Neurofeedback: A promising tool for the self-regulation of emotion networks

Differential magnetic resonance neurofeedback modulations across extrinsic (visual) and intrinsic (default-mode) nodes of the human cortex

Previous advances in magnetic resonance imaging allow the analysis of blood oxygen level-dependent signals in real time, thus opening the possibility of feeding an index of these signals back to scanned human participants. However, it is still not known to what extent different cortical networks may differ in their sensitivity to such internally generated neurofeedback (NF). Here, we compare NF efficacy across six cortical regions including: early and high-order visual areas and the posterior parietal lobe, a prominent node of the default mode network (DMN). Our results reveal a consistent difference in NF activation across these areas. Sham controls ruled out a role of attention/arousal in these effects. These differences are suggestive of a relationship to the relative reliance on intrinsic information, moving from early visual cortex (lowest) to the DMN (highest). Interestingly, the visual parahippocampal place area showed NF activation closer to the DMN node. The results are compatible with the notion of the DMN as an intrinsically oriented system.

Manipulating motor performance and memory through real-time fMRI neurofeedback

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Neurofeedback training of motor cortex shortens reaction times.

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Self-regulation of parahippocampal cortex activity interferes with memory encoding.

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Differential neurofeedback reveals double dissociation between neurofeedback target areas.

Task performance depends on ongoing brain activity which can be influenced by attention, arousal, or motivation. However, such modulating factors of cognitive efficiency are unspecific, can be difficult to control, and are not suitable to facilitate neural processing in a regionally specific manner. Here, we non-pharmacologically manipulated regionally specific brain activity using technically sophisticated real-time fMRI neurofeedback. This was accomplished by training participants to simultaneously control ongoing brain activity in circumscribed motor and memory-related brain areas, namely the supplementary motor area and the parahippocampal cortex. We found that learned voluntary control over these functionally distinct brain areas caused functionally specific behavioral effects, i.e. shortening of motor reaction times and specific interference with memory encoding. The neurofeedback approach goes beyond improving cognitive efficiency by unspecific psychological factors such as attention, arousal, or motivation. It allows for directly manipulating sustained activity of task-relevant brain regions in order to yield specific behavioral or cognitive effects.

Neurofeedback and networks of depression

Recent advances in imaging technology and in the understanding of neural circuits relevant to emotion, motivation, and depression have boosted interest and experimental work in neuromodulation for affective disorders. Real-time functional magnetic resonance imaging (fMRI) can be used to train patients in the self regulation of these circuits, and thus complement existing neurofeedback technologies based on electroencephalography (EEG). EEG neurofeedback for depression has mainly been based on models of altered hemispheric asymmetry. fMRI-based neurofeedback (fMRI-NF) can utilize functional localizer scans that allow the dynamic adjustment of the target areas or networks for self-regulation training to individual patterns of emotion processing. An initial application of fMRI-NF in depression has produced promising clinical results, and further clinical trials are under way. Challenges lie in the design of appropriate control conditions for rigorous clinical trials, and in the transfer of neurofeedback protocols from the laboratory to mobile devices to enhance the sustainability of any clinical benefits.

Representation of cognitive reappraisal goals in frontal gamma oscillations

Recently, numerous efforts have been made to understand the neural mechanisms underlying cognitive regulation of emotion, such as cognitive reappraisal. Many studies have reported that cognitive control of emotion induces increases in neural activity of the control system, including the prefrontal cortex and the dorsal anterior cingulate cortex, and increases or decreases (depending upon the regulation goal) in neural activity of the appraisal system, including the amygdala and the insula. It has been hypothesized that information about regulation goals needs to be processed through interactions between the control and appraisal systems in order to support cognitive reappraisal. However, how this information is represented in the dynamics of cortical activity remains largely unknown. To address this, we investigated temporal changes in gamma band activity (35-55 Hz) in human electroencephalograms during a cognitive reappraisal task that was comprised of three reappraisal goals: to decease, maintain, or increase emotional responses modulated by affect-laden pictures. We examined how the characteristics of gamma oscillations, such as spectral power and large-scale phase synchronization, represented cognitive reappraisal goals. We found that left frontal gamma power decreased, was sustained, or increased when the participants suppressed, maintained, or amplified their emotions, respectively. This change in left frontal gamma power appeared during an interval of 1926 to 2453 ms after stimulus onset. We also found that the number of phase-synchronized pairs of gamma oscillations over the entire brain increased when participants regulated their emotions compared to when they maintained their emotions. These results suggest that left frontal gamma power may reflect cortical representation of emotional states modulated by cognitive reappraisal goals and gamma phase synchronization across whole brain regions may reflect emotional regulatory efforts to achieve these goals. Our study may provide the basis for an electroencephalogram-based neurofeedback system for the cognitive regulation of emotion.

