Closed-loop neuromodulation for studying spontaneous activity and causality

Enhancing creativity with covert neurofeedback: causal evidence for default-executive network coupling in creative thinking

Creativity neuroscience has consistently reported increased functional connectivity between the default mode network and the executive control network supports creative cognition, potentially reflecting coordination of generative and evaluative cognitive processes. However, evidence has been purely correlational-no causal demonstrations show that default mode network-executive control network interaction specifically drives creative performance. We sought causal evidence for default mode network-executive control network coupling in creative thinking using functional near-infrared spectroscopy-brain connectivity neurofeedback, which can endogenously modify functional connectivity through reinforcement learning. Importantly, we employed covert neurofeedback, where participants were unaware of the specific brain activity being trained, allowing for unbiased evaluation of cognitive and neural impacts. In a default-executive neurofeedback condition (n = 15), we entrained coupling between the medial prefrontal cortex and the dorsolateral prefrontal cortex, hubs of the default mode network and executive control network, respectively. We compared this with a default-motor condition (n = 15), entraining coupling between the medial prefrontal cortex and the supplementary motor area. Approximately 24 h later, default-executive neurofeedback led to increased coupling between the default mode network and the executive control network during a creative thinking task (generating creative object uses), extending to broader default mode network regions. Behaviorally, we observed a double dissociation: The default-executive condition increased idea originality, while the default-motor condition improved go/no-go reaction times. We thus provide the first evidence that default mode network-executive control network coupling causally enhances creative performance.

The emergent property of inhibitory control: implications of intermittent network-based fNIRS neurofeedback training

Background

Studies have shown that inhibitory control is supported by frontal cortex and small-world brain networks. However, it remains unclear how regulating the topology changes the inhibitory control. We investigated the effects of small-worldness upregulation training on resting-state networks via fNIRS neurofeedback training, which will contribute to a deeper insight of inhibitory control.

Methods

A five-day training session was used to regulate the small-worldness of the frontal cortex, and the color-word Stroop task was tested before and after training. Fifty healthy adults were recruited and randomly assigned to the sham feedback group (sham group), or intermittent fNIRS-based brain network feedback group (fNIRS-NF group). On the basis of the exclusion of incomplete data, 45 valid data sets were retained and analyzed (sham: 21, fNIRS-NF: 24).

Results

Training increased resting-state small-worldness and improved Stroop task performance, with a significant correlation between these changes (r = -0.32, p = 0.032). The fNIRS-NF group exhibited reduced hemodynamic activation (βvalue decreased, indicating lower cognitive load) during posttest and follow-up. Notably, the right dorsolateral prefrontal cortex (dlPFC) showed greater intra-regional connectivity increases than the left dlPFC, suggesting asymmetric plasticity.

Conclusion

Intermittent fNIRS neurofeedback effectively modulates resting-state small-world networks and enhances inhibitory control, with effects sustained for at least one week. These findings highlight small-worldness as a novel target for cognitive interventions.

Music in the loop: a systematic review of current neurofeedback methodologies using music

Music, a universal element in human societies, possesses a profound ability to evoke emotions and influence mood. This systematic review explores the utilization of music to allow self-control of brain activity and its implications in clinical neuroscience. Focusing on music-based neurofeedback studies, it explores methodological aspects and findings to propose future directions. Three key questions are addressed: the rationale behind using music as a stimulus, its integration into the feedback loop, and the outcomes of such interventions. While studies emphasize the emotional link between music and brain activity, mechanistic explanations are lacking. Additionally, there is no consensus on the imaging or behavioral measures of neurofeedback success. The review suggests considering whole-brain neural correlates of music stimuli and their interaction with target brain networks and reward mechanisms when designing music-neurofeedback studies. Ultimately, this review aims to serve as a valuable resource for researchers, facilitating a deeper understanding of music's role in neurofeedback and guiding future investigations.

