A Multi-Target Motor Imagery Training Using Bimodal EEG-fMRI Neurofeedback: A Pilot Study in Chronic Stroke Patients

Electroencephalography Neurofeedback Training with Focus on the State of Attention: An Investigation Using Source Localization and Effective Connectivity

Identifying brain activity and flow direction can help in monitoring the effectiveness of neurofeedback tasks that aim to treat cognitive deficits. The goal of this study was to compare the neuronal electrical activity of the cortex between individuals from two groups-low and high difficulty-based on a spatial analysis of electroencephalography (EEG) acquired through neurofeedback sessions. These sessions require the subjects to maintain their state of attention when executing a task. EEG data were collected during three neurofeedback sessions for each person, including theta and beta frequencies, followed by a comprehensive preprocessing. The inverse solution based on cortical current density was applied to identify brain regions related to the state of attention. Thereafter, effective connectivity between those regions was estimated using the Directed Transfer Function. The average cortical current density of the high-difficulty group demonstrated that the medial prefrontal, dorsolateral prefrontal, and temporal regions are related to the attentional state. In contrast, the low-difficulty group presented higher current density values in the central regions. Furthermore, for both theta and beta frequencies, for the high-difficulty group, flows left and entered several regions, unlike the low-difficulty group, which presented flows leaving a single region. In this study, we identified which brain regions are related to the state of attention in individuals who perform more demanding tasks (high-difficulty group).

EEG-based sensorimotor neurofeedback for motor neurorehabilitation in children and adults: A scoping review

OBJECTIVE

Therapeutic interventions for children and young people with dystonia and dystonic/dyskinetic cerebral palsy are limited. EEG-based neurofeedback is emerging as a neurorehabilitation tool. This scoping review maps research investigating EEG-based sensorimotor neurofeedback in adults and children with neurological motor impairments, including augmentative strategies.

METHODS

MEDLINE, CINAHL and Web of Science databases were searched up to 2023 for relevant studies. Study selection and data extraction were conducted independently by at least two reviewers.

RESULTS

Of 4380 identified studies, 133 were included, only three enrolling children. The most common diagnosis was adult-onset stroke (77%). Paradigms mostly involved upper limb motor imagery or motor attempt. Common neurofeedback modes included visual, haptic and/or electrical stimulation. EEG parameters varied widely and were often incompletely described. Two studies applied augmentative strategies. Outcome measures varied widely and included classification accuracy of the Brain-Computer Interface, degree of enhancement of mu rhythm modulation or other neurophysiological parameters, and clinical/motor outcome scores. Few studies investigated whether functional outcomes related specifically to the EEG-based neurofeedback.

CONCLUSIONS

There is limited evidence exploring EEG-based sensorimotor neurofeedback in individuals with movement disorders, especially in children. Further clarity of neurophysiological parameters is required to develop optimal paradigms for evaluating sensorimotor neurofeedback.

SIGNIFICANCE

The expanding field of sensorimotor neurofeedback offers exciting potential as a non-invasive therapy. However, this needs to be balanced by robust study design and detailed methodological reporting to ensure reproducibility and validation that clinical improvements relate to induced neurophysiological changes.

Global trends and research hotspots of stroke and magnetic resonance imaging: A bibliometric analysis

BACKGROUND

In this study, we used CiteSpace and VOSviewer to create a bibliometric visualization of research papers relating to stroke and magnetic resonance imaging (MRI) between 2000 and 2022. To fully understand the trends and hotspots in MRI and stroke research and provide new perspectives for future studies.

METHODS

The Web of Science Core Collection was selected as the source of data for this paper. Using CiteSpace and VOSviewer, publications were analyzed for authors, countries, institutions, journals, references, and keywords.