Variability in Depressive Symptoms of Cognitive Deficit and Cognitive Bias During the First 2 Years After Diagnosis in Australian Men With Prostate Cancer

The incidence and contribution to total depression of the depressive symptoms of cognitive deficit and cognitive bias in prostate cancer (PCa) patients were compared from cohorts sampled during the first 2 years after diagnosis. Survey data were collected from 394 patients with PCa, including background information, treatments, and disease status, plus total scores of depression and scores for subscales of the depressive symptoms of cognitive bias and cognitive deficit via the Zung Self-Rating Depression Scale. The sample was divided into eight 3-monthly time-since-diagnosis cohorts and according to depression severity. Mean scores for the depressive symptoms of cognitive deficit were significantly higher than those for cognitive bias for the whole sample, but the contribution of cognitive bias to total depression was stronger than that for cognitive deficit. When divided according to overall depression severity, patients with clinically significant depression showed reversed patterns of association between the two subsets of cognitive symptoms of depression and total depression compared with those patients who reported less severe depression. Differences in the incidence and contribution of these two different aspects of the cognitive symptoms of depression for patients with more severe depression argue for consideration of them when assessing and diagnosing depression in patients with PCa. Treatment requirements are also different between the two types of cognitive symptoms of depression, and several suggestions for matching treatment to illness via a personalized medicine approach are discussed.

Online spatial normalization for real-time FMRI

Real-time functional magnetic resonance imaging (rtfMRI) is a recently emerged technique that demands fast data processing within a single repetition time (TR), such as a TR of 2 seconds. Data preprocessing in rtfMRI has rarely involved spatial normalization, which can not be accomplished in a short time period. However, spatial normalization may be critical for accurate functional localization in a stereotactic space and is an essential procedure for some emerging applications of rtfMRI. In this study, we introduced an online spatial normalization method that adopts a novel affine registration (AFR) procedure based on principal axes registration (PA) and Gauss-Newton optimization (GN) using the self-adaptive β parameter, termed PA-GN(β) AFR and nonlinear registration (NLR) based on discrete cosine transform (DCT). In AFR, PA provides an appropriate initial estimate of GN to induce the rapid convergence of GN. In addition, the β parameter, which relies on the change rate of cost function, is employed to self-adaptively adjust the iteration step of GN. The accuracy and performance of PA-GN(β) AFR were confirmed using both simulation and real data and compared with the traditional AFR. The appropriate cutoff frequency of the DCT basis function in NLR was determined to balance the accuracy and calculation load of the online spatial normalization. Finally, the validity of the online spatial normalization method was further demonstrated by brain activation in the rtfMRI data.

The covariation of independent and dependant variables in neurofeedback: a proposal framework to identify cognitive processes and brain activity variables

This methodological article proposes a framework for analysing the relationship between cognitive processes and brain activity using variables measured by neurofeedback (NF) carried out by functional Magnetic Resonance Imagery (fMRI NF). Cognitive processes and brain activity variables can be analysed as either the dependant variable or the independent variable. Firstly, we propose two traditional approaches, defined in the article as the "neuropsychological" approach (NP) and the "psychophysiology" approach (PP), to extract dependent and independent variables in NF protocols. Secondly, we suggest that NF can be inspired by the style of inquiry used in neurophenomenology. fMRI NF allows participants to experiment with his or her own cognitive processes and their effects on brain region of interest (ROI) activations simultaneously. Thus, we suggest that fMRI NF could be improved by implementing "the elicitation interview method", which allows the investigator to gather relevant verbatim from participants' introspection on subjective experiences.

Comparison of real-time water proton spectroscopy and echo-planar imaging sensitivity to the BOLD effect at 3 T and at 7 T

Gradient-echo echo-planar imaging (GE EPI) is the most commonly used approach to assess localized blood oxygen level dependent (BOLD) signal changes in real-time. Alternatively, real-time spin-echo single-voxel spectroscopy (SE SVS) has recently been introduced for spatially specific BOLD neurofeedback at 3 T and at 7 T. However, currently it is not known how neurofeedback based on real-time SE SVS compares to real-time GE EPI-based. We therefore compared both methods at high (3 T) and at ultra-high (7 T) magnetic field strengths. We evaluated standard quality measures of both methods for signals originating from the motor cortex, the visual cortex, and for a neurofeedback condition. At 3 T, the data quality of the real-time SE SVS and GE EPI R2* estimates were comparable. At 7 T, the data quality of the real-time GE EPI acquisitions was superior compared to those of the real-time SE SVS. Despite the somehow lower data quality of real-time SE SVS compared to GE EPI at 7 T, SE SVS acquisitions might still be an interesting alternative. Real-time SE SVS allows for a direct and subject-specific T2* estimation and thus for a physiologically more plausible neurofeedback signal.

Real-time FMRI neurofeedback training of amygdala activity in patients with major depressive disorder

BACKGROUND

Amygdala hemodynamic responses to positive stimuli are attenuated in major depressive disorder (MDD), and normalize with remission. Real-time functional MRI neurofeedback (rtfMRI-nf) offers a non-invasive method to modulate this regional activity. We examined whether depressed participants can use rtfMRI-nf to enhance amygdala responses to positive autobiographical memories, and whether this ability alters symptom severity.

METHODS

Unmedicated MDD subjects were assigned to receive rtfMRI-nf from either left amygdala (LA; experimental group, n = 14) or the horizontal segment of the intraparietal sulcus (HIPS; control group, n = 7) and instructed to contemplate happy autobiographical memories (AMs) to raise the level of a bar representing the hemodynamic signal from the target region to a target level. This 40s Happy condition alternated with 40s blocks of rest and counting backwards. A final Transfer run without neurofeedback information was included.

