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Abstract: Neurofeedback based on real-time fMRI is an emerging technique that can be used to train voluntary control of brain activity. Such brain training has been shown to lead to behavioral effects that are specific to the functional role of the targeted brain area. However, real-time fMRI-based neurofeedback so far was limited to mainly training localized brain activity within a region of interest. Here, we overcome this limitation by presenting near real-time dynamic causal modeling in order to provide feedback information based on connectivity between brain areas rather than activity within a single brain area. Using a visual–spatial attention paradigm, we show that participants can voluntarily control a feedback signal that is based on the Bayesian model comparison between two predefined model alternatives, i.e. the connectivity between left visual cortex and left parietal cortex vs. the connectivity between right visual cortex and right parietal cortex. Our new approach thus allows for training voluntary control over specific functional brain networks. Because most mental functions and most neurological disorders are associated with network activity rather than with activity in a single brain region, this novel approach is an important methodological innovation in order to more directly target functionally relevant brain networks. [Copyright &y& Elsevier]

Abstract: Recent fMRI studies demonstrated that functional connectivity is altered following cognitive tasks (e.g., learning) or due to various neurological disorders. We tested whether real-time fMRI-based neurofeedback can be a tool to voluntarily reconfigure brain network interactions. To disentangle learning-related from regulation-related effects, we first trained participants to voluntarily regulate activity in the auditory cortex (training phase) and subsequently asked participants to exert learned voluntary self-regulation in the absence of feedback (transfer phase without learning). Using independent component analysis (ICA), we found network reconfigurations (increases in functional network connectivity) during the neurofeedback training phase between the auditory target region and (1) the auditory pathway; (2) visual regions related to visual feedback processing; (3) insula related to introspection and self-regulation and (4) working memory and high-level visual attention areas related to cognitive effort. Interestingly, the auditory target region was identified as the hub of the reconfigured functional networks without a-priori assumptions. During the transfer phase, we again found specific functional connectivity reconfiguration between auditory and attention network confirming the specific effect of self-regulation on functional connectivity. Functional connectivity to working memory related networks was no longer altered consistent with the absent demand on working memory. We demonstrate that neurofeedback learning is mediated by widespread changes in functional connectivity. In contrast, applying learned self-regulation involves more limited and specific network changes in an auditory setup intended as a model for tinnitus. Hence, neurofeedback training might be used to promote recovery from neurological disorders that are linked to abnormal patterns of brain connectivity. [Copyright &y& Elsevier]

Abstract: Recent advances in brain imaging have improved the measure of neural processes related to perceptual, cognitive and affective functions, yet the relation between brain activity and subjective experience remains poorly characterized. In part, it is a challenge to obtain reliable accounts of participant's experience in such studies. Here we addressed this limitation by utilizing experienced meditators who are expert in introspection. We tested a novel method to link objective and subjective data, using real-time fMRI (rt-fMRI) to provide participants with feedback of their own brain activity during an ongoing task. We provided real-time feedback during a focused attention task from the posterior cingulate cortex, a hub of the default mode network shown to be activated during mind-wandering and deactivated during meditation. In a first experiment, both meditators and non-meditators reported significant correspondence between the feedback graph and their subjective experience of focused attention and mind-wandering. When instructed to volitionally decrease the feedback graph, meditators, but not non-meditators, showed significant deactivation of the posterior cingulate cortex. We were able to replicate these results in a separate group of meditators using a novel step-wise rt-fMRI discovery protocol in which participants were not provided with prior knowledge of the expected relationship between their experience and the feedback graph (i.e., focused attention versus mind-wandering). These findings support the feasibility of using rt-fMRI to link objective measures of brain activity with reports of ongoing subjective experience in cognitive neuroscience research, and demonstrate the generalization of expertise in introspective awareness to novel contexts. [Copyright &y& Elsevier]

In the past decade, neuroimaging research has begun to identify key brain regions involved in self-referential processing, most consistently midline structures such as the posterior cingulate cortex (PCC). The majority of studies have employed cognitive tasks such as judgment about trait adjectives or mind wandering, that have been associated with increased PCC activity. Conversely, tasks that share an element of present-centered attention (being "on task"), ranging from working memory to meditation, have been associated with decreased PCC activity. Given the complexity of cognitive processes that likely contribute to these tasks, the specific contribution of the PCC to self-related processes still remains unknown. Building on this prior literature, recent studies have employed sampling methods that more precisely link subjective experience to brain activity, such as real-time fMRI neurofeedback. This recent work suggests that PCC activity may represent a sub-component cognitive process of self-reference - "getting caught up in" one's experience. For example, getting caught up in a drug craving or a particular viewpoint. In this paper, we will review evidence across a number of different domains of cognitive neuroscience that converges in activation and deactivation of the PCC including recent neurophenomenological studies of PCC activity using real-time fMRI neurofeedback. [ABSTRACT FROM AUTHOR]