RESULTS

We found 1423 papers after searching and removing duplicates, which indicated an upward trend over the previous 23 years. Fiebach J.B. is the most published author (21 publications), Hacke W. is the most cited author (213 citations), and the United States (449 publications) and Harvard University (86 publications) are the most prolific nations and institutions. Stroke is the journal with the most co-citations (1275) and the most papers (171) published. The most representative reference was the 1995 article by Marler et al, which received 115 citations and had the top 3 co-occurring keywords: stroke, magnetic resonance imaging, and MRI. The article by Nogueria et al showed the strongest citation burst at the end of 2022 (strength = 17.32). High-frequency keywords in recent years are time, association, functional connectivity, thrombectomy, and rehabilitation.

CONCLUSION

This study provides a scientific perspective on stroke and MRI research, provides valuable information for researchers to understand the current status of research, hotspots, and trends, and guides future research directions.

Two is better? combining EEG and fMRI for BCI and neurofeedback: a systematic review

Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are two commonly used non-invasive techniques for measuring brain activity in neuroscience and brain-computer interfaces (BCI).Objective. In this review, we focus on the use of EEG and fMRI in neurofeedback (NF) and discuss the challenges of combining the two modalities to improve understanding of brain activity and achieve more effective clinical outcomes. Advanced technologies have been developed to simultaneously record EEG and fMRI signals to provide a better understanding of the relationship between the two modalities. However, the complexity of brain processes and the heterogeneous nature of EEG and fMRI present challenges in extracting useful information from the combined data.Approach. We will survey existing EEG-fMRI combinations and recent studies that exploit EEG-fMRI in NF, highlighting the experimental and technical challenges.Main results. We made a classification of the different combination of EEG-fMRI for NF, we provide a review of multimodal analysis methods for EEG-fMRI features. We also survey the current state of research on EEG-fMRI in the different existing NF paradigms. Finally, we also identify some of the remaining challenges in this field.Significance. By exploring EEG-fMRI combinations in NF, we are advancing our knowledge of brain function and its applications in clinical settings. As such, this review serves as a valuable resource for researchers, clinicians, and engineers working in the field of neural engineering and rehabilitation, highlighting the promising future of EEG-fMRI-based NF.

EEG-LLAMAS: A low-latency neurofeedback platform for artifact reduction in EEG-fMRI

Simultaneous EEG-fMRI is a powerful multimodal technique for imaging the brain, but its use in neurofeedback experiments has been limited by EEG noise caused by the MRI environment. Neurofeedback studies typically require analysis of EEG in real time, but EEG acquired inside the scanner is heavily contaminated with ballistocardiogram (BCG) artifact, a high-amplitude artifact locked to the cardiac cycle. Although techniques for removing BCG artifacts do exist, they are either not suited to real-time, low-latency applications, such as neurofeedback, or have limited efficacy. We propose and validate a new open-source artifact removal software called EEG-LLAMAS (Low Latency Artifact Mitigation Acquisition Software), which adapts and advances existing artifact removal techniques for low-latency experiments. We first used simulations to validate LLAMAS in data with known ground truth. We found that LLAMAS performed better than the best publicly-available real-time BCG removal technique, optimal basis sets (OBS), in terms of its ability to recover EEG waveforms, power spectra, and slow wave phase. To determine whether LLAMAS would be effective in practice, we then used it to conduct real-time EEG-fMRI recordings in healthy adults, using a steady state visual evoked potential (SSVEP) task. We found that LLAMAS was able to recover the SSVEP in real time, and recovered the power spectra collected outside the scanner better than OBS. We also measured the latency of LLAMAS during live recordings, and found that it introduced a lag of less than 50 ms on average. The low latency of LLAMAS, coupled with its improved artifact reduction, can thus be effectively used for EEG-fMRI neurofeedback. A limitation of the method is its use of a reference layer, a piece of EEG equipment which is not commercially available, but can be assembled in-house. This platform enables closed-loop experiments which previously would have been prohibitively difficult, such as those that target short-duration EEG events, and is shared openly with the neuroscience community.