RESULTS

Participants in the experimental group upregulated their amygdala responses during positive AM recall. Significant pre-post scan decreases in anxiety ratings and increases in happiness ratings were evident in the experimental versus control group. A whole brain analysis showed that during the transfer run, participants in the experimental group had increased activity compared to the control group in left superior temporal gyrus and temporal polar cortex, and right thalamus.

CONCLUSIONS

Using rtfMRI-nf from the left amygdala during recall of positive AMs, depressed subjects were able to self-regulate their amygdala response, resulting in improved mood. Results from this proof-of-concept study suggest that rtfMRI-nf training with positive AM recall holds potential as a novel therapeutic approach in the treatment of depression.

Toward a new application of real-time electrophysiology: online optimization of cognitive neurosciences hypothesis testing

Brain-computer interfaces (BCIs) mostly rely on electrophysiological brain signals. Methodological and technical progress has largely solved the challenge of processing these signals online. The main issue that remains, however, is the identification of a reliable mapping between electrophysiological measures and relevant states of mind. This is why BCIs are highly dependent upon advances in cognitive neuroscience and neuroimaging research. Recently, psychological theories became more biologically plausible, leading to more realistic generative models of psychophysiological observations. Such complex interpretations of empirical data call for efficient and robust computational approaches that can deal with statistical model comparison, such as approximate Bayesian inference schemes. Importantly, the latter enable the optimization of a model selection error rate with respect to experimental control variables, yielding maximally powerful designs. In this paper, we use a Bayesian decision theoretic approach to cast model comparison in an online adaptive design optimization procedure. We show how to maximize design efficiency for individual healthy subjects or patients. Using simulated data, we demonstrate the face- and construct-validity of this approach and illustrate its extension to electrophysiology and multiple hypothesis testing based on recent psychophysiological models of perception. Finally, we discuss its implications for basic neuroscience and BCI itself.

Decoding of single-trial auditory mismatch responses for online perceptual monitoring and neurofeedback

Multivariate pattern classification methods are increasingly applied to neuroimaging data in the context of both fundamental research and in brain-computer interfacing approaches. Such methods provide a framework for interpreting measurements made at the single-trial level with respect to a set of two or more distinct mental states. Here, we define an approach in which the output of a binary classifier trained on data from an auditory mismatch paradigm can be used for online tracking of perception and as a neurofeedback signal. The auditory mismatch paradigm is known to induce distinct perceptual states related to the presentation of high- and low-probability stimuli, which are reflected in event-related potential (ERP) components such as the mismatch negativity (MMN). The first part of this paper illustrates how pattern classification methods can be applied to data collected in an MMN paradigm, including discussion of the optimization of preprocessing steps, the interpretation of features and how the performance of these methods generalizes across individual participants and measurement sessions. We then go on to show that the output of these decoding methods can be used in online settings as a continuous index of single-trial brain activation underlying perceptual discrimination. We conclude by discussing several potential domains of application, including neurofeedback, cognitive monitoring and passive brain-computer interfaces.

Adolescent depression: stress and reward dysfunction

After participating in this educational activity, the physician should be better able to 1. Evaluate the relationship between reward processes, stress, and depression. 2. Assess the characteristics of the three etiological models of stress and reward processes. 3. Identify the biological basis for stress and reward processes. Adolescence is a peak period for the onset of depression, and it is also a time marked by substantial stress as well as neural development within the brain reward circuitry. In this review, we provide a selective overview of current animal and human research investigating the relationship among reward processes, stress, and depression. Three separate, but related, etiological models examine the differential roles that stress may play in relation to reward dysfunction and adolescent depression. First, the reward mediation model suggests that both acute and chronic stress contribute to reward deficits, which, in turn, potentiate depressive symptoms or increase the risk for depression. Second, in line with the stress generation perspective, it is plausible that premorbid reward-related dysfunction generates stress--in particular, interpersonal stress--which then leads to the manifestation of depressive symptoms. Third, consistent with a diathesis-stress model, the interaction between stress and premorbid reward dysfunction may contribute to the onset of depression. Given the equifinal nature of depression, these models could shed important light on different etiological pathways during adolescence, particularly as they may relate to understanding the heterogeneity of depression. To highlight the translational potential of these insights, a hypothetical case study is provided as a means of demonstrating the importance of targeting reward dysfunction in both assessment and treatment of adolescent depression.

Diagnostic and therapeutic utility of neuroimaging in depression: an overview

A growing number of studies have used neuroimaging to further our understanding of how brain structure and function are altered in major depression. More recently, these techniques have begun to show promise for the diagnosis and treatment of depression, both as aids to conventional methods and as methods in their own right. In this review, we describe recent neuroimaging findings in the field that might aid diagnosis and improve treatment accuracy. Overall, major depression is associated with numerous structural and functional differences in neural systems involved in emotion processing and mood regulation. Furthermore, several studies have shown that the structure and function of these systems is changed by pharmacological and psychological treatments of the condition and that these changes in candidate brain regions might predict clinical response. More recently, "machine learning" methods have used neuroimaging data to categorize individual patients according to their diagnostic status and predict treatment response. Despite being mostly limited to group-level comparisons at present, with the introduction of new methods and more naturalistic studies, neuroimaging has the potential to become part of the clinical armamentarium and may improve diagnostic accuracy and inform treatment choice at the patient level.

