Integration of limbic self-neuromodulation with psychotherapy for complex post-traumatic stress disorder: treatment rationale and case study.

Treatment Rationale: Exposure to repeated sexual trauma, particularly during childhood, often leads to protracted mental health problems. Childhood adversity is specifically associated with complex posttraumatic stress disorder (PTSD) presentation, which is particularly tenacious and treatment refractory, and features severe emotion dysregulation. Augmentation approaches have been suggested to enhance treatment efficacy in PTSD thus integrating first-line psychotherapy with mechanistically informed self-neuromodulation procedures (i.e. neurofeedback) may pave the way to enhanced clinical outcomes. A central neural mechanism of PTSD and emotion dysregulation involves amygdala hyperactivity that can be volitionally regulated by neurofeedback. We outline a treatment rationale that includes a detailed justification for the potential of combining psychotherapy and NF and delineate mechanisms of change. We illustrate key processes of reciprocal interactions between neurofeedback engagement and therapeutic goals.Case Study: We describe a clinical case of a woman with complex PTSD due to early and repetitive childhood sexual abuse using adjunctive neurofeedback as an augmentation to an ongoing, stable, traditional treatment plan. The woman participated in (a) ten sessions of neurofeedback by the use of an fMRI-inspired EEG model of limbic related activity (Amygdala Electrical-Finger-Print; AmygEFP-NF), (b) traditional weekly individual psychotherapy, (c) skills group. Before and after NF training period patient was blindly assessed for PTSD symptoms, followed by a 1, 3- and 6-months self-report follow-up. We demonstrate mechanisms of change as well as the clinical effectiveness of adjunctive treatment as indicated by reduced PTSD symptoms and improved daily functioning within this single case.Conclusions: We outline an integrative neuropsychological framework for understanding the unique mechanisms of change conferring value to conjoining NF applications with trauma-focused psychotherapy in complex PTSD.

Self-neuromodulation procedures that regulates limbic-related activity in adjunction to therapy show clinical effectivity in complex PTSD.We present an integrative perspective of neurofeedback embedded in psychotherapy, illustrated by a single case report.A single case provides an illustration of the potential utility of multifaced treatment including psychotherapy with adjunctive neurofeedback.

Amygdala-related electroencephalogram neurofeedback as add-on therapy for treatment-resistant childhood sexual abuse posttraumatic stress disorder: feasibility study.

AIM: Childhood sexual abuse (CSA) among women is an alarmingly prevalent traumatic experience that often leads to debilitating and treatment-refractory posttraumatic stress disorder (PTSD), raising the need for novel adjunctive therapies. Neuroimaging investigations systematically report that amygdala hyperactivity is the most consistent and reliable neural abnormality in PTSD and following childhood abuse, raising the potential of implementing volitional neural modulation using neurofeedback (NF) aimed at down-regulating amygdala activity. This study aimed to reliably probe limbic activity but overcome the limited applicability of functional magnetic resonance imaging (fMRI) NF by using a scalable electroencephalogram NF probe of amygdala-related activity, termed amygdala electrical-finger-print (amyg-EFP) in a randomized controlled trial. METHOD: Fifty-five women with CSA-PTSD who were in ongoing intensive trauma-focused psychotherapy for a minimum of 1 year but still met Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) PTSD criteria were randomized to either 10 add-on sessions of amyg-EFP-NF training (test group) or continuing psychotherapy (control group). Participants were blindly assessed for PTSD symptoms before and after the NF training period, followed by self-reported clinical follow-up at 1, 3, and 6 months, as well as one session of amygdala real-time fMRI-NF before and after NF training period. RESULTS: Participants in the test group compared with the control group demonstrated a marginally significant immediate reduction in PTSD symptoms, which progressively improved during the follow-up period. In addition, successful neuromodulation during NF training was demonstrated. CONCLUSION: This feasibility study for patients with treatment-resistant CSA-PTSD indicates that amyg-EFP-NF is a viable and efficient intervention.

Pilot Study of Prism EFP NeuroFeedback in Adult ADHD.

