Through real-time neurofeedback, users/patients are empowered to adopt and apply mental strategies by causally influencing their own personalised brain signals, which can potentially produce longer-lasting neurobiological and behavioural impact. Despite neurofeedback’s long-standing therapeutic potential, the relevant methodological advances required to bridge translational gap are fairly recent. The emergence of simultaneous EEG-fMRI modelling and 7T fMRI neurofeedback, coupled with longitudinal and controlled study designs, is enabling deeper insights into brain dynamics, brain-behaviour relationships and the translational value of neurofeedback technology. Real-time physiological artifact corrections and real-time functional connectivity calculations have additionally introduced greater rigor and innovation into neurofeedback paradigms. Therefore, it is timely to discuss these important methodological advances that are ushering in improved translational capabilities for neurofeedback and neuroimaging. The symposium will bring together four internationally recognized neuroimaging experts, with interdisciplinary proficiencies in EEG-fMRI modelling, psychiatry, engineering, psychology and neuroscience, to discuss recent pioneering work with fMRI and multimodal neurofeedback that can further translation. Our symposium will appeal to methodologists and engineers interested in understanding multimodal (EEG-fMRI) brain function modelling and advanced real-time neuroimaging, neuroscientists curious about the neurobiological effects associated with real-time neurofeedback training, as well as clinicians/psychiatrists invested in exploring the translational and therapeutic value of personalised brain-based technologies.
Symposium attendees will be provided with the knowledge to:
i) understand key elements of real-time neuroimaging and neurofeedback that can influence behavioural and neurobiological outcomes,
ii) appreciate the utility of multi-modal neuroimaging and EEG-fMRI modelling for translational impact, and
iii) identify and utilise best practices for designing neurofeedback experiments and conducting real-time neuroimaging analysis.
Our symposium will appeal to methodologists and engineers interested in cutting-edge real-time neuroimaging and multimodal (EEG-fMRI) modelling and measurements, neuroscientists curious about the neurobiological underpinnings of non-invasive and personalised real-time neurofeedback training, as well as clinicians and psychiatrists exploring the translational and therapeutic relevance of personalised brain-based technologies. The breadth of talks will ensure that the symposium appeals to those without computational experience/expertise.
Reward processing is essential for our mental- and physical health. I will present two paths in neurofeedback (NF) research for harnessing reward brain circuit activation towards mental and physical health augmentation.
The first path addresses the scalability gap of functional MRI neurofeedback (fMRI-NF) by developing a scalable fMRI-informed EEG model related to reward activity in the ventral-striatum (VS) – a major node of the brain's reward system. Such an EEG based prediction model of VS-BOLD activation may enable ecological monitoring and modulation of reward-related neural processing without the use of the stationary and costly fMRI. To establish this EEG model, we collected simultaneous EEG-fMRI data from two cohorts of healthy individuals while listening to selected pleasurable music – a highly rewarding stimulus known to engage the mesolimbic circuit. We used these cross-modal data to construct a generic regression model for predicting the concurrently acquired Blood-Oxygen-Level-Dependent (BOLD) signal from the VS using spectro-temporal features from the EEG signal (termed hereby VS-Electrical Finger Print; VS-EFP). We then validated the EEG model on the second dataset and showed its functionality by training individuals to upregulate the VS-EFP model and measured modified reward processing neuronally (fMRI) and behaviorally (tasks and scales).
The second path addresses the mind-body gap by testing the effect of NF training to upregulate mesolimbic fMRI activity on immune reactivity following Hepatitis vaccination. In a pre-registered, triple-blinded controlled design, 85 healthy individuals were assigned to one out of three groups: (1) test group of mesolimbic reward circuit NF (N=34), (2) an active control group (N=34) allowing to disentangle target-specific from non-specific effects; and (3) no-NF control group (N=17). Each NF participant underwent four fMRI-NF training sessions and received a vaccination in order to challenge their immune system either after training (group 1 &2) or at a similar time lag (group3). We found that greater NF upregulation in the mesolimbic circuit resulted in greater Hepatitis IgG count following vaccination and that this modulation was mediated by mental strategies associated with reward consumption.
The promising results obtained via the described paths of research, together pave the way for future use of scalable self-neuromodulation of the reward circuit in humans for alleviating mood disturbance as well as for empowering body immunity.
