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BACKGROUND: Low hippocampal volume could serve as an early risk factor for Post-traumatic Stress Disorder (PTSD) in interaction with other brain anomalies of developmental origin. One such anomaly may well be a presence of large Cavum Septum Pellucidum (CSP), which has been loosely associated with PTSD. Here, we performed a longitudinal prospective study of recent trauma survivors. We hypothesized that at one-month after trauma exposure, the relation between hippocampal volume and PTSD symptom severity will be moderated by CSP volume, and that this early interaction will account for persistent PTSD symptoms at subsequent time-points. METHODS: 171 adults (87 females, average age=34.22, range=18–65) admitted to a general hospital’s emergency department following a traumatic event, underwent clinical assessment and structural MRI within one-month after trauma. Follow-up clinical evaluations were conducted at six (n=97) and fourteen (n=78) months after trauma. Hippocampus and CSP volumes were measured automatically by FreeSurfer software and verified manually by a neuroradiologist. RESULTS: At one-month following trauma, CSP volume significantly moderated the relation between hippocampal volume and PTSD severity (p=0.026), and this interaction further predicted symptom severity at fourteen months post-trauma (p=0.018). Specifically, individuals with smaller hippocampus and larger CSP at one-month post-trauma, showed more severe symptoms at one- and fourteen months following trauma exposure. CONCLUSIONS: Our study provides evidence for an early neuroanatomical risk factors for PTSD, which could also predict the progression of the disorder in the year following trauma exposure. Such a simple-to-acquire neuroanatomical signature for PTSD could guide early management, as well as long-term monitoring. TRIAL REGISTRATION: Neurobehavioral Moderators of Post-traumatic Disease Trajectories. ClinicalTrials.gov registration: NCT03756545. https://clinicaltrials.gov/ct2/show/NCT03756545

Amygdala dysregulation is core to multiple psychiatric disorders. Real-time fMRI enables Amygdala self-modulation through NeuroFeedback (NF).Despite a surge in Amygdala-NF studies, a systematic quantification of self-modulation is lacking. Amygdala-NF dissemination is further restricted by absence of unifying framework dictating design choices and insufficient understanding of neural changes underlying successful self-modulation.The current meta-analysis of Amygdala-NF literature found that real-time feedback facilitates learned self-modulation more than placebo. Intriguingly, while we found that variability in design choices could be explained by the targeted domain, this was rarely highlighted by authors. Lastly, reanalysis of six fMRI data-sets (n=151), revealed that successful Amygdala down-modulation is coupled with deactivation of posterior insula and Default-Mode-Network major nodes, pointing to regulation related processes.While findings point to Amygdala self-modulation as a learned skill that could modify brain functionality, further placebo-controlled trials are necessary to prove clinical efficacy. We further suggest that studies should explicitly target neuro-behavioral domain, design studies accordingly and include ‘target engagement’ measures. We exemplify this idea through a ‘process-based’ NF approach for PTSD.

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., 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.

Highlights • We propose a novel end-to-end neural network that employs resting-state and task-based functional MRI (fMRI) datasets, obtained one month after trauma exposure, to predict PTSD one, six and 14-months after the exposure. • The method utilizes connectivity maps extracted from pairs of brain regions which are subsequently updated by applying the algorithmic technique of pairwise attention. • The proposed deep learning method predicts PTSD status, PTSD symptom clusters and survival analysis within the prospective design. We demonstrate a significant improvement in performance on all the datasets and experiments in comparison to other relevant analytical techniques. • Pairwise association analysis reveals several significant functional connectivity patterns, in line with previous PTSD neuroimaging literature., Early intervention following exposure to a traumatic life event could change the clinical path from the development of post traumatic stress disorder (PTSD) to recovery, hence the interest in early detection and underlying biological mechanisms involved in the development of post traumatic sequelae. We introduce a novel end-to-end neural network that employs resting-state and task-based functional MRI (fMRI) datasets, obtained one month after trauma exposure, to predict PTSD symptoms at one-, six- and fourteen-months after the exposure. FMRI data, as well as PTSD status and symptoms, were collected from adults at risk for PTSD development, after admission to emergency room following a traumatic event. Our computational method utilized a per-region encoder to extract brain regions embedding, which were subsequently updated by applying the algorithmic technique of pairwise attention. The affinities obtained between each pair of regions were combined to create a pairwise co-activation map used to perform multi-label classification. The results demonstrate that the novel method’s performance in predicting PTSD symptoms, in a prospective manner, outperforms previous analytical techniques reported in the fMRI literature, all trained on the same dataset. We further show a high predictive ability for predicting PTSD symptom clusters and PTSD persistence. To the best of our knowledge, this is the first deep learning method applied on fMRI data with respect to prospective clinical outcomes, to predict PTSD status, severity and symptom clusters. Future work could further delineate the mechanisms that underlie such a prediction, and potentially improve single patient characterization.

