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.
Real-time functional magnetic resonance imaging (rt-fMRI) has revived the translational perspective of neurofeedback (NF). Particularly for stress management, targeting deeply located limbic areas involved in stress processing 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 costeffectiveness) of the approach, particularly for clinical purposes. 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). 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 training 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 activity 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 nonpharmacological yet neuroscience-based training to prevent stress-induced psychopathology.
Functional MRI neurofeedback (NF) allows humans to self-modulate neural patterns in specific brain areas. This technique is regarded as a promising tool to translate neuroscientific knowledge into brain-guided psychiatric interventions. However, its clinical implementation is restricted by unstandardized methodological practices, by clinical definitions that are poorly grounded in neurobiology, and by lack of a unifying framework that dictates experimental choices. Here we put forward a new framework, termed 'process-based NF', which endorses a process-oriented characterization of mental dysfunctions to form precise and effective psychiatric treatments. This framework relies on targeting specific dysfunctional mental processes by modifying their underlying neural mechanisms and on applying process-specific contextual feedback interfaces. Finally, process-based NF offers designs and a control condition that address the methodological shortcomings of current approaches, thus paving the way for a precise and personalized neuromodulation. The use of functional MRI (fMRI) in neurofeedback (fMRI-NF) has brought new hope to the field of self-guided neuromodulation. fMRI-NF allows individuals to modulate spatially localized neural patterns in real-time, using contingent rewarding feedback. Accumulating evidence suggests that in many cases, attaining significant neural modulations in line with the task protocol (i.e., NF success) is followed by corresponding mental and behavioural changes1, thus contributing to bridging the gap between brain functionality and our mental experience. Despite this promising prospect, the utilization of fMRI-NF for basic science as well as for clinical purposes has been slower than expected. This may be due to various methodological constraints, such as the lack of proper control conditions and inadequate blinding and randomization, as well as the relatively small sample sizes that characterize the field. Furthermore, brain-guided interventions do not correspond with current psychiatric categorization, which traditionally relies on subjective reports rather than on
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.
Highlights Randomized clinical trial with a novel self-neuromodulation training in PTSD. Demonstration of feasibility of an fMRI-informed EEG model of Amygdala modulation (AmygEFP). Individually-tailored trauma-related content as the training feedback interface. Results showed reduction of PTSD symptoms following AmygEFP trauma-related feedback training.
In the past decade neurofeedback (NF) has become the focus of a growing body of research. With real-time functional magnetic resonance imaging (fMRI) enabling online monitoring of emotion-related areas, such as the amygdala, many have begun testing its therapeutic benefits. However, most existing NF procedures still use monotonic unimodal interfaces, thus possibly limiting user engagement and weakening learning efficiency. The current study tested a novel multi-sensory NF animated scenario (AS) aimed at enhancing user experience and improving learning. We examined whether relative to a simple uni-modal 2D interface, learning via an interface of complex multi-modal 3D scenario will result in improved NF learning. As a neural-probe, we used the recently developed fMRI-inspired EEG model of amygdala activity ("amygdala-EEG finger print"; amygdala-EFP), enabling low-cost and mobile limbic NF training. Amygdala-EFP was reflected in the AS by the unrest level of a hospital waiting room in which virtual characters become impatient, approach the admission desk and complain loudly. Successful downregulation was reflected as an ease in the room unrest level. We tested whether relative to a standard uni-modal 2D graphic thermometer (TM) interface, this AS could facilitate more effective learning and improve the training experience. Thirty participants underwent two separated NF sessions (1 week apart) practicing downregulation of the amygdala-EFP signal. In the first session, half trained via the AS and half via a TM interface. Learning efficiency was tested by three parameters: (a) effect size of the change in amygdala-EFP following training, (b) sustainability of the learned downregulation in the absence of online feedback, and (c) transferability to an unfamiliar context. Comparing amygdala-EFP signal amplitude between the last and the first NF trials revealed that the AS produced a higher effect size. In addition, NF via the AS showed better sustainability, as indicated by a no-feedback trial conducted in session 2 and better transferability to a new unfamiliar interface. Lastly, participants reported that the AS was more engaging and more motivating than the TM. Together, these results demonstrate the promising potential of integrating realistic virtual environments in NF to enhance learning and improve user's experience.
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-ofconcept, 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.
Socio-emotional encounters involve a resonance of others' affective states, known as affect sharing (AS); and attribution of mental states to others, known as theory-ofmind (ToM). Empathy necessitates the integration of both processes, yet their interaction during emotional episodes and subsequent generation of inferences on others' affective states has rarely been tested. To address this, we developed a novel experimental design, wherein we manipulated AS by presenting nonverbal emotionally negative movies twice-each time accompanied by one of two soundtracks that accentuated either somatic cues or externally generated sounds. Movies were followed by questions addressing affective-ToM (emotional inferences), cognitive-ToM (inferences on beliefs and knowledge), and non-ToM aspects. Results revealed a neural differentiation between AS, affective-ToM, and cognitive-ToM. AS movies activated regions that have been implicated in emotional (e.g., amygdala) and somatosensory processing, and synchronized brain activity between participants in the latter. Affective-ToM activated the middle insula, limbic regions, and both ventral and dorsal portions of the medial prefrontal cortex (ventral medial prefrontal cortex[VMPFC] and dorsal medial prefrontal cortex [DMPFC], respectively), whereas cognitive-ToM activated posteromedial and lateral-prefrontal and temporal cortices.Critically, AS movies specifically altered neural activation in AS and ToM-related regions during subsequent affective-ToM inferences, most notably in the DMPFC.Moreover, DMPFC-VMPFC connectivity correlated with affective-ToM accuracy, when such questions followed AS movies. Our results associate empathic processes with designated neural activations and shed light on how neuro-behavioral indices of affective ToM are shaped by preceding somatic engagement.
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