Search

Real-time functional magnetic resonance imaging (rt-fMRI) has revived the translational perspective of neurofeedback (NF)1. Particularly for stress management, targeting deeply located limbic areas involved in stress processing2 has paved new paths for brain-guided interventions. However, the high cost and immobility of fMRI constitute a challenging drawback for the scalability (accessibility and cost-effectiveness) of the approach, particularly for clinical purposes3. The current study aimed to overcome the limited applicability of rt-fMRI by using an electroencephalography (EEG) model endowed with improved spatial resolution, derived from simultaneous EEG–fMRI, to target amygdala activity (termed amygdala electrical fingerprint (Amyg-EFP))4–6. Healthy individuals (n = 180) undergoing a stressful military training programme were randomly assigned to six Amyg-EFP-NF sessions or one of two controls (control-EEG-NF or NoNF), taking place at the military training base. The results demonstrated specificity of NF learning to the targeted Amyg-EFP signal, which led to reduced alexithymia and faster emotional Stroop, indicating better stress coping following Amyg-EFP-NF relative to controls. Neural target engagement was demonstrated in a follow-up fMRI-NF, showing greater amygdala blood-oxygen-level-dependent downregulation and amygdala–ventromedial prefrontal cortex functional connectivity following Amyg-EFP-NF relative to NoNF. Together, these results demonstrate limbic specificity and efficacy of Amyg-EFP-NF during a stressful period, pointing to a scalable non-pharmacological yet neuroscience-based training to prevent stress-induced psychopathology. A new study by Keynan and colleagues provides evidence that training in amygdala self-regulation via EEG neurofeedback (‘electrical fingerprint’) results in neurobehavioural markers of stress resilience in a cohort of individuals undergoing military training.

Neuro-Feedback (NF) is a particular form of bio-feedback, which feeds back brain activity to the individual in real-time, to allow for training of controlled regulation of the brain in order to improve performance. Functional magnetic resonance imaging (fMRI) spatial localization allows for a high-quality real-time targeting of sub-cortical brain regions. Yet, until now, real-time-fMRI-NF treatments were limited to training brain activity localized within a region of interest. Conversely, since most mental functions are associated with functional integration of networks, limiting the treatment to one region may be a serious hurdle for an effective treatment. Thus, broader network perspective features can be of great value. In this study we developed a novel network-based rt-fMRI-NF procedure that obtains feedback derived from networks’ influence features as constructed by a graph theory method entitled the dependency network analysis (D\(_\mathrm{{EP}}\)NA), thus training the subject to control an explicit brain region’s influence on the network. In a proof-of-concept pilot study conducted on ten healthy subjects we demonstrated the feasibility of such a network based probe to be volitionally up-regulated. We further propose that this approach will ultimately provide a clinical therapeutic tool for an individually-tailored intervention protocol aimed at improving different mental processes and cognitive abilities.

Invasive brain–computer interfaces (BCI) provide better signal quality in terms of spatial localization, frequencies and signal/noise ratio, in addition to giving access to deep brain regions that play important roles in cognitive or affective processes. Despite some anecdotal attempts, little work has explored the possibility of integrating such BCI input into more sophisticated interactive systems like those which can be developed with game engines. In this article, we integrated an amygdala depth electrode recorder with a virtual environment controlling a virtual crowd. Subjects were asked to down regulate their amygdala using the level of unrest in the virtual room as feedback on how successful they were. We report early results which suggest that users adapt very easily to this paradigm and that the timing and fluctuations of amygdala activity during self-regulation can be matched by crowd animation in the virtual room. This suggests that depth electrodes could also serve as high-performance affective interfaces, notwithstanding their strictly limited availability, justified on medical grounds only.

The original and corrected Acknowledgements are shown in the accompanying Author Correction.