Altered resting-state functional connectivity of basolateral and centromedial amygdala complexes in posttraumatic stress disorder

The amygdala is a major structure that orchestrates defensive reactions to environmental threats and is implicated in hypervigilance and symptoms of heightened arousal in posttraumatic stress disorder (PTSD). The basolateral and centromedial amygdala (CMA) complexes are functionally heterogeneous, with distinct roles in learning and expressing fear behaviors. PTSD differences in amygdala-complex function and functional connectivity with cortical and subcortical structures remain unclear. Recent military veterans with PTSD (n=20) and matched trauma-exposed controls (n=22) underwent a resting-state fMRI scan to measure task-free synchronous blood-oxygen level dependent activity. Whole-brain voxel-wise functional connectivity of basolateral and CMA seeds was compared between groups. The PTSD group had stronger functional connectivity of the basolateral amygdala (BLA) complex with the pregenual anterior cingulate cortex (ACC), dorsomedial prefrontal cortex, and dorsal ACC than the trauma-exposed control group (p<0.05; corrected). The trauma-exposed control group had stronger functional connectivity of the BLA complex with the left inferior frontal gyrus than the PTSD group (p<0.05; corrected). The CMA complex lacked connectivity differences between groups. We found PTSD modulates BLA complex connectivity with prefrontal cortical targets implicated in cognitive control of emotional information, which are central to explanations of core PTSD symptoms. PTSD differences in resting-state connectivity of BLA complex could be biasing processes in target regions that support behaviors central to prevailing laboratory models of PTSD such as associative fear learning. Further research is needed to investigate how differences in functional connectivity of amygdala complexes affect target regions that govern behavior, cognition, and affect in PTSD.

Real-time neurofeedback using functional MRI could improve down-regulation of amygdala activity during emotional stimulation: a proof-of-concept study

The amygdala is a central target of emotion regulation. It is overactive and dysregulated in affective and anxiety disorders and amygdala activity normalizes with successful therapy of the symptoms. However, a considerable percentage of patients do not reach remission within acceptable duration of treatment. The amygdala could therefore represent a promising target for real-time functional magnetic resonance imaging (rtfMRI) neurofeedback. rtfMRI neurofeedback directly improves the voluntary regulation of localized brain activity. At present, most rtfMRI neurofeedback studies have trained participants to increase activity of a target, i.e. up-regulation. However, in the case of the amygdala, down-regulation is supposedly more clinically relevant. Therefore, we developed a task that trained participants to down-regulate activity of the right amygdala while being confronted with amygdala stimulation, i.e. negative emotional faces. The activity in the functionally-defined region was used as online visual feedback in six healthy subjects instructed to minimize this signal using reality checking as emotion regulation strategy. Over a period of four training sessions, participants significantly increased down-regulation of the right amygdala compared to a passive viewing condition to control for habilitation effects. This result supports the concept of using rtfMRI neurofeedback training to control brain activity during relevant stimulation, specifically in the case of emotion, and has implications towards clinical treatment of emotional disorders.

Self-regulation of the anterior insula: Reinforcement learning using real-time fMRI neurofeedback

The anterior insula (AI) plays a key role in affective processing, and insular dysfunction has been noted in several clinical conditions. Real-time functional MRI neurofeedback (rtfMRI-NF) provides a means of helping people learn to self-regulate activation in this brain region. Using the Blood Oxygenated Level Dependant (BOLD) signal from the right AI (RAI) as neurofeedback, we trained participants to increase RAI activation. In contrast, another group of participants was shown 'control' feedback from another brain area. Pre- and post-training affective probes were shown, with subjective ratings and skin conductance response (SCR) measured. We also investigated a reward-related reinforcement learning model of rtfMRI-NF. In contrast to the controls, we hypothesised a positive linear increase in RAI activation in participants shown feedback from this region, alongside increases in valence ratings and SCR to affective probes. Hypothesis-driven analyses showed a significant interaction between the RAI/control neurofeedback groups and the effect of self-regulation. Whole-brain analyses revealed a significant linear increase in RAI activation across four training runs in the group who received feedback from RAI. Increased activation was also observed in the caudate body and thalamus, likely representing feedback-related learning. No positive linear trend was observed in the RAI in the group receiving control feedback, suggesting that these data are not a general effect of cognitive strategy or control feedback. The control group did, however, show diffuse activation across the putamen, caudate and posterior insula which may indicate the representation of false feedback. No significant training-related behavioural differences were observed for valence ratings, or SCR. In addition, correlational analyses based on a reinforcement learning model showed that the dorsal anterior cingulate cortex underpinned learning in both groups. In summary, these data demonstrate that it is possible to regulate the RAI using rtfMRI-NF within one scanning session, and that such reward-related learning is mediated by the dorsal anterior cingulate.