OBJECTIVE: A pilot study to preliminarily examine the effects of Prism EFP NeuroFeedback (NF) in adult ADHD. METHOD: Prism EFP NF is a form of NF specifically designed to target emotional dysregulation (ED) through down regulation of amygdala activity. Prism EFP NF has been shown to improve other disorders with significant ED. Nine participants with adult ADHD received an open trial of Prism EFP NF consisting of fifteen sessions over 8 weeks; all completed at least 5 weeks of treatment with seven completing all 8 weeks. Outcomes were assessed by change in ADHD symptoms from baseline to End of Treatment. RESULTS: About two-third reduction was seen in total DSM ADHD symptom scores (primary outcome measure) with improvement observed in all other clinical measures. No significant adverse events were seen. CONCLUSION: This preliminary trial found substantial effects of Prism EFP NF on ADHD/ED symptoms and global impairment.

Limbic self-neuromodulation as a novel treatment option for emotional dysregulation in premenstrual dysphoric disorder (PMDD); a proof-of-concept study.

AIM: To assess the efficacy of a novel neurofeedback (NF) method, targeting limbic activity, to treat emotional dysregulation related to premenstrual dysphoric disorder (PMDD). METHODS: We applied a NF probe targeting limbic activity using a functional magnetic resonance imaging-inspired electroencephalogram model (termed Amyg-EFP-NF) in a double-blind randomized controlled trial. A frontal alpha asymmetry probe (AAS-NF), served as active control. Twenty-seven participants diagnosed with PMDD (mean age = 33.57 years, SD = 5.67) were randomly assigned to Amyg-EFP-NF or AAS-NF interventions with a 2:1 ratio, respectively. The treatment protocol consisted of 11 NF sessions through three menstrual cycles, and a follow-up assessment 3 months thereafter. The primary outcome measure was improvement in the Revised Observer Version of the Premenstrual Tension Syndrome Rating Scale (PMTS-OR). RESULTS: A significant group by time effect was observed for the core symptom subscale of the PMTS-OR, with significant improvement observed at follow-up for the Amyg-EFP group compared with the AAS group [F(1, 15)=4.968, P = 0.042]. This finding was specifically robust for reduction in anger [F(1, 15) = 22.254, P < 0.001]. A significant correlation was found between learning scores and overall improvement in core symptoms (r = 0.514, P = 0.042) suggesting an association between mechanism of change and clinical improvement. CONCLUSION: Our preliminary findings suggest that Amyg-EFP-NF may serve as an affordable and accessible non-invasive treatment option for emotional dysregulation in women suffering from PMDD. Our main limitations were the relatively small number of participants and the lack of a sham-NF placebo arm.

Neural and functional validation of fMRI-informed EEG model of right inferior frontal gyrus activity.

The right inferior frontal gyrus (rIFG) is a region involved in the neural underpinning of cognitive control across several domains such as inhibitory control and attentional allocation process. Therefore, it constitutes a desirable neural target for brain-guided interventions such as neurofeedback (NF). To date, rIFG-NF has shown beneficial ability to rehabilitate or enhance cognitive functions using functional Magnetic Resonance Imaging (fMRI-NF). However, the utilization of fMRI-NF for clinical purposes is severely limited, due to its poor scalability. The present study aimed to overcome the limited applicability of fMRI-NF by developing and validating an EEG model of fMRI-defined rIFG activity (hereby termed "Electrical FingerPrint of rIFG"; rIFG-EFP). To validate the computational model, we employed two experiments in healthy individuals. The first study (n = 14) aimed to test the target engagement of the model by employing rIFG-EFP-NF training while simultaneously acquiring fMRI. The second study (n = 41) aimed to test the functional outcome of two sessions of rIFG-EFP-NF using a risk preference task (known to depict cognitive control processes), employed before and after the training. Results from the first study demonstrated neural target engagement as expected, showing associated rIFG-BOLD signal changing during simultaneous rIFG-EFP-NF training. Target anatomical specificity was verified by showing a more precise prediction of the rIFG-BOLD by the rIFG-EFP model compared to other EFP models. Results of the second study suggested that successful learning to up-regulate the rIFG-EFP signal through NF can reduce one's tendency for risk taking, indicating improved cognitive control after two sessions of rIFG-EFP-NF. Overall, our results confirm the validity of a scalable NF method for targeting rIFG activity by using an EEG probe.