The capacity for subjects to learn to volitionally control localized brain activity using neurofeedback is actively being investigated.We aimed to investigate the ability of healthy volunteers to quickly learn to use visual feedback during real-time functional MRI (rtfMRI) to modulate brain activity within their anterior right insular cortex (RIC) localized during a blink suppression task, an approach of possible interest in the use of rtfMRI to reduce urges. The RIC region of interest (RIC-ROI) was functionally localized using a blink suppression task, and blood-oxygen level dependent (BOLD) signal changes within RIC-ROI used to create a constantly updating display fed back to the subject in the scanner. Subjects were instructed to use emotional imagery to try and increase activity within RIC-ROI during four feedback training runs (FB1-FB4). A "control" run (CNTRL) before training and a "transfer" run (XSFR) after training were performed without feedback to assess for baseline abilities and learning effects. Fourteen participants completed all neurofeedback training runs. At the group-level, increased BOLD activity was seen in the anterior RIC during all the FB runs, but a significant increase in the functionally defined RIC-ROI was only attained during FB2. In atlas-defined insular cortex ROIs, significant increases were seen bilaterally during the CNTRL, FB1, FB2, and FB4 runs. Increased activity within the insular cortices did not show lateralization. Training did, however, result in a significant increase in functional connectivity between the RIC-ROI and the medial frontal gyrus when comparing FB4 to FB1. Since neurofeedback training did not lead to an increase in BOLD signal across all feedback runs, we suggest that learning to control one's brain activity in this fashion may require longer or repeated rtfMRI training sessions. [ABSTRACT FROM AUTHOR]

There is a recent increase in the use of multivariate analysis and pattern classification in prediction and real-time feedback of brain states from functional imaging signals and mapping of spatio-temporal patterns of brain activity. Here we present MANAS, a generalized software toolbox for performing online and offline classification of fMRI signals. MANAS has been developed using MATLAB, LIBSVM, and SVMlight packages to achieve a cross-platform environment. MANAS is targeted for neuroscience investigations and brain rehabilitation applications, based on neurofeedback and brain-computer interface (BCI) paradigms. MANAS provides two different approaches for real-time classification: subject dependent and subject independent classification. In this article, we present the methodology of real-time subject dependent and subject independent pattern classification of fMRI signals; the MANAS software architecture and subsystems; and finally demonstrate the use of the system with experimental results. [ABSTRACT FROM AUTHOR]

Abstract: In February of 2012, the first international conference on real time functional magnetic resonance imaging (rtfMRI) neurofeedback was held at the Swiss Federal Institute of Technology Zurich (ETHZ), Switzerland. This review summarizes progress in the field, introduces current debates, elucidates open questions, and offers viewpoints derived from the conference. The review offers perspectives on study design, scientific and clinical applications, rtfMRI learning mechanisms and future outlook. [Copyright &y& Elsevier]

ABSTRACT Numerous research groups are now using analysis of blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI) results and relaying back information about regional activity in their brains to participants in the scanner in 'real time'. In this study, we explored the feasibility of self-regulation of frontal cortical activation using real-time fMRI (rtfMRI) neurofeedback in nicotine-dependent cigarette smokers during exposure to smoking cues. Ten cigarette smokers were shown smoking-related visual cues in a 3 Tesla MRI scanner to induce their nicotine craving. Participants were instructed to modify their craving using rtfMRI feedback with two different approaches. In a 'reduce craving' paradigm, participants were instructed to 'reduce' their craving, and decrease the anterior cingulate cortex (ACC) activity. In a separate 'increase resistance' paradigm, participants were asked to increase their resistance to craving and to increase middle prefrontal cortex (mPFC) activity. We found that participants were able to significantly reduce the BOLD signal in the ACC during the 'reduce craving' task ( P = 0.028). There was a significant correlation between decreased ACC activation and reduced craving ratings during the 'reduce craving' session ( P = 0.011). In contrast, there was no modulation of the BOLD signal in mPFC during the 'increase resistance' session. These preliminary results suggest that some smokers may be able to use neurofeedback via rtfMRI to voluntarily regulate ACC activation and temporarily reduce smoking cue-induced craving. Further research is needed to determine the optimal parameters of neurofeedback rtfMRI, and whether it might eventually become a therapeutic tool for nicotine dependence. [ABSTRACT FROM AUTHOR]

Real-time functional magnetic resonance imaging (rt-fMRI) has recently gained interest as a possible means to facilitate the learning of certain behaviors. However, rt-fMRI is limited by processing speed and available software, and continued development is needed for rt-fMRI to progress further and become feasible for clinical use. In this work, we present an open-source rt-fMRI system for biofeedback powered by a novel Graphics Processing Unit (GPU) accelerated motion correction strategy as part of the BioImage Suite project (). Our system contributes to the development of rt-fMRI by presenting a motion correction algorithm that provides an estimate of motion with essentially no processing delay as well as a modular rt-fMRI system design. Using empirical data from rt-fMRI scans, we assessed the quality of motion correction in this new system. The present algorithm performed comparably to standard (non real-time) offline methods and outperformed other real-time methods based on zero order interpolation of motion parameters. The modular approach to the rt-fMRI system allows the system to be flexible to the experiment and feedback design, a valuable feature for many applications. We illustrate the flexibility of the system by describing several of our ongoing studies. Our hope is that continuing development of open-source rt-fMRI algorithms and software will make this new technology more accessible and adaptable, and will thereby accelerate its application in the clinical and cognitive neurosciences. [ABSTRACT FROM AUTHOR]

The article focuses on a study that evaluates the use of real time-functional magnetic resonance imaging (fMRI) in the treatment of addictive disorders and nicotine dependence. It highlights ion the challenges associated with the development of real time-fMRI in the treatment of addiction disorders. It suggests that real time-fMRI can prove to be a beneficial method for treating nicotine dependence.

