Optimizing real time fMRI neurofeedback for therapeutic discovery and development

Revealing the spatiotemporal brain dynamics of covert speech compared with overt speech: A simultaneous EEG-fMRI study

Covert speech (CS) refers to speaking internally to oneself without producing any sound or movement. CS is involved in multiple cognitive functions and disorders. Reconstructing CS content by brain-computer interface (BCI) is also an emerging technique. However, it is still controversial whether CS is a truncated neural process of overt speech (OS) or involves independent patterns. Here, we performed a word-speaking experiment with simultaneous EEG-fMRI. It involved 32 participants, who generated words both overtly and covertly. By integrating spatial constraints from fMRI into EEG source localization, we precisely estimated the spatiotemporal dynamics of neural activity. During CS, EEG source activity was localized in three regions: the left precentral gyrus, the left supplementary motor area, and the left putamen. Although OS involved more brain regions with stronger activations, CS was characterized by an earlier event-locked activation in the left putamen (peak at 262 ms versus 1170 ms). The left putamen was also identified as the only hub node within the functional connectivity (FC) networks of both OS and CS, while showing weaker FC strength towards speech-related regions in the dominant hemisphere during CS. Path analysis revealed significant multivariate associations, indicating an indirect association between the earlier activation in the left putamen and CS, which was mediated by reduced FC towards speech-related regions. These findings revealed the specific spatiotemporal dynamics of CS, offering insights into CS mechanisms that are potentially relevant for future treatment of self-regulation deficits, speech disorders, and development of BCI speech applications.

Functional and structural connectivity success predictors of real-time fMRI neurofeedback targeting DLPFC: Contributions from central executive, salience, and default mode networks

Real-time functional magnetic resonance imaging (rt-fMRI) neurofeedback (NF), a training method for the self-regulation of brain activity, has shown promising results as a neurorehabilitation tool, depending on the ability of the patient to succeed in neuromodulation. This study explores connectivity-based structural and functional success predictors in an NF n-back working memory paradigm targeting the dorsolateral prefrontal cortex (DLPFC). We established as the NF success metric the linear trend on the ability to modulate the target region during NF runs and performed a linear regression model considering structural and functional connectivity (intrinsic and seed-based) metrics. We found a positive correlation between NF success and the default mode network (DMN) intrinsic functional connectivity and a negative correlation with the DLPFC-precuneus connectivity during the 2-back condition, indicating that success is associated with larger uncoupling between DMN and the executive network. Regarding structural connectivity, the salience network emerges as the main contributor to success. Both functional and structural classification models showed good performance with 77% and 86% accuracy, respectively. Dynamic switching between DMN, salience network and central executive network seems to be the key for neurofeedback success, independently indicated by functional connectivity on the localizer run and structural connectivity data.

ENIGMA's simple seven: Recommendations to enhance the reproducibility of resting-state fMRI in traumatic brain injury

Resting state functional magnetic resonance imaging (rsfMRI) provides researchers and clinicians with a powerful tool to examine functional connectivity across large-scale brain networks, with ever-increasing applications to the study of neurological disorders, such as traumatic brain injury (TBI). While rsfMRI holds unparalleled promise in systems neurosciences, its acquisition and analytical methodology across research groups is variable, resulting in a literature that is challenging to integrate and interpret. The focus of this narrative review is to address the primary methodological issues including investigator decision points in the application of rsfMRI to study the consequences of TBI. As part of the ENIGMA Brain Injury working group, we have collaborated to identify a minimum set of recommendations that are designed to produce results that are reliable, harmonizable, and reproducible for the TBI imaging research community. Part one of this review provides the results of a literature search of current rsfMRI studies of TBI, highlighting key design considerations and data processing pipelines. Part two outlines seven data acquisition, processing, and analysis recommendations with the goal of maximizing study reliability and between-site comparability, while preserving investigator autonomy. Part three summarizes new directions and opportunities for future rsfMRI studies in TBI patients. The goal is to galvanize the TBI community to gain consensus for a set of rigorous and reproducible methods, and to increase analytical transparency and data sharing to address the reproducibility crisis in the field.

No threat: Emotion regulation neurofeedback for police special forces recruits

Police officers of the Special Forces are confronted with highly demanding situations in terms of stress, high tension and threats to their lives. Their tasks are specifically high-risk operations, such as arrests of armed suspects and anti-terror interventions. Improving the emotion regulation skills of police officers might be a vital investment, supporting them to stay calm and focused. A promising approach is training emotion regulation by using real-time (rt-) fMRI neurofeedback. Specifically, downregulating activity in key areas of the fronto-limbic emotion regulation network in the presence of threatening stimuli. Thirteen recruits of the Dutch police special forces underwent six weekly rt-fMRI sessions, receiving neurofeedback from individualized regions of their emotion regulation network. Their task was to reduce the image size of threatening images, wherein the image size represented their brain activity. A reduction in image size represented successful downregulation. Participants were free to use their preferred regulation strategy. A control group of fifteen recruits received no neurofeedback. Both groups completed behavioural tests (image rating on evoked valence and arousal, questionnaire) before and after the neurofeedback training. We hypothesized that the neurofeedback group would improve in downregulation and would score better than the control group on the behavioural tests after the neurofeedback training. Neurofeedback training resulted in a significant decrease in image size (t(12) = 2.82, p = .015) and a trend towards decreased activation in the target regions (t(10) = 1.82, p = .099) from the first to the last session. Notably, subjects achieved downregulation below the pre-stimulus baseline in the last two sessions. No relevant differences between groups were found in the behavioural tasks. Through the training of rt-fMRI neurofeedback, participants learned to downregulate the activity in individualized areas of the emotion regulation network, by using their own preferred strategies. The lack of behavioural between-group differences may be explained by floor effects. Tasks that are close to real-life situations may be needed to uncover behavioural correlates of this emotion regulation training.

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.

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.

Neuropragmatics: A scientometric review

Neuropragmatics investigates brain functions and neural activities responsible for pragmatic language abilities, often impaired in disorders such as hemisphere damage, autism, schizophrenia, and neurodegenerative disorders. This study examined the development of neuropragmatics and existing neuroimaging evidence using bibliometric and scientometric indicators, analyzing 4,247 documents published between 1967 and 2022 with CiteSpace and VOSviewer. Our cluster analysis revealed key themes. 1) Language comprehension loss due to brain injury: Studies exploring the impact of brain injuries on language comprehension and underlying neural mechanisms. 2) Right hemisphere damage and pragmatic language skills: Research focusing on the relationship between right hemisphere damage and pragmatic language abilities, investigating impairments in social language use and potential neural correlates. 3) Traumatic brain injury and social communication assessment: Research on traumatic brain injury effects on social communication skills, using various assessment tools to evaluate communication effectiveness in social situations. These clusters provide valuable insights into the neuropragmatics field and serve as a framework for future investigations. By building upon existing knowledge, researchers can improve our understanding of brain functions, language behavior, and enhance rehabilitation for individuals with pragmatic language impairments.

Repetitive transcranial magnetic stimulation for stroke rehabilitation: insights into the molecular and cellular mechanisms of neuroinflammation

Stroke is a leading cause of mortality and disability worldwide, with most survivors reporting dysfunctions of motor, sensation, deglutition, cognition, emotion, and speech, etc. Repetitive transcranial magnetic stimulation (rTMS), one of noninvasive brain stimulation (NIBS) techniques, is able to modulate neural excitability of brain regions and has been utilized in neurological and psychiatric diseases. Moreover, a large number of studies have shown that the rTMS presents positive effects on function recovery of stroke patients. In this review, we would like to summarized the clinical benefits of rTMS for stroke rehabilitation, including improvements of motor impairment, dysphagia, depression, cognitive function, and central post-stroke pain. In addition, this review will also discuss the molecular and cellular mechanisms underlying rTMS-mediated stroke rehabilitation, especially immune regulatory mechanisms, such as regulation of immune cells and inflammatory cytokines. Moreover, the neuroimaging technique as an important tool in rTMS-mediated stroke rehabilitation has been discussed, to better understanding the mechanisms underlying the effects of rTMS. Finally, the current challenges and future prospects of rTMS-mediated stroke rehabilitation are also elucidated with the intention to accelerate its widespread clinical application.

Targeted neurorehabilitation strategies in post-stroke aphasia

BACKGROUND

Aphasia is a debilitating language impairment, affecting millions of people worldwide. About 40% of stroke survivors develop chronic aphasia, resulting in life-long disability.

OBJECTIVE

This review examines extrinsic and intrinsic neuromodulation techniques, aimed at enhancing the effects of speech and language therapies in stroke survivors with aphasia.

METHODS

We discuss the available evidence supporting the use of transcranial direct current stimulation (tDCS), repetitive transcranial magnetic stimulation, and functional MRI (fMRI) real-time neurofeedback in aphasia rehabilitation.

RESULTS

This review systematically evaluates studies focusing on efficacy and implementation of specialized methods for post-treatment outcome optimization and transfer to functional skills. It considers stimulation target determination and various targeting approaches. The translation of neuromodulation interventions to clinical practice is explored, emphasizing generalization and functional communication. The review also covers real-time fMRI neurofeedback, discussing current evidence for efficacy and essential implementation parameters. Finally, we address future directions for neuromodulation research in aphasia.

CONCLUSIONS

This comprehensive review aims to serve as a resource for a broad audience of researchers and clinicians interested in incorporating neuromodulation for advancing aphasia care.

Exploring personalized structural connectomics for moderate to severe traumatic brain injury

Graph theoretical analysis of the structural connectome has been employed successfully to characterize brain network alterations in patients with traumatic brain injury (TBI). However, heterogeneity in neuropathology is a well-known issue in the TBI population, such that group comparisons of patients against controls are confounded by within-group variability. Recently, novel single-subject profiling approaches have been developed to capture inter-patient heterogeneity. We present a personalized connectomics approach that examines structural brain alterations in five chronic patients with moderate to severe TBI who underwent anatomical and diffusion magnetic resonance imaging. We generated individualized profiles of lesion characteristics and network measures (including personalized graph metric GraphMe plots, and nodal and edge-based brain network alterations) and compared them against healthy reference cases (N = 12) to assess brain damage qualitatively and quantitatively at the individual level. Our findings revealed alterations of brain networks with high variability between patients. With validation and comparison to stratified, normative healthy control comparison cohorts, this approach could be used by clinicians to formulate a neuroscience-guided integrative rehabilitation program for TBI patients, and for designing personalized rehabilitation protocols based on their unique lesion load and connectome.

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.

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.

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.

Neurofeedback-guided kinesthetic motor imagery training in Parkinson's disease: Randomized trial

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Parkinson’s disease causes difficulty with sustained motor performance.

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Insula and dorsomedial frontal cortex (dmFC) are implicated in motivating movement.

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Regulation of insula-dmFC functional connectivity with neurofeedback (NF) failed.

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Motor imagery practice regardless of NF improved motor function and body awareness.

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Visual imagery practice without NF also improved motor function.

Background

Parkinson’s disease (PD) causes difficulty with maintaining the speed, size, and vigor of movements, especially when they are internally generated. We previously proposed that the insula is important in motivating intentional movement via its connections with the dorsomedial frontal cortex (dmFC). We demonstrated that subjects with PD can increase the right insula-dmFC functional connectivity using fMRI-based neurofeedback (NF) combined with kinesthetic motor imagery (MI). The current study is a randomized clinical trial testing whether NF-guided kinesthetic MI training can improve motor performance and increase task-based and resting-state right insula-dmFC functional connectivity in subjects with PD.

Methods

We assigned nondemented subjects with mild PD (Hoehn & Yahr stage ≤ 3) to the experimental kinesthetic MI with NF (MI-NF, n = 22) and active control visual imagery (VI, n = 22) groups. Only the MI-NF group received NF-guided MI training (10–12 runs). The NF signal was based on the right insula-dmFC functional connectivity strength. All subjects also practiced their respective imagery tasks at home daily for 4 weeks. Post-training changes in 1) task-based and resting-state right insula-dmFC functional connectivity were the primary imaging outcomes, and 2) MDS-UPDRS motor exam and motor function scores were the primary and secondary clinical outcomes, respectively.

Results

The MI-NF group was not significantly different from the VI group in any of the primary imaging or clinical outcome measures. The MI-NF group reported subjective improvement in kinesthetic body awareness. There was significant and comparable improvement only in motor function scores in both groups (secondary clinical outcome). This improvement correlated with NF regulation of the right insula-dmFC functional connectivity only in the MI-NF group. Both groups showed specific training effects in whole-brain functional connectivity with distinct neural circuits supporting kinesthetic motor and visual imagery (exploratory imaging outcome).

Conclusions

The functional connectivity-based NF regulation was unsuccessful, however, both kinesthetic MI and VI practice improved motor function in our cohort with mild PD.

Neurofeedback through the lens of reinforcement learning

Despite decades of experimental and clinical practice, the neuropsychological mechanisms underlying neurofeedback (NF) training remain obscure. NF is a unique form of reinforcement learning (RL) task, during which participants are provided with rewarding feedback regarding desired changes in neural patterns. However, key RL considerations - including choices during practice, prediction errors, credit-assignment problems, or the exploration-exploitation tradeoff - have infrequently been considered in the context of NF. We offer an RL-based framework for NF, describing different internal states, actions, and rewards in common NF protocols, thus fashioning new proposals for characterizing, predicting, and hastening the course of learning. In this way we hope to advance current understanding of neural regulation via NF, and ultimately to promote its effectiveness, personalization, and clinical utility.

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.

Depressive symptoms reduce when dorsolateral prefrontal cortex-precuneus connectivity normalizes after functional connectivity neurofeedback

Depressive disorders contribute heavily to global disease burden; This is possibly because patients are often treated homogeneously, despite having heterogeneous symptoms with differing underlying neural mechanisms. A novel treatment that can directly influence the neural circuit relevant to an individual patient's subset of symptoms might more precisely and thus effectively aid in the alleviation of their specific symptoms. We tested this hypothesis in a proof-of-concept study using fMRI functional connectivity neurofeedback. We targeted connectivity between the left dorsolateral prefrontal cortex/middle frontal gyrus and the left precuneus/posterior cingulate cortex, because this connection has been well-established as relating to a specific subset of depressive symptoms. Specifically, this connectivity has been shown in a data-driven manner to be less anticorrelated in patients with melancholic depression than in healthy controls. Furthermore, a posterior cingulate dominant state-which results in a loss of this anticorrelation-is expected to specifically relate to an increase in rumination symptoms such as brooding. In line with predictions, we found that, with neurofeedback training, the more a participant normalized this connectivity (restored the anticorrelation), the more related (depressive and brooding symptoms), but not unrelated (trait anxiety), symptoms were reduced. Because these results look promising, this paradigm next needs to be examined with a greater sample size and with better controls. Nonetheless, here we provide preliminary evidence for a correlation between the normalization of a neural network and a reduction in related symptoms. Showing their reproducibility, these results were found in two experiments that took place several years apart by different experimenters. Indicative of its potential clinical utility, effects of this treatment remained one-two months later.Clinical trial registration: Both experiments reported here were registered clinical trials (UMIN000015249, jRCTs052180169).

Closed-loop neuromodulation for studying spontaneous activity and causality

Having established that spontaneous brain activity follows meaningful coactivation patterns and correlates with behavior, researchers have turned their attention to understanding its function and behavioral significance. We suggest closed-loop neuromodulation as a neural perturbation tool uniquely well suited for this task. Closed-loop neuromodulation has primarily been viewed as an interventionist tool to teach subjects to directly control their own brain activity. We examine an alternative operant conditioning model of closed-loop neuromodulation which, through implicit feedback, can manipulate spontaneous activity at the network level, without violating the spontaneous or endogenous nature of the signal, thereby providing a direct test of network causality.

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.

How Machine Learning is Powering Neuroimaging to Improve Brain Health

This report presents an overview of how machine learning is rapidly advancing clinical translational imaging in ways that will aid in the early detection, prediction, and treatment of diseases that threaten brain health. Towards this goal, we aresharing the information presented at a symposium, "Neuroimaging Indicators of Brain Structure and Function - Closing the Gap Between Research and Clinical Application", co-hosted by the McCance Center for Brain Health at Mass General Hospital and the MIT HST Neuroimaging Training Program on February 12, 2021. The symposium focused on the potential for machine learning approaches, applied to increasingly large-scale neuroimaging datasets, to transform healthcare delivery and change the trajectory of brain health by addressing brain care earlier in the lifespan. While not exhaustive, this overview uniquely addresses many of the technical challenges from image formation, to analysis and visualization, to synthesis and incorporation into the clinical workflow. Some of the ethical challenges inherent to this work are also explored, as are some of the regulatory requirements for implementation. We seek to educate, motivate, and inspire graduate students, postdoctoral fellows, and early career investigators to contribute to a future where neuroimaging meaningfully contributes to the maintenance of brain health.

Resting-state brain activity can predict target-independent aptitude in fMRI-neurofeedback training

Neurofeedback (NF) aptitude, which refers to an individual's ability to change brain activity through NF training, has been reported to vary significantly from person to person. The prediction of individual NF aptitudes is critical in clinical applications to screen patients suitable for NF treatment. In the present study, we extracted the resting-state functional brain connectivity (FC) markers of NF aptitude, independent of NF-targeting brain regions. We combined the data from fMRI-NF studies targeting four different brain regions at two independent sites (obtained from 59 healthy adults and six patients with major depressive disorder) to collect resting-state fMRI data associated with aptitude scores in subsequent fMRI-NF training. We then trained the multiple regression models to predict the individual NF aptitude scores from the resting-state fMRI data using a discovery dataset from one site and identified six resting-state FCs that predicted NF aptitude. Subsequently, the reproducibility of the prediction model was validated using independent test data from another site. The identified FC model revealed that the posterior cingulate cortex was the functional hub among the brain regions and formed predictive resting-state FCs, suggesting that NF aptitude may be involved in the attentional mode-orientation modulation system's characteristics in task-free resting-state brain activity.

Brain circuits for pain and its treatment

[Figure: see text].

Weighted neurofeedback facilitates greater self-regulation of functional connectivity between the primary motor area and cerebellum

Objective.Brain-computer interface (BCI) is a tool that can be used to train brain self-regulation and influence specific activity patterns, including functional connectivity, through neurofeedback. The functional connectivity of the primary motor area (M1) and cerebellum play a critical role in motor recovery after a brain injury, such as stroke. The objective of this study was to determine the feasibility of achieving control of the functional connectivity between M1 and the cerebellum in healthy subjects. Additionally, we aimed to compare the brain self-regulation of two different feedback modalities and their effects on motor performance.Approach.Nine subjects were trained with a real-time functional magnetic resonance imaging BCI system. Two groups were conformed: equal feedback group (EFG), which received neurofeedback that weighted the contribution of both regions of interest (ROIs) equally, and weighted feedback group (WFG) that weighted each ROI differentially (30% cerebellum; 70% M1). The magnitude of the brain activity induced by self-regulation was evaluated with the blood-oxygen-level-dependent (BOLD) percent change (BPC). Functional connectivity was assessed using temporal correlations between the BOLD signal of both ROIs. A finger-tapping task was included to evaluate the effect of brain self-regulation on motor performance.Main results.A comparison between the feedback modalities showed that WFG achieved significantly higher BPC in M1 than EFG. The functional connectivity between ROIs during up-regulation in WFG was significantly higher than EFG. In general, both groups showed better tapping speed in the third session compared to the first. For WFG, there were significant correlations between functional connectivity and tapping speed.Significance.The results show that it is possible to train healthy individuals to control M1-cerebellum functional connectivity with rtfMRI-BCI. Besides, it is also possible to use a weighted feedback approach to facilitate a higher activity of one region over another.

Resting state dynamic functional connectivity in children with attention deficit/hyperactivity disorder

Attention deficit/hyperactivity disorder (ADHD) is characterized by inattention, hyperactivity and impulsivity. In this study, we investigated group differences in dynamic functional connectivity (dFC) between 113 children with inattentive (46 ADHD_I) and combined (67 ADHD_C) ADHD and 76 typically developing (TD) children using resting-state functional MRI data. For dynamic connectivity analysis, the data were first decomposed into 100 independent components, among which 88 were classified into eight well-known resting-state networks (RSNs). Three discrete FC states were then identified using k-means clustering and used to estimate transition probabilities between states in both patient and control groups using a hidden Markov model. Our results showed state-dependent alterations in intra and inter-network connectivity in both ADHD subtypes in comparison with TD. Spending less time than healthy controls in state 1, both ADHD_Iand ADHD_Cwere characterized with weaker intra-hemispheric connectivity with functional asymmetries. In this state, ADHD_Ifurther showed weaker inter-hemispheric connectivity. The patients spent more time in state 2, exhibiting characteristic abnormalities in corticosubcortical and corticocerebellar connectivity. In state 3, a less frequently state observed across the ADHD and TD children, ADHD_Cwas differentiated from ADHD_Iby significant alterations in FC between bilateral temporal regions and other brain areas in comparison with TD. Across all three states, several strategic brain regions, mostly bilateral, exhibited significant alterations in both static functional connectivity (sFC) and dFC in the ADHD groups compared to TD, including inferior, middle and superior temporal gyri, middle frontal gyri, insula, anterior cingulum cortex, precuneus, calcarine, fusiform, superior motor area, and cerebellum. Our results show distributed abnormalities in sFC and dFC between different large-scale RSNs including cortical and subcortical regions in both ADHD subtypes compared to TD. Our findings show that the dynamic changes in brain FC can better explain the underlying pathophysiology of ADHD such as deficits in visual cognition, attention, memory and emotion processing, and cognitive and motor control.

The impact of real-time fMRI denoising on online evaluation of brain activity and functional connectivity

Objective. Comprehensive denoising is imperative in functional magnetic resonance imaging (fMRI) analysis to reliably evaluate neural activity from the blood oxygenation level dependent signal. In real-time fMRI, however, only a minimal denoising process has been applied and the impact of insufficient denoising on online brain activity estimation has not been assessed comprehensively. This study evaluated the noise reduction performance of online fMRI processes in a real-time estimation of regional brain activity and functional connectivity.Approach.We performed a series of real-time processing simulations of online fMRI processing, including slice-timing correction, motion correction, spatial smoothing, signal scaling, and noise regression with high-pass filtering, motion parameters, motion derivatives, global signal, white matter/ventricle average signals, and physiological noise models with image-based retrospective correction of physiological motion effects (RETROICOR) and respiration volume per time (RVT).Main results.All the processing was completed in less than 400 ms for whole-brain voxels. Most processing had a benefit for noise reduction except for RVT that did not work due to the limitation of the online peak detection. The global signal regression, white matter/ventricle signal regression, and RETROICOR had a distinctive noise reduction effect, depending on the target signal, and could not substitute for each other. Global signal regression could eliminate the noise-associated bias in the mean dynamic functional connectivity across time.Significance.The results indicate that extensive real-time denoising is possible and highly recommended for real-time fMRI applications.

Initial Results of Tests Using GSR Biofeedback as a New Neurorehabilitation Technology Complementing Pharmacological Treatment of Patients with Schizophrenia

Galvanic skin response (GSR) Biofeedback uses training to reduce tension and anxiety and improve concentration and self-regulation. The study was aimed to evaluate this method as a form of rehabilitation and quantify the outcomes achieved by patients undergoing training using this technique. Six schizophrenic patients were enrolled in the study and underwent training based on the relaxation training module (CENTER), concentration training module (BALANCE), and self-regulation training module (INSECTS). Training sessions were held twice a week for 6 weeks. From the total group of subjects involved in the study, two patients had a statistically significant increase in measured values after the CENTER exercise, indicating that relaxation was achieved. Four patients showed a statistically significant decrease in measured values after the BALANCE exercise, which was reflective of an improvement in concentration. Three patients had a statistically significant decrease in measured values after the INSECTS exercise, which indicated an improvement in self-regulation. GSR Biofeedback may be used to complement the pharmacological treatment of patients diagnosed with schizophrenia.

Matched neurofeedback during fMRI differentially activates reward-related circuits in active and sham groups

BACKGROUND AND PURPOSE

Functional MRI neurofeedback (fMRI-nf) leverages the brain's ability to self-regulate its own activity. However, self-regulation processes engaged during fMRI-nf are incompletely understood. Here, we used matched feedback in an fMRI-nf experimental protocol to investigate whether brain processes recognize true neurofeedback signals.

METHODS

We implemented an existing fMRI-nf protocol to train lateralized motor activity using a finger-tap task in conjunction with real-time feedback. Twelve healthy, right-handed, adult participants were assigned into age- and sex-matched active and sham study groups. Matched participant pairs received the same visual feedback, based on brain activity of the participant from the active group. We compared group-averaged activation maps before, during, and after neurofeedback, and analyzed changes in lateralized motor activity due to neurofeedback.

RESULTS

Active and sham groups demonstrated different brain activation to the same feedback during neurofeedback. In particular, there was higher activation in visual cortex, secondary somatosensory cortex, and right inferior frontal gyrus in the active group compared to the sham group. Conversely, sham participants demonstrated higher activation in anterior cingulate cortex, left frontal pole, and posterior superior temporal gyrus. Despite differing brain activations during neurofeedback, neither group demonstrated significant improvement in lateralized motor activity from pre to postfeedback scan in the same session. We also observed no significant difference between pre and postfeedback activation maps, suggesting that no significant finger-tap related functional reorganization had occurred.

CONCLUSIONS

These findings suggest that fMRI neurofeedback paradigms that monitor or incorporate activity from regions reported here would provide enhanced efficacy for research investigation and clinical intervention.

Neural correlates of cognitive bias modification for interpretation

The effectiveness of cognitive bias modification for interpretation (CBM-I), a treatment method employed to reduce social anxiety (SA), has been examined. However, the neural correlates of CBM-I remain unclear, and we aimed to elucidate brain activities during intervention and activity changes associated with CBM-I effectiveness in a pre–post intervention comparison. Healthy participants divided into two groups (CBM, control) were scanned before, during and after intervention using functional magnetic resonance imaging. Ambiguous social situations followed by positive outcomes were repeatedly imagined by the CBM group during intervention, while half of the outcomes in the control group were negative. Whole-brain analysis revealed that activation of the somatomotor and somatosensory areas, occipital lobe, fusiform gyrus and thalamus during intervention was significantly greater in the CBM than in the control group. Furthermore, altered activities in the somatomotor and somatosensory areas, occipital lobe and posterior cingulate gyrus during interpreting ambiguous social situations showed a significant group × change in SA interaction. Our result suggests that when facing ambiguous social situations, positive imagery instilled by CBM-I is recalled, and interpretations are modified to contain social reward. These findings may help to suggest an alternative manner of enhancing CBM-I effectiveness from a cognitive-neuroscience perspective.

Real-world stress resilience is associated with the responsivity of the locus coeruleus

Individuals may show different responses to stressful events. Here, we investigate the neurobiological basis of stress resilience, by showing that neural responsitivity of the noradrenergic locus coeruleus (LC-NE) and associated pupil responses are related to the subsequent change in measures of anxiety and depression in response to prolonged real-life stress. We acquired fMRI and pupillometry data during an emotional-conflict task in medical residents before they underwent stressful emergency-room internships known to be a risk factor for anxiety and depression. The LC-NE conflict response and its functional coupling with the amygdala was associated with stress-related symptom changes in response to the internship. A similar relationship was found for pupil-dilation, a potential marker of LC-NE firing. Our results provide insights into the noradrenergic basis of conflict generation, adaptation and stress resilience.

Brain-Computer Interfaces in Neurorecovery and Neurorehabilitation

Recent advances in brain-computer interface technology to restore and rehabilitate neurologic function aim to enable persons with disabling neurologic conditions to communicate, interact with the environment, and achieve other key activities of daily living and personal goals. Here we evaluate the principles, benefits, challenges, and future directions of brain-computer interfaces in the context of neurorehabilitation. We then explore the clinical translation of these technologies and propose an approach to facilitate implementation of brain-computer interfaces for persons with neurologic disease.

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

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

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

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

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.

Putamen volume predicts real-time fMRI neurofeedback learning success across paradigms and neurofeedback target regions

Real-time fMRI guided neurofeedback training has gained increasing interest as a noninvasive brain regulation technique with the potential to modulate functional brain alterations in therapeutic contexts. Individual variations in learning success and treatment response have been observed, yet the neural substrates underlying the learning of self-regulation remain unclear. Against this background, we explored potential brain structural predictors for learning success with pooled data from three real-time fMRI data sets. Our analysis revealed that gray matter volume of the right putamen could predict neurofeedback learning success across the three data sets (n = 66 in total). Importantly, the original studies employed different neurofeedback paradigms during which different brain regions were trained pointing to a general association with learning success independent of specific aspects of the experimental design. Given the role of the putamen in associative learning this finding may reflect an important role of instrumental learning processes and brain structural variations in associated brain regions for successful acquisition of fMRI neurofeedback-guided self-regulation.

Brain network dynamics fingerprints are resilient to data heterogeneity

CONTEXT

Large multi-site neuroimaging datasets have significantly advanced our quest to understand brain-behavior relationships and to develop biomarkers of psychiatric and neurodegenerative disorders. Yet, such data collections come at a cost, as the inevitable differences across samples may lead to biased or erroneous conclusions.

OBJECTIVE

We aim to validate the estimation of individual brain network dynamics fingerprints and appraise sources of variability in large resting-state functional magnetic resonance imaging (rs-fMRI) datasets by providing a novel point of view based on data-driven dynamical models.

APPROACH

Previous work has investigated this critical issue in terms of effects on static measures, such as functional connectivity and brain parcellations. Here, we utilize dynamical models (Hidden Markov models - HMM) to examine how diverse scanning factors in multi-site fMRI recordings affect our ability to infer the brain's spatiotemporal wandering between large-scale networks of activity. Specifically, we leverage a stable HMM trained on the Human Connectome Project (homogeneous) dataset, which we then apply to an heterogeneous dataset of traveling subjects scanned under a multitude of conditions.

MAIN RESULTS

Building upon this premise, we first replicate previous work on the emergence of non-random sequences of brain states. We next highlight how these time-varying brain activity patterns are robust subject-specific fingerprints. Finally, we suggest these fingerprints may be used to assess which scanning factors induce high variability in the data.

SIGNIFICANCE

These results demonstrate that we can i) use large scale dataset to train models that can be then used to interrogate subject-specific data, ii) recover the unique trajectories of brain activity changes in each individual, but also iii) urge caution as our ability to infer such patterns is affected by how, where and when we do so.

Behavioral Medicine Methods in Treatment of Somatic Conditions

Background

The aim of this article is to present a short review of noninvasive, nonpharmacological treatment methods used in somatic illnesses that fall under the umbrella of approach called behavioral medicine.

Methods

The narrative review method was applied in the study. Science paper databases, including PubMed, had been used to retrieve papers on therapeutic methods used in clinical setting that meet the broad criteria of behavioral medicine definition as stated in the Charter of International Behavioral Medicine Society

Results

Main groups of methods, disorders in which they are being employed and their effectiveness, have been identified.

Conclusions

Behavioral medicine is grouping treatment methods and interventions that hold large potential for clinical setting. Two groups of methods can be distinguished by the scrutiny and level of evidence gathered in their effectiveness assessment; for biofeedback, guided imagery, and hypnosis techniques, comprehensive evidence reports in the framework of U.S. Evidence Synthesis Program exist. Meditation techniques, disclosure therapies, and relaxation methods are less well assessed. Broader employment of behavioral medicine therapies in clinical setting is possible after addressing two major problems in the field, which are deficiencies in quality evidence of effectiveness for many of the methods and their insufficiencies in underlying therapeutic mechanism knowledge.

Low Cost MR Compatible Haptic Stimulation with Application to fMRI Neurofeedback

The most common feedback displays in the fMRI environment are visual, e.g., in which participants try to increase or decrease the level of a thermometer. However, haptic feedback is increasingly valued in computer interaction tasks, particularly for real-time fMRI feedback. fMRI-neurofeedback is a clinical intervention that has not yet taken advantage of this trend. Here we describe a low-cost, user-friendly, MR-compatible system that can provide graded haptic vibrotactile stimulation in an initial application to fMRI neurofeedback. We also present a feasibility demonstration showing that we could successfully set up the system and obtain data in the context of a neurofeedback paradigm. We conclude that vibrotactile stimulation using this low-cost system is a viable method of feedback presentation, and encourage neurofeedback researchers to incorporate this type of feedback into their studies.

Pyneal: Open Source Real-Time fMRI Software

Increasingly, neuroimaging researchers are exploring the use of real-time functional magnetic resonance imaging (rt-fMRI) as a way to access a participant's ongoing brain function throughout a scan. This approach presents novel and exciting experimental applications ranging from monitoring data quality in real time, to delivering neurofeedback from a region of interest, to dynamically controlling experimental flow, or interfacing with remote devices. Yet, for those interested in adopting this method, the existing software options are few and limited in application. This presents a barrier for new users, as well as hinders existing users from refining techniques and methods. Here we introduce a free, open-source rt-fMRI package, the Pyneal toolkit, designed to address this limitation. The Pyneal toolkit is python-based software that offers a flexible and user friendly framework for rt-fMRI, is compatible with all three major scanner manufacturers (GE, Siemens, Phillips), and, critically, allows fully customized analysis pipelines. In this article, we provide a detailed overview of the architecture, describe how to set up and run the Pyneal toolkit during an experimental session, offer tutorials with scan data that demonstrate how data flows through the Pyneal toolkit with example analyses, and highlight the advantages that the Pyneal toolkit offers to the neuroimaging community.

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.

Advances in biofeedback and neurofeedback studies on smoking

Smoking is a leading cause of morbidity and premature death constituting a global health challenge. Although, pharmacological and behavioral approaches comprise the mainstay of smoking cessation interventions, the efficacy and safety of pharmacotherapy is not demonstrated for some populations. Non-pharmacological approaches, such as biofeedback (BF) and neurofeedback (NF) could facilitate self-regulation of predisposing factors of relapse such as craving and stress. The current review aims to aggregate the existing evidence regarding the effects of BF and NF training on smokers. Relevant studies were identified through searching in Scopus, PubMed and Cochrane Library, and through hand-searching the references of screened articles. Peer-reviewed controlled and uncontrolled studies, where BF and/or NF training was administered, were included and evaluated according to PICOS framework. Narrative qualitative synthesis of ten eligible studies was performed, aggregated into three categories according to training provided. BF outcomes seem to be affected by smoking behavior prior to training; individualized EEG NF training holds promise for modulating craving-related response while minimizing the required number of sessions. Real-time fMRI NF studies concluded that nicotine-dependent individuals could modulate craving-related brain responses, while mixed results were revealed regarding smokers' ability to modulate brain responses related to resistance towards the urge to smoke. BF and NF training seem to facilitate modulation of autonomous and/or central nervous system activity while also transferring this learned self-regulation to behavioral outcomes. BF and NF training should a) address remaining issues on specificity and scientific validity, b) target diverse demographics, and c) produce robust reproducible methodologies and clinical guidelines for relevant health care providers, in order to be considered as viable complementary tools to standard smoking cessation care.

Quality and denoising in real-time functional magnetic resonance imaging neurofeedback: A methods review

Neurofeedback training using real-time functional magnetic resonance imaging (rtfMRI-NF) allows subjects voluntary control of localised and distributed brain activity. It has sparked increased interest as a promising non-invasive treatment option in neuropsychiatric and neurocognitive disorders, although its efficacy and clinical significance are yet to be determined. In this work, we present the first extensive review of acquisition, processing and quality control methods available to improve the quality of the neurofeedback signal. Furthermore, we investigate the state of denoising and quality control practices in 128 recently published rtfMRI-NF studies. We found: (a) that less than a third of the studies reported implementing standard real-time fMRI denoising steps, (b) significant room for improvement with regards to methods reporting and (c) the need for methodological studies quantifying and comparing the contribution of denoising steps to the neurofeedback signal quality. Advances in rtfMRI-NF research depend on reproducibility of methods and results. Notably, a systematic effort is needed to build up evidence that disentangles the various mechanisms influencing neurofeedback effects. To this end, we recommend that future rtfMRI-NF studies: (a) report implementation of a set of standard real-time fMRI denoising steps according to a proposed COBIDAS-style checklist (https://osf.io/kjwhf/), (b) ensure the quality of the neurofeedback signal by calculating and reporting community-informed quality metrics and applying offline control checks and (c) strive to adopt transparent principles in the form of methods and data sharing and support of open-source rtfMRI-NF software. Code and data for reproducibility, as well as an interactive environment to explore the study data, can be accessed at https://github.com/jsheunis/quality-and-denoising-in-rtfmri-nf.

Grant Report on the Effects of Childhood Maltreatment on Neurocircuitry in Adolescent Depression

This report describes the protocol for an ongoing project funded by the National Institutes of Health (R01MH108155) that is focused on effects of childhood maltreatment (MALTX) on neurocircuitry changes associated with adolescent major depressive disorder (MDD). Extant clinical and neuroimaging literature on MDD is reviewed, which has relied on heterogeneous samples that do not parse out the unique contribution of MALTX on neurobiological changes in MDD. Employing a 2 × 2 study design (controls with no MALTX or MDD, MALTX only, MDD only, and MDD + MALTX), and based on a cohesive theoretical model that incorporates behavioral, cognitive and neurobiological domains, we describe the multi-modal neuroimaging techniques used to test whether structural and functional alterations in the fronto-limbic and fronto-striatal circuits associated with adolescent MDD are moderated by MALTX. We hypothesize that MDD + MALTX youth will show alterations in the fronto-limbic circuit, with reduced connectivity between the amygdala (AMG) and the prefrontal cortex (PFC), as the AMG is sensitive to stress/threat during development. Participants with MDD will exhibit increased functional connectivity between the AMG and PFC due to self-referential negative emotions. Lastly, MDD + MALTX will only show changes in motivational/anticipatory aspects of the fronto-striatal circuit, and MDD will exhibit changes in motivational and consummatory/outcome aspects of reward-processing. Our goal is to identify distinct neural substrates associated with MDD due to MALTX compared to other causes, as these markers could be used to more effectively predict treatment outcome, index treatment response, and facilitate alternative treatments for adolescents who do not respond well to traditional approaches.

The role of the subgenual anterior cingulate cortex in dorsomedial prefrontal-amygdala neural circuitry during positive-social emotion regulation

Positive-social emotions mediate one's cognitive performance, mood, well-being, and social bonds, and represent a critical variable within therapeutic settings. It has been shown that the upregulation of positive emotions in social situations is associated with increased top-down signals that stem from the prefrontal cortices (PFC) which modulate bottom-up emotional responses in the amygdala. However, it remains unclear if positive-social emotion upregulation of the amygdala occurs directly through the dorsomedial PFC (dmPFC) or indirectly linking the bilateral amygdala with the dmPFC via the subgenual anterior cingulate cortex (sgACC), an area which typically serves as a gatekeeper between cognitive and emotion networks. We performed functional MRI (fMRI) experiments with and without effortful positive-social emotion upregulation to demonstrate the functional architecture of a network involving the amygdala, the dmPFC, and the sgACC. We found that effortful positive-social emotion upregulation was associated with an increase in top-down connectivity from the dmPFC on the amygdala via both direct and indirect connections with the sgACC. Conversely, we found that emotion processes without effortful regulation increased network modulation by the sgACC and amygdala. We also found that more anxious individuals with a greater tendency to suppress emotions and intrusive thoughts, were likely to display decreased amygdala, dmPFC, and sgACC activity and stronger connectivity strength from the sgACC onto the left amygdala during effortful emotion upregulation. Analyzed brain network suggests a more general role of the sgACC in cognitive control and sheds light on neurobiological informed treatment interventions.

Volitional Modulation of the Left DLPFC Neural Activity Based on a Pain Empathy Paradigm-A Potential Novel Therapeutic Target for Pain

The ability to perceive and feel another person' pain as if it were one's own pain, e.g., pain empathy, is related to brain activity in the "pain-matrix" network. A non-core region of this network in Dorsolateral Prefrontal Cortex (DLPFC) has been suggested as a modulator of the attentional-cognitive dimensions of pain processing in the context of pain empathy. We conducted a neurofeedback experiment using real-time functional magnetic resonance imaging (rt-fMRI-NF) to investigate the association between activity in the left DLPFC (our neurofeedback target area) and the perspective assumed by the participant ("first-person"/"Self" or "third-person"/"Other" perspective of a pain-inducing stimulus), based on a customized pain empathy task. Our main goals were to assess the participants' ability to volitionally modulate activity in their own DLPFC through an imagery task of pain empathy and to investigate into which extent this ability depends on feedback. Our results demonstrate participants' ability to significantly modulate brain activity of the neurofeedback target area for the "first-person"/"Self" and "third-person"/"Other" perspectives. Results of both perspectives show that the participants were able to modulate (with statistical significance) the activity already in the first run of the session, in spite of being naïve to the task and even in the absence of feedback information. Moreover, they improved modulation throughout the session, particularly in the "Self" perspective. These results provide new insights on the role of DLPFC in pain and pain empathy mechanisms and validate the proposed protocol, paving the way for future interventional studies in clinical populations with empathic deficits.

Neuromodulation of brain activation associated with addiction: A review of real-time fMRI neurofeedback studies

Real-time functional magnetic resonance imaging neurofeedback (rtfMRI-nf) has emerged in recent years as an imaging modality used to examine volitional control over targeted brain activity. rtfMRI-nf has also been applied clinically as a way to train individuals to self-regulate areas of the brain, or circuitry, involved in various disorders. One such application of rtfMRI-nf has been in the domain of addictive behaviors, including substance use. Given the pervasiveness of substance use and the challenges of existing treatments to sustain abstinence, rtfMRI-nf has been identified as a promising treatment tool. rtfMRI-nf has also been used in basic science research in order to test the ability to modulate brain function involved in addiction. This review focuses first on providing an overview of recent rtfMRI-nf studies in substance-using populations, specifically nicotine, alcohol, and cocaine users, aimed at reducing craving-related brain activation. Next, rtfMRI-nf studies targeting reward responsivity and emotion regulation in healthy samples are reviewed in order to examine the extent to which areas of the brain involved in addiction can be self-regulated using neurofeedback. We propose that future rtfMRI-nf studies could be strengthened by improvements to study design, sample selection, and more robust strategies in the development and assessment of rtfMRI-nf as a clinical treatment. Recommendations for ways to accomplish these improvements are provided. rtfMRI-nf holds much promise as an imaging modality that can directly target key brain regions involved in addiction, however additional studies are needed in order to establish rtfMRI-nf as an effective, and practical, treatment for addiction.

Earliest amyloid and tau deposition modulate the influence of limbic networks during closed-loop hippocampal downregulation

Research into hippocampal self-regulation abilities may help determine the clinical significance of hippocampal hyperactivity throughout the pathophysiological continuum of Alzheimer's disease. In this study, we aimed to identify the effects of amyloid-β peptide 42 (amyloid-β42) and phosphorylated tau on the patterns of functional connectomics involved in hippocampal downregulation. We identified 48 cognitively unimpaired participants (22 with elevated CSF amyloid-β peptide 42 levels, 15 with elevated CSF phosphorylated tau levels, mean age of 62.705 ± 4.628 years), from the population-based 'Alzheimer's and Families' study, with baseline MRI, CSF biomarkers, APOE genotyping and neuropsychological evaluation. We developed a closed-loop, real-time functional MRI neurofeedback task with virtual reality and tailored it for training downregulation of hippocampal subfield cornu ammonis 1 (CA1). Neurofeedback performance score, cognitive reserve score, hippocampal volume, number of apolipoprotein ε4 alleles and sex were controlled for as confounds in all cross-sectional analyses. First, using voxel-wise multiple regression analysis and controlling for CSF biomarkers, we identified the effect of healthy ageing on eigenvector centrality, a measure of each voxel's overall influence based on iterative whole-brain connectomics, during hippocampal CA1 downregulation. Then, controlling for age, we identified the effects of abnormal CSF amyloid-β42 and phosphorylated tau levels on eigenvector centrality during hippocampal CA1 downregulation. Across subjects, our main findings during hippocampal downregulation were: (i) in the absence of abnormal biomarkers, age correlated with eigenvector centrality negatively in the insula and midcingulate cortex, and positively in the inferior temporal gyrus; (ii) abnormal CSF amyloid-β42 (<1098) correlated negatively with eigenvector centrality in the anterior cingulate cortex and primary motor cortex; and (iii) abnormal CSF phosphorylated tau levels (>19.2) correlated with eigenvector centrality positively in the ventral striatum, anterior cingulate and somatosensory cortex, and negatively in the precuneus and orbitofrontal cortex. During resting state functional MRI, similar eigenvector centrality patterns in the cingulate had previously been associated to CSF biomarkers in mild cognitive impairment and dementia patients. Using the developed closed-loop paradigm, we observed such patterns, which are characteristic of advanced disease stages, during a much earlier presymptomatic phase. In the absence of CSF biomarkers, our non-invasive, interactive, adaptive and gamified neuroimaging procedure may provide important information for clinical prognosis and monitoring of therapeutic efficacy. We have released the developed paradigm and analysis pipeline as open-source software to facilitate replication studies.

Using mind control to modify cue-reactivity in AUD: the impact of mindfulness-based relapse prevention on real-time fMRI neurofeedback to modify cue-reactivity in alcohol use disorder: a randomized controlled trial

BACKGROUND

Alcohol Use Disorder is a severe mental disorder affecting the individuals concerned, their family and friends and society as a whole. Despite its high prevalence, novel treatment options remain rather limited. Two innovative interventions used for treating severe disorders are the use of real-time functional magnetic resonance imaging neurofeedback that targets brain regions related to the disorder, and mindfulness-based treatments. In the context of the TRR SFB 265 C04 "Mindfulness-based relapse prevention as an addition to rtfMRI NFB intervention for patients with Alcohol Use Disorder (MiND)" study, both interventions will be combined to a state-of-the art intervention that will use mindfulness-based relapse prevention to improve the efficacy of a real-time neurofeedback intervention targeting the ventral striatum, which is a brain region centrally involved in cue-reactivity to alcohol-related stimuli.

METHODS/DESIGN

After inclusion, N = 88 patients will be randomly assigned to one of four groups. Two of those groups will receive mindfulness-based relapse prevention. All groups will receive two fMRI sessions and three real-time neurofeedback sessions in a double-blind manner and will regulate either the ventral striatum or the auditory cortex as a control region. Two groups will additionally receive five sessions of mindfulness-based relapse prevention prior to the neurofeedback intervention. After the last fMRI session, the participants will be followed-up monthly for a period of 3 months for an assessment of the relapse rate and clinical effects of the intervention.

DISCUSSION

The results of this study will give further insights into the efficacy of real-time functional magnetic resonance imaging neurofeedback interventions for the treatment of Alcohol Use Disorder. Additionally, the study will provide further insight on neurobiological changes in the brain caused by the neurofeedback intervention as well as by the mindfulness-based relapse prevention. The outcome might be useful to develop new treatment approaches targeting mechanisms of Alcohol Use Disorder with the goal to reduce relapse rates after discharge from the hospital.

TRIAL REGISTRATION

This trial is pre-registered at clinicaltrials.gov (trial identifier: NCT04366505; WHO Universal Trial Number (UTN): U1111-1250-2964). Registered 30 March 2020, published 29 April 2020.

Connectome-wide search for functional connectivity locus associated with pathological rumination as a target for real-time fMRI neurofeedback intervention

Real-time fMRI neurofeedback (rtfMRI-nf) enables noninvasive targeted intervention in brain activation with high spatial specificity. To achieve this promise of rtfMRI-nf, we introduced and demonstrated a data-driven framework to design a rtfMRI-nf intervention through the discovery of precise target location associated with clinical symptoms and neurofeedback signal optimization. Specifically, we identified the functional connectivity locus associated with rumination symptoms, utilizing a connectome-wide search in resting-state fMRI data from a large cohort of mood and anxiety disorder individuals (N = 223) and healthy controls (N = 45). Then, we performed a rtfMRI simulation analysis to optimize the online functional connectivity neurofeedback signal for the identified functional connectivity. The connectome-wide search was performed in the medial prefrontal cortex and the posterior cingulate cortex/precuneus brain regions to identify the precise location of the functional connectivity associated with rumination severity as measured by the ruminative response style (RRS) scale. The analysis found that the functional connectivity between the loci in the precuneus (-6, -54, 48 mm in MNI) and the right temporo-parietal junction (RTPJ; 49, -49, 23 mm) was positively correlated with RRS scores (depressive, p < 0.001; brooding, p < 0.001; reflective, p = 0.002) in the mood and anxiety disorder group. We then performed a rtfMRI processing simulation to optimize the online computation of the precuneus-RTPJ connectivity. We determined that the two-point method without a control region was appropriate as a functional connectivity neurofeedback signal with less dependence on signal history and its accommodation of head motion. The present study offers a discovery framework for the precise location of functional connectivity targets for rtfMRI-nf intervention, which could help directly translate neuroimaging findings into clinical rtfMRI-nf interventions.