Neurofeedback through the lens of reinforcement learning.

Despite decades of experimental and clinical practice, the neuropsychological mechanisms underlying neurofeedback (NF) training remain obscure. NF is a unique form of reinforcement learning (RL) task, during which participants are provided with rewarding feedback regarding desired changes in neural patterns. However, key RL considerations - including choices during practice, prediction errors, credit-assignment problems, or the exploration-exploitation tradeoff - have infrequently been considered in the context of NF. We offer an RL-based framework for NF, describing different internal states, actions, and rewards in common NF protocols, thus fashioning new proposals for characterizing, predicting, and hastening the course of learning. In this way we hope to advance current understanding of neural regulation via NF, and ultimately to promote its effectiveness, personalization, and clinical utility.

Feasibility and utility of amygdala neurofeedback.

Amygdala NeuroFeedback (NF) have the potential of being a valuable non-invasive intervention tool in many psychiatric disporders. However, the feasibility and best practices of this method have not been systematically examined. The current article presents a review of amygdala-NF studies, an analytic summary of study design parameters, and examination of brain mechanisms related to successful amygdala-NF performance. A meta-analysis of 33 publications showed that real amygdala-NF facilitates learned modulation compared to control conditions. In addition, while variability in study dsign parameters is high, these design choices are implicitly organized by the targeted valence domain (positive or negative). However, in most cases the neuro-behavioral effects of targeting such domains were not directly assessed. Lastly, re-analyzing six data sets of amygdala-fMRI-NF revealed that successful amygdala down-modulation is coupled with deactivation of the posterior insula and nodes in the Default-Mode-Network. Our findings suggest that amygdala self-modulation can be acquired using NF. Yet, additional controlled studies, relevant behavioral tasks before and after NF intervention, and neural 'target engagement' measures are critically needed to establish efficacy and specificity. In addition, the fMRI analysis presented here suggest that common accounts regarding the brain network involved in amygdala NF might reflect unsuccessful modulation attempts rather than successful modulation.

An Investigation of Awareness and Metacognition in Neurofeedback with the Amygdala Electrical Fingerprint.

Awareness theory posits that individuals connected to a brain-computer interface can learn to estimate and discriminate their brain states. We used the amygdala Electrical Fingerprint (amyg-EFP) - a functional Magnetic Resonance Imaging-inspired Electroencephalogram surrogate of deep brain activation - to investigate whether participants could accurately estimate their own brain activation. Ten participants completed up to 20 neurofeedback runs and estimated their amygdala-EFP activation (depicted as a thermometer) and confidence in this rating during each trial. We analysed data using multilevel models, predicting the real thermometer position with participant rated position and adjusted for activation during the previous trial. Hypotheses on learning regulation and improvement of estimation were not confirmed. However, participant ratings were significantly associated with the amyg-EFP signal. Higher rating accuracy also predicted higher subjective confidence in the rating. This proof-of-concept study introduces an approach to study awareness with fMRI-informed neurofeedback and provides initial evidence for metacognition in neurofeedback.

Amygdala electrical-finger-print (AmygEFP) NeuroFeedback guided by individually-tailored Trauma script for post-traumatic stress disorder: Proof-of-concept.

BACKGROUND: Amygdala activity dysregulation plays a central role in post-traumatic stress disorder (PTSD). Hence learning to self-regulate one's amygdala activity may facilitate recovery. PTSD is further characterized by abnormal contextual processing related to the traumatic memory. Therefore, provoking the personal traumatic narrative while training amygdala down-regulation could enhance clinical efficacy. We report the results of a randomized controlled trial (NCT02544971) of a novel self-neuromodulation procedure (i.e. NeuroFeedback) for PTSD, aimed at down-regulating limbic activity while receiving feedback from an auditory script of a personal traumatic narrative. To scale-up applicability, neural activity was probed by an fMRI-informed EEG model of amygdala activity, termed Amygdala Electrical Finger-Print (AmygEFP). METHODS: Fifty-nine adults meeting DSM-5 criteria for PTSD were randomized between three groups: Trauma-script feedback interface (Trauma-NF) or Neutral feedback interface (Neutral-NF), and a control group of No-NF (to control for spontaneous recovery). Before and immediately after 15 NF training sessions patients were blindly assessed for PTSD symptoms and underwent one session of amygdala fMRI-NF for transferability testing. Follow-up clinical assessment was performed at 3- and 6-months following NF treatment. RESULTS: Patients in both NF groups learned to volitionally down-regulate AmygEFP signal and demonstrated a greater reduction in PTSD symptoms and improved down-regulation of the amygdala during fMRI-NF, compared to the No-NF group. The Trauma-NF group presented the largest immediate clinical improvement. CONCLUSIONS: This proof-of-concept study indicates the feasibility of the AmygEFP-NF process-driven as a scalable intervention for PTSD and illustrates its clinical potential. Further investigation is warranted to elucidate the contribution of AmygEFP-NF beyond exposure and placebo effects.