Talma Hendler, PhD
, Tel Aviv University and Tel Aviv Sourasky Medical Center Tel Aviv, Israel
Real-time fMRI neurofeedback has shown promising results, yet its clinical efficacy remains a topic of debate due to factors such as the lack of control conditions in many studies, potential noise and placebo effects, and an incomplete understanding of its underlying mechanisms. A primary challenge is the susceptibility of the fMRI signal to various types of noise, especially physiological variations. In closed-loop feedback training, if participants, whether consciously or unconsciously, discern that they can influence feedback signals based on physiological state changes, the objective of NF training may transition from modulating neural activity to altering physiological states. To address this challenge, we introduced an advanced real-time fMRI processing system, RTPSpy. This system delivers comprehensive noise reduction, including physiological noise, ensuring precise feedback on brain activation states. In this session, I will outline the capabilities of RTPSpy, with a focus on its proficiency in counteracting physiological noise. I will showcase the influence of physiological noise on fMRI signals and the benefits of real-time processing in mitigating it. Furthermore, I will delve into our unique approach to simultaneous EEG-fMRI recording, aiming to elucidate the mechanisms of neurofeedback training. By integrating EEG and fMRI data, we can obtain a temporally resolved view that distinguishes brain activity during the initial response to feedback signals and subsequent self-regulatory phases. I will present our findings underscoring the pivotal role of the brain's reaction to feedback signals in the efficacy of neurofeedback training. In addition, I will present initial results illustrating how the combined EEG-fMRI analysis can distinctly differentiate between the brain's response to feedback signals and self-regulatory activations.
Masaya Misaki, PhD
, Laureate Institute for Brain Research Tulsa, OK
Disrupted neural patterns are linked to psychiatric vulnerabilities, such as Repetitive Negative Thinking (RNT) in depression and disturbances due to early life adversity (ELA). Neuroanatomically targeted fMRI neurofeedback (NF) has shown promise in safely and non-invasively mitigating the disrupted neural patterns through learned self-regulation of brain activity. I will present two studies that use real-time fMRI NF (with real-time physiological correction using RTPSpy) employing different mental strategies to target 2 main types of clinical cohorts, i.e., depression (reappraisal technique), and ELA (mindfulness). These studies use 2 distinct methods of neurofeedback – functional connectivity-based neurofeedback (FC-NF), and BOLD activation-based feedback.
Study 1: Evidence has shown that enhanced resting-state FC between the precuneus/posterior cingulate cortex (PCC) and right temporoparietal junction (rTPJ) correlates with increased rumination (RNT). Therefore, we employed fMRI neurofeedback to modulate PCC-rTPJ FC in individuals (N=39) with MDD. We found that MDD patients receiving active neurofeedback showed significant reductions in negative rumination (RNT) and enhancement in quality of daily-life mental strategies.
Study 2: ELA disrupts activity in the default-mode network, which influences emotional awareness. Hence, we employed neurofeedback-augmented mindfulness training (NAMT) to modulate the DMN by specifically targeting PCC BOLD activation. 43 ELA (randomized to active or sham NF) and 40 healthy (active NF) adolescents underwent NAMT. We found that although ELA-exposed adolescents experienced greater difficulty in down-regulating their PCC activation compared to healthy controls, they showed improvements in state-mindfulness and affective responses post-neurofeedback training.
These investigations highlight the utility of FC and BOLD activation-based real-time NF aimed at patients and at-risk populations. While FC neurofeedback with cognitive reappraisal shows promise in mitigating RNT in depression, activation-based neurofeedback with mindfulness aimed at adolescents with ELA may need further tailoring for effective translation. The overarching theme of my talk is the promising avenue of personalized neurofeedback treatments for varied patient groups.
There is growing evidence that real-time fMRI neurofeedback can empower neurotypical and psychiatric populations to quickly learn non-invasive self-regulation of brain function and subsequently improve well-being or mitigate dysfunction. Advancements in ultra-high field 7 Tesla MRI have facilitated substantially greater neuroanatomical precision with sub-second temporal resolution which can potentially further the efficacy of fMRI neurofeedback. However, very few studies have attempted real-time 7T fMRI neurofeedback, as achieving meaningful real-time processing of such high-dimensional data at sub-second sampling rates can be technically challenging. Furthermore, neurofeedback studies often do not incorporate adequate follow-up assessments that can disambiguate the immediate and longer-term translational impact of brief and precise neurofeedback training. Therefore, to investigate the translational impact of neuroanatomically precise high-resolution fMRI neurofeedback, we first established real-time neurofeedback capacity at our 7 Tesla MRI centre (with TR=800 ms; isometric fMRI voxel size = 1.6 mm). Subsequently, we performed the first comprehensive placebo-controlled 7 Tesla fMRI neurofeedback trial with intensive longitudinal behavioural sampling. We aimed to teach neurotypical beginners to meditate correctly and efficiently using precise real-time neurofeedback guidance. Following the neurofeedback-augmented meditation training, we assessed its immediate as well as longer-term (e.g., 1-week, 3-6 months) translational impact on memory, attention, meditation performance, anxiety and distress. Additionally, we performed ecological sampling of state mindfulness associated with daily at-home meditation practice for a week following the neurofeedback training. We found that precise neurofeedback-meditation training produces greater deactivation in the posterior cingulate cortex, compared to active placebo (non-contingent sham neurofeedback). Furthermore, we found that this training can produce greater improvements in meditation practice and associated mental health benefits compared to the active control group. I further discuss how neurofeedback learning transfers outside the MRI scanner, and the far-reaching translational capacity of such comprehensive neurofeedback study designs in enabling multimodal comparisons and inferences (e.g., comparison with same translational outcomes from an equivalent but independent EEG neurofeedback experiment).