An introduction is presented in which the editor discusses various reports within the issue on topics including cognitive and behavioral therapies, magneto encephalography and addictive behaviors.

Paraphilia is a set of disorders characterized by abnormal sexual desires. Perhaps most discussed amongst them, pedophilia is a complex interaction of disturbances of the emotional, cognitive and sexual experience. Using new imaging techniques such as functional magnetic resonance imaging, neural correlates of emotional, sexual and cognitive abnormalities and interactions have been investigated. As described on the basis of current research, altered patterns of brain activity, especially in the frontal areas of the brain, are seen in pedophilia. Building on these results, the analysis of neural correlates of impaired psychological functions opens the opportunity to further explore sexual deviances, which may contribute ultimately to the development of tools for risk assessment, classification methods and new therapeutic approaches. [ABSTRACT FROM AUTHOR]

Purpose: This pilot study aims to test the reliability and clinical validity of the assessment of cerebrovascular reactivity (CVR) with real-time blood-oxygen-level-dependent (BOLD) fMRI (rtCVR) in comparison with standard off-line processing in children with moyamoya disease. Methods: Eight consecutive pediatric patients with moyamoya cerebral arteriopathy underwent BOLD fMRI CVR studies either on a 1.5-T or on a 3-T scanner with breath-holding techniques in six patients and under general anesthesia (GA) in three patients. One patient had undergone CVR study initially without and later with GA, and another had undergone CVR study before and after a revascularization procedure. The off-line and real-time processing of the data was done, and the results were compared for general quality and adequacy of the study (scale 0-3, 3 being the best) and for the presence or absence of abnormal reactivity and the location, pattern, and extent of abnormal reactivity. Results: A total of 20 CVR series each was studied on real-time functional MRI and off-line analysis (eight patients, ten sessions, two series per session). All eight sessions done under GA were rated as quality class 3 (100 %), whereas only 5 of the 12 (41.7 %) studies with breath-holding technique were considered to be of best quality (class 3). In comparison to the off-line processing, the overall sensitivity of the rtCVR technique was 90 % with a specificity of 100 %. Conclusions: rtCVR assessment in children with moyamoya disease is feasible, and the results are comparable to that of standard off-line analysis. The results from off-line analysis are only available, at the earliest, several hours after the MRI has been completed. rtCVR can overcome this difficulty and may be equally reliable. [ABSTRACT FROM AUTHOR]

Real-time functional magnetic resonance imaging (rtfMRI) is a novel technique that has allowed subjects to achieve self-regulation of circumscribed brain regions. Despite its anticipated therapeutic benefits, there is no report on successful application of this technique in psychiatric populations. The objectives of the present study were to train schizophrenia patients to achieve volitional control of bilateral anterior insula cortex on multiple days, and to explore the effect of learned self-regulation on face emotion recognition (an extensively studied deficit in schizophrenia) and on brain network connectivity. Nine patients with schizophrenia were trained to regulate the hemodynamic response in bilateral anterior insula with contingent rtfMRI neurofeedback, through a 2-weeks training. At the end of the training stage, patients performed a face emotion recognition task to explore behavioral effects of learned self-regulation. A learning effect in self-regulation was found for bilateral anterior insula, which persisted through the training. Following successful self-regulation, patients recognized disgust faces more accurately and happy faces less accurately. Improvements in disgust recognition were correlated with levels of self-activation of right insula. RtfMRI training led to an increase in the number of the incoming and outgoing effective connections of the anterior insula. This study shows for the first time that patients with schizophrenia can learn volitional brain regulation by rtfMRI feedback training leading to changes in the perception of emotions and modulations of the brain network connectivity. These findings open the door for further studies of rtfMRI in severely ill psychiatric populations, and possible therapeutic applications. Hum Brain Mapp, 2013. © 2011 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Brain-computer interfaces (BCIs) allow people with severe neurological impairment and without ability to control their muscles to regain some control over their environment. The BCI user performs a mental task to regulate brain activity, which is measured and translated into commands controlling some external device. We here show that healthy participants are capable of navigating a robot by covertly shifting their visuospatial attention. Covert Visuospatial Attention (COVISA) constitutes a very intuitive brain function for spatial navigation and does not depend on presented stimuli or on eye movements. Our robot is equipped with motors and a camera that sends visual feedback to the user who can navigate it from a remote location. We used an ultrahigh field MRI scanner (7 Tesla) to obtain fMRI signals that were decoded in real time using a support vector machine. Four healthy subjects with virtually no training succeeded in navigating the robot to at least three of four target locations. Our results thus show that with COVISA BCI, realtime robot navigation can be achieved. Since the magnitude of the fMRI signal has been shown to correlate well with the magnitude of spectral power changes in the gamma frequency band in signals measured by intracranial electrodes, the COVISA concept may in future translate to intracranial application in severely paralyzed people. [ABSTRACT FROM AUTHOR]