Predictors of real-time fMRI neurofeedback performance and improvement - A machine learning mega-analysis.

Real-time fMRI neurofeedback is an increasingly popular neuroimaging technique that allows an individual to gain control over his/her own brain signals, which can lead to improvements in behavior in healthy participants as well as to improvements of clinical symptoms in patient populations. However, a considerably large ratio of participants undergoing neurofeedback training do not learn to control their own brain signals and, consequently, do not benefit from neurofeedback interventions, which limits clinical efficacy of neurofeedback interventions. As neurofeedback success varies between studies and participants, it is important to identify factors that might influence neurofeedback success. Here, for the first time, we employed a big data machine learning approach to investigate the influence of 20 different design-specific (e.g. activity vs. connectivity feedback), region of interest-specific (e.g. cortical vs. subcortical) and subject-specific factors (e.g. age) on neurofeedback performance and improvement in 608 participants from 28 independent experiments. With a classification accuracy of 60% (considerably different from chance level), we identified two factors that significantly influenced neurofeedback performance: Both the inclusion of a pre-training no-feedback run before neurofeedback training and neurofeedback training of patients as compared to healthy participants were associated with better neurofeedback performance. The positive effect of pre-training no-feedback runs on neurofeedback performance might be due to the familiarization of participants with the neurofeedback setup and the mental imagery task before neurofeedback training runs. Better performance of patients as compared to healthy participants might be driven by higher motivation of patients, higher ranges for the regulation of dysfunctional brain signals, or a more extensive piloting of clinical experimental paradigms. Due to the large heterogeneity of our dataset, these findings likely generalize across neurofeedback studies, thus providing guidance for designing more efficient neurofeedback studies specifically for improving clinical neurofeedback-based interventions. To facilitate the development of data-driven recommendations for specific design details and subpopulations the field would benefit from stronger engagement in open science research practices and data sharing.

Can we predict real-time fMRI neurofeedback learning success from pretraining brain activity?

Neurofeedback training has been shown to influence behavior in healthy participants as well as to alleviate clinical symptoms in neurological, psychosomatic, and psychiatric patient populations. However, many real-time fMRI neurofeedback studies report large inter-individual differences in learning success. The factors that cause this vast variability between participants remain unknown and their identification could enhance treatment success. Thus, here we employed a meta-analytic approach including data from 24 different neurofeedback studies with a total of 401 participants, including 140 patients, to determine whether levels of activity in target brain regions during pretraining functional localizer or no-feedback runs (i.e., self-regulation in the absence of neurofeedback) could predict neurofeedback learning success. We observed a slightly positive correlation between pretraining activity levels during a functional localizer run and neurofeedback learning success, but we were not able to identify common brain-based success predictors across our diverse cohort of studies. Therefore, advances need to be made in finding robust models and measures of general neurofeedback learning, and in increasing the current study database to allow for investigating further factors that might influence neurofeedback learning.

Is there a prediction network? Meta-analytic evidence for a cortical-subcortical network likely subserving prediction.

