Neurofeedback (NF) is a research and clinical technique, characterized by live demonstration of brain activation to the subject. The technique has become increasingly popular as a tool for the training of brain self-regulation, fueled by the superiority in spatial resolution and fidelity brought along with real-time analysis of fMRI (functional magnetic resonance imaging) data, compared to the more traditional EEG (electroencephalography) approach. NF learning is a complex phenomenon and a controversial discussion on its feasibility and mechanisms has arisen in the literature. Critical aspects of the design of fMRI-NF studies include the localization of neural targets, cognitive and operant aspects of the training procedure, personalization of training, and the definition of training success, both through neural effects and (for studies with therapeutic aims) through clinical effects. In this paper, we argue that a developmental perspective should inform neural target selection particularly for pediatric populations, and different success metrics may allow in-depth analysis of NF learning. The relevance of the functional neuroanatomy of NF learning for brain target selection is discussed. Furthermore, we address controversial topics such as the role of strategy instructions, sometimes given to subjects in order to facilitate learning, and the timing of feedback. Discussion of these topics opens sight on problems that require further conceptual and empirical work, in order to improve the impact that fMRI-NF could have on basic and applied research in future.

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.

Sleep deprivation has been shown recently to alter emotional processing possibly associated with reduced frontal regulation. Such impairments can ultimately fail adaptive attempts to regulate emotional processing (also known as cognitive control of emotion), although this hypothesis has not been examined directly. Therefore, we explored the influence of sleep deprivation on the human brain using two different cognitive-emotional tasks, recorded using fMRI and EEG. Both tasks involved irrelevant emotional and neutral distractors presented during a competing cognitive challenge, thus creating a continuous demand for regulating emotional processing. Results reveal that, although participants showed enhanced limbic and electrophysiological reactions to emotional distractors regardless of their sleep state, they were specifically unable to ignore neutral distracting information after sleep deprivation. As a consequence, sleep deprivation resulted in similar processing of neutral and negative distractors, thus disabling accurate emotional discrimination. As expected, these findings were further associated with a decrease in prefrontal connectivity patterns in both EEG and fMRI signals, reflecting a profound decline in cognitive control of emotion. Notably, such a decline was associated with lower REM sleep amounts, supporting a role for REM sleep in overnight emotional processing. Altogether, our findings suggest that losing sleep alters emotional reactivity by lowering the threshold for emotional activation, leading to a maladaptive loss of emotional neutrality.

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

IntroductionThe scientific study of the role of cannabis in pain medicine still lags far behind the growing use driven by public approval. Accumulated clinical experience is therefore an important source of knowledge. However, no study to date has targeted physicians who actually use cannabis in their daily practice.MethodsRegistered, active, board-certified pain specialists in Israel (n=79) were asked to complete a Web-based survey. The survey was developed using the Qualtrics Online Survey Software. Questions were formulated as multiple-choice questions, and these addressed three areas of interest: 1) doctors’ personal experience; 2) the role of cannabis in pain medicine; and 3) cannabis medicalization and legalization.ResultsSixty-four percent of all practicing pain specialists in Israel responded. Almost all prescribe cannabis. Among them, 63% find cannabis moderately to highly effective, 56% have encountered mild or no side effects, and only 5% perceive it as significantly harmful. Common indications are neuropathic pain (65%), oncological pain (50%), arthralgias (25%), and any intractable pain (29%). Leading contraindications are schizophrenia (76%), pregnancy/breastfeeding (65%), and age <18 years (59%). Only 12% rated cannabis as more hazardous than opiates. On a personal note, 45% prefer cannabis for themselves or a family member. Lastly, 54% would like to see cannabis legalized in Israel.ConclusionIn this survey, pain clinicians experienced in prescribing cannabis over prolonged periods view it as an effective and relatively safe treatment for chronic pain, based on their own experience. Their responses suggest a possible change of paradigm from using cannabis as the last resort.

Volitional neural modulation using neurofeedback has been indicated as a potential treatment for chronic conditions that involve peripheral and central neural dysregulation. Here we utilized neurofeedback in patients suffering from Fibromyalgia-a chronic pain syndrome that involves sleep disturbance and emotion dysregulation. These ancillary symptoms, which have an amplification effect on pain, are known to be mediated by heightened limbic activity. In order to reliably probe limbic activity in a scalable manner fit for EEG-neurofeedback training, we utilized an Electrical Finger Print (EFP) model of amygdala-BOLD signal (termed Amyg-EFP), that has been successfully validated in our lab in the context of volitional neuromodulation. We anticipated that Amyg-EFP-neurofeedback training aimed at limbic down modulation should improve chronic pain in patients suffering from Fibromyalgia, by balancing disturbed indices for sleep and affect. We further expected that improved clinical status would correspond to successful training as indicated by improved down modulation of the Amygdala-EFP signal. Thirty-Four Fibromyalgia patients (31F; age 35.6±11.82) participated in a randomized placebo-controlled trial with biweekly Amyg-EFP-neurofeedback sessions and placebo of sham neurofeedback (n=9) for a total duration of five consecutive weeks. Following training, participants in the Real-neurofeedback group were divided into good (n=13) or poor (n=12) modulators according to their success in the neurofeedback training. Before and after treatment, self-reports on pain, depression, anxiety, fatigue and sleep quality were obtained, as well as objective sleep Indices. Long-term clinical follow-up was made available, within up to three years of the neurofeedback training completion. REM latency and objective sleep quality index were robustly improved following the treatment course only in the Real-neurofeedback group (both time*group p<0.05) and to a greater extent among good modulators (both time*sub-group p<0.05). In contrast, self-report measures did not reveal a treatment-specific response at the end of the treatment. However, the follow-up assessment revealed a delayed improvement

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