to transfer haemodynamic-based neurofeedback protocols that have already been proven and validated by fMRI to mobile fNIRS. However, this undertaking is not always easy, especially since fNIRS novices may miss important fNIRS-specific methodological challenges. This review is aimed at researchers from different fields who seek to exploit the unique capabilities of fNIRS for neurofeedback. It carefully addresses fNIRS-specific challenges and offers suggestions for possible solutions. If the challenges raised are addressed and further developed, fNIRS could emerge as a useful neurofeedback technique with its own unique application potential-the targeted training of brain activity in real-world environments, thereby significantly expanding the scope and scalability of haemodynamic-based neurofeedback applications.This article is part of the theme issue 'Neurofeedback: new territories and neurocognitive mechanisms of endogenous neuromodulation'.

Regulation of craving for real-time fMRI neurofeedback based on individual classification

In previous real-time functional magnetic resonance imaging neurofeedback (rtfMRI-NF) studies on smoking craving, the focus has been on within-region activity or between-region connectivity, neglecting the potential predictive utility of broader network activity. Moreover, there is debate over the use and relative predictive power of individual-specific and group-level classifiers. This study aims to further advance rtfMRI-NF for substance use disorders by using whole-brain rtfMRI-NF to assess smoking craving-related brain patterns, evaluate the performance of group-level or individual-level classification (n = 31) and evaluate the performance of an optimized classifier across repeated NF runs. Using real-time individual-level classifiers derived from whole-brain support vector machines, we found that classification accuracy between crave and no-crave conditions and between repeated NF runs increased across repeated runs at both individual and group levels. In addition, individual-level accuracy was significantly greater than group-level accuracy, highlighting the potential increased utility of an individually trained whole-brain classifier for volitional control over brain patterns to regulate smoking craving. This study provides evidence supporting the feasibility of using whole-brain rtfMRI-NF to modulate smoking craving-related brain responses and the potential for learning individual strategies through optimization across repeated feedback runs. This article is part of the theme issue 'Neurofeedback: new territories and neurocognitive mechanisms of endogenous neuromodulation'.

Individualized functional magnetic resonance imaging neuromodulation enhances visuospatial perception: a proof-of-concept study

This proof-of-concept study uses individualized functional magnetic resonance imaging neuromodulation (iNM) to explore the mechanisms that enhance BOLD signals in visuospatial perception (VP) networks that are crucial for navigation. Healthy participants (n = 8) performed a VP up- and down-direction discrimination task at full and subthreshold coherence through peripheral vision, and superimposed direction through visual imagery (VI) at central space under iNM and control conditions. iNM targets individualized anatomical and functional middle- and medial-superior temporal (MST) networks that control VP. We found that iNM engaged selective exteroceptive and interoceptive attention (SEIA) and motor planning (MP) networks. Specifically, iNM increased overall: (i) area under the curve of the BOLD magnitude: 100% in VP (but decreased for weak coherences), 21-47% in VI, 26-59% in MP and 48-76% in SEIA through encoding; and (ii) classification performance for each direction, coherence and network through decoding, predicting stimuli from brain maps. Our findings, derived from encoding and decoding models, suggest that mechanisms induced by iNM are causally linked in enhancing visuospatial networks and demonstrate iNM as a feasibility treatment for low-vision patients with cortical blindness or visuospatial impairments that precede cognitive decline.This article is part of the theme issue 'Neurofeedback: new territories and neurocognitive mechanisms of endogenous neuromodulation'.

Closed-loop fMRI at the mesoscopic scale of columns and layers: Can we do it and why would we want to?

Technological advances in fMRI including ultra-high magnetic fields (≥ 7 T) and acquisition methods that increase spatial specificity have paved the way for studies of the human cortex at the scale of layers and columns. This mesoscopic scale promises an improved mechanistic understanding of human cortical function so far only accessible to invasive animal neurophysiology. In recent years, an increasing number of studies have applied such methods to better understand the cortical function in perception and cognition. This future perspective article asks whether closed-loop fMRI studies could equally benefit from these methods to achieve layer and columnar specificity. We outline potential applications and discuss the conceptual and concrete challenges, including data acquisition and volitional control of mesoscopic brain activity. We anticipate an important role of fMRI with mesoscopic resolution for closed-loop fMRI and neurofeedback, yielding new insights into brain function and potentially clinical applications.This article is part of the theme issue 'Neurofeedback: new territories and neurocognitive mechanisms of endogenous neuromodulation'.

Getting stress-related disorders under control: the untapped potential of neurofeedback

Stress-related disorders are among the biggest global health challenges. Despite significant progress in understanding their neurocognitive basis, the promise of applying insights from fundamental research to prevention and treatment remains largely unfulfilled. We argue that neurofeedback - a method for training voluntary control over brain activity - has the potential to fill this translational gap. We provide a contemporary perspective on neurofeedback as endogenous neuromodulation that can target complex brain network dynamics, is transferable to real-world scenarios outside a laboratory or treatment facility, can be trained prospectively, and is individually adaptable. This makes neurofeedback a prime candidate for a personalized preventive neuroscience-based intervention strategy that focuses on the ecological momentary neuromodulation of stress-related brain networks in response to actual stressors in real life.

Amygdala fMRI-A Critical Appraisal of the Extant Literature

Even before the advent of fMRI, the amygdala occupied a central space in the affective neurosciences. Yet this amygdala-centred view on emotion processing gained even wider acceptance after the inception of fMRI in the early 1990s, a landmark that triggered a goldrush of fMRI studies targeting the amygdala in vivo. Initially, this amygdala fMRI research was mostly confined to task-activation studies measuring the magnitude of the amygdala's response to emotional stimuli. Later, interest began to shift more towards the study of the amygdala's resting-state functional connectivity and task-based psychophysiological interactions. Later still, the test-retest reliability of amygdala fMRI came under closer scrutiny, while at the same time, amygdala-based real-time fMRI neurofeedback gained widespread popularity. Each of these major subdomains of amygdala fMRI research has left its marks on the field of affective neuroscience at large. The purpose of this review is to provide a critical assessment of this literature. By integrating the insights garnered by these research branches, we aim to answer the question: What part (if any) can amygdala fMRI still play within the current landscape of affective neuroscience? Our findings show that serious questions can be raised with regard to both the reliability and validity of amygdala fMRI. These conclusions force us to cast doubt on the continued viability of amygdala fMRI as a core pilar of the affective neurosciences.

Uncovering the power of neurofeedback: a meta-analysis of its effectiveness in treating major depressive disorders

Neurofeedback, a non-invasive intervention, has been increasingly used as a potential treatment for major depressive disorders. However, the effectiveness of neurofeedback in alleviating depressive symptoms remains uncertain. To address this gap, we conducted a comprehensive meta-analysis to evaluate the efficacy of neurofeedback as a treatment for major depressive disorders. We conducted a comprehensive meta-analysis of 22 studies investigating the effects of neurofeedback interventions on depression symptoms, neurophysiological outcomes, and neuropsychological function. Our analysis included the calculation of Hedges' g effect sizes and explored various moderators like intervention settings, study designs, and demographics. Our findings revealed that neurofeedback intervention had a significant impact on depression symptoms (Hedges' g = -0.600) and neurophysiological outcomes (Hedges' g = -0.726). We also observed a moderate effect size for neurofeedback intervention on neuropsychological function (Hedges' g = -0.418). As expected, we observed that longer intervention length was associated with better outcomes for depressive symptoms (β = -4.36, P < 0.001) and neuropsychological function (β = -2.89, P = 0.003). Surprisingly, we found that shorter neurofeedback sessions were associated with improvements in neurophysiological outcomes (β = 3.34, P < 0.001). Our meta-analysis provides compelling evidence that neurofeedback holds promising potential as a non-pharmacological intervention option for effectively improving depressive symptoms, neurophysiological outcomes, and neuropsychological function in individuals with major depressive disorders.

Mapping the evolution of neurofeedback research: a bibliometric analysis of trends and future directions

Introduction

This study conducts a bibliometric analysis on neurofeedback research to assess its current state and potential future developments.

Methods

It examined 3,626 journal articles from the Web of Science (WoS) using co-citation and co-word methods.

Results

The co-citation analysis identified three major clusters: "Real-Time fMRI Neurofeedback and Self-Regulation of Brain Activity," "EEG Neurofeedback and Cognitive Performance Enhancement," and "Treatment of ADHD Using Neurofeedback." The co-word analysis highlighted four key clusters: "Neurofeedback in Mental Health Research," "Brain-Computer Interfaces for Stroke Rehabilitation," "Neurofeedback for ADHD in Youth," and "Neural Mechanisms of Emotion and Self-Regulation with Advanced Neuroimaging.

Discussion

This in-depth bibliometric study significantly enhances our understanding of the dynamic field of neurofeedback, indicating its potential in treating ADHD and improving performance. It offers non-invasive, ethical alternatives to conventional psychopharmacology and aligns with the trend toward personalized medicine, suggesting specialized solutions for mental health and rehabilitation as a growing focus in medical practice.

Two is better? combining EEG and fMRI for BCI and neurofeedback: a systematic review

Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are two commonly used non-invasive techniques for measuring brain activity in neuroscience and brain-computer interfaces (BCI).Objective. In this review, we focus on the use of EEG and fMRI in neurofeedback (NF) and discuss the challenges of combining the two modalities to improve understanding of brain activity and achieve more effective clinical outcomes. Advanced technologies have been developed to simultaneously record EEG and fMRI signals to provide a better understanding of the relationship between the two modalities. However, the complexity of brain processes and the heterogeneous nature of EEG and fMRI present challenges in extracting useful information from the combined data.Approach. We will survey existing EEG-fMRI combinations and recent studies that exploit EEG-fMRI in NF, highlighting the experimental and technical challenges.Main results. We made a classification of the different combination of EEG-fMRI for NF, we provide a review of multimodal analysis methods for EEG-fMRI features. We also survey the current state of research on EEG-fMRI in the different existing NF paradigms. Finally, we also identify some of the remaining challenges in this field.Significance. By exploring EEG-fMRI combinations in NF, we are advancing our knowledge of brain function and its applications in clinical settings. As such, this review serves as a valuable resource for researchers, clinicians, and engineers working in the field of neural engineering and rehabilitation, highlighting the promising future of EEG-fMRI-based NF.

No evidence from a negative mood induction fMRI task for frontal functional asymmetry as a suitable neurofeedback target

Frontal functional asymmetry (FA) has been proposed as a potential target for neurofeedback (NFB) training for mental disorders but most FA NFB studies used electroencephalography while the investigations of FA NFB in functional magnetic resonance imaging (fMRI) are rather limited. In this study, we aimed at identifying functional asymmetry effects in fMRI and exploring its potential as a target for fMRI NFB studies by re-analyzing an existing data set containing a resting state measurement and a sad mood induction task of n = 30 participants with remitted major depressive disorder and n = 30 matched healthy controls. We applied low-frequency fluctuations (ALFF), fractional ALFF, and regional homogeneity and estimated functional asymmetry in both a voxel-wise and regional manner. We assessed functional asymmetry during rest and negative mood induction as well as functional asymmetry changes between the phases, and associated the induced mood change with the change in functional asymmetry. Analyses were conducted within as well as between groups. Despite extensive analyses, we identified only very limited effects. While some tests showed nominal significance, our results did not contain any clear identifiable patterns of effects that would be expected if a true underlying effect would be present. In conclusion, we do not find evidence for FA effects related to negative mood in fMRI, which questions the usefulness of FA measures for real-time fMRI neurofeedback as a treatment approach for affective disorders.

Training volitional control of the theory of mind network with real-time fMRI neurofeedback

Is there a way improve our ability to understand the minds of others? Towards addressing this question, here, we conducted a single-arm, proof-of-concept study to evaluate whether real-time fMRI neurofeedback (rtfMRI-NF) from the temporo-parietal junction (TPJ) leads to volitional control of the neural network subserving theory of mind (ToM; the process by which we attribute and reason about the mental states of others). As additional aims, we evaluated the strategies used to self-regulate the network and whether volitional control of the ToM network was moderated by participant characteristics and associated with improved performance on behavioral measures. Sixteen participants underwent fMRI while completing a task designed to individually-localize the TPJ, and then three separate rtfMRI-NF scans during which they completed multiple runs of a training task while receiving intermittent, activation-based feedback from the TPJ, and one run of a transfer task in which no neurofeedback was provided. Region-of-interest analyses demonstrated volitional control in most regions during the training tasks and during the transfer task, although the effects were smaller in magnitude and not observed in one of the neurofeedback targets for the transfer task. Text analysis demonstrated that volitional control was most strongly associated with thinking about prior social experiences when up-regulating the neural signal. Analysis of behavioral performance and brain-behavior associations largely did not reveal behavior changes except for a positive association between volitional control in RTPJ and changes in performance on one ToM task. Exploratory analysis suggested neurofeedback-related learning occurred, although some degree of volitional control appeared to be conferred with the initial self-regulation strategy provided to participants (i.e., without the neurofeedback signal). Critical study limitations include the lack of a control group and pre-rtfMRI transfer scan, which prevents a more direct assessment of neurofeedback-induced volitional control, and a small sample size, which may have led to an overestimate and/or unreliable estimate of study effects. Nonetheless, together, this study demonstrates the feasibility of training volitional control of a social cognitive brain network, which may have important clinical applications. Given the study's limitations, findings from this study should be replicated with more robust experimental designs.

Self-regulation of visual word form area activation with real-time fMRI neurofeedback

The Visual Word Form Area (VWFA) is a key region of the brain's reading network and its activation has been shown to be strongly associated with reading skills. Here, for the first time, we investigated whether voluntary regulation of VWFA activation is feasible using real-time fMRI neurofeedback. 40 adults with typical reading skills were instructed to either upregulate (UP group, N = 20) or downregulate (DOWN group, N = 20) their own VWFA activation during six neurofeedback training runs. The VWFA target region was individually defined based on a functional localizer task. Before and after training, also regulation runs without feedback ("no-feedback runs") were performed. When comparing the two groups, we found stronger activation across the reading network for the UP than the DOWN group. Further, activation in the VWFA was significantly stronger in the UP group than the DOWN group. Crucially, we observed a significant interaction of group and time (pre, post) for the no-feedback runs: The two groups did not differ significantly in their VWFA activation before neurofeedback training, but the UP group showed significantly stronger activation than the DOWN group after neurofeedback training. Our results indicate that upregulation of VWFA activation is feasible and that, once learned, successful upregulation can even be performed in the absence of feedback. These results are a crucial first step toward the development of a potential therapeutic support to improve reading skills in individuals with reading impairments.

Inhibitory control as a potential treatment target for obesity

OBJECTIVES

Strong reward responsiveness to food and insufficient inhibitory control are thought to be implicated in the development and maintenance of obesity. This narrative review addresses the role of inhibitory control in obesity and weight loss, and in how far inhibitory control is a promising target for weight loss interventions.

METHODS

PubMed, Web of Science, and Google Scholar were searched for papers up to May 2021. 41 papers were included.

RESULTS

Individuals with obesity have poorer food-specific inhibitory control, particularly when hungry, and less concurrent activation of inhibitory brain areas. Moreover, this was strongly predictive of future weight gain. More activation of inhibitory brain areas, on the other hand, was predictive of weight loss: individuals with successful weight loss initially show inhibitory brain activity comparable to that of normal weight individuals. When successful weight maintenance is achieved for at least 1 year, this inhibitory activity is further increased. Interventions targeting inhibitory control in obese individuals have divergent effects. Firstly, food-specific inhibitory control training is particularly effective for people with low inhibitory control and high BMI. Secondly, neuromodulation paradigms are rather heterogeneous: although rTMS to the left dorsolateral prefrontal cortex induced some weight-loss, multiple sessions of tDCS reduced food consumption (desire) and induced weight loss in two thirds of the papers. Thirdly, neurofeedback results in successful upregulation of brain activity and connectivity, but occasionally leads to increased food intake. In conclusion, inhibitory control is implicated in obesity. It can be targeted to promote weight loss although major weight losses have not been achieved.

Metaheuristic Optimization-Based Feature Selection for Imagery and Arithmetic Tasks: An fNIRS Study

In recent decades, the brain-computer interface (BCI) has emerged as a leading area of research. The feature selection is vital to reduce the dataset's dimensionality, increase the computing effectiveness, and enhance the BCI's performance. Using activity-related features leads to a high classification rate among the desired tasks. This study presents a wrapper-based metaheuristic feature selection framework for BCI applications using functional near-infrared spectroscopy (fNIRS). Here, the temporal statistical features (i.e., the mean, slope, maximum, skewness, and kurtosis) were computed from all the available channels to form a training vector. Seven metaheuristic optimization algorithms were tested for their classification performance using a k-nearest neighbor-based cost function: particle swarm optimization, cuckoo search optimization, the firefly algorithm, the bat algorithm, flower pollination optimization, whale optimization, and grey wolf optimization (GWO). The presented approach was validated based on an available online dataset of motor imagery (MI) and mental arithmetic (MA) tasks from 29 healthy subjects. The results showed that the classification accuracy was significantly improved by utilizing the features selected from the metaheuristic optimization algorithms relative to those obtained from the full set of features. All of the abovementioned metaheuristic algorithms improved the classification accuracy and reduced the feature vector size. The GWO yielded the highest average classification rates (p < 0.01) of 94.83 ± 5.5%, 92.57 ± 6.9%, and 85.66 ± 7.3% for the MA, MI, and four-class (left- and right-hand MI, MA, and baseline) tasks, respectively. The presented framework may be helpful in the training phase for selecting the appropriate features for robust fNIRS-based BCI applications.

A Hybrid GCN and Filter-Based Framework for Channel and Feature Selection: An fNIRS-BCI Study

In this study, a channel and feature selection methodology is devised for brain-computer interface (BCI) applications using functional near-infrared spectroscopy (fNIRS). A graph convolutional network (GCN) is employed to select the appropriate and correlated fNIRS channels. Furthermore, in the feature extraction phase, the performance of two filter-based feature selection algorithms, (i) the minimum redundancy maximum relevance (mRMR) and (ii) ReliefF, is investigated. The five most commonly used temporal statistical features (i.e., mean, slope, maximum, skewness, and kurtosis) are used, whereas the conventional support vector machine (SVM) is utilized as a classifier for training and testing. The proposed methodology is validated using an available online dataset of motor imagery (left- and right-hand), mental arithmetic, and baseline tasks. First, the efficacy of the proposed methodology is shown for two-class BCI applications (i.e., left- vs. right-hand motor imagery and mental arithmetic vs. baseline). Second, the proposed framework is applied to four-class BCI applications (i.e., left- vs. right-hand motor imagery vs. mental arithmetic vs. baseline). The results show that the number of appropriate channels and features was significantly reduced, resulting in a significant increase in classification accuracy for both two-class and four-class BCI applications, respectively. Furthermore, both mRMR (i.e., 87.8% for motor imagery, 87.1% for mental arithmetic, and 78.7% for four-class) and ReliefF (i.e., 90.7% for motor imagery, 93.7% for mental arithmetic, and 81.6% for four-class) yielded high average classification accuracy $\left(p<0.05\right)$ . However, the results of the ReliefF algorithm are more stable and significant.

Neural and behavioral adaptations to frontal theta neurofeedback training: A proof of concept study

Previous neurofeedback research has shown training-related frontal theta increases and performance improvements on some executive tasks in real feedback versus sham control groups. However, typical sham control groups receive false or non-contingent feedback, making it difficult to know whether observed differences between groups are associated with accurate contingent feedback or other cognitive mechanisms (motivation, control strategies, attentional engagement, fatigue, etc.). To address this question, we investigated differences between two frontal theta training groups, each receiving accurate contingent feedback, but with different top-down goals: (1) increase and (2) alternate increase/decrease. We hypothesized that the increase group would exhibit greater increases in frontal theta compared to the alternate group, which would exhibit lower frontal theta during down- versus up-modulation blocks over sessions. We also hypothesized that the alternate group would exhibit greater performance improvements on a Go-NoGo shooting task requiring alterations in behavioral activation and inhibition, as the alternate group would be trained with greater task specificity, suggesting that receiving accurate contingent feedback may be the more salient learning mechanism underlying frontal theta neurofeedback training gains. Thirty young healthy volunteers were randomly assigned to increase or alternate groups. Training consisted of an orientation session, five neurofeedback training sessions (six blocks of six 30-s trials of FCz theta modulation (4-7 Hz) separated by 10-s rest intervals), and six Go-NoGo testing sessions (four blocks of 90 trials in both Low and High time-stress conditions). Multilevel modeling revealed greater frontal theta increases in the alternate group over training sessions. Further, Go-NoGo task performance increased at a greater rate in the increase group (accuracy and reaction time, but not commission errors). Overall, these results reject our hypotheses and suggest that changes in frontal theta and performance outcomes were not explained by reinforcement learning afforded by accurate contingent feedback. We discuss our findings in terms of alternative conceptual and methodological considerations, as well as limitations of this research.

Real-time fMRI neurofeedback as a new treatment for psychiatric disorders: A meta-analysis

Neurofeedback using real-time functional MRI (RT-fMRI-NF) is an innovative technique that allows to voluntarily modulate a targeted brain response and its associated behavior. Despite promising results in the current literature, its effectiveness on symptoms management in psychiatric disorders is not yet clearly demonstrated. Here, we provide 1) a state-of-art qualitative review of RT-fMRI-NF studies aiming at alleviating clinical symptoms in a psychiatric population; 2) a quantitative evaluation (meta-analysis) of RT-fMRI-NF effectiveness on various psychiatric disorders and 3) methodological suggestions for future studies. Thirty-one clinical trials focusing on psychiatric disorders were included and categorized according to standard diagnostic categories. Among the 31 identified studies, 22 consisted of controlled trials, of which only eight showed significant clinical improvement in the experimental vs. control group after the training. Nine studies found an effect at follow-up on ADHD symptoms, emotion dysregulation, facial emotion processing, depressive symptoms, hallucinations, psychotic symptoms, and specific phobia. Within-group meta-analysis revealed large effects of the NF training on depressive symptoms right after the training (g = 0.81, p < 0.01) and at follow-up (g = 1.19, p < 0.01), as well as medium effects on anxiety (g = 0.44, p = 0.01) and emotion regulation (g = 0.48, p < 0.01). Between-group meta-analysis showed a medium effect on depressive symptoms (g = 0.49, p < 0.01) and a large effect on anxiety (g = 0.77, p = 0.01). However, the between-studies heterogeneity is very high. The use of RT-fMRI-NF as a treatment for psychiatric symptoms is promising, however, further double-blind, multicentric, randomized-controlled trials are warranted.

Inducing a mental context for associative memory formation with real-time fMRI neurofeedback

Memory, one of the hallmarks of human cognition, can be modified when humans voluntarily modulate neural population activity using neurofeedback. However, it is currently unknown whether neurofeedback can influence the integration of memories, and whether memory is facilitated or impaired after such neural perturbation. In this study, participants memorized objects while we provided them with abstract neurofeedback based on their brain activity patterns in the ventral visual stream. This neurofeedback created an implicit face or house context in the brain while memorizing the objects. The results revealed that participants created associations between each memorized object and its implicit context solely due to the neurofeedback manipulation. Our findings shed light onto how memory formation can be influenced by synthetic memory tags with neurofeedback and advance our understanding of mnemonic processing.

Reduced homotopic interhemispheric connectivity in psychiatric disorders: evidence for both transdiagnostic and disorder specific features

There is considerable interest in the significance of structural and functional connections between the two brain hemispheres in terms of both normal function and in relation to psychiatric disorders. In recent years, many studies have used voxel mirrored homotopic connectivity analysis of resting state data to investigate the importance of connectivity between homotopic regions in the brain hemispheres in a range of neuropsychiatric disorders. The current review summarizes findings from these voxel mirrored homotopic connectivity studies in individuals with autism spectrum disorder, addiction, attention deficit hyperactivity disorder, anxiety and depression disorders, and schizophrenia, as well as disorders such as Alzheimer's disease, mild cognitive impairment, epilepsy, and insomnia. Overall, other than attention deficit hyperactivity disorder, studies across psychiatric disorders report decreased homotopic resting state functional connectivity in the default mode, attention, salience, sensorimotor, social cognition, visual recognition, primary visual processing, and reward networks, which are often associated with symptom severity and/or illness onset/duration. Decreased homotopic resting state functional connectivity may therefore represent a transdiagnostic marker for general psychopathology. In terms of disorder specificity, the extensive decreases in homotopic resting state functional connectivity in autism differ markedly from attention deficit hyperactivity disorder, despite both occurring during early childhood and showing extensive co-morbidity. A pattern of more posterior than anterior regions showing reductions in schizophrenia is also distinctive. Going forward, more studies are needed to elucidate the functions of these homotopic functional connections in both health and disorder and focusing on associations with general psychopathology, and not only on disorder specific symptoms.

Cortical networks underlying successful control of nociceptive processing using real-time fMRI

Real-time fMRI (rt-fMRI) enables self-regulation of neural activity in localized brain regions through neurofeedback. Previous studies showed successful up- and down-regulation of neural activity in the anterior cingulate cortex (ACC) and the insula (Ins) during nociceptive stimulation. Such self-regulation capacity is, however, variable across subjects, possibly related to the ability of cognitive top-down control of pain. Moreover, how specific brain areas interact to enable successful regulation of nociceptive processing and neurofeedback-based brain modulation is not well understood. A connectivity analysis framework in the frequency domain was used to examine the up- or down-regulation in the ACC and Ins and pain intensity and unpleasantness ratings were assessed. We found that successful up- and down-regulation was mediated by the ACC and by its functional connectivity with the Ins and secondary somatosensory cortex. There was no significant relationship between successful up- or downregulation and pain ratings. These findings demonstrate functional interactions between brain areas involved in nociceptive processing during regulation of ACC and Ins activity, and the relevance of the frequency domain connectivity analysis for real-time fMRI. Moreover, despite successful neural regulation, there was no change in pain ratings, suggesting that pain is a complex perception, which may be more difficult to modify than other sensory or emotional processes.

A real-time fMRI neurofeedback system for the clinical alleviation of depression with a subject-independent classification of brain states: A proof of principle study

Most clinical neurofeedback studies based on functional magnetic resonance imaging use the patient's own neural activity as feedback. The objective of this study was to create a subject-independent brain state classifier as part of a real-time fMRI neurofeedback (rt-fMRI NF) system that can guide patients with depression in achieving a healthy brain state, and then to examine subsequent clinical changes. In a first step, a brain classifier based on a support vector machine (SVM) was trained from the neural information of happy autobiographical imagery and motor imagery blocks received from a healthy female participant during an MRI session. In the second step, 7 right-handed female patients with mild or moderate depressive symptoms were trained to match their own neural activity with the neural activity corresponding to the “happiness emotional brain state” of the healthy participant. The training (4 training sessions over 2 weeks) was carried out using the rt-fMRI NF system guided by the brain-state classifier we had created. Thus, the informative voxels previously obtained in the first step, using SVM classification and Effect Mapping, were used to classify the Blood-Oxygen-Level Dependent (BOLD) activity of the patients and converted into real-time visual feedback during the neurofeedback training runs. Improvements in the classifier accuracy toward the end of the training were observed in all the patients [Session 4–1 Median = 6.563%; Range = 4.10–27.34; Wilcoxon Test (0), 2-tailed p = 0.031]. Clinical improvement also was observed in a blind standardized clinical evaluation [HDRS CE2-1 Median = 7; Range 2 to 15; Wilcoxon Test (0), 2-tailed p = 0.016], and in self-report assessments [BDI-II CE2-1 Median = 8; Range 1–15; Wilcoxon Test (0), 2-tailed p = 0.031]. In addition, the clinical improvement was still present 10 days after the intervention [BDI-II CE3-2_Median = 0; Range −1 to 2; Wilcoxon Test (0), 2-tailed p = 0.50/ HDRS CE3-2 Median = 0; Range −1 to 2; Wilcoxon Test (0), 2-tailed p = 0.625]. Although the number of participants needs to be increased and a control group included to confirm these findings, the results suggest a novel option for neural modulation and clinical alleviation in depression using noninvasive stimulation technologies.

Frontostriatal circuitry as a target for fMRI-based neurofeedback interventions: A systematic review

Dysregulated frontostriatal circuitries are viewed as a common target for the treatment of aberrant behaviors in various psychiatric and neurological disorders. Accordingly, experimental neurofeedback paradigms have been applied to modify the frontostriatal circuitry. The human frontostriatal circuitry is topographically and functionally organized into the "limbic," the "associative," and the "motor" subsystems underlying a variety of affective, cognitive, and motor functions. We conducted a systematic review of the literature regarding functional magnetic resonance imaging-based neurofeedback studies that targeted brain activations within the frontostriatal circuitry. Seventy-nine published studies were included in our survey. We assessed the efficacy of these studies in terms of imaging findings of neurofeedback intervention as well as behavioral and clinical outcomes. Furthermore, we evaluated whether the neurofeedback targets of the studies could be assigned to the identifiable frontostriatal subsystems. The majority of studies that targeted frontostriatal circuitry functions focused on the anterior cingulate cortex, the dorsolateral prefrontal cortex, and the supplementary motor area. Only a few studies (n = 14) targeted the connectivity of the frontostriatal regions. However, post-hoc analyses of connectivity changes were reported in more cases (n = 32). Neurofeedback has been frequently used to modify brain activations within the frontostriatal circuitry. Given the regulatory mechanisms within the closed loop of the frontostriatal circuitry, the connectivity-based neurofeedback paradigms should be primarily considered for modifications of this system. The anatomical and functional organization of the frontostriatal system needs to be considered in decisions pertaining to the neurofeedback targets.

Can neurofeedback provide evidence of direct brain-behavior causality?

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

Using causal methods to map symptoms to brain circuits in neurodevelopment disorders: moving from identifying correlates to developing treatments

A wide variety of model systems and experimental techniques can provide insight into the structure and function of the human brain in typical development and in neurodevelopmental disorders. Unfortunately, this work, whether based on manipulation of animal models or observational and correlational methods in humans, has a high attrition rate in translating scientific discovery into practicable treatments and therapies for neurodevelopmental disorders.With new computational and neuromodulatory approaches to interrogating brain networks, opportunities exist for "bedside-to bedside-translation" with a potentially shorter path to therapeutic options. Specifically, methods like lesion network mapping can identify brain networks involved in the generation of complex symptomatology, both from acute onset lesion-related symptoms and from focal developmental anomalies. Traditional neuroimaging can examine the generalizability of these findings to idiopathic populations, while non-invasive neuromodulation techniques such as transcranial magnetic stimulation provide the ability to do targeted activation or inhibition of these specific brain regions and networks. In parallel, real-time functional MRI neurofeedback also allow for endogenous neuromodulation of specific targets that may be out of reach for transcranial exogenous methods.Discovery of novel neuroanatomical circuits for transdiagnostic symptoms and neuroimaging-based endophenotypes may now be feasible for neurodevelopmental disorders using data from cohorts with focal brain anomalies. These novel circuits, after validation in large-scale highly characterized research cohorts and tested prospectively using noninvasive neuromodulation and neurofeedback techniques, may represent a new pathway for symptom-based targeted therapy.

Real-Time fMRI Neurofeedback Training Changes Brain Degree Centrality and Improves Sleep in Chronic Insomnia Disorder: A Resting-State fMRI Study

Background

Chronic insomnia disorder (CID) is considered a major public health problem worldwide. Therefore, innovative and effective technical methods for studying the pathogenesis and clinical comprehensive treatment of CID are urgently needed.

Methods

Real-time fMRI neurofeedback (rtfMRI-NF), a new intervention, was used to train 28 patients with CID to regulate their amygdala activity for three sessions in 6 weeks. Resting-state fMRI data were collected before and after training. Then, voxel-based degree centrality (DC) method was used to explore the effect of rtfMRI-NF training. For regions with altered DC, we determined the specific connections to other regions that most strongly contributed to altered functional networks based on DC. Furthermore, the relationships between the DC value of the altered regions and changes in clinical variables were determined.

Results

Patients with CID showed increased DC in the right postcentral gyrus, Rolandic operculum, insula, and superior parietal gyrus and decreased DC in the right supramarginal gyrus, inferior parietal gyrus, angular gyrus, middle occipital gyrus, and middle temporal gyrus. Seed-based functional connectivity analyses based on the altered DC regions showed more details about the altered functional networks. Clinical scores in Pittsburgh sleep quality index, insomnia severity index (ISI), Beck depression inventory, and Hamilton anxiety scale decreased. Furthermore, a remarkable positive correlation was found between the changed ISI score and DC values of the right insula.

Conclusions

This study confirmed that amygdala-based rtfMRI-NF training altered the intrinsic functional hubs, which reshaped the abnormal functional connections caused by insomnia and improved the sleep of patients with CID. These findings contribute to our understanding of the neurobiological mechanism of rtfMRI-NF in insomnia treatment. However, additional double-blinded controlled clinical trials with larger sample sizes need to be conducted to confirm the effect of rtfMRI-NF from this initial study.

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.

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.

Improved Regional Homogeneity in Chronic Insomnia Disorder After Amygdala-Based Real-Time fMRI Neurofeedback Training

BACKGROUND

Chronic insomnia disorder (CID) is a highly prevalent sleep disorder, which influences people's daily life and is even life threatening. However, whether the resting-state regional homogeneity (ReHo) of disrupted brain regions in CID can be reshaped to normal after treatment remains unclear.

METHODS

A novel intervention real-time functional magnetic resonance imaging neurofeedback (rtfMRI-NF) was used to train 28 CID patients to regulate the activity of the left amygdala for three sessions in 6 weeks. The ReHo methodology was adopted to explore its role on resting-state fMRI data, which were collected before and after training. Moreover, the relationships between changes of clinical variables and ReHo value of altered regions were determined.

RESULTS

Results showed that the bilateral dorsal medial pre-frontal cortex, supplementary motor area (SMA), and left dorsal lateral pre-frontal cortex had decreased ReHo values, whereas the bilateral cerebellum anterior lobe (CAL) had increased ReHo values after training. Some clinical scores markedly decreased, including Pittsburgh Sleep Quality Index, Insomnia Severity Index, Beck Depression Inventory, and Hamilton Anxiety Scale (HAMA). Additionally, the ReHo values of the left CAL were positively correlated with the change in the Hamilton depression scale score, and a remarkable positive correlation was found between the ReHo values of the right SMA and the HAMA score.

CONCLUSION

Our study provided an objective evidence that amygdala-based rtfMRI-NF training could reshape abnormal ReHo and improve sleep in patients with CID. The improved ReHo in CID provides insights into the neurobiological mechanism for the effectiveness of this intervention. However, larger double-blinded sham-controlled trials are needed to confirm our results from this initial study.

Neuroanatomical differences in the memory systems of intellectual giftedness and typical development

INTRODUCTION

Studying neuro-structural markers of intellectual giftedness (IG) will inform scientific understanding of the processes helping children excel academically.

METHODS

Structural and diffusion-weighted MRI was used to compare regional brain shape and connectivity of 12 children with average to high average IQ and 18 IG children, defined as having IQ greater than 145.

RESULTS

IG had larger subcortical structures and more robust white matter microstructural organization between those structures in regions associated with explicit memory. TD had more connected, larger subcortical structures in regions associated with implicit memory.

CONCLUSIONS

It was found that the memory systems within brains of children with exceptional intellectual abilities are differently sized and connected compared to the brains of typically developing children. These different neurodevelopmental trajectories suggest different learning strategies. A spectrum of intelligence types is envisioned, facilitated by different ratios of implicit and explicit system, which was validated using a large external dataset.

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.

Four methods of brain pattern analyses of fMRI signals associated with wrist extension versus wrist flexion studied for potential use in future motor learning BCI

OBJECTIVE

In stroke survivors, a treatment-resistant problem is inability to volitionally differentiate upper limb wrist extension versus flexion. When one intends to extend the wrist, the opposite occurs, wrist flexion, rendering the limb non-functional. Conventional therapeutic approaches have had limited success in achieving functional recovery of patients with chronic and severe upper extremity impairments. Functional magnetic resonance imaging (fMRI) neurofeedback is an emerging strategy that has shown potential for stroke rehabilitation. There is a lack of information regarding unique blood-oxygenation-level dependent (BOLD) cortical activations uniquely controlling execution of wrist extension versus uniquely controlling wrist flexion. Therefore, a first step in providing accurate neural feedback and training to the stroke survivor is to determine the feasibility of classifying (or differentiating) brain activity uniquely associated with wrist extension from that of wrist flexion, first in healthy adults.

APPROACH

We studied brain signal of 10 healthy adults, who performed wrist extension and wrist flexion during fMRI data acquisition. We selected four types of analyses to study the feasibility of differentiating brain signal driving wrist extension versus wrist flexion, as follows: 1) general linear model (GLM) analysis; 2) support vector machine (SVM) classification; 3) 'Winner Take All'; and 4) Relative Dominance.

RESULTS

With these four methods and our data, we found that few voxels were uniquely active during either wrist extension or wrist flexion. SVM resulted in only minimal classification accuracies. There was no significant difference in activation magnitude between wrist extension versus flexion; however, clusters of voxels showed extension signal > flexion signal and other clusters vice versa. Spatial patterns of activation differed among subjects.

SIGNIFICANCE

We encountered a number of obstacles to obtaining clear group results in healthy adults. These obstacles included the following: high variability across healthy adults in all measures studied; close proximity of uniquely active voxels to voxels that were common to both the extension and flexion movements; in general, higher magnitude of signal for the voxels common to both movements versus the magnitude of any given uniquely active voxel for one type of movement. Our results indicate that greater precision in imaging will be required to develop a truly effective method for differentiating wrist extension versus wrist flexion from fMRI data.

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.

Neurofeedback and the Aging Brain: A Systematic Review of Training Protocols for Dementia and Mild Cognitive Impairment

Dementia describes a set of symptoms that occur in neurodegenerative disorders and that is characterized by gradual loss of cognitive and behavioral functions. Recently, non-invasive neurofeedback training has been explored as a potential complementary treatment for patients suffering from dementia or mild cognitive impairment. Here we systematically reviewed studies that explored neurofeedback training protocols based on electroencephalography or functional magnetic resonance imaging for these groups of patients. From a total of 1,912 screened studies, 10 were included in our final sample (N = 208 independent participants in experimental and N = 81 in the control groups completing the primary endpoint). We compared the clinical efficacy across studies, and evaluated their experimental designs and reporting quality. In most studies, patients showed improved scores in different cognitive tests. However, data from randomized controlled trials remains scarce, and clinical evidence based on standardized metrics is still inconclusive. In light of recent meta-research developments in the neurofeedback field and beyond, quality and reporting practices of individual studies are reviewed. We conclude with recommendations on best practices for future studies that investigate the effects of neurofeedback training in dementia and cognitive impairment.

Neuroenhancement: State of the Art and Future Perspectives

Pharmacological neuroenhancement refers to the non-medical use of prescription drugs, alcohol, illegal drugs, or the so-called soft enhancers for the purpose of improving cognition, mood, pro-social behavior, or work and academic performance. This phenomenon is undoubtedly more frequent than previously supposed especially amongst university students. The aim of the present paper was to carefully review and comment on the available literature on neuroenhancement, according to Prisma guidelines. The results showed a great use of all prescribed drugs (benzodiazepines, antidepressants, antipsychotics, nootropic compounds, and especially stimulants) as neuroenhancers amongst healthy subjects, although probably the real prevalence is underestimated. The use of illicit drugs and soft enhancers is similarly quite common. Data on the improvement of cognition by other compounds, such as oxytocin and pheromones, or non-pharmacological techniques, specifically deep brain stimulation and transcranial magnetic stimulation, are still limited. In any case, if it is true that human beings are embedded by the desire to overcome the limits of their intrinsic nature, neuroenhancement practices put into question the concept of authenticity. Therefore, the problem appears quite complex and requires to be deepened and analyzed with no prejudice, although within an ethical conceptual frame.

From reversal to normal: Robust improvement in conflict adaptation through real-time functional near infrared spectroscopy-based neurofeedback training

Conflict adaptation refers to the improved conflict control induced after experiencing conflict and is a prominent index of adaptive cognitive control. Reversal of conflict adaptation may be maladaptive and predictive of certain mental disorders. Here, we employed real-time functional near infrared spectroscopy-based neurofeedback training, with the left dorsolateral prefrontal cortex as the target brain area, to investigate whether reversal of conflict adaptation during a word-color Stroop task could be recovered to be normal. Healthy human individuals with reversal pattern of conflict adaptation in the pretest were randomly assigned into the experimental or control groups. Distributed training for 80 min led to greater improvements in the experimental group who received real neurofeedback compared to those in the control group who received sham neurofeedback. These results indicated causal evidence for understanding the generation of conflict adaptation and heighten the prospects of clinical application of neurofeedback training.

Resting state differences between successful and unsuccessful restrained eaters

Restrained eating is a popular weight loss strategy for young women that tends to have limited effectiveness over extended periods of time. Although previous studies have explored and identified possible personality and behavior differences between successful and unsuccessful restrained eaters (REs), there has been a paucity of research on neurophysiological differences.Towards addressing this gap, we assessed brain resting state (Rs) differences in groups of unsuccessful REs (N = 39) and successful REs (N = 31). In line with hypotheses, unsuccessful REs displayed reduced regional homogeneity in brain regions involved in cognitive control (inferior parietal lobe) compared to successful REs. Regions involved in conflict monitoring (anterior cingulate cortex) were also observed to be comparatively less active in the unsuccessful RE group. Finally, based on analyses of independent components and seed-based functional connectivity, regions involved in conflict monitoring and cognitive control, especially those localized within the frontoparietal network, showed weaker connectivities among unsuccessful REs compared to their successful counterparts.These results underscore specific brain Rs differences between successful REs and unsuccessful REs in regions implicated in cognitive control and conflict monitoring.

Psychotherapy in the Framework of Embodied Cognition-Does Interpersonal Synchrony Influence Therapy Success?

Mental health problems remain among the main generators of costs within and beyond the health care system. Psychotherapy, the tool of choice in their treatment, is qualified by social interaction, and cooperation within the therapist-patient-dyad. Research into the factors influencing therapy success to date is neither exhaustive nor conclusive. Among many others, the quality of the relationship between therapist and patient stands out regardless of the followed psychotherapy school. Emerging research points to a connection between interpersonal synchronization within the sessions and therapy outcome. Consequently, it can be considered significant for the shaping of this relationship. The framework of Embodied Cognition assumes bodily and neuronal correlates of thinking. Therefore, the present paper reviews investigations on interpersonal, non-verbal synchrony in two domains: firstly, studies on interpersonal synchrony in psychotherapy are reviewed (synchronization of movement). Secondly, findings on neurological correlates of interpersonal synchrony (assessed with EEG, fMRI, fNIRS) are summarized in a narrative manner. In addition, the question is asked whether interpersonal synchrony can be achieved voluntarily on an individual level. It is concluded that there might be mechanisms which could give more insights into therapy success, but as of yet remain uninvestigated. Further, the framework of embodied cognition applies more to the current body of evidence than classical cognitivist views. Nevertheless, deeper research into interpersonal physical and neurological processes utilizing the framework of Embodied Cognition emerges as a possible route of investigation on the road to lower drop-out rates, improved and quality-controlled therapeutic interventions, thereby significantly reducing healthcare costs.

Neurofeedback training in major depressive disorder: A systematic review of clinical efficacy, study quality and reporting practices

Major depressive disorder (MDD) is the leading cause of disability worldwide. Neurofeedback training has been suggested as a potential additional treatment option for MDD patients not reaching remission from standard care (i.e., psychopharmacology and psychotherapy). Here we systematically reviewed neurofeedback studies employing electroencephalography, or functional magnetic resonance-based protocols in depressive patients. Of 585 initially screened studies, 24 were included in our final sample (N = 480 patients in experimental and N = 194 in the control groups completing the primary endpoint). We evaluated the clinical efficacy across studies and attempted to group studies according to the control condition categories currently used in the field that affect clinical outcomes in group comparisons. In most studies, MDD patients showed symptom improvement superior to the control group(s). However, most articles did not comply with the most stringent study quality and reporting practices. We conclude with recommendations on best practices for experimental designs and reporting standards for neurofeedback training.

Targeting hippocampal hyperactivity with real-time fMRI neurofeedback: protocol of a single-blind randomized controlled trial in mild cognitive impairment

BACKGROUND

Several fMRI studies found hyperactivity in the hippocampus during pattern separation tasks in patients with Mild Cognitive Impairment (MCI; a prodromal stage of Alzheimer's disease). This was associated with memory deficits, subsequent cognitive decline, and faster clinical progression. A reduction of hippocampal hyperactivity with an antiepileptic drug improved memory performance. Pharmacological interventions, however, entail the risk of side effects. An alternative approach may be real-time fMRI neurofeedback, during which individuals learn to control region-specific brain activity. In the current project we aim to test the potential of neurofeedback to reduce hippocampal hyperactivity and thereby improve memory performance.

METHODS

In a single-blind parallel-group study, we will randomize n = 84 individuals (n = 42 patients with MCI, n = 42 healthy elderly volunteers) to one of two groups receiving feedback from either the hippocampus or a functionally independent region. Percent signal change of the hemodynamic response within the respective target region will be displayed to the participant with a thermometer icon. We hypothesize that only feedback from the hippocampus will decrease hippocampal hyperactivity during pattern separation and thereby improve memory performance.

DISCUSSION

Results of this study will reveal whether real-time fMRI neurofeedback is able to reduce hippocampal hyperactivity and thereby improve memory performance. In addition, the results of this study may identify predictors of successful neurofeedback as well as the most successful regulation strategies.

TRIAL REGISTRATION

The study has been registered with clinicaltrials.gov on the 16th of July 2019 (trial identifier: NCT04020744 ).

The efficacy of real-time functional magnetic resonance imaging neurofeedback for psychiatric illness: A meta-analysis of brain and behavioral outcomes

Real-time functional magnetic resonance imaging neurofeedback (rtfMRI-NF) has gained popularity as an experimental treatment for a variety of psychiatric illnesses. However, there has yet to be a quantitative review regarding its efficacy. Here, we present the first meta-analysis of rtfMRI-NF for psychiatric disorders, evaluating its impact on brain and behavioral outcomes. Our literature review identified 17 studies and 105 effect sizes across brain and behavioral outcomes. We find that rtfMRI-NF produces a medium-sized effect on neural activity during training (g = .59, 95 % CI [.44, .75], p < .0001), a large-sized effect after training when no neurofeedback is provided (g = .84, 95 % CI [.37, 1.31], p = .005), and small-sized effects for behavioral outcomes (symptoms g = .37, 95 % CI [.16, .58], p = .002; cognition g = .23, 95 % CI [-.33, .78], p = .288). Mixed-effects analyses revealed few moderators. Together, these data suggest a positive impact of rtfMRI-NF on brain and behavioral outcomes, although more research is needed to determine how rtfMRI-NF works, for whom, and under what circumstances.

Picking Your Brains: Where and How Neuroscience Tools Can Enhance Marketing Research

The use of neuroscience tools to study consumer behavior and the decision making process in marketing has improved our understanding of cognitive, neuronal, and emotional mechanisms related to marketing-relevant behavior. However, knowledge about neuroscience tools that are used in consumer neuroscience research is scattered. In this article, we present the results of a literature review that aims to provide an overview of the available consumer neuroscience tools and classifies them according to their characteristics. We analyse a total of 219 full-texts in the area of consumer neuroscience. Our findings suggest that there are seven tools that are currently used in consumer neuroscience research. In particular, electroencephalography (EEG) and eye tracking (ET) are the most commonly used tools in the field. We also find that consumer neuroscience tools are used to study consumer preferences and behaviors in different marketing domains such as advertising, branding, online experience, pricing, product development and product experience. Finally, we identify two ready-to-use platforms, namely iMotions and GRAIL that can help in integrating the measurements of different consumer neuroscience tools simultaneously. Measuring brain activity and physiological responses on a common platform could help by (1) reducing time and costs for experiments and (2) linking cognitive and emotional aspects with neuronal processes. Overall, this article provides relevant input in setting directions for future research and for business applications in consumer neuroscience. We hope that this study will provide help to researchers and practitioners in identifying available, non-invasive and useful tools to study consumer behavior.

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.

fMRI volume classification using a 3D convolutional neural network robust to shifted and scaled neuronal activations

Deep-learning methods based on deep neural networks (DNNs) have recently been successfully utilized in the analysis of neuroimaging data. A convolutional neural network (CNN) is a type of DNN that employs a convolution kernel that covers a local area of the input sample and moves across the sample to provide a feature map for the subsequent layers. In our study, we hypothesized that a 3D-CNN model with down-sampling operations such as pooling and/or stride would have the ability to extract robust feature maps from the shifted and scaled neuronal activations in a single functional MRI (fMRI) volume for the classification of task information associated with that volume. Thus, the 3D-CNN model would be able to ameliorate the potential misalignment of neuronal activations and over-/under-activation in local brain regions caused by imperfections in spatial alignment algorithms, confounded by variability in blood-oxygenation-level-dependent (BOLD) responses across sessions and/or subjects. To this end, the fMRI volumes acquired from four sensorimotor tasks (left-hand clenching, right-hand clenching, auditory attention, and visual stimulation) were used as input for our 3D-CNN model to classify task information using a single fMRI volume. The classification performance of the 3D-CNN was systematically evaluated using fMRI volumes obtained from various minimal preprocessing scenarios applied to raw fMRI volumes that excluded spatial normalization to a template and those obtained from full preprocessing that included spatial normalization. Alternative classifier models such as the 1D fully connected DNN (1D-fcDNN) and support vector machine (SVM) were also used for comparison. The classification performance was also assessed for several k-fold cross-validation (CV) schemes, including leave-one-subject-out CV (LOOCV). Overall, the classification results of the 3D-CNN model were superior to that of the 1D-fcDNN and SVM models. When using the fully-processed fMRI volumes with LOOCV, the mean error rates (± the standard error of the mean) for the 3D-CNN, 1D-fcDNN, and SVM models were 2.1% (± 0.9), 3.1% (± 1.2), and 4.1% (± 1.5), respectively (p = 0.041 from a one-way ANOVA). The error rates for 3-fold CV were higher (2.4% ± 1.0, 4.2% ± 1.3, and 10.1% ± 2.0; p < 0.0003 from a one-way ANOVA). The mean error rates also increased considerably using the raw fMRI 3D volume data without preprocessing (26.2% for the 3D-CNN, 75.0% for the 1D-fcDNN, and 75.0% for the SVM). Furthermore, the ability of the pre-trained 3D-CNN model to handle shifted and scaled neuronal activations was demonstrated in an online scenario for five-class classification (i.e., four sensorimotor tasks and the resting state) using the real-time fMRI of three participants. The resulting classification accuracy was 78.5% (± 1.4), 26.7% (± 5.9), and 21.5% (± 3.1) for the 3D-CNN, 1D-fcDNN, and SVM models, respectively. The superior performance of the 3D-CNN compared to the 1D-fcDNN was verified by analyzing the resulting feature maps and convolution filters that handled the shifted and scaled neuronal activations and by utilizing an independent public dataset from the Human Connectome Project.