SSVEP modulation via non-volitional neurofeedback: anin silicoproof of concept

Objective.Neuronal oscillatory patterns are believed to underpin multiple cognitive mechanisms. Accordingly, compromised oscillatory dynamics were shown to be associated with neuropsychiatric conditions. Therefore, the possibility of modulating, or controlling, oscillatory components of brain activity as a therapeutic approach has emerged. Typical non-invasive brain-computer interfaces based on EEG have been used to decode volitional motor brain signals for interaction with external devices. Here we aimed at feedback through visual stimulation which returns directly back to the visual cortex.Approach.Our architecture permits the implementation of feedback control-loops capable of controlling, or at least modulating, visual cortical activity. As this type of neurofeedback depends on early visual cortical activity, mainly driven by external stimulation it is called non-volitional or implicit neurofeedback. Because retino-cortical 40-100 ms delays in the feedback loop severely degrade controller performance, we implemented a predictive control system, called a Smith-Predictor (SP) controller, which compensates for fixed delays in the control loop by building an internal model of the system to be controlled, in this case the EEG response to stimuli in the visual cortex.Main results. Response models were obtained by analyzing, EEG data (n= 8) of experiments using periodically inverting stimuli causing prominent parieto-occipital oscillations, the steady-state visual evoked potentials (SSVEPs). Averaged subject-specific SSVEPs, and associated retina-cortical delays, were subsequently used to obtain the SP controller's linear, time-invariant models of individual responses. The SSVEP models were first successfully validated against the experimental data. When placed in closed loop with the designed SP controller configuration, the SSVEP amplitude level oscillated around several reference values, accounting for inter-individual variability.Significance. In silicoandin vivodata matched, suggesting model's robustness, paving the way for the experimental validation of this non-volitional neurofeedback system to control the amplitude of abnormal brain oscillations in autism and attention and hyperactivity deficits.

From lab to life: challenges and perspectives of fNIRS for haemodynamic-based neurofeedback in real-world environments

Neurofeedback allows individuals to monitor and self-regulate their brain activity, potentially improving human brain function. Beyond the traditional electrophysiological approach using primarily electroencephalography, brain haemodynamics measured with functional magnetic resonance imaging (fMRI) and more recently, functional near-infrared spectroscopy (fNIRS) have been used (haemodynamic-based neurofeedback), particularly to improve the spatial specificity of neurofeedback. Over recent years, especially fNIRS has attracted great attention because it offers several advantages over fMRI such as increased user accessibility, cost-effectiveness and mobility-the latter being the most distinct feature of fNIRS. The next logical step would be to transfer haemodynamic-based neurofeedback protocols that have already been proven and validated by fMRI to mobile fNIRS. However, this undertaking is not always easy, especially since fNIRS novices may miss important fNIRS-specific methodological challenges. This review is aimed at researchers from different fields who seek to exploit the unique capabilities of fNIRS for neurofeedback. It carefully addresses fNIRS-specific challenges and offers suggestions for possible solutions. If the challenges raised are addressed and further developed, fNIRS could emerge as a useful neurofeedback technique with its own unique application potential-the targeted training of brain activity in real-world environments, thereby significantly expanding the scope and scalability of haemodynamic-based neurofeedback applications.This article is part of the theme issue 'Neurofeedback: new territories and neurocognitive mechanisms of endogenous neuromodulation'.

Toward neuroscientific understanding in posttraumatic growth: Scoping review identifying electrophysiological neurofeedback training targets for brain-based research

Improved neural understanding of posttraumatic growth (PTG) is required for effective trauma care. PTG is the advantageous psychological change some individuals derive from their struggle to overcome trauma. This comprehensive review critically examined the limited neural PTG research, to identify electrophysiological training targets for future research examining neurofeedback to enhance PTG, and provides novel insights into the emerging neural theory of PTG. PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) informed the process. Findings from the studies N=8 (participants N=765) revealed PTG was correlated with left-lateralised alpha frequency power patterns. Specifically, PTG was associated with lower left frontal alpha power, higher left central alpha power, and lower parietal alpha power. Differences between studies may identify different components of PTG-related neural circuitry, or represent variations in PTG and sub-factor strength, mechanistic differences between studies, or the potential confounding presence of posttraumatic stress disorder (PTSD). While lower alpha power has been associated with higher PTSD in existing literature, higher left central alpha power was associated with lower PTSD. Therefore, alpha upregulation neurofeedback delivered over the sensorimotor cortices of the brain, around left central EEG electrode C3, presented the most promising neurofeedback target.