In the past decade, several studies have shown that Neurofeedback (NFB) by functional magnetic resonance imaging can alter the functional coupling of targeted and non-targeted areas. However, the causal mechanisms underlying these changes remain uncertain. Here, we applied a whole-brain dynamical model to estimate Effective Connectivity (EC) profiles of resting-state data acquired before and immediately after a single-session NFB training for 17 participants who underwent motor imagery NFB training and 16 healthy controls who received sham feedback. Within-group and between-group classification analyses revealed that only for the NFB group it was possible to accurately discriminate between the 2 resting-state sessions. NFB training-related signatures were reflected in a support network of direct connections between areas involved in reward processing and implicit learning, together with regions belonging to the somatomotor, control, attention, and default mode networks, identified through a recursive-feature elimination procedure. By applying a data-driven approach to explore NFB-induced changes in spatiotemporal dynamics, we demonstrated that these regions also showed decreased switching between different brain states (i.e. metastability) only following real NFB training. Overall, our findings contribute to the understanding of NFB impact on the whole brain's structure and function by shedding light on the direct connections between brain areas affected by NFB training., (© The Author(s) 2022. Published by Oxford University Press.)

The aim of this study was to explore the potential of default mode network (DMN) functional connectivity for predicting the success of smoking cessation in patients with tobacco dependence in the context of a real-time function al MRI (RT-fMRI) neurofeedback (NF) supported therapy.Fifty-four tobacco-dependent patients underwent three RT-fMRI-NF sessions including resting-state functional connectivity (RSFC) runs over a period of 4 weeks during professionally assisted smoking cessation. Patients were randomized into two groups that performed either active NF of an addiction-related brain region or sham NF. After preprocessing, the RSFC baseline data were statistically evaluated using seed-based ROI (SBA) approaches taking into account the smoking status of patients after 3 months (abstinence/relapse).The results of the real study group showed a widespread functional connectivity in the relapse subgroup (n = 10) exceeding the DMN template and mainly low correlations and anticorrelations in the within-seed analysis. In contrast, the connectivity pattern of the abstinence subgroup (n = 8) primarily contained the core DMN in the seed-to-whole-brain analysis and a left lateralized correlation pattern in the within-seed analysis. Calculated Multi-Subject Dictionary Learning (MSDL) matrices showed anticorrelations between DMN regions and salience regions in the abstinence group. Concerning the sham group, results of the relapse subgroup (n = 4) and the abstinence subgroup (n = 6) showed similar trends only in the within-seed analysis.In the setting of a RT-fMRI-NF-assisted therapy, a widespread intrinsic DMN connectivity and a low negative coupling between the DMN and the salience network (SN) in patients with tobacco dependency during early withdrawal may be useful as an early indicator of later therapy nonresponse.

We outline general theoretical and practical implications of what we promote as enactive cinema for the neuroscientific study of online socio-emotional interaction. In a real-time functional magnetic resonance imaging (rt-fMRI) setting, participants are immersed in cinematic experiences that simulate social situations. While viewing, their physiological reactions—including brain responses—are tracked, representing implicit and unconscious experiences of the on-going social situations. These reactions, in turn, are analyzed in real-time and fed back to modify the cinematic sequences they are viewing while being scanned. Due to the engaging cinematic content, the proposed setting focuses on living-by in terms of shared psycho-physiological epiphenomena of experience rather than active coping in terms of goal-oriented motor actions. It constitutes a means to parametrically modify stimuli that depict social situations and their broader environmental contexts. As an alternative to studying the variation of brain responses as a function of a priori fixed stimuli, this method can be applied to survey the range of stimuli that evoke similar responses across participants at particular brain regions of interest. [ABSTRACT FROM AUTHOR]

Real-time fMRI permits simultaneous measurement and observation of brain activity during an ongoing task. One of the most challenging applications of real-time fMRI in neuroscientific and clinical research is the possibility of acquiring volitional control of localized brain activity using real-time fMRI–based neurofeedback protocols. Real-time fMRI allows the experimenter to noninvasively manipulate brain activity as an independent variable to observe the effects on behavior. Real-time fMRI neurofeedback studies demonstrated that learned control of the local brain activity leads to specific changes in behavior. Here, the authors describe the implementation and application of real-time fMRI with particular emphasis on the self-regulation of local brain activity and the investigation of brain-function relationships. Real-time fMRI represents a promising new approach to cognitive neuroscience that could complement traditional neuroimaging techniques by providing more causal insights into the functional role of circumscribed brain regions in behavior. [ABSTRACT FROM PUBLISHER]