Probing fMRI brain connectivity and activity changes during emotion regulation by EEG neurofeedback

Despite the existence of several emotion regulation studies using neurofeedback, interactions among a small number of regions were evaluated, and therefore, further investigation is needed to understand the interactions of the brain regions involved in emotion regulation. We implemented electroencephalography (EEG) neurofeedback with simultaneous functional magnetic resonance imaging (fMRI) using a modified happiness-inducing task through autobiographical memories to upregulate positive emotion. Then, an explorative analysis of whole brain regions was done to understand the effect of neurofeedback on brain activity and the interaction of whole brain regions involved in emotion regulation. The participants in the control and experimental groups were asked to do emotion regulation while viewing positive images of autobiographical memories and getting sham or real (based on alpha asymmetry) EEG neurofeedback, respectively. The proposed multimodal approach quantified the effects of EEG neurofeedback in changing EEG alpha power, fMRI blood oxygenation level-dependent (BOLD) activity of prefrontal, occipital, parietal, and limbic regions (up to 1.9% increase), and functional connectivity in/between prefrontal, parietal, limbic system, and insula in the experimental group. New connectivity links were identified by comparing the brain functional connectivity between experimental conditions (Upregulation and View blocks) and also by comparing the brain connectivity of the experimental and control groups. Psychometric assessments confirmed significant changes in positive and negative mood states in the experimental group by neurofeedback. Based on the exploratory analysis of activity and connectivity among all brain regions involved in emotion regions, we found significant BOLD and functional connectivity increases due to EEG neurofeedback in the experimental group, but no learning effect was observed in the control group. The results reveal several new connections among brain regions as a result of EEG neurofeedback which can be justified according to emotion regulation models and the role of those regions in emotion regulation and recalling positive autobiographical memories.

Simultaneous real-time EEG-fMRI neurofeedback: A systematic review

Neurofeedback (NF) is a biofeedback technique that teaches individuals self-control of brain functions by measuring brain activations and providing an online feedback signal to modify emotional, cognitive, and behavioral functions. NF approaches typically rely on a single modality, such as electroencephalography (EEG-NF) or a brain imaging technique, such as functional magnetic resonance imaging (fMRI-NF). The introduction of simultaneous EEG-fMRI tools has opened up the possibility of combining the high temporal resolution of EEG with the high spatial resolution of fMRI, thereby increasing the accuracy of NF. However, only a few studies have actively combined both techniques. In this study, we conducted a systematic review of EEG-fMRI-NF studies (N = 17) to identify the potential and effectiveness of this non-invasive treatment for neurological conditions. The systematic review revealed a lack of homogeneity among the studies, including sample sizes, acquisition methods in terms of simultaneity of the two procedures (unimodal EEG-NF and fMRI-NF), therapeutic targets field, and the number of sessions. Indeed, because most studies are based on a single session of NF, it is difficult to draw any conclusions regarding the therapeutic efficacy of NF. Therefore, further research is needed to fully understand non-clinical and clinical potential of EEG-fMRI-NF.

Self-modulation of motor cortex activity after stroke: a randomized controlled trial