Effects of one session of theta or high alpha neurofeedback on EEG activity and working memory

Neurofeedback techniques provide participants immediate feedback on neuronal signals, enabling them to modulate their brain activity. This technique holds promise to unveil brain-behavior relationship and offers opportunities for neuroenhancement. Establishing causal relationships between modulated brain activity and behavioral improvements requires rigorous experimental designs, including appropriate control groups and large samples. Our primary objective was to examine whether a single neurofeedback session, designed to enhance working memory through the modulation of theta or high-alpha frequencies, elicits specific changes in electrophysiological and cognitive outcomes. Additionally, we explored predictors of successful neuromodulation. A total of 101 healthy adults were assigned to groups trained to increase frontal theta, parietal high alpha, or random frequencies (active control group). We measured resting-state EEG, working memory performance, and self-reported psychological states before and after one neurofeedback session. Although our analyses revealed improvements in electrophysiological and behavioral outcomes, these gains were not specific to the experimental groups. An increase in the frequency targeted by the training has been observed for the theta and high alpha groups, but training designed to increase randomly selected frequencies appears to induce more generalized neuromodulation compared with targeting a specific frequency. Among all the predictors of neuromodulation examined, resting theta and high alpha amplitudes predicted specifically the increase of those frequencies during the training. These results highlight the challenge of integrating a control group based on enhancing randomly selected frequency bands and suggest potential avenues for optimizing interventions (e.g., by including a control group trained in both up- and down-regulation).

Auditory processing up to cortex is maintained during sleep spindles

Sleep spindles are transient 11-16 Hz brain oscillations generated by thalamocortical circuits. Their role in memory consolidation is well established, but how they play a role in sleep continuity and protection of memory consolidation against interference is unclear. One theory posits that spindles or a neural refractory period following their offset act as a gating mechanism, blocking sensory information en route to the cortex at the level of the thalamus. An alternative model posits that spindles do not participate in the suppression of neural responses to sound, although they can be produced in response to sound. We present evidence from three experiments using electroencephalography and magnetoencephalography in humans that examine different evoked responses in the presence of and following sleep spindles. The results provide convergent empirical evidence suggesting that auditory processing up to cortex is maintained during sleep spindles, and their refractory periods.

Wireless closed-loop deep brain stimulation using microelectrode array probes

Deep brain stimulation (DBS), including optical stimulation and electrical stimulation, has been demonstrated considerable value in exploring pathological brain activity and developing treatments for neural disorders. Advances in DBS microsystems based on implantable microelectrode array (MEA) probes have opened up new opportunities for closed-loop DBS (CL-DBS) in situ. This technology can be used to detect damaged brain circuits and test the therapeutic potential for modulating the output of these circuits in a variety of diseases simultaneously. Despite the success and rapid utilization of MEA probe-based CL-DBS microsystems, key challenges, including excessive wired communication, need to be urgently resolved. In this review, we considered recent advances in MEA probe-based wireless CL-DBS microsystems and outlined the major issues and promising prospects in this field. This technology has the potential to offer novel therapeutic options for psychiatric disorders in the future.

CAMBA framework: Unveiling the brain asymmetry alterations and longitudinal changes after stroke using resting-state EEG

Hemispheric asymmetry or lateralization is a fundamental principle of brain organization. However, it is poorly understood to what extent the brain asymmetries across different levels of functional organizations are evident in health or altered in brain diseases. Here, we propose a framework that integrates three degrees of brain interactions (isolated nodes, node-node, and edge-edge) into a unified analysis pipeline to capture the sliding window-based asymmetry dynamics at both the node and hemisphere levels. We apply this framework to resting-state EEG in healthy and stroke populations and investigate the stroke-induced abnormal alterations in brain asymmetries and longitudinal asymmetry changes during poststroke rehabilitation. We observe that the mean asymmetry in patients was abnormally enhanced across different frequency bands and levels of brain interactions, with these abnormal patterns strongly associated with the side of the stroke lesion. Compared to healthy controls, patients displayed significant alterations in asymmetry fluctuations, disrupting and reconfiguring the balance of inter-hemispheric integration and segregation. Additionally, analyses reveal that specific abnormal asymmetry metrics in patients tend to move towards those observed in healthy controls after short-term brain-computer interface rehabilitation. Furthermore, preliminary evidence suggests that baseline clinical and asymmetry features can predict poststroke improvements in the Fugl-Meyer assessment of the lower extremity (mean absolute error of about 2). Overall, these findings advance our understanding of hemispheric asymmetry. Our framework offers new insights into the mechanisms underlying brain alterations and recovery after a brain lesion, may help identify prognostic biomarkers, and can be easily extended to different functional modalities.

EEG Phase Can Be Predicted with Similar Accuracy across Cognitive States after Accounting for Power and Signal-to-Noise Ratio

EEG phase is increasingly used in cognitive neuroscience, brain-computer interfaces, and closed-loop stimulation devices. However, it is unknown how accurate EEG phase prediction is across cognitive states. We determined the EEG phase prediction accuracy of parieto-occipital alpha waves across rest and task states in 484 participants over 11 public datasets. We were able to track EEG phase accurately across various cognitive conditions and datasets, especially during periods of high instantaneous alpha power and signal-to-noise ratio (SNR). Although resting states generally have higher accuracies than task states, absolute accuracy differences were small, with most of these differences attributable to EEG power and SNR. These results suggest that experiments and technologies using EEG phase should focus more on minimizing external noise and waiting for periods of high power rather than inducing a particular cognitive state.

Self-regulation learning as active inference: dynamic causal modeling of an fMRI neurofeedback task

Introduction

Learning to self-regulate brain activity by neurofeedback has been shown to lead to changes in the brain and behavior, with beneficial clinical and non-clinical outcomes. Neurofeedback uses a brain-computer interface to guide participants to change some feature of their brain activity. However, the neural mechanism of self-regulation learning remains unclear, with only 50% of the participants succeeding in achieving it. To bridge this knowledge gap, our study delves into the neural mechanisms of self-regulation learning via neurofeedback and investigates the brain processes associated with successful brain self-regulation.

Methods

We study the neural underpinnings of self-regulation learning by employing dynamical causal modeling (DCM) in conjunction with real-time functional MRI data. The study involved a cohort of 18 participants undergoing neurofeedback training targeting the supplementary motor area. A critical focus was the comparison between top-down hierarchical connectivity models proposed by Active Inference and alternative bottom-up connectivity models like reinforcement learning.

Results

Our analysis revealed a crucial distinction in brain connectivity patterns between successful and non-successful learners. Particularly, successful learners evinced a significantly stronger top-down effective connectivity towards the target area implicated in self-regulation. This heightened top-down network engagement closely resembles the patterns observed in goal-oriented and cognitive control studies, shedding light on the intricate cognitive processes intertwined with self-regulation learning.

Discussion

The findings from our investigation underscore the significance of cognitive mechanisms in the process of self-regulation learning through neurofeedback. The observed stronger top-down effective connectivity in successful learners indicates the involvement of hierarchical cognitive control, which aligns with the tenets of Active Inference. This study contributes to a deeper understanding of the neural dynamics behind successful self-regulation learning and provides insights into the potential cognitive architecture underpinning this process.

Somatosensory Event-Related Potential as an Electrophysiological Correlate of Endogenous Spatial Tactile Attention: Prospects for Electrotactile Brain-Computer Interface for Sensory Training

Tactile attention tasks are used in the diagnosis and treatment of neurological and sensory processing disorders, while somatosensory event-related potentials (ERP) measured by electroencephalography (EEG) are used as neural correlates of attention processes. Brain-computer interface (BCI) technology provides an opportunity for the training of mental task execution via providing online feedback based on ERP measures. Our recent work introduced a novel electrotactile BCI for sensory training, based on somatosensory ERP; however, no previous studies have addressed specific somatosensory ERP morphological features as measures of sustained endogenous spatial tactile attention in the context of BCI control. Here we show the morphology of somatosensory ERP responses induced by a novel task introduced within our electrotactile BCI platform i.e., the sustained endogenous spatial electrotactile attention task. By applying pulsed electrical stimuli to the two proximal stimulation hotspots at the user's forearm, stimulating sequentially the mixed branches of radial and median nerves with equal probability of stimuli occurrence, we successfully recorded somatosensory ERPs for both stimulation locations, in the attended and unattended conditions. Waveforms of somatosensory ERP responses for both mixed nerve branches showed similar morphology in line with previous reports on somatosensory ERP components obtained by stimulation of exclusively sensory nerves. Moreover, we found statistically significant increases in ERP amplitude on several components, at both stimulation hotspots, while sustained endogenous spatial electrotactile attention task is performed. Our results revealed the existence of general ERP windows of interest and signal features that can be used to detect sustained endogenous tactile attention and classify between spatial attention locations in 11 healthy subjects. The current results show that features of N140, P3a and P3b somatosensory ERP components are the most prominent global markers of sustained spatial electrotactile attention, over all subjects, within our novel electrotactile BCI task/paradigm, and this work proposes the features of those components as markers of sustained endogenous spatial tactile attention in online BCI control. Immediate implications of this work are the possible improvement of online BCI control within our novel electrotactile BCI system, while these finding can be used for other tactile BCI applications in the diagnosis and treatment of neurological disorders by employing mixed nerve somatosensory ERPs and sustained endogenous electrotactile attention task as control paradigms.

A new error-monitoring brain-computer interface based on reinforcement learning for people with autism spectrum disorders

Objective.Brain-computer interfaces (BCIs) are emerging as promising cognitive training tools in neurodevelopmental disorders, as they combine the advantages of traditional computerized interventions with real-time tailored feedback. We propose a gamified BCI based on non-volitional neurofeedback for cognitive training, aiming at reaching a neurorehabilitation tool for application in autism spectrum disorders (ASDs).Approach.The BCI consists of an emotional facial expression paradigm controlled by an intelligent agent that makes correct and wrong actions, while the user observes and judges the agent's actions. The agent learns through reinforcement learning (RL) an optimal strategy if the participant generates error-related potentials (ErrPs) upon incorrect agent actions. We hypothesize that this training approach will allow not only the agent to learn but also the BCI user, by participating through implicit error scrutiny in the process of learning through operant conditioning, making it of particular interest for disorders where error monitoring processes are altered/compromised such as in ASD. In this paper, the main goal is to validate the whole methodological BCI approach and assess whether it is feasible enough to move on to clinical experiments. A control group of ten neurotypical participants and one participant with ASD tested the proposed BCI approach.Main results.We achieved an online balanced-accuracy in ErrPs detection of 81.6% and 77.1%, respectively for two different game modes. Additionally, all participants achieved an optimal RL strategy for the agent at least in one of the test sessions.Significance.The ErrP classification results and the possibility of successfully achieving an optimal learning strategy, show the feasibility of the proposed methodology, which allows to move towards clinical experimentation with ASD participants to assess the effectiveness of the approach as hypothesized.

Can neurofeedback provide evidence of direct brain-behavior causality?

Neurofeedback is a procedure that measures brain activity in real-time and presents it as feedback to an individual, thus allowing them to self-regulate brain activity with effects on cognitive processes inferred from behavior. One common argument is that neurofeedback studies can reveal how the measured brain activity causes a particular cognitive process. The causal claim is often made regarding the measured brain activity being manipulated as an independent variable, similar to brain stimulation studies. However, this causal inference is vulnerable to the argument that other upstream brain activities change concurrently and cause changes in the brain activity from which feedback is derived. In this paper, we outline the inference that neurofeedback may causally affect cognition by indirect means. We further argue that researchers should remain open to the idea that the trained brain activity could be part of a "causal network" that collectively affects cognition rather than being necessarily causally primary. This particular inference may provide a better translation of evidence from neurofeedback studies to the rest of neuroscience. We argue that the recent advent of multivariate pattern analysis, when combined with implicit neurofeedback, currently comprises the strongest case for causality. Our perspective is that although the burden of inferring direct causality is difficult, it may be triangulated using a collection of various methods in neuroscience. Finally, we argue that the neurofeedback methodology provides unique advantages compared to other methods for revealing changes in the brain and cognitive processes but that researchers should remain mindful of indirect causal effects.

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.