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.

Training negative connectivity patterns between the dorsolateral prefrontal cortex and amygdala through fMRI-based neurofeedback to target adolescent socially-avoidant behaviour.

Social anxiety is prevalent in adolescence. Given its role in maintaining fears, reducing social avoidance through cognitive reappraisal may help attenuate social anxiety. We used fMRI-based neurofeedback (NF) to increase 'adaptive' patterns of negative connectivity between the dorsolateral prefrontal cortex (DLPFC) and the amygdala to change reappraisal ability, and alter social avoidance and approach behaviours in adolescents. Twenty-seven female participants aged 13-17 years with varying social anxiety levels completed a fMRI-based NF training task where they practiced cognitive reappraisal strategies, whilst receiving real-time feedback of DLPFC-amygdala connectivity. All participants completed measures of cognitive reappraisal and social approach-avoidance behaviour before and after NF training. Avoidance of happy faces was associated with greater social anxiety pre-training. Participants who were unable to acquire a more negative pattern of connectivity through NF training displayed significantly greater avoidance of happy faces at post-training compared to pre-training. These 'maladaptive' participants also reported significant decreases in re-appraisal ability from pre to post-training. In contrast, those who were able to acquire a more 'adaptive' connectivity pattern did not show these changes in social avoidance and re-appraisal. Future research could consider using strategies to improve the capacity of NF training to boost youth social-approach behaviour.

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.

Modulatory effects of dynamic fMRI-based neurofeedback on emotion regulation networks in adolescent females.

Research has shown that difficulties with emotion regulation abilities in childhood and adolescence increase the risk for developing symptoms of mental disorders, e.g anxiety. We investigated whether functional magnetic resonance imaging (fMRI)-based neurofeedback (NF) can modulate brain networks supporting emotion regulation abilities in adolescent females. We performed three experiments (Experiment 1: N â€‹= â€‹18; Experiment 2: N â€‹= â€‹30; Experiment 3: N â€‹= â€‹20). We first compared different NF implementations regarding their effectiveness of modulating prefrontal cortex (PFC)-amygdala functional connectivity (fc). Further we assessed the effects of fc-NF on neural measures, emotional/metacognitive measures and their associations. Finally, we probed the mechanism underlying fc-NF by examining concentrations of inhibitory and excitatory neurotransmitters. Results showed that NF implementations differentially modulate PFC-amygdala fc. Using the most effective NF implementation we observed important relationships between neural and emotional/metacognitive measures, such as practice-related change in fc was related with change in thought control ability. Further, we found that the relationship between state anxiety prior to the MRI session and the effect of fc-NF was moderated by GABA concentrations in the PFC and anterior cingulate cortex. To conclude, we were able to show that fc-NF can be used in adolescent females to shape neural and emotional/metacognitive measures underlying emotion regulation. We further show that neurotransmitter concentrations moderate fc-NF-effects.

Challenge Accepted? Individual Performance Gains for Motor Imagery Practice with Humanoid Robotic EEG Neurofeedback.

Optimizing neurofeedback (NF) and brain-computer interface (BCI) implementations constitutes a challenge across many fields and has so far been addressed by, among others, advancing signal processing methods or predicting the user's control ability from neurophysiological or psychological measures. In comparison, how context factors influence NF/BCI performance is largely unexplored. We here investigate whether a competitive multi-user condition leads to better NF/BCI performance than a single-user condition. We implemented a foot motor imagery (MI) NF with mobile electroencephalography (EEG). Twenty-five healthy, young participants steered a humanoid robot in a single-user condition and in a competitive multi-user race condition using a second humanoid robot and a pseudo competitor. NF was based on 8-30 Hz relative event-related desynchronization (ERD) over sensorimotor areas. There was no significant difference between the ERD during the competitive multi-user condition and the single-user condition but considerable inter-individual differences regarding which condition yielded a stronger ERD. Notably, the stronger condition could be predicted from the participants' MI-induced ERD obtained before the NF blocks. Our findings may contribute to enhance the performance of NF/BCI implementations and highlight the necessity of individualizing context factors.

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.

Modulating hemispheric lateralization by brain stimulation yields gain in mental and physical activity.

Imagery plays an important role in our life. Motor imagery is the mental simulation of a motor act without overt motor output. Previous studies have documented the effect of motor imagery practice. However, its translational potential for patients as well as for athletes, musicians and other groups, depends largely on the transfer from mental practice to overt physical performance. We used bilateral transcranial direct current stimulation (tDCS) over sensorimotor areas to modulate neural lateralization patterns induced by unilateral mental motor imagery and the performance of a physical motor task. Twenty-six healthy older adults participated (mean age = 67.1 years) in a double-blind cross-over sham-controlled study. We found stimulation-related changes at the neural and behavioural level, which were polarity-dependent. Specifically, for the hand contralateral to the anode, electroencephalographic activity induced by motor imagery was more lateralized and motor performance improved. In contrast, for the hand contralateral to the cathode, hemispheric lateralization was reduced. The stimulation-related increase and decrease in neural lateralization were negatively related. Further, the degree of stimulation-related change in neural lateralization correlated with the stimulation-related change on behavioural level. These convergent neurophysiological and behavioural effects underline the potential of tDCS to improve mental and physical motor performance.

High-Intensity Chronic Stroke Motor Imagery Neurofeedback Training at Home: Three Case Reports.

Motor imagery (MI) with neurofeedback has been suggested as promising for motor recovery after stroke. Evidence suggests that regular training facilitates compensatory plasticity, but frequent training is difficult to integrate into everyday life. Using a wireless electroencephalogram (EEG) system, we implemented a frequent and efficient neurofeedback training at the patients' home. Aiming to overcome maladaptive changes in cortical lateralization patterns we presented a visual feedback, representing the degree of contralateral sensorimotor cortical activity and the degree of sensorimotor cortex lateralization. Three stroke patients practiced every other day, over a period of 4 weeks. Training-related changes were evaluated on behavioral, functional, and structural levels. All 3 patients indicated that they enjoyed the training and were highly motivated throughout the entire training regime. EEG activity induced by MI of the affected hand became more lateralized over the course of training in all three patients. The patient with a significant functional change also showed increased white matter integrity as revealed by diffusion tensor imaging, and a substantial clinical improvement of upper limb motor functions. Our study provides evidence that regular, home-based practice of MI neurofeedback has the potential to facilitate cortical reorganization and may also increase associated improvements of upper limb motor function in chronic stroke patients.

Motor Imagery Impairment in Postacute Stroke Patients.

Not much is known about how well stroke patients are able to perform motor imagery (MI) and which MI abilities are preserved after stroke. We therefore applied three different MI tasks (one mental chronometry task, one mental rotation task, and one EEG-based neurofeedback task) to a sample of postacute stroke patients (n = 20) and age-matched healthy controls (n = 20) for addressing the following questions: First, which of the MI tasks indicate impairment in stroke patients and are impairments restricted to the paretic side? Second, is there a relationship between MI impairment and sensory loss or paresis severity? And third, do the results of the different MI tasks converge? Significant differences between the stroke and control groups were found in all three MI tasks. However, only the mental chronometry task and EEG analysis revealed paresis side-specific effects. Moreover, sensitivity loss contributed to a performance drop in the mental rotation task. The findings indicate that although MI abilities may be impaired after stroke, most patients retain their ability for MI EEG-based neurofeedback. Interestingly, performance in the different MI measures did not strongly correlate, neither in stroke patients nor in healthy controls. We conclude that one MI measure is not sufficient to fully assess an individual's MI abilities.

Simultaneous EEG-fNIRS reveals how age and feedback affect motor imagery signatures.

Stroke frequently results in motor impairment. Motor imagery (MI), the mental practice of movements, has been suggested as a promising complement to other therapeutic approaches facilitating motor rehabilitation. Of particular potential is the combination of MI with neurofeedback (NF). However, MI NF protocols have been largely optimized only in younger healthy adults, although strokes occur more frequently in older adults. The present study examined the influence of age on the neural correlates of MI supported by electroencephalogram (EEG)-based NF and on the neural correlates of motor execution. We adopted a multimodal neuroimaging framework focusing on EEG-derived event-related desynchronization (ERD%) and oxygenated (HbO) and deoxygenated hemoglobin (HbR) concentrations simultaneously acquired using functional near-infrared spectroscopy (fNIRS). ERD%, HbO concentration and HbR concentration were compared between younger (mean age: 24.4 years) and older healthy adults (mean age: 62.6 years). During MI, ERD% and HbR concentration were less lateralized in older adults than in younger adults. The lateralization-by-age interaction was not significant for movement execution. Moreover, EEG-based NF was related to an increase in task-specific activity when compared to the absence of feedback in both older and younger adults. Finally, significant modulation correlations were found between ERD% and hemodynamic measures despite the absence of significant amplitude correlations. Overall, the findings suggest a complex relationship between age and movement-related activity in electrophysiological and hemodynamic measures. Our results emphasize that the age of the actual end-user should be taken into account when designing neurorehabilitation protocols.

Lateralization patterns of covert but not overt movements change with age: An EEG neurofeedback study.

The mental practice of movements has been suggested as a promising add-on therapy to facilitate motor recovery after stroke. In the case of mentally practised movements, electroencephalogram (EEG) can be utilized to provide feedback about an otherwise covert act. The main target group for such an intervention are elderly patients, though research so far is largely focused on young populations (<30 years). The present study therefore aimed to examine the influence of age on the neural correlates of covert movements (CMs) in a real-time EEG neurofeedback framework. CM-induced event-related desynchronization (ERD) was studied in young (mean age: 23.6 years) and elderly (mean age: 62.7 years) healthy adults. Participants performed covert and overt hand movements. CMs were based on kinesthetic motor imagery (MI) or quasi-movements (QM). Based on previous studies investigating QM in the mu frequency range (8-13Hz) QM were expected to result in more lateralized ERD% patterns and accordingly higher classification accuracies. Independent of CM strategy the elderly were characterized by a significantly reduced lateralization of ERD%, due to stronger ipsilateral ERD%, and in consequence, reduced classification accuracies. QM were generally perceived as more vivid, but no differences were evident between MI and QM in ERD% or classification accuracies. EEG feedback enhanced task-related activity independently of strategy and age. ERD% measures of overt and covert movements were strongly related in young adults, whereas in the elderly ERD% lateralization is dissociated. In summary, we did not find evidence in support of more pronounced ERD% lateralization patterns in QM. Our finding of a less lateralized activation pattern in the elderly is in accordance to previous research and with the idea that compensatory processes help to overcome neurodegenerative changes related to normal ageing. Importantly, it indicates that EEG neurofeedback studies should place more emphasis on the age of the potential end-users.

Real-time EEG feedback during simultaneous EEG-fMRI identifies the cortical signature of motor imagery.

Motor imagery (MI) combined with real-time electroencephalogram (EEG) feedback is a popular approach for steering brain-computer interfaces (BCI). MI BCI has been considered promising as add-on therapy to support motor recovery after stroke. Yet whether EEG neurofeedback indeed targets specific sensorimotor activation patterns cannot be unambiguously inferred from EEG alone. We combined MI EEG neurofeedback with concurrent and continuous functional magnetic resonance imaging (fMRI) to characterize the relationship between MI EEG neurofeedback and activation in cortical sensorimotor areas. EEG signals were corrected online from interfering MRI gradient and ballistocardiogram artifacts, enabling the delivery of real-time EEG feedback. Significantly enhanced task-specific brain activity during feedback compared to no feedback blocks was present in EEG and fMRI. Moreover, the contralateral MI related decrease in EEG sensorimotor rhythm amplitude correlated inversely with fMRI activation in the contralateral sensorimotor areas, whereas a lateralized fMRI pattern did not necessarily go along with a lateralized EEG pattern. Together, the findings indicate a complex relationship between MI EEG signals and sensorimotor cortical activity, whereby both are similarly modulated by EEG neurofeedback. This finding supports the potential of MI EEG neurofeedback for motor rehabilitation and helps to better understand individual differences in MI BCI performance.

Mobile EEG and its potential to promote the theory and application of imagery-based motor rehabilitation.

Studying the brain in its natural state remains a major challenge for neuroscience. Solving this challenge would not only enable the refinement of cognitive theory, but also provide a better understanding of cognitive function in the type of complex and unpredictable situations that constitute daily life, and which are often disturbed in clinical populations. With mobile EEG, researchers now have access to a tool that can help address these issues. In this paper we present an overview of technical advancements in mobile EEG systems and associated analysis tools, and explore the benefits of this new technology. Using the example of motor imagery (MI) we will examine the translational potential of MI-based neurofeedback training for neurological rehabilitation and applied research.