Predictive coding is an increasingly influential and ambitious concept in neuroscience viewing the brain as a 'hypothesis testing machine' that constantly strives to minimize prediction error, the gap between its predictions and the actual sensory input. Despite the invaluable contribution of this framework to the formulation of brain function, its neuroanatomical foundations have not been fully defined. To address this gap, we conducted activation likelihood estimation (ALE) meta-analysis of 39 neuroimaging studies of three functional domains (action perception, language and music) inherently involving prediction. The ALE analysis revealed a widely distributed brain network encompassing regions within the inferior and middle frontal gyri, anterior insula, premotor cortex, pre-supplementary motor area, temporoparietal junction, striatum, thalamus/subthalamus and the cerebellum. This network is proposed to subserve domain-general prediction and its relevance to motor control, attention, implicit learning and social cognition is discussed in light of the predictive coding scheme. Better understanding of the presented network may help advance treatments of neuropsychiatric conditions related to aberrant prediction processing and promote cognitive enhancement in healthy individuals.

Current progress in real-time functional magnetic resonance-based neurofeedback: Methodological challenges and achievements.

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.

Acute psychosocial stress alters thalamic network centrality.

Acute stress triggers a broad psychophysiological response that is adaptive if rapidly activated and terminated. While the brain controls the stress response, it is strongly affected by it. Previous research of stress effects on brain activation and connectivity has mainly focused on pre-defined brain regions or networks, potentially missing changes in the rest of the brain. We here investigated how both stress reactivity and stress recovery are reflected in whole-brain network topology and how changes in functional connectivity relate to other stress measures. Healthy young males (n = 67) completed the Trier Social Stress Test or a control task. From 60 min before until 105 min after stress onset, blocks of resting-state fMRI were acquired. Subjective, autonomic, and endocrine measures of the stress response were assessed throughout the experiment. Whole-brain network topology was quantified using Eigenvector centrality (EC) mapping, which detects central hubs of a network. Stress influenced subjective affect, autonomic activity, and endocrine measures. EC differences between groups as well as before and after stress exposure were found in the thalamus, due to widespread connectivity changes in the brain. Stress-driven EC increases in the thalamus were significantly correlated with subjective stress ratings and showed non-significant trends for a correlation with heart rate variability and saliva cortisol. Furthermore, increases in thalamic EC and in saliva cortisol persisted until 105 min after stress onset. We conclude that thalamic areas are central for information processing after stress exposure and may provide an interface for the stress response in the rest of the body and in the mind.

Effect of deactivation of activity patterns related to smoking cue reactivity on nicotine addiction.

With approximately 75% of smokers resuming cigarette smoking after using the Gold Standard Programme for smoking cessation, investigation into novel therapeutic approaches is warranted. Typically, smoking cue reactivity is crucial for smoking behaviour. Here we developed a novel closed-loop, smoking cue reactivity patterns EEG-based neurofeedback protocol and evaluated its therapeutic efficacy on nicotine addiction. During an evoked smoking cue reactivity task participants' brain activity patterns corresponding to smoking cues were obtained with multivariate pattern analysis of all EEG channels data, then during neurofeedback the EEG activity patterns of smoking cue reactivity were continuously deactivated with adaptive closed-loop training. In a double-blind, placebo-controlled, randomized clinical trial, 60 nicotine-dependent participants were assigned to receive two neurofeedback training sessions (∼1 h/session) either from their own brain (n = 30, real-feedback group) or from the brain activity pattern of a matched participant (n = 30, yoked-feedback group). Cigarette craving and craving-related P300 were assessed at pre-neurofeedback and post-neurofeedback. The number of cigarettes smoked per day was assessed at baseline, 1 week, 1 month, and 4 months following the final neurofeedback visit. In the real-feedback group, participants successfully deactivated EEG activity patterns of smoking cue reactivity. The real-feedback group showed significant decrease in cigarette craving and craving-related P300 amplitudes compared with the yoked-feedback group. The rates of cigarettes smoked per day at 1 week, 1 month and 4 months follow-up decreased 30.6%, 38.2%, and 27.4% relative to baseline in the real-feedback group, compared to decreases of 14.0%, 13.7%, and 5.9% in the yoked-feedback group. The neurofeedback effects on craving change and smoking amount at the 4-month follow-up were further predicted by neural markers at pre-neurofeedback. This novel neurofeedback training approach produced significant short-term and long-term effects on cigarette craving and smoking behaviour, suggesting the neurofeedback protocol described herein is a promising brain-based tool for treating addiction.