Prefrontal control of the amygdala during real-time fMRI neurofeedback training of emotion regulation

We observed in a previous study (PLoS ONE 6:e24522) that the self-regulation of amygdala activity via real-time fMRI neurofeedback (rtfMRI-nf) with positive emotion induction was associated, in healthy participants, with an enhancement in the functional connectivity between the left amygdala (LA) and six regions of the prefrontal cortex. These regions included the left rostral anterior cingulate cortex (rACC), bilateral dorsomedial prefrontal cortex (DMPFC), bilateral superior frontal gyrus (SFG), and right medial frontopolar cortex (MFPC). Together with the LA, these six prefrontal regions thus formed the functional neuroanatomical network engaged during the rtfMRI-nf procedure. Here we perform a structural vector autoregression (SVAR) analysis of the effective connectivity for this network. The SVAR analysis demonstrates that the left rACC plays an important role during the rtfMRI-nf training, modulating the LA and the other network regions. According to the analysis, the rtfMRI-nf training leads to a significant enhancement in the time-lagged effect of the left rACC on the LA, potentially consistent with the ipsilateral distribution of the monosynaptic projections between these regions. The training is also accompanied by significant increases in the instantaneous (contemporaneous) effects of the left rACC on four other regions – the bilateral DMPFC, the right MFPC, and the left SFG. The instantaneous effects of the LA on the bilateral DMPFC are also significantly enhanced. Our results are consistent with a broad literature supporting the role of the rACC in emotion processing and regulation. Our exploratory analysis provides, for the first time, insights into the causal relationships within the network of regions engaged during the rtfMRI-nf procedure targeting the amygdala. It suggests that the rACC may constitute a promising target for rtfMRI-nf training along with the amygdala in patients with affective disorders, particularly posttraumatic stress disorder (PTSD).

Experiencing your brain: neurofeedback as a new bridge between neuroscience and phenomenology

Neurophenomenology is a scientific research program aimed to combine neuroscience with phenomenology in order to study human experience. Nevertheless, despite several explicit implementations, the integration of first-person data into the experimental protocols of cognitive neuroscience still faces a number of epistemological and methodological challenges. Notably, the difficulties to simultaneously acquire phenomenological and neuroscientific data have limited its implementation into research projects. In our paper, we propose that neurofeedback paradigms, in which subjects learn to self-regulate their own neural activity, may offer a pragmatic way to integrate first-person and third-person descriptions. Here, information from first- and third-person perspectives is braided together in the iterative causal closed loop, creating experimental situations in which they reciprocally constrain each other. In real-time, the subject is not only actively involved in the process of data acquisition, but also assisted to directly influence the neural data through conscious experience. Thus, neurofeedback may help to gain a deeper phenomenological-physiological understanding of downward causations whereby conscious activities have direct causal effects on neuronal patterns. We discuss possible mechanisms that could mediate such effects and indicate a number of directions for future research.

Modulation of functionally localized right insular cortex activity using real-time fMRI-based neurofeedback

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.

The role of peer rejection in adolescent depression

Adolescence is a period of major risk for depression, which is associated with negative personal, social, and educational outcomes. Yet, in comparison to adult models of depression, very little is known about the specific psychosocial stressors that contribute to adolescent depression, and whether these can be targeted by interventions. In this review, we consider the role of peer rejection. First, we present a comprehensive review of studies using innovative experimental paradigms to understand the role of peer rejection in adolescent depression. We show how reciprocal relationships between peer rejection and depressive symptoms across adolescence powerfully shape and maintain maladaptive trajectories. Second, we consider how cognitive biases and their neurobiological substrates may explain why some adolescents are more vulnerable to the effects of, and perhaps exposure to, peer rejection compared to others. Finally, we draw attention to emerging cognitive and functional magnetic resonance imaging-based neurofeedback training, which by modifying aspects of information processing may promote more adaptive responses to peer rejection. A better understanding of the mechanisms underlying adolescent depression may not only alleviate symptoms during a period of substantial developmental challenges, but may also reduce the burden of the disorder across the lifespan.

Differences in neurobiological pathways of four "clinical content" subtypes of depression

Although often considered as a mental disorder, depression is best described as a behavioral-neurobiological phenomenon. In addition, although usually reported as a unitary diagnosis, major depressive episode is composed of a range of different symptoms that can occur in nearly 1500 possible combinations to fulfill the required diagnostic criterion. To investigate and describe the underlying behavioral and neurobiological substrates of these symptoms, they were clustered into "clinical content" subtypes of depression according to their predominant common behavioral characteristics. These subtypes were then found to possess different neurobiological pathways that argue for different treatment approaches.

Improving the neural mechanisms of cognition through the pursuit of happiness

This paper reviews evidence on the neural basis of how positive mood states can modulate cognition, particularly during creative problem-solving. Studies performed over the past few decades demonstrate that individuals in a positive mood engage in a broader scope of attention, enhancing their access to distant and unusual semantic associations, and increasing task-shifting and problem-solving capacities. In this review, we summarize these behavioral studies; we then present recent findings on the changes in brain activation patterns that are induced by a positive mood when participants engage in problem-solving tasks and show how these relate to task performance. Additionally, we integrate findings on the neuromodulatory influence of positive mood on cognition as mediated by dopaminergic signaling in the prefrontal cortex and we describe how this system can go awry during pathological states of elevated mood as in mania. Finally, we describe current and future research directions using psychotherapeutic and real-time fMRI neurofeedback approaches to up-regulate positive mood and facilitate optimal creative cognitive performance. We conclude with some speculations on the clinical implications of this emerging area of research.