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. publishedVersion

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.

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 and data sharing.

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 interindividual 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 pre-training 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 pre-training 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.

We present a deep neural network method that enables learning of a personal representation from samples acquired while subjects are performing a self neuro-feedback task, guided by functional MRI (fMRI). The neurofeedback task (watch vs. regulate) provides the subjects with continuous feedback, contingent on the down-regulation of their Amygdala signal. The representation is learned by a self-supervised recurrent neural network that predicts the Amygdala activity in the next fMRI frame given recent fMRI frames and is conditioned on the learned individual representation. We show that our personal representation, learned solely using fMRI images, improves the next-frame prediction considerably and, more importantly, yields superior performance in linear prediction of psychiatric traits, compared to performing such predictions based on clinical data and personality tests. Our code is attached as supplementary and the data would be shared subject to ethical approvals.

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., Many real‐time fMRI neurofeedback studies report large inter‐individual differences in learning success, but the factors that cause this vast variability between participants remain unknown. Here, we used a meta‐analytic approach including data from 24 different neurofeedback studies with a total of 401 participants to determine whether levels of activity in target brain regions during pretraining functional localizer or no‐feedback runs could predict neurofeedback learning success. We were not able to identify common brain‐based success predictors across our diverse cohort of studies.

BackgroundLow hippocampal volume could serve as an early risk factor for Post-traumatic Stress Disorder (PTSD) in interaction with other brain anomalies of developmental origin. One such anomaly may well be a presence of large Cavum Septum Pellucidum (CSP), which has been loosely associated with PTSD. Here, we performed a longitudinal prospective study of recent trauma survivors. We hypothesized that at one-month after trauma exposure, the relation between hippocampal volume and PTSD symptom severity will be moderated by CSP volume, and that this early interaction will account for persistent PTSD symptoms at subsequent time-points.Methods171 adults (87 females, average age=34.22, range=18-65) admitted to a general hospital’s emergency department following a traumatic event, underwent clinical assessment and structural MRI within one-month after trauma. Follow-up clinical evaluations were conducted at six (n=97) and fourteen (n=78) months after trauma. Hippocampus and CSP volumes were measured automatically by FreeSurfer software and verified manually by a neuroradiologist.ResultsAt one-month following trauma, CSP volume significantly moderated the relation between hippocampal volume and PTSD severity (p=0.026), and this interaction further predicted symptom severity at fourteen months post-trauma (p=0.018). Specifically, individuals with smaller hippocampus and larger CSP at one-month post-trauma, showed more severe symptoms at one-and fourteen months following trauma exposure.ConclusionsOur study provides evidence for an early neuroanatomical risk factors for PTSD, which could also predict the progression of the disorder in the year following trauma exposure. Such a simple-to-acquire neuroanatomical signature for PTSD could guide early management, as well as long-term monitoring.Trial RegistrationNeurobehavioral Moderators of Post-traumatic Disease Trajectories. ClinicalTrials.gov registration:NCT03756545.https://clinicaltrials.gov/ct2/show/NCT03756545

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.

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 in chronic pain and subjective sleep experience, evident only in the Real-neurofeedback group (both 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 training 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 necessary link between somatic and affective dysregulation that can be successfully targeted using neurofeedback.

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.

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

This paper discusses the exclusion of veterans with combat PTSD (CPTSD) from eligibility for the Purple Heart (PH). The main argument is that this exclusion is unjustified and that it strengthens the stigma attached to the traumatized veterans, with detrimental implications to their wellbeing. In the context of the politics of recognition, the history of the term PTSD, and with support evidence from brain studies, the paper contends that in the case of combat veterans, posttraumatic stress should be termed PTSI (posttraumatic stress injury) rather than PTSD (posttraumatic stress disorder). The proposed alteration in terminology may enable eligibility of posttraumatic combat veterans’ for the Purple Heart, and consequently mitigate the stigma of their wounds, help to deconstruct their misrecognition as inferior to physiologically wounded, increase their willingness to seek aid, and improve their chances to heal.

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.