Electrical fingerprint of the amygdala guides neurofeedback training for stress resilience.

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.

Process-based framework for precise neuromodulation.

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.

Volitional limbic neuromodulation exerts a beneficial clinical effect on Fibromyalgia.

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 amplificating 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 would improve chronic pain in patients suffering from Fibromyalgia, by reducing sleep disorder improving emotion regulation. We further expected that improved clinical status would correspond with successful training as indicated by improved down modulation of the Amygdala-EFP signal. Thirty-Four Fibromyalgia patients (31F; age 35.6 ±â€¯11.82) participated in a randomized placebo-controlled trial with biweekly Amyg-EFP-neurofeedback sessions or 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 (time × group p < 0.05) and to a greater extent among good modulators (time × sub-group p < 0.05). In contrast, self-report measures did not reveal a treatment-specific response at the end of the neurofeedback training. However, the follow-up assessment revealed a delayed improvement in chronic pain and subjective sleep experience, evident only in the real-neurofeedback group (time × group p < 0.05). Moderation analysis showed that the enduring clinical effects on pain evident in the follow-up assessment were predicted by the immediate improvements following training in objective sleep and subjective affect measures. Our findings suggest that Amyg-EFP-neurofeedback that specifically targets limbic activity down modulation offers a successful principled approach for volitional EEG based neuromodulation treatment in Fibromyalgia patients. Importantly, it seems that via its immediate sleep improving effect, the neurofeedback training induced a delayed reduction in the target subjective symptom of chronic pain, far and beyond the immediate placebo effect. This indirect approach to chronic pain management reflects the substantial link between somatic and affective dysregulation that can be successfully targeted using neurofeedback.

Cannabis analgesia in chronic neuropathic pain is associated with altered brain connectivity.

OBJECTIVE: To characterize the functional brain changes involved in δ-9-tetrahydrocannabinol (THC) modulation of chronic neuropathic pain. METHODS: Fifteen patients with chronic radicular neuropathic pain participated in a randomized, double-blind, placebo-controlled trial employing a counterbalanced, within-subjects design. Pain assessments and functional resting state brain scans were performed at baseline and after sublingual THC administration. We examined functional connectivity of the anterior cingulate cortex (ACC) and pain-related network dynamics using graph theory measures. RESULTS: THC significantly reduced patients' pain compared to placebo. THC-induced analgesia was correlated with a reduction in functional connectivity between the anterior cingulate cortex (ACC) and the sensorimotor cortex. Moreover, the degree of reduction was predictive of the response to THC. Graph theory analyses of local measures demonstrated reduction in network connectivity in areas involved in pain processing, and specifically in the dorsolateral prefrontal cortex (DLPFC), which were correlated with individual pain reduction. CONCLUSION: These results suggest that the ACC and DLPFC, 2 major cognitive-emotional modulation areas, and their connections to somatosensory areas, are functionally involved in the analgesic effect of THC in chronic pain. This effect may therefore be mediated through induction of functional disconnection between regulatory high-order affective regions and the sensorimotor cortex. Moreover, baseline functional connectivity between these brain areas may serve as a predictor for the extent of pain relief induced by THC.

Monitoring and control of amygdala neurofeedback involves distributed information processing in the human brain.

Brain-computer interfaces provide conscious access to neural activity by means of brain-derived feedback ("neurofeedback"). An individual's abilities to monitor and control feedback are two necessary processes for effective neurofeedback therapy, yet their underlying functional neuroanatomy is still being debated. In this study, healthy subjects received visual feedback from their amygdala response to negative pictures. Activation and functional connectivity were analyzed to disentangle the role of brain regions in different processes. Feedback monitoring was mapped to the thalamus, ventromedial prefrontal cortex (vmPFC), ventral striatum (VS), and rostral PFC. The VS responded to feedback corresponding to instructions while rPFC activity differentiated between conditions and predicted amygdala regulation. Control involved the lateral PFC, anterior cingulate, and insula. Monitoring and control activity overlapped in the VS and thalamus. Extending current neural models of neurofeedback, this study introduces monitoring and control of feedback as anatomically dissociated processes, and suggests their important role in voluntary neuromodulation.