Real-time functional MRI neurofeedback allows individuals to self-modulate their ongoing brain activity. This may be a useful tool in clinical disorders that are associated with altered brain activity patterns. Motor impairment after stroke has previously been associated with decreased laterality of motor cortex activity. Here we examined whether chronic stroke survivors were able to use real-time fMRI neurofeedback to increase laterality of motor cortex activity and assessed effects on motor performance and on brain structure and function. We carried out a randomized, double-blind, sham-controlled trial (ClinicalTrials.gov: NCT03775915) in which 24 chronic stroke survivors with mild to moderate upper limb impairment experienced three training days of either Real (n = 12) or Sham (n = 12) neurofeedback. Assessments of brain structure, brain function and measures of upper-limb function were carried out before and 1 week after neurofeedback training. Additionally, measures of upper-limb function were repeated 1 month after neurofeedback training. Primary outcome measures were (i) changes in lateralization of motor cortex activity during movements of the stroke-affected hand throughout neurofeedback training days; and (ii) changes in motor performance of the affected limb on the Jebsen Taylor Test (JTT). Stroke survivors were able to use Real neurofeedback to increase laterality of motor cortex activity within (P = 0.019), but not across, training days. There was no group effect on the primary behavioural outcome measure, which was average JTT performance across all subtasks (P = 0.116). Secondary analysis found improvements in the performance of the gross motor subtasks of the JTT in the Real neurofeedback group compared to Sham (P = 0.010). However, there were no improvements on the Action Research Arm Test or the Upper Extremity Fugl-Meyer score (both P > 0.5). Additionally, decreased white-matter asymmetry of the corticospinal tracts was detected 1 week after neurofeedback training (P = 0.008), indicating that the tracts become more similar with Real neurofeedback. Changes in the affected corticospinal tract were positively correlated with participants neurofeedback performance (P = 0.002). Therefore, here we demonstrate that chronic stroke survivors are able to use functional MRI neurofeedback to self-modulate motor cortex activity in comparison to a Sham control, and that training is associated with improvements in gross hand motor performance and with white matter structural changes.

Dynamic functional connectivity estimation for neurofeedback emotion regulation paradigm with simultaneous EEG-fMRI analysis

Joint Analysis of EEG and fMRI datasets can bring new insight into brain mechanisms. In this paper, we employed the recently introduced Correlated Coupled Tensor Matrix Factorization (CCMTF) method for analysis of the emotion regulation paradigm based on EEG frontal asymmetry neurofeedback in the alpha frequency band with simultaneous fMRI. CCMTF method assumes that the co-variations of the common dimension (temporal dimension) between EEG and fMRI are correlated and not necessarily identical. The results of the CCMTF method suggested that EEG and fMRI had similar covariations during the transition of brain activities from resting states to task (view and upregulation) states and these covariations followed an increasing trend. The fMRI shared spatial component showed activations in the limbic system, DLPFC, OFC, and VLPC regions, which were consistent with the previous studies and were linked to EEG frequency patterns in the range of 1-15 Hz with a correlation value close to 0.75. The estimated regions from the CCMTF method were then used as the candidate nodes for dynamic functional connectivity (dFC) analysis, in which the changes in connectivity from view to upregulation states were examined. The results of the dFC analysis were compared with a Normalized Mutual information (NMI) based approach in two different frequency ranges (1-15 and 15-40 Hz) as the NMI method was applied to the vectors of dFC nodes of EEG and fMRI data. The results of the two methods illustrated that the relation between EEG and fMRI datasets was mostly in the frequency range of 1-15 Hz. These relations were both in the brain activations and the dFCs between the two modalities. This paper suggests that the CCMTF method is a capable approach for extracting the shared information between EEG and fMRI data and can reveal new information about brain functions and their connectivity without solving the EEG inverse problem or analyzing different frequency bands.

Research and application advances in rehabilitation assessment of stroke

Stroke has a high incidence and disability rate, and rehabilitation is an effective means to reduce the disability rate of patients. To systematize rehabilitation assessment, which is the foundation for rehabilitation therapy, we summarize the assessment methods commonly used in research and clinical applications, including the various types of stroke rehabilitation scales and their applicability, and related biomedical detection technologies, including surface electromyography (sEMG), motion analysis systems, transcranial magnetic stimulation (TMS), magnetic resonance imaging (MRI), and combinations of different techniques. We also introduce some assessment techniques that are still in the experimental phase, such as the prospective application of artificial intelligence (AI) with optical correlation tomography (OCT) in stroke rehabilitation. This review provides a useful bibliography for the assessment of not only the severity of stroke injury, but also the therapeutic effects of stroke rehabilitation, and establishes a solid base for the future development of stroke rehabilitation skills.