Background: Although functional magnetic resonance imaging (fMRI) is in widespread research use, the safety of this approach has not been extensively quantitatively evaluated. Real-time fMRI (rtfMRI)-based training paradigms use fMRI neurofeedback and cognitive strategies to alter regional brain activation, and are currently being evaluated as a novel approach to treat neurological and psychiatric conditions. Purpose: The purpose of this study is to determine the incidence and severity of any adverse events that might be caused by changes in brain activation brought about through fMRI or through rtfMRI-based training paradigms. Method: Quantitative adverse event self-report data were obtained from 641 functional imaging scans in 114 chronic pain patients participating in a research clinical trial examining repeated fMRI scans and rtfMRI-based training. Participants recorded potential adverse events during non-scanning baseline, fMRI scanning, or rtfMRI-based training sessions. Results: There were no significant increases in the number of reported adverse events following fMRI or rtfMRI scanning sessions compared to baseline non-scanning sessions in a chronic pain trial ( N = 88). There were no reported adverse events of any kind for over 90% of sessions during the course of rtfMRI-based training. When adverse events were reported, they were almost exclusively mild or moderate in severity and similar to those observed in a non-scanning baseline session. There was no increase in adverse events reported by participants receiving feedback from any of four brain regions during repeated rtfMRI-based training scans compared to non-scanning baseline sessions. For chronic pain patients completing the rtfMRI-based training paradigm including up to a total of nine scan sessions ( N = 69), neither the number nor severity of reported events increased during the fMRI or rtfMRI scanning portions of the paradigm. There were no significant increases in the number of reported adverse events in participants who withdrew from the study. Conclusion: Repeated fMRI scanning and rtfMRI training, consisting of repeated fMRI scanning in conjunction with cognitive strategies and real-time feedback from several regions of interest in multiple brain systems to control brain region activation, were not associated with an increase in adverse event number or severity. These results demonstrate the safety of repetitive fMRI scanning paradigms similar to those in use in many laboratories worldwide, as well as the safety rtfMRI-based training paradigms. [ABSTRACT FROM AUTHOR]

Abstract: The advantage of using a virtual reality (VR) paradigm in fMRI is the possibility to interact with highly realistic environments. This extends the functions of standard fMRI paradigms, where the volunteer usually has a passive role, for example, watching a simple movie paradigm without any stimulus interactions. From that point of view the combined usage of VR and real-time fMRI offers great potential to identify underlying cognitive mechanisms such as spatial navigation, attention, semantic and episodic memory, as well as neurofeedback paradigms. However, the design and the implementation of a VR stimulus paradigm as well as the integration into an existing MR scanner framework are very complex processes. To support the modeling and usage of VR stimuli we developed and implemented a VR stimulus application based on C++. This software allows the fast and easy presentation of VR environments for fMRI studies without any additional expert knowledge. Furthermore, it provides for the reception of real-time data analysis values a bidirectional communication interface. In addition, the internal plugin interface enables users to extend the functionality of the software with custom programmed C++ plugins. The VR stimulus framework was tested in several performance tests and a spatial navigation study. According to the post-experimental interview, all subjects described immersive experiences and a high attentional load inside the artifical environment. Results from other VR spatial memory studies confirm the neuronal activation that was detected in parahippocampal areas, cuneus, and occipital regions. [Copyright &y& Elsevier]

Previous studies have shown that healthy participants learn to control local brain activity with operant training by using real-time functional magnetic resonance imaging (rt-fMRI). Very little data exist, however, on the dynamics of interaction between critical brain regions during rt-fMRI-based training. Here, we examined self-regulation of stimulus-elicited insula activation and performed a psychophysiological interaction (PPI) analysis of real-time self-regulation data. During voluntary up-regulation of the left anterior insula in the presence of threat-related pictures, differential activations were observed in the ventrolateral prefrontal cortex, the frontal operculum, the middle cingulate cortex and the right insula. Down-regulation in comparison to no-regulation revealed additional activations in right superior temporal cortex, right inferior parietal cortex and right middle frontal cortex. There was a significant learning effect over sessions during up-regulation, documented by a significant improvement of anterior insula control over time. Connectivity analysis revealed that successful up-regulation of the activity in left anterior insula while viewing aversive pictures was directly modulated by dorsomedial and ventrolateral prefrontal cortex. Down-regulation of activity was more difficult to achieve and no learning effect was observed. More extensive training might be necessary for successful down-regulation. These findings illustrate the functional interactions between different brain areas during regulation of anterior insula activity in the presence of threat-related stimuli. [ABSTRACT FROM AUTHOR]

Abstract: Functional MRI neurofeedback (fMRI NF) is an emerging technique that trains subjects to regulate their brain activity while they manipulate sensory stimulus representations of fMRI signals in “real-time”. Here we report an fMRI NF study of brain activity associated with kinesthetic motor imagery (kMI), analyzed using partial least squares (PLS), a multivariate analysis technique. Thirteen healthy young adult subjects performed kMI involving each hand separately, with NF training targeting regions of interest (ROIs) in the left and right primary motor cortex (M1). Throughout, subjects attempted to maximize a laterality index (LI) of brain activity—the difference in activity between the contralateral ROI (relative to the hand involved in kMI) and the ipsilateral M1 ROI—while receiving real-time updates on a visual display. Six of 13 subjects were successful in increasing the LI value, whereas the other 7 were not successful and performed similarly to 5 control subjects who received sham NF training. Ability to suppress activity in the ipsilateral M1 ROI was the primary driver of successful NF performance. Multiple PLS analyses depicted activated networks of brain regions involved with imagery, self-awareness, and feedback processing, and additionally showed that activation of the task positive network was correlated with task performance. These results indicate that fMRI NF of kMI is capable of modulating brain activity in primary motor regions in a subset of the population. In the future, such methods may be useful in the development of NF training methods for enhancing motor rehabilitation following stroke. [Copyright &y& Elsevier]

Abstract: In this study, we investigated the efficacy of a real-time functional magnetic resonance imaging (rtfMRI)-based neurofeedback method for the modulation of the effective connectivity (EC) of causality between attention-related neuronal activities. In participants who received the feedback of attention-related neuronal activity, the EC estimated from Granger causality analysis was reinforced within the task-related network, such as between the bilateral cingulate gyri and frontal cortices, whereas the EC between the task-related network and task-unrelated resting-state network, including the inferior parietal lobule, was diminished. On the other hand, only marginal changes were observed in participants who received “sham” feedback. This “dynamic” characteristic measure of EC based on causality may be useful for evaluating the efficacy of methods designed to modulate brain networks, including rtfMRI-based neurofeedback. [Copyright &y& Elsevier]

Background. Despite the availability of various options for movement restoration in stroke patients, there is no effective treatment for patients who show little or no functional recovery of upper limb motor function. Objective. The present study explored the feasibility of real-time functional magnetic resonance imaging brain–computer interface (fMRI-BCI) as a new tool for rehabilitation of this patient population. Methods. Healthy adults and chronic subcortical stroke patients with residual movement were trained for 3 days to regulate the blood oxygenation level dependent (BOLD) response in the ventral premotor cortex (PMv), a secondary motor area with extensive anatomic connections with the primary motor cortex. Effect of learned modulation of the PMv was evaluated with BOLD signal changes across training sessions, transcranial magnetic stimulation (TMS), and a visuomotor task. Results. fMRI-BCI feedback training showed learning with a significantly increasing BOLD signal in the PMv over sessions. Participants’ capability to learn self-regulation was found to depend linearly on intracortical facilitation and correlated negatively with intracortical inhibition measured by TMS prior to feedback training. After training, intracortical inhibition decreased significantly with the volitional increase of the BOLD response in the PMv, indicating a beneficial effect of self-regulation training on motor cortical output. Conclusion. The study provides first evidence for the therapeutic potential of fMRI-BCI in stroke rehabilitation. [ABSTRACT FROM PUBLISHER]

Abstract: Real-time fMRI allows analysis and visualization of the brain activity online, i.e. within one repetition time. It can be used in neurofeedback applications where subjects attempt to control an activation level in a specified region of interest (ROI) of their brain. The signal derived from the ROI is contaminated with noise and artifacts, namely with physiological noise from breathing and heart beat, scanner drift, motion-related artifacts and measurement noise. We developed a Bayesian approach to reduce noise and to remove artifacts in real-time using a modified Kalman filter. The system performs several signal processing operations: subtraction of constant and low-frequency signal components, spike removal and signal smoothing. Quantitative feedback signal quality analysis was used to estimate the quality of the neurofeedback time series and performance of the applied signal processing on different ROIs. The signal-to-noise ratio (SNR) across the entire time series and the group event-related SNR (eSNR) were significantly higher for the processed time series in comparison to the raw data. Applied signal processing improved the t-statistic increasing the significance of blood oxygen level-dependent (BOLD) signal changes. Accordingly, the contrast-to-noise ratio (CNR) of the feedback time series was improved as well. In addition, the data revealed increase of localized self-control across feedback sessions. The new signal processing approach provided reliable neurofeedback, performed precise artifacts removal, reduced noise, and required minimal manual adjustments of parameters. Advanced and fast online signal processing algorithms considerably increased the quality as well as the information content of the control signal which in turn resulted in higher contingency in the neurofeedback loop. [Copyright &y& Elsevier]

The cognitive neurosciences combine behavioral experiments with acquiring physiological data from different modalities, such as electroencephalography, magnetoencephalography, transcranial magnetic stimulation, and functional magnetic resonance imaging, all of which require excellent timing. A simple framework is proposed in which uni- and multimodal experiments can be conducted with minimal adjustments when one switches between modalities. The framework allows the beginner to quickly become productive and the expert to be flexible and not constrained by the tool by building on existing software such as MATLAB and the Psychophysics Toolbox, which already are serving a large community. The framework allows running standard experiments but also supports and facilitates exciting new possibilities for real-time neuroimaging and state-dependent stimulation. [ABSTRACT FROM AUTHOR]