Real-time fMRI neurofeedback: progress and challenges

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.

The day-after effect: long term, Hebbian-like restructuring of resting-state fMRI patterns induced by a single epoch of cortical activation

During rest, the cerebral cortex displays rich, coordinated patterns of spontaneous activity. The mechanism that shapes these patterns is largely unknown. Here we demonstrate that a Hebbian-like, sustained process plays a role in focusing these coherent patterns. Human subjects used an fMRI-based neurofeedback (NF) paradigm to intensely activate the dorsal anterior cingulate cortex for a single epoch (30 min). Resting-state correlations between all of the cortical voxels' BOLD time courses (functional connectivity) were mapped before, immediately after, and one day after the NF session. We found that the single epoch of cortical activation induced a lasting restructuring of the functional connections according to a Hebbian-like rule. Therefore, the change (increase and decrease) in functional connectivity strength of cortical voxels during rest reflected the level of their prior coactivation during the NF epoch. Interestingly, the effect was significantly enhanced 1 d after the NF activation epoch. The effect was evident in each subject individually, indicating its potential as a diagnostic window into the personal history of prior brain activations of both healthy and abnormal individuals.

Decoding speech perception by native and non-native speakers using single-trial electrophysiological data

Brain-computer interfaces (BCIs) are systems that use real-time analysis of neuroimaging data to determine the mental state of their user for purposes such as providing neurofeedback. Here, we investigate the feasibility of a BCI based on speech perception. Multivariate pattern classification methods were applied to single-trial EEG data collected during speech perception by native and non-native speakers. Two principal questions were asked: 1) Can differences in the perceived categories of pairs of phonemes be decoded at the single-trial level? 2) Can these same categorical differences be decoded across participants, within or between native-language groups? Results indicated that classification performance progressively increased with respect to the categorical status (within, boundary or across) of the stimulus contrast, and was also influenced by the native language of individual participants. Classifier performance showed strong relationships with traditional event-related potential measures and behavioral responses. The results of the cross-participant analysis indicated an overall increase in average classifier performance when trained on data from all participants (native and non-native). A second cross-participant classifier trained only on data from native speakers led to an overall improvement in performance for native speakers, but a reduction in performance for non-native speakers. We also found that the native language of a given participant could be decoded on the basis of EEG data with accuracy above 80%. These results indicate that electrophysiological responses underlying speech perception can be decoded at the single-trial level, and that decoding performance systematically reflects graded changes in the responses related to the phonological status of the stimuli. This approach could be used in extensions of the BCI paradigm to support perceptual learning during second language acquisition.

Volitional reduction of anterior cingulate cortex activity produces decreased cue craving in smoking cessation: a preliminary real-time fMRI study

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.

Neuroimaging methods for adolescent substance use disorder prevention science

Magnetic resonance imaging (MRI) methods safely provide in vivo indicators of cerebral macrostructure, microstructure, and activation that can be examined in relation to substance use disorder (SUD) risks and effects. This article will provide an overview of MRI approaches, including volumetric measures, diffusion tensor imaging, and functional MRI, that have been applied to studies of adolescent neuromaturation in relationship to risk phenotypes and adolescent SUD. To illustrate these applications, examples of research findings will be presented. MRI indicators have demonstrated that neurobiological maturation continues throughout adolescence. MRI research has suggested that variations in neurobiological maturation may contribute to SUD risk, and that substance use adversely influences adolescent brain development. Directly measured neurobiological variables may be viable preventive intervention targets and outcome indicators. Further research is needed to provide definitive findings on neurodevelopmental immaturity as an SUD risk and to determine the directions such observations suggest for advancing prevention science.

Plasticity during childhood and adolescence: innovative approaches to investigating neurocognitive development

Adolescence is a period of profound change, which holds substantial developmental milestones, but also unique challenges to the individual. In this opinion paper, we highlight the potential of combining two recently developed behavioural and neural training techniques (cognitive bias modification and functional magnetic neuroimaging-based neurofeedback) into a research approach that could help make the most of increased levels of plasticity during childhood and adolescence. We discuss how this powerful combination could be used to explore changing brain-behaviour relationships throughout development in the context of emotion processing, a cognitive domain that exhibits continuous development throughout the second decade of life. By targeting both behaviour and brain response, we would also be in an excellent position to define sensible time windows for enhancing plasticity, thereby allowing for targeted intervention approaches that can help improve emotion processing in both typically and atypically developing populations.

Real-time automated spectral assessment of the BOLD response for neurofeedback at 3 and 7T

Echo-planar imaging is the dominant functional MRI data acquisition scheme for evaluating the BOLD signal. To date, it remains the only approach providing neurofeedback from spatially localized brain activity. Real-time functional single-voxel proton spectroscopy (fSVPS) may be an alternative for spatially specific BOLD neurofeedback at 7T because it allows for a precise estimation of the local T2* signal, EPI-specific artifacts may be avoided, and the signal contrast may increase. In order to explore and optimize this alternative neurofeedback approach, we tested fully automated real-time fSVPS spectral estimation procedures to approximate T2* BOLD signal changes from the unsuppressed water peak, i.e. lorentzian non-linear complex spectral fit (LNLCSF) in frequency and frequency-time domain. The proposed approaches do not require additional spectroscopic localizers in contrast to conventional T2* approximation based on linear regression of the free induction decay (FID). For methods comparison, we evaluated quality measures for signals from the motor and the visual cortex as well as a real-time feedback condition at high (3T) and at ultra-high (7T) magnetic field strengths. Using these methods, we achieved reliable and fast water peak spectral parameter estimations. At 7T, we observed an absolute increase of spectra line narrowing due to the BOLD effect, but quality measures did not improve due to artifactual line broadening. Overall, the automated fSVPS approach can be used to assess dynamic spectral changes in real-time, and to provide localized T2* neurofeedback at 3 and 7T.

Pattern classification of valence in depression

Neuroimaging biomarkers of depression have potential to aid diagnosis, identify individuals at risk and predict treatment response or course of illness. Nevertheless none have been identified so far, potentially because no single brain parameter captures the complexity of the pathophysiology of depression. Multi-voxel pattern analysis (MVPA) may overcome this issue as it can identify patterns of voxels that are spatially distributed across the brain. Here we present the results of an MVPA to investigate the neuronal patterns underlying passive viewing of positive, negative and neutral pictures in depressed patients. A linear support vector machine (SVM) was trained to discriminate different valence conditions based on the functional magnetic resonance imaging (fMRI) data of nine unipolar depressed patients. A similar dataset obtained in nine healthy individuals was included to conduct a group classification analysis via linear discriminant analysis (LDA). Accuracy scores of 86% or higher were obtained for each valence contrast via patterns that included limbic areas such as the amygdala and frontal areas such as the ventrolateral prefrontal cortex. The LDA identified two areas (the dorsomedial prefrontal cortex and caudate nucleus) that allowed group classification with 72.2% accuracy. Our preliminary findings suggest that MVPA can identify stable valence patterns, with more sensitivity than univariate analysis, in depressed participants and that it may be possible to discriminate between healthy and depressed individuals based on differences in the brain's response to emotional cues.

Connectivity-based neurofeedback: dynamic causal modeling for real-time fMRI

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.

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We adapt DCM for use in neurofeedback experiments.

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Participants can control a DCM-based neurofeedback signal.

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Real-time DCM allows for voluntary control over brain connectivity.

Improving visual perception through neurofeedback

Perception depends on the interplay of ongoing spontaneous activity and stimulus-evoked activity in sensory cortices. This raises the possibility that training ongoing spontaneous activity alone might be sufficient for enhancing perceptual sensitivity. To test this, we trained human participants to control ongoing spontaneous activity in circumscribed regions of retinotopic visual cortex using real-time functional MRI-based neurofeedback. After training, we tested participants using a new and previously untrained visual detection task that was presented at the visual field location corresponding to the trained region of visual cortex. Perceptual sensitivity was significantly enhanced only when participants who had previously learned control over ongoing activity were now exercising control and only for that region of visual cortex. Our new approach allows us to non-invasively and non-pharmacologically manipulate regionally specific brain activity and thus provide "brain training" to deliver particular perceptual enhancements.

Deconstructing the familiality of variability in momentary negative and positive affect

OBJECTIVE

The daily life, affective phenotypes of momentary negative affect (NA), positive affect (PA) variability and NA variability are associated with future depressive symptomatology. This study investigates the extent to which genetic and environmental factors contribute to the inter-individual differences in these daily life, affective phenotypes.

METHOD

Two hundred and seventy-nine female twins from the Flemish (Belgium) general population participated in an experience sampling study measuring affect in daily life. Structural equation modelling was used to fit univariate and bivariate models.

RESULTS

Genetic factors explained, respectively, 18%, 18% and 35% of the inter-individual differences in momentary NA, PA variability and NA variability. Non-shared environmental factors were found to explain the remaining inter-individual variation. In addition, 41% of the association between positive and NA variability was attributed to shared genetic factors.

CONCLUSION

Results of this study show that daily life patterns of affective expression are subject to substantial environmental influence. Prospective assessments of the effect of interventions on these expressions may therefore represent a powerful tool to prevent transition from subclinical depressive symptomatology to a clinical outcome or to reduce symptomatology in those with clinical depression.

WITHDRAWN: Neurofeedback-mediated self-regulation of the dopaminergic midbrain

The publisher regrets due to an error in the publishing process the above articles was accidentally withdrawn and has now been published in (Neuroimage 83C December 2013 https://doi.org/10.1016/j.neuroimage.2013.05.115). The publisher would like to apologize for any inconvenience caused.