Toward an Adapted Neurofeedback for Post-stroke Motor Rehabilitation: State of the Art and Perspectives

Stroke is a severe health issue, and motor recovery after stroke remains an important challenge in the rehabilitation field. Neurofeedback (NFB), as part of a brain–computer interface, is a technique for modulating brain activity using on-line feedback that has proved to be useful in motor rehabilitation for the chronic stroke population in addition to traditional therapies. Nevertheless, its use and applications in the field still leave unresolved questions. The brain pathophysiological mechanisms after stroke remain partly unknown, and the possibilities for intervention on these mechanisms to promote cerebral plasticity are limited in clinical practice. In NFB motor rehabilitation, the aim is to adapt the therapy to the patient’s clinical context using brain imaging, considering the time after stroke, the localization of brain lesions, and their clinical impact, while taking into account currently used biomarkers and technical limitations. These modern techniques also allow a better understanding of the physiopathology and neuroplasticity of the brain after stroke. We conducted a narrative literature review of studies using NFB for post-stroke motor rehabilitation. The main goal was to decompose all the elements that can be modified in NFB therapies, which can lead to their adaptation according to the patient’s context and according to the current technological limits. Adaptation and individualization of care could derive from this analysis to better meet the patients’ needs. We focused on and highlighted the various clinical and technological components considering the most recent experiments. The second goal was to propose general recommendations and enhance the limits and perspectives to improve our general knowledge in the field and allow clinical applications. We highlighted the multidisciplinary approach of this work by combining engineering abilities and medical experience. Engineering development is essential for the available technological tools and aims to increase neuroscience knowledge in the NFB topic. This technological development was born out of the real clinical need to provide complementary therapeutic solutions to a public health problem, considering the actual clinical context of the post-stroke patient and the practical limits resulting from it.

Emerging Limb Rehabilitation Therapy After Post-stroke Motor Recovery

Stroke, including hemorrhagic and ischemic stroke, refers to the blood supply disorder in the local brain tissue for various reasons (aneurysm, occlusion, etc.). It leads to regional brain circulation imbalance, neurological complications, limb motor dysfunction, aphasia, and depression. As the second-leading cause of death worldwide, stroke poses a significant threat to human life characterized by high mortality, disability, and recurrence. Therefore, the clinician has to care about the symptoms of stroke patients in the acute stage and formulate an effective postoperative rehabilitation plan to facilitate the recovery in patients. We summarize a novel application and update of the rehabilitation therapy in limb motor rehabilitation of stroke patients to provide a potential future stroke rehabilitation strategy.

Simultaneous EEG-fMRI: What Have We Learned and What Does the Future Hold?

Simultaneous EEG-fMRI has developed into a mature measurement technique in the past 25 years. During this time considerable technical and analytical advances have been made, enabling valuable scientific contributions to a range of research fields. This review will begin with an introduction to the measurement principles involved in EEG and fMRI and the advantages of combining these methods. The challenges faced when combining the two techniques will then be considered. An overview of the leading application fields where EEG-fMRI has made a significant contribution to the scientific literature and emerging applications in EEG-fMRI research trends is then presented.

Closed-loop Modulation of the Self-regulating Brain: A Review on Approaches, Emerging Paradigms, and Experimental Designs

Closed-loop approaches, setups, and experimental designs have been applied within the field of neuroscience to enhance the understanding of basic neurophysiology principles (closed-loop neuroscience; CLNS) and to develop improved procedures for modulating brain circuits and networks for clinical purposes (closed-loop neuromodulation; CLNM). The contents of this review are thus arranged into the following sections. First, we describe basic research findings that have been made using CLNS. Next, we provide an overview of the application, rationale, and therapeutic aspects of CLNM for clinical purposes. Finally, we summarize methodological concerns and critics in clinical practice of neurofeedback and novel applications of closed-loop perspective and techniques to improve and optimize its experiments. Moreover, we outline the theoretical explanations and experimental ideas to test animal models of neurofeedback and discuss technical issues and challenges associated with implementing closed-loop systems. We hope this review is helpful for both basic neuroscientists and clinical/ translationally-oriented scientists interested in applying closed-loop methods to improve mental health and well-being.

The impact of neurofeedback on effective connectivity networks in chronic stroke patients: an exploratory study

Objective.In this study, we assessed the impact of electroencephalography-functional magnetic resonance imaging (EEG-fMRI) neurofeedback (NF) on connectivity strength and direction in bilateral motor cortices in chronic stroke patients. Most of the studies using NF or brain computer interfaces for stroke rehabilitation have assessed treatment effects focusing on successful activation of targeted cortical regions. However, given the crucial role of brain network reorganization for stroke recovery, our broader aim was to assess connectivity changes after an NF training protocol targeting localized motor areas.Approach.We considered changes in fMRI connectivity after a multisession EEG-fMRI NF training targeting ipsilesional motor areas in nine stroke patients. We applied the dynamic causal modeling and parametric empirical Bayes frameworks for the estimation of effective connectivity changes. We considered a motor network including both ipsilesional and contralesional premotor, supplementary and primary motor areas.Main results.Our results indicate that NF upregulation of targeted areas (ipsilesional supplementary and primary motor areas) not only modulated activation patterns, but also had a more widespread impact on fMRI bilateral motor networks. In particular, inter-hemispheric connectivity between premotor and primary motor regions decreased, and ipsilesional self-inhibitory connections were reduced in strength, indicating an increase in activation during the NF motor task.Significance.To the best of our knowledge, this is the first work that investigates fMRI connectivity changes elicited by training of localized motor targets in stroke. Our results open new perspectives in the understanding of large-scale effects of NF training and the design of more effective NF strategies, based on the pathophysiology underlying stroke-induced deficits.

Crossing time windows optimization based on mutual information for hybrid BCI

Hybrid EEG-fNIRS brain-computer interface (HBCI) is widely employed to enhance BCI performance. EEG and fNIRS signals are combined to increase the dimensionality of the information. Time windows are used to select EEG and fNIRS singles synchronously. However, it ignores that specific modal signals have their own characteristics, when the task is stimulated, the information between the modalities will mismatch at the moment, which has a significant impact on the classification performance. Here we propose a novel crossing time windows optimization for mental arithmetic (MA) based BCI. The EEG and fNIRS signals were segmented separately by sliding time windows. Then crossing time windows (CTW) were combined with each one segment from EEG and fNIRS selected independently. Furthermore, EEG and fNIRS features were extracted using Filter Bank Common Spatial Pattern (FBCSP) and statistical methods from each sample. Mutual information was calculated for FBCSP and statistical features to characterize the discrimination of crossing time windows, and the optimal window would be selected based on the largest mutual information. Finally, a sparse structured framework of Fisher Lasso feature selection (FLFS) was designed to select the joint features, and conventional Linear Discriminant Analysis (LDA) was employed to perform classification. We used proposed method for a MA dataset. The classification accuracy of the proposed method is 92.52 ± 5.38% and higher than other methods, which shows the rationality and superiority of the proposed method.

Application of fMRI and Simultaneous fMRI-EEG Neurofeedback in Post-Stroke Motor Rehabilitation

This article discusses the contribution of fMRI- and fMRI-EEG-neurofeedback into recovery of motor function in two subacute stroke patients during the early post-stroke period. Premotor and supplementary motor zones of the cortex were chosen as the targets of voluntary control. Patient 1 received 6 sessions of motor imagery-based fMRI neurofeedback of secondary motor areas activity and Patient 2 received a similar course with the addition of μ- and β-EEG activity suppression. Both reduced the motor deficit severity, improved on the quality of life, and increased the C3/C4 coherence to other central leads within EEG μ-band. Patient 1 reliably increased the fMRI signal in target areas and improved on the strength and speed of hand movements. Patient 2 (fMRI-EEG) mastered the EEG activity regulation to a greater degree. The authors conclude that pure fMRI neurofeedback and bi-modal fMