Real-time fMRI Neurofeedback Research Articles

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.

Enhanced efficacy of CBT following augmentation with amygdala rtfMRI neurofeedback in depression

BackgroundDespite cognitive behavioral therapy (CBT) being a standard treatment in major depressive disorder (MDD), nearly half of patients do not respond. As one of the predictors of CBT's efficacy is amygdala reactivity to positive information, which is often decreased in MDD, we explored whether real-time fMRI neurofeedback (rtfMRI-nf) training to increase amygdala responses during positive memory recall prior CBT would enhance its efficacy. MethodsIn a double-blind, placebo controlled, randomized clinical trial, 35 adults with MDD received two sessions of rtfMRI-nf training to increase their amygdala (experimental group, n = 16) or parietal (control group, n = 19) responses during positive memory neurofeedback prior to receiving 10 CBT sessions. Depressive symptomatology was monitored between the rtfMRI sessions, the first three, 9th and 10th sessions of CBT and at 6 months and 1 year follow-up. ResultsParticipants in the experimental group showed decreased depressive symptomatology and higher remission rates at 6 months and 1 year follow-up than the control group. Analysis of CBT content highlighted that participants in the experimental group focused more on positive thinking and behaviors than the control group. LimitationsThe study was relatively small and not sufficiently powered to detect small effects. ConclusionsCBT, when combined with amygdala neurofeedback, results in sustained clinical changes and leads to long-lasting clinical improvement, potentially by increasing focus on positive memories and cognitions.

Effectiveness of Neurofeedback Training for Patients with Personality Disorders: A Systematic Review.

Objective: Personality disorders are serious psychiatric conditions, and some studies have examined neurofeedback training as a potential alternative treatment to improve cognitive and clinical symptoms in patients with such disorders. Here, we aimed to provide a first systematic review of such trials and present existing evidence regarding this treatment for individuals with personality disorders. Method : A systematic search of peer-reviewed English journal articles was conducted for this study to identify original studies on fMRI and EEG neurofeedback treatment protocols in patients with personality disorders up to January 2023. PubMed, Web of Science, ProQuest, Cochrane Library, and Google Scholar databases were queried through the keywords "neurofeedback," "biofeedback," and "personality disorder," as well as their related Mesh synonyms. Results: Totally, five studies were included in our systematic review. Two studies utilized EEG neurofeedback protocols, while three articles used real-time fMRI neurofeedback protocols. The types of studies were non-randomized, not-blinded case reports, case series, and single-arm trials with a high risk of bias. EEG neurofeedback protocols applied more training sessions and reported improvements in patients' neuropsychological and behavioral functions after treatment. Furthermore, fMRI-based neurofeedback studies reported neurophysiological changes, such as a shift in vmPFC-amygdala connectivity, towards healthy states following treatment. Moreover, behavioral symptoms of patients were reported to be improved after fMRI neurofeedback. Conclusion: Neurofeedback studies investigating this therapeutic technique for personality disorders are still very preliminary, and no strict conclusions can be drawn at this time. Therefore, further basic and clinical investigations are required to address several open methodological and technical questions and establish consensus and standardization, which will eventually lead to translational works.

Pattern classification based on the amygdala does not predict an individual's response to emotional stimuli.

Functional magnetic resonance imaging (fMRI) studies have often recorded robust univariate group effects in the amygdala of subjects exposed to emotional stimuli. Yet it is unclear to what extent this effect also holds true when multi-voxel pattern analysis (MVPA) is applied at the level of the individual participant. Here we sought to answer this question. To this end, we combined fMRI data from two prior studies (N = 112). For each participant, a linear support vector machine was trained to decode the valence of emotional pictures (negative, neutral, positive) based on brain activity patterns in either the amygdala (primary region-of-interest analysis) or the whole-brain (secondary exploratory analysis). The accuracy score of the amygdala-based pattern classifications was statistically significant for only a handful of participants (4.5%) with a mean and standard deviation of 37% ± 5% across all subjects (range: 28-58%; chance-level: 33%). In contrast, the accuracy score of the whole-brain pattern classifications was statistically significant in roughly half of the participants (50.9%), and had an across-subjects mean and standard deviation of 49% ± 6% (range: 33-62%). The current results suggest that the information conveyed by the emotional pictures was encoded by spatially distributed parts of the brain, rather than by the amygdala alone, and may be of particular relevance to studies that seek to target the amygdala in the treatment of emotion regulation problems, for example via real-time fMRI neurofeedback training.

Selecting an optimal real-time fMRI neurofeedback method for alcohol craving control training.

Real-time fMRI neurofeedback (rt-fMRI-NF) is a technique in which information about an individual's neural state is given back to them, typically to enable and reinforce neuromodulation. Its clinical potential has been demonstrated in several applications, but lack of evidence on optimal parameters limits clinical utility of the technique. This study aimed to identify optimal parameters for rt-fMRI-NF-aided craving regulation training in alcohol use disorder (AUD). Adults with AUD (n = 30) participated in a single-session study of four runs of rt-fMRI-NF where they downregulated "craving-related" brain activity. They received one of three types of neurofeedback: multi-region of interest (ROI), support vector machine with continuous feedback (cSVM), and support vector machine with intermittent feedback (iSVM). Performance was assessed on the success rate, change in neural downregulation, and change in self-reported craving for alcohol. Participants had more successful trials in run 4 versus 1, as well as improved downregulation of the insula, anterior cingulate, and dorsolateral prefrontal cortex (dlPFC). Greater downregulation of the latter two regions predicted greater reduction in craving. iSVM performed significantly worse than the other two methods. Downregulation of the striatum and dlPFC, enabled by ROI but not cSVM neurofeedback, was correlated with a greater reduction in craving. rt-fMRI-NF training for downregulation of alcohol craving in individuals with AUD shows potential for clinical use, though this pilot study should be followed with a larger randomized-control trial before clinical meaningfulness can be established. Preliminary results suggest an advantage of multi-ROI over SVM and intermittent feedback approaches.

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.

Amygdala downregulation training using fMRI neurofeedback in post-traumatic stress disorder: a randomized, double-blind trial

Hyperactivation of amygdala is a neural marker for post-traumatic stress disorder (PTSD) and improvement in control over amygdala activity has been associated with treatment success in PTSD. In this randomized, double-blind clinical trial we evaluated the efficacy of a real-time fMRI neurofeedback intervention designed to train control over amygdala activity following trauma recall. Twenty-five patients with PTSD completed three sessions of neurofeedback training in which they attempted to downregulate the feedback signal after exposure to personalized trauma scripts. For subjects in the active experimental group (N = 14), the feedback signal was from a functionally localized region of their amygdala associated with trauma recall. For subjects in the control group (N = 11), yoked-sham feedback was provided. Changes in control over the amygdala and PTSD symptoms served as the primary and secondary outcome measurements, respectively. We found significantly greater improvements in control over amygdala activity in the active group than in the control group 30-days following the intervention. Both groups showed improvements in symptom scores, however the symptom reduction in the active group was not significantly greater than in the control group. Our finding of greater improvement in amygdala control suggests potential clinical application of neurofeedback in PTSD treatment. Thus, further development of amygdala neurofeedback training in PTSD treatment, including evaluation in larger samples, is warranted.

186. Real-Time fMRI Neurofeedback as a Technique for Emotion Regulation Process: A Pilot Study

186. Real-Time fMRI Neurofeedback as a Technique for Emotion Regulation Process: A Pilot Study

Testing the efficacy of real-time fMRI neurofeedback for training people who smoke daily to upregulate neural responses to nondrug rewards.

Although the use of nondrug rewards (e.g., money) to facilitate smoking cessation is widespread, recent research has found that such rewards may be least effective when people who smoke cigarettes are tempted to do so. Specifically, among people who smoke, the neural response to nondrug rewards appears blunted when access to cigarettes is anticipated, and this blunting is linked to a decrease in willingness to refrain from smoking to earn a monetary incentive. Accordingly, methods to enhance the value of nondrug rewards may be theoretically and clinically important. The current proof-of-concept study tested if real-time fMRI neurofeedback training augments the ability to upregulate responses in reward-related brain areas relative to a no-feedback control condition in people who smoke. Adults (n = 44, age range = 20-44) who reported smoking >5 cigarettes per day completed the study. Those in the intervention group (n = 22, 5 females) were trained to upregulate brain responses using feedback of ongoing striatal activity (i.e., a dynamic "thermometer" that reflected ongoing changes of fMRI signal intensity in the striatum) in a single neurofeedback session with three training runs. The control group (n = 22, 5 females) underwent a nearly identical procedure but received no neurofeedback. Those who received neurofeedback training demonstrated significantly greater increases in striatal BOLD activation while attempting to think about something rewarding compared to controls, but this effect was present only during the first training run. Future neurofeedback research with those who smoke should explore how to make neurofeedback training more effective for the self-regulation of reward-related brain activities.

Framework for Accurate Classification of Self-Reported Stress From Multisession Functional MRI Data of Veterans With Posttraumatic Stress

Background: Posttraumatic stress disorder (PTSD) is a significant burden among combat Veterans returning from the wars in Iraq and Afghanistan. While empirically supported treatments have demonstrated reductions in PTSD symptomatology, there remains a need to improve treatment effectiveness. Functional magnetic resonance imaging (fMRI) neurofeedback has emerged as a possible treatment to ameliorate PTSD symptom severity. Virtual reality (VR) approaches have also shown promise in increasing treatment compliance and outcomes. To facilitate fMRI neurofeedback-associated therapies, it would be advantageous to accurately classify internal brain stress levels while Veterans are exposed to trauma-associated VR imagery. Methods: Across 2 sessions, we used fMRI to collect neural responses to trauma-associated VR-like stimuli among male combat Veterans with PTSD symptoms (N = 8). Veterans reported their self-perceived stress level on a scale from 1 to 8 every 15 s throughout the fMRI sessions. In our proposed framework, we precisely sample the fMRI data on cortical gray matter, blurring the data along the gray-matter manifold to reduce noise and dimensionality while preserving maximum neural information. Then, we independently applied 3 machine learning (ML) algorithms to this fMRI data collected across 2 sessions, separately for each Veteran, to build individualized ML models that predicted their internal brain states (self-reported stress responses). Results: We accurately classified the 8-class self-reported stress responses with a mean (± standard error) root mean square error of 0.6 (± 0.1) across all Veterans using the best ML approach. Conclusions: The findings demonstrate the predictive ability of ML algorithms applied to whole-brain cortical fMRI data collected during individual Veteran sessions. The framework we have developed to preprocess whole-brain cortical fMRI data and train ML models across sessions would provide a valuable tool to enable individualized real-time fMRI neurofeedback during VR-like exposure therapy for PTSD.

Individual- and Connectivity-Based Real-Time fMRI Neurofeedback to Modulate Emotion-Related Brain Responses in Patients with Depression: A Pilot Study.

Individual real-time functional magnetic resonance imaging neurofeedback (rtfMRI NF) might be a promising adjuvant in treating depressive symptoms. Further studies showed functional variations and connectivity-related changes in the dorsolateral prefrontal cortex (dlPFC) and the insular cortex. The aim of this pilot study was to investigate whether individualized connectivity-based rtfMRI NF training can improve symptoms in depressed patients as an adjunct to a psychotherapeutic programme. The novel strategy chosen for this was to increase connectivity between individualized regions of interest, namely the insula and the dlPFC. Sixteen patients diagnosed with major depressive disorder (MDD, ICD-10) and 19 matched healthy controls (HC) participated in a rtfMRI NF training consisting of two sessions with three runs each, within an interval of one week. RtfMRI NF was applied during a sequence of negative emotional pictures to modulate the connectivity between the dlPFC and the insula. The MDD REAL group was divided into a Responder and a Non-Responder group. Patients with an increased connectivity during the second NF session or during both the first and the second NF session were identified as "MDD REAL Responder" (N = 6). Patients that did not show any increase in connectivity and/or a decreased connectivity were identified as "MDD REAL Non-Responder" (N = 7). Before the rtfMRI sessions, patients with MDD showed higher neural activation levels in ventromedial PFC and the insula than HC; by contrast, HC revealed increased hemodynamic activity in visual processing areas (primary visual cortex and visual association cortex) compared to patients with MDD. The comparison of hemodynamic responses during the first compared to during the last NF session demonstrated significantly increased BOLD-activation in the medial orbitofrontal cortex (mOFC) in patients and HC, and additionally in the lateral OFC in patients with MDD. These findings were particularly due to the MDD Responder group, as the MDD Non-Responder group showed no increase in this region during the last NF run. There was a decrease of neural activation in emotional processing brain regions in both groups in the last NF run compared to the first: HC showed differences in the insula, parahippocampal gyrus, basal ganglia, and cingulate gyrus. Patients with MDD demonstrated deceased responses in the parahippocampal gyrus. There was no significant reduction of BDI scores after NF training in patients. Increased neural activation in the insula and vmPFC in MDD suggests an increased emotional reaction in patients with MDD. The activation of the mOFC could be associated with improved control-strategies and association-learning processes. The increased lOFC activation could indicate a stronger sensitivity to failed NF attempts in MDD. A stronger involvement of visual processing areas in HC may indicate better adaptation to negative emotional stimuli after repeated presentation. Overall, the rtfMRI NF had an impact on neurobiological mechanisms, but not on psychometric measures in patients with MDD.

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.

Real-Time fMRI Neurofeedback Training as a Neurorehabilitation Approach on Depressive Disorders: A Systematic Review of Randomized Control Trials.

Real-time functional magnetic resonance imaging neurofeedback (rt-fMRI-nf) training is an emerging intervention for neurorehabilitation. However, its translation into clinical use on participants with clinical depression is unclear, the effect estimates from randomized control trials and the certainty of the supporting evidence on the effect estimates are unknown. As the number of studies on neurofeedback increases every year, and better quality evidence becomes available, we evaluate the evidence of all randomized control trials available on the clinical application of rt-fMRI-nf training on participants with clinical depression. We performed electronic searches in Pubmed, Embase, CENTRAL, rtFIN database, Epistemonikos, trial registers, reference lists, other systematic reviews, conference abstracts, and cross-citation in Google Scholar. Reviewers independently selected studies, extracted data and evaluated the risk of bias. The certainty of the evidence was judged using the GRADE framework. This review complies with PRISMA guidelines and was submitted to PROSPERO registration. We found 435 results. After the selection process, we included 11 reports corresponding to four RCTs. The effect of rt-fMRI-nf on improving the severity of clinical depression scores demonstrated a tendency to favor the intervention; however, the general effect was not significant. At end of treatment, SMD (standardized mean difference): -0.32 (95% CI -0.73 to 0.10). At follow-up, SMD: -0.33 (95% CI -0.91, 1.25). All the studies showed changes in BOLD fMRI activation after training; however, only one study confirmed regulation success during a transfer run. Whole-brain analyses suggests that rt-fMRI nf may alter activity patterns in brain networks. More studies are needed to evaluate quality of life, acceptability, adverse effects, cognitive tasks, and physiology measures. We conclude that the current evidence on the effect of rt-fMRI-nf training for decision-making outcomes in patients with clinical depression is still based on low certainty of the evidence.

The efficacy of neurofeedback for alcohol use disorders - a systematic review

Background Alcoholism is a serious social, economic, and public health problem. Alcoholism can affect the gastrointestinal, neurological, cardiovascular, and respiratory systems, and it can be fatal, costing the healthcare system huge amounts of money. Despite the availability of cognitive-behavioral and psychosocial therapies, alcoholism has a high recurrence rate and a dismal prognosis, with a wide inter-individual variation. As a result, better or adjuvant therapies that improve or facilitate alcoholism therapy are required. We conducted a systematic review to look into the published studies that reported the effectiveness of non-pharmacological neurofeedback interventions in patients with alcohol use disorders. Methods PubMed, Google Scholar, The Cochrane Library, Science Direct, and Clinicaltrial.gov were searched until April 04, 2022. Original articles of any design reporting on the use of neurofeedback approaches in the treatment of alcohol use disorders were included. Information related to study design, participants, control group, neuromodulation therapy, number of sessions, and key findings of the study were extracted. The Joanna Briggs Institute’s (JBI) Critical Appraisal Checklist for Studies was used to assess the quality of studies. Results 20 research articles (including 618 participants) were retrieved and included for qualitative analysis. The sample size ranged from one (case report) to eighty, with years of publication ranging from 1977 to 2022. Nine of the 20 articles included in the study were conducted in the United States, followed by Germany, the United Kingdom, India, the Netherlands, and South Korea. Out of the 20 studies included, 8 (40%) had a moderate risk of bias, while the other i.e., 60% had a low risk of bias. The effectiveness of various neurological treatments in the treatment of alcohol use disorders was established in these 20 studies. There have been 11 studies on EEG neurofeedback training, three studies on real-time FMRI neurofeedback, two studies each on transcranial direct current stimulation and transcranial magnetic stimulation, and one study each on deep brain stimulation and theta burst stimulation. These alternative neurological therapies have been demonstrated to lower alcohol cravings and consumption temporarily, reduce anxiety and depression scores, reduce relapse rates, and increase control of brain activity. Conclusion: The use of various neuromodulation approaches to the treatment of alcohol use disorder shows promise. However, more research with larger sample size is required.

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.

Real-time fMRI neurofeedback compared to cognitive behavioral therapy in a pilot study for the treatment of mild and moderate depression

Real-time functional magnetic resonance imaging (rt-fMRI) neurofeedback was found to reduce depressive symptoms. However, no direct comparison of drug-free patients with an active psychotherapy control group is available. The present study compared rt-fMRI neurofeedback with cognitive behavioral therapy, as the standard treatment in patients declining anti-depressants. Twenty adult, drug-free patients with mild or moderate depression were non-randomly assigned either to a course of eight half-hour sessions of neurofeedback targeting the left medial prefrontal cortex (N = 12) or to a 16-session course of cognitive behavioral therapy (N = 8). Montgomery–Asberg Depression Rating Scale was introduced at baseline, mid-treatment, and end-treatment points. In each group, 8 patients each remained in the study to a mid-treatment evaluation and 6 patients each to the study end-point. ANOVA revealed a depression reduction with a significant effect of Time (F(3,6) = 19.0, p < 0.001, η2 = 0.76). A trend to greater improvement in the cognitive behavioral therapy group compared to neurofeedback emerged (Group × Time; p = 0.078). Percent signal change in the region of interest between up- and down-regulation conditions was significantly correlated with session number (Pearson’s r = 0.85, p < 0.001) indicating a learning effect. As limitations, small sample size could lead to insufficient power and non-random allocation to selection bias. Both neurofeedback and cognitive behavioral therapy improved mild and moderate depression. Neurofeedback was not superior to cognitive behavioral therapy. Noteworthy, the neurofeedback training course was associated with continuous improvement in the self-regulation skill, without plateau. This study delivers data to plan clinical trials comparing neurofeedback with cognitive behavioral interventions.

Real-time neurofeedback to alter interpretations of a naturalistic narrative

We explored the potential of using real-time fMRI (rt-fMRI) neurofeedback training to bias interpretations of naturalistic narrative stimuli. Participants were randomly assigned to one of two possible conditions, each corresponding to a different interpretation of an ambiguous spoken story. While participants listened to the story in the scanner, neurofeedback was used to reward neural activity corresponding to the assigned interpretation. After scanning, final interpretations were assessed. While neurofeedback did not change story interpretations on average, participants with higher levels of decoding accuracy during the neurofeedback procedure were more likely to adopt the assigned interpretation; additional control conditions are needed to establish the role of individualized feedback in driving this result. While naturalistic stimuli introduce a unique set of challenges in providing effective and individualized neurofeedback, we believe that this technique holds promise for individualized cognitive therapy.

Real-Time Functional Magnetic Resonance Imaging Dyadic Neurofeedback for Emotion Regulation: A Proof-of-Concept Study.

Real-time fMRI (rt-fMRI) neurofeedback can be used to non-invasively modulate brain activity and has shown initial effectiveness in symptom reduction for psychiatric disorders. Neurofeedback paradigms often target the neurocircuitry underlying emotion regulation, as difficulties with emotion regulation are common across many psychiatric conditions. Adolescence is a key period for the development of emotion regulation, with the parent-adolescent relationship providing an important context for learning how to modulate one’s emotions. Here, we present evidence for a novel extension of rt-fMRI neurofeedback wherein a second person (the parent) views neurofeedback from the focal participant (adolescent) and attempts to regulate the other person’s brain activity. In this proof-of-concept study, mother-adolescent dyads (n = 6; all female) participated in a dyadic neurofeedback protocol, during which they communicated via active noise-canceling microphones and headphones. During the scan, adolescents described current emotionally upsetting situations in their lives, and their mothers responded while viewing neurofeedback from the adolescent’s right anterior insular cortex (aIC)—a key hub for emotion-related processing. The mother was instructed to supportively respond to her daughter’s negative emotions and attempt to downregulate the aIC activity. Mean right aIC activation during each run was calculated for each adolescent participant, and results revealed a downward trend across the session (β = −0.17, SEβ = 0.19, Cohen’s f2 = 0.03). Results of this proof-of-concept study support further research using dyadic neurofeedback to target emotion-related processing. Future applications may include therapist-client dyads and continued research with parents and children.Clinical Trial Registration[www.ClinicalTrials.gov], identifier [NCT03929263].

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.

P655. Real-Time fMRI Neurofeedback Augmented Mindfulness Training in Maltreated Youth: A Randomized Sham-Controlled Trial

Maltreated youth evidence disruptions in the integrity of the default mode network, which is associated with poorer psychological outcomes. Mindfulness training (MT) improves psychological outcomes in maltreated youth, and neuroimaging studies identify the posterior cingulate cortex (PCC) as the key neural target for MT. This novel study examined the feasibility of training maltreated youth to self-regulate their PCC activity using real-time fMRI neurofeedback (rtfMRI-nf) augmented mindfulness training.