Real-time functional magnetic resonance imaging can be used to feed back signal changes from the brain to participants such that they can train to modulate activation levels in specific brain areas. Here we present the first study combining up-regulation of brain areas for positive emotions with psychometric measures to assess the effect of successful self-regulation on subsequent mood. We localized brain areas associated with positive emotions through presentation of standardized pictures with positive valence. Participants up-regulated activation levels in their target area during specific periods, alternating with rest. Participants attained reliable self-control of the target area by the last of three seven-minute runs. This training effect was supported by an extensive network outside the targeted brain region, including higher sensory areas, paralimbic and orbitofrontal cortex. Self-control of emotion areas was not accompanied by clear changes in self-reported emotions; trend-level improvements on depression scores were counteracted by increases on measures of fatigue, resulting in no overall mood improvement. It is possible that benefits of self-control of emotion networks may only appear in people who display abnormal emotional homeostasis. The use of only a single, short, training session, overlap between positive and negative emotion networks and aversive reactions to the scanning environment may have prevented the detection of subtle changes in mood. [ABSTRACT FROM AUTHOR]

Social learning is essential for adaptive behavior in humans. Neurofeedback based on functional magnetic resonance imaging (fMRI) trains control over localized brain activity. It can disentangle learning processes at the neural level and thus investigate the mechanisms of operant conditioning with explicit social reinforcers. In a pilot study, a computer-generated face provided a positive feedback (smiling) when activity in the anterior cingulate cortex (ACC) increased and gradually returned to a neutral expression when the activity dropped. One female volunteer without previous experience in fMRI underwent training based on a social reinforcer. Directly before and after the neurofeedback runs, neural responses to a cognitive interference task (Simon task) were recorded. We observed a significant increase in activity within ACC during the neurofeedback blocks, correspondent with the a-priori defined anatomical region of interest. In the course of the neurofeedback training, the subject learned to regulate ACC activity and could maintain the control even without direct feedback. Moreover, ACC was activated significantly stronger during Simon task after the neurofeedback training when compared to before. Localized brain activity can be controlled by social reward. The increased ACC activity transferred to a cognitive task with the potential to reduce cognitive interference. Systematic studies are required to explore long-term effects on social behavior and clinical applications. [ABSTRACT FROM AUTHOR]

Background: A promising new approach to cognitive neuroscience based on real-time functional magnetic resonance imaging (rtfMRI) demonstrated that the learned regulation of the neurophysiological activity in circumscribed brain regions can be used as an independent variable to observe its effects on behavior. Here, for the first time, we investigated the modulatory effect of learned regulation of blood oxygenation level-dependent (BOLD) response in the left anterior insula on the perception of visual emotional stimuli. Methods: Three groups of participants (n = 27) were tested: two underwent four rtfMRI training sessions receiving either specific (n = 9) or unspecific feedback (n = 9) of the insula''s BOLD response, respectively, and one group used emotional imagery alone (n = 9) without rtfMRI feedback. During training, all groups were required to assess aversive and neutral pictures. Results: Participants able to significantly increase BOLD signal in the target region rated the aversive pictures more negatively. We measured a significant correlation between enhanced left anterior insula activity and increased negative valence ratings of the aversive stimuli. Control groups performing either rtfMRI training with unspecific feedback or an emotional imagery training alone were not able to significantly enhance activity in the left anterior insula and did not show changes in subjective emotional responses. Conclusions: This study corroborates traditional neuroimaging studies demonstrating a critical role of the anterior insula in the explicit appraisal of emotional stimuli and indicates the adopted approach as a potential tool for clinical applications in emotional disorders. [Copyright &y& Elsevier]

• Mapping of population receptive_fields within seconds for millions of voxels. • Online estimation of receptive_fields using gradient-descent. • Fast pRF parameter estimation for real-time applications. • Model-free approach for pRF mapping. Population receptive field (pRF) mapping is a popular tool in computational neuroimaging that allows for the investigation of receptive field properties, their topography and interrelations in health and disease. Furthermore, the possibility to invert population receptive fields provides a decoding model for constructing stimuli from observed cortical activation patterns. This has been suggested to pave the road towards pRF-based brain-computer interface (BCI) communication systems, which would be able to directly decode internally visualized letters from topographically organized brain activity. A major stumbling block for such an application is, however, that the pRF mapping procedure is computationally heavy and time consuming. To address this, we propose a novel and fast pRF mapping procedure that is suitable for real-time applications. The method is built upon hashed-Gaussian encoding of the stimulus, which tremendously reduces computational resources. After the stimulus is encoded, mapping can be performed using either ridge regression for fast offline analyses or gradient descent for real-time applications. We validate our model-agnostic approach in silico , as well as on empirical fMRI data obtained from 3T and 7T MRI scanners. Our approach is capable of estimating receptive fields and their parameters for millions of voxels in mere seconds. This method thus facilitates real-time applications of population receptive field mapping. [ABSTRACT FROM AUTHOR]

Abstract: In functional MRI (fMRI) complex experiments and applications require increasingly complex parameter handling as the experimental setup usually consists of separated soft- and hardware systems. Advanced real-time applications such as neurofeedback-based training or brain computer interfaces (BCIs) may even require adaptive changes of the paradigms and experimental setup during the measurement. This would be facilitated by an automated management of the overall workflow and a control of the communication between all experimental components. We realized a concept based on an XML software framework called Experiment Description Language (EDL). All parameters relevant for real-time data acquisition, real-time fMRI (rtfMRI) statistical data analysis, stimulus presentation, and activation processing are stored in one central EDL file, and processed during the experiment. A usability study comparing the central EDL parameter management with traditional approaches showed an improvement of the complete experimental handling. Based on this concept, a feasibility study realizing a dynamic rtfMRI-based brain computer interface showed that the developed system in combination with EDL was able to reliably detect and evaluate activation patterns in real-time. The implementation of a centrally controlled communication between the subsystems involved in the rtfMRI experiments reduced potential inconsistencies, and will open new applications for adaptive BCIs. [Copyright &y& Elsevier]

Abstract: Introduction: In patients with tumours in or near the motor cortex reliable intra-operative identification of the precentral gyrus can be difficult due to anatomical dislocation. Maps of functional magnetic resonance imaging (fMRI) based on the blood oxygen level dependent (BOLD) effect are used to localize eloquent functional areas of the brain but require postprocessing for reduction of false positive activations. We set the focus of this study on the evaluation of feasibility and clinical usefulness of using real-time fMRI t-maps without postprocessing for pre-operative planning and intra-operative localization of functional motor areas. Methods: Real-time fMRI t-maps from a 3-T MRI scanner were coregistered with MRI data. Ten patients were operated under general anaesthesia using 3D neuronavigation with integrated real-time fMRI t-maps. Surgical and functional outcome was compared to results of 12 patients who previously underwent wake surgeries. Results: Good neurological outcome was achieved in all treated patients. Main activation clusters on fMRI real-time maps were easily identified. Coregistered real-time fMRI data without additional postprocessing were useful in planning the surgical approach. However, due to brain shift and large voxel size of BOLD contrast signals on t-maps exact localization of borders between tumours and functional areas was not possible intra-operatively. Conclusion: Our method is very simple to use and effective in guiding the neurosurgeon safely through minimally invasive craniotomies to tumours in eloquent areas without setting lesions to functional areas. Furthermore, the neurosurgeon is more independent when tumour location requires acquisition of fMRI data for pre-operative planning and intra-operative navigation. [Copyright &y& Elsevier]

Abstract: Recent advances in functional magnetic resonance imaging (fMRI) data acquisition and processing techniques have made real-time fMRI (rtfMRI) of localized brain areas feasible, reliable and less susceptible to artefacts. Previous studies have shown that healthy subjects learn to control local brain activity with operant training by using rtfMRI-based neurofeedback. In the present study, we investigated whether healthy subjects could voluntarily gain control over right anterior insular activity. Subjects were provided with continuously updated information of the target ROI’s level of activation by visual feedback. All participants were able to successfully regulate BOLD-magnitude in the right anterior insular cortex within three sessions of 4 min each. Training resulted in a significantly increased activation cluster in the anterior portion of the right insula across sessions. An increased activity was also found in the left anterior insula but the percent signal change was lower than in the target ROI. Two different control conditions intended to assess the effects of non-specific feedback and mental imagery demonstrated that the training effect was not due to unspecific activations or non feedback-related cognitive strategies. Both control groups showed no enhanced activation across the sessions, which confirmed our main hypothesis that rtfMRI feedback is area-specific. The increased activity in the right anterior insula during training demonstrates that the effects observed are anatomically specific and self-regulation of right anterior insula only is achievable. This is the first group study investigating the volitional control of emotionally relevant brain region by using rtfMRI training and confirms that self-regulation of local brain activity with rtfMRI is possible. [Copyright &y& Elsevier]

Abstract: An fMRI technique based on real-time analysis was applied to evaluate the advantages of dynamic monitoring of the t-statistics based on a general linear model. The temporal change of the t-statistics in V1 and V4 under four conditions of visual stimuli covering different visual fields with or without coloring was estimated using an incremental analysis and a sliding window analysis (SWA). The SWA not only visualized the dynamic change of the activation in response to the task conditions and switching, but also enabled us to evaluate the temporal correlation of the t-statistics among the four visual areas. It was suggested that the activity in the V4 was bilaterally organized, and the altering color stimuli gave stronger stimulation to the V1 than did the black and white stimuli. Although the activation map at each time point represents the brain activity during several task and rest blocks, a SWA will be useful to evaluate the transition of neuronal activation in response to several sequential task conditions. An incremental analysis will be useful to monitor the ongoing activation in real-time during the scan, since it gives a higher t-value according to the accumulation of volume data. These two methods will be complementary. [Copyright &y& Elsevier]

Abstract: A voluntary head motion suppression method using feedback to subjects of their own head motion information is demonstrated. A real-time fMRI system was developed on standard MR imaging hardware for this purpose. The head motion information was simplified as a four-way arrow display that changed color from green to red when a composite head motion index went beyond a specified threshold. The arrow indicators were integrated into a version of the commonly used visual N-BACK task. Results suggest a significant suppression of head motion consistently in all subjects while the influence on task performance and brain activation was minimal. It is proposed that under certain experimental conditions, voluntary head motion suppression may feasibly be employed without significant compromise of fMRI data. [Copyright &y& Elsevier]