Recent evidence suggests that learned self-regulation of localized brain activity in deep limbic areas such as the amygdala, may alleviate symptoms of affective disturbances. Thus far self-regulation of amygdala activity could be obtained only via fMRI guided neurofeedback, an expensive and immobile procedure. EEG on the other hand is relatively inexpensive and can be easily implemented in any location. However the clinical utility of EEG neurofeedback for affective disturbances remains limited due to low spatial resolution, which hampers the targeting of deep limbic areas such as the amygdala. We introduce an EEG prediction model of amygdala activity from a single electrode. The gold standard used for training is the fMRI-BOLD signal in the amygdala during simultaneous EEG/fMRI recording. The suggested model is based on a time/frequency representation of the EEG data with varying time-delay. Previous work has shown a strong inhomogeneity among subjects as is reflected by the models created to predict the amygdala BOLD response from EEG data. In that work, different models were constructed for different subjects. In this work, we carefully analyzed the inhomogeneity among subjects and were able to construct a single model for the majority of the subjects. We introduce a method for inhomogeneity assessment. This enables us to demonstrate a choice of subjects for which a single model could be derived. We further demonstrate the ability to modulate brain-activity in a neurofeedback setting using feedback generated by the model. We tested the effect of the neurofeedback training by showing that new subjects can learn to down-regulate the signal amplitude compared to a sham group, which received a feedback obtained by a different participant. This EEG based model can overcome substantial limitations of fMRI-NF. It can enable investigation of NF training using multiple sessions and large samples in various locations.

The amygdala has a pivotal role in processing traumatic stress; hence, gaining control over its activity could facilitate adaptive mechanism and recovery. To date, amygdala volitional regulation could be obtained only via real-time functional magnetic resonance imaging (fMRI), a highly inaccessible procedure. The current article presents high-impact neurobehavioral implications of a novel imaging approach that enables bedside monitoring of amygdala activity using fMRI-inspired electroencephalography (EEG), hereafter termed amygdala-electrical fingerprint (amyg-EFP). Simultaneous EEG/fMRI indicated that the amyg-EFP reliably predicts amygdala-blood oxygen level-dependent activity. Implementing the amyg-EFP in neurofeedback demonstrated that learned downregulation of the amyg-EFP facilitated volitional downregulation of amygdala-blood oxygen level-dependent activity via real-time fMRI and manifested as reduced amygdala reactivity to visual stimuli. Behavioral evidence further emphasized the therapeutic potential of this approach by showing improved implicit emotion regulation following amyg-EFP neurofeedback. Additional EFP models denoting different brain regions could provide a library of localized activity for low-cost and highly accessible brain-based diagnosis and treatment.

International audience; The recent development of Brain-Computer Interfaces (BCI) for Virtual Worlds has resulted in a growing interest in realistic visual feedback. In this paper, we investigate the potential role of Virtual Agents in neurofeedback (NF) systems, which constitute an important paradigm for BCI. We discuss the potential of virtual agents to contribute to the success of NF in the specific context of affective BCI. Throughout the paper, we illustrate our presentation with two fully implemented NF prototypes featuring virtual agents. The first is an interactive narrative in which the user empathises with the feature character; the second, an emotion regulation system in which virtual crowd behaviour becomes a metaphor for arousal, as the user attempts to down-regulate their affective state.

The recent development of Brain-Computer Interfaces (BCI) to Virtual World has resulted in a growing interest in realistic visual feedback. In this paper, we investigate the potential role of Virtual Agents in neurofeedback systems, which constitute an important paradigm for BCI. We discuss the potential impact of virtual agents on some important determinants of neurofeedback in the context of affective BCI. Throughout the paper, we illustrate our presentation with two fully implemented neurofeedback prototypes featuring virtual agents: the first is an interactive narrative in which the user empathises with the character through neurofeedback; the second recreates a natural environment in which crowd behaviour becomes a metaphor for arousal and the user engages in emotional regulation.

This paper discusses the potential of Brain-Computer Interfaces based on neurofeedback methods to support emotional control and pursue the goal of emotional control as a mechanism for human augmentation in specific contexts. We illustrate this discussion through two proof-of-concept, fully-implemented experiments: one controlling disposition towards virtual characters using pre-frontal alpha asymmetry, and the other aimed at controlling arousal through activity of the amygdala. In the first instance, these systems are intended to explore augmentation technologies that would be incorporated into various media-based systems rather than permanently affect user behaviour.