Neural correlates of the behavioral-autonomic interaction response to potentially threatening stimuli

Subjective assessment of emotional valence is typically associated with both brain activity and autonomic arousal. Accurately assessing emotional salience is particularly important when perceiving threat. We sought to characterize the neural correlates of the interaction between behavioral and autonomic responses to potentially threatening visual and auditory stimuli. Twenty-five healthy male subjects underwent fMRI scanning whilst skin conductance responses (SCR) were recorded. One hundred and eighty pictures, sentences, and sounds were assessed as “harmless” or “threatening.” Individuals' stimulus-locked, phasic SCRs and trial-by-trial behavioral assessments were entered as regressors into a flexible factorial design to establish their separate autonomic and behavioral neural correlates, and convolved to examine psycho-autonomic interaction (PAI) effects. Across all stimuli, “threatening,” compared with “harmless” behavioral assessments were associated with mainly frontal and precuneus activation with specific within-modality activations including bilateral parahippocampal gyri (pictures), bilateral anterior cingulate cortex (ACC) and frontal pole (sentences), and right Heschl's gyrus and bilateral temporal gyri (sounds). Across stimulus modalities SCRs were associated with activation of parieto-occipito-thalamic regions, an activation pattern which was largely replicated within-modality. In contrast, PAI analyses revealed modality-specific activations including right fusiform/parahippocampal gyrus (pictures), right insula (sentences), and mid-cingulate gyrus (sounds). Phasic SCR activity was positively correlated with an individual's propensity to assess stimuli as “threatening.” SCRs may modulate cognitive assessments on a “harmless–threatening” dimension, thereby modulating affective tone and hence behavior.

Integrative neuroimaging in mood disorders

PURPOSE OF REVIEW

Neuroimaging has become a central technique of biological psychiatry and is uniquely suited to assess functional and structural brain changes in psychiatric patients in vivo. In this review, we highlight several recent developments that may enable the transition of psychiatric neuroimaging from laboratory to clinic.

RECENT FINDINGS

We describe recent trends in refining imaging techniques for brain microstructure (diffusion imaging) and neurochemistry (magnetic resonance spectroscopy of neurotransmitters and metabolites) and their application to patients with mood disorders and individuals at risk, such as first-degree relatives. We also survey recent progress in imaging-guided deep brain stimulation (DBS), imaging-based (neurofeedback) therapies and studies looking at their convergent anatomical targets. These new interventional techniques, which aim to modulate brain circuits of emotion and motivation highlighted by functional imaging studies, have shown promising effects in several small studies.

SUMMARY

The mapping of brain patterns associated with risk to develop mood disorders may pave the way for diagnostic/prognostic applications of neuroimaging. The neuromodulation techniques of DBS and neurofeedback, which target dysfunctional or compensatory circuits identified by functional imaging, may take neuroimaging into a new, therapeutic domain.

NIRS-based neurofeedback learning systems for controlling activity of the prefrontal cortex

The aim of this study was to develop a NIRS-based neurofeedback system to modulate activity in the prefrontal cortex (PFC). We evaluated the effectiveness of the system in terms of separability of changes in oxy-Hb and its derivative. Training with neurofeedback resulted in higher separability than training without neurofeedback or no training, suggesting that the neurofeedback system could enhance self-control of PFC activity. Interestingly, the dorsolateral PFC exhibited enhanced activity and high separability after neurofeedback training. These observations suggest that the neurofeedback system might be useful for training subjects to regulate emotions by self-control of dorsolateral PFC activity.

Enactive cinema paves way for understanding complex real-time social interaction in neuroimaging experiments

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.

Real-time self-regulation of emotion networks in patients with depression

Many patients show no or incomplete responses to current pharmacological or psychological therapies for depression. Here we explored the feasibility of a new brain self-regulation technique that integrates psychological and neurobiological approaches through neurofeedback with functional magnetic resonance imaging (fMRI). In a proof-of-concept study, eight patients with depression learned to upregulate brain areas involved in the generation of positive emotions (such as the ventrolateral prefrontal cortex (VLPFC) and insula) during four neurofeedback sessions. Their clinical symptoms, as assessed with the 17-item Hamilton Rating Scale for Depression (HDRS), improved significantly. A control group that underwent a training procedure with the same cognitive strategies but without neurofeedback did not improve clinically. Randomised blinded clinical trials are now needed to exclude possible placebo effects and to determine whether fMRI-based neurofeedback might become a useful adjunct to current therapies for depression.

Brain-computer interfaces: a neuroscience paradigm of social interaction? A matter of perspective

A number of recent studies have put human subjects in true social interactions, with the aim of better identifying the psychophysiological processes underlying social cognition. Interestingly, this emerging Neuroscience of Social Interactions (NSI) field brings up challenges which resemble important ones in the field of Brain-Computer Interfaces (BCI). Importantly, these challenges go beyond common objectives such as the eventual use of BCI and NSI protocols in the clinical domain or common interests pertaining to the use of online neurophysiological techniques and algorithms. Common fundamental challenges are now apparent and one can argue that a crucial one is to develop computational models of brain processes relevant to human interactions with an adaptive agent, whether human or artificial. Coupled with neuroimaging data, such models have proved promising in revealing the neural basis and mental processes behind social interactions. Similar models could help BCI to move from well-performing but offline static machines to reliable online adaptive agents. This emphasizes a social perspective to BCI, which is not limited to a computational challenge but extends to all questions that arise when studying the brain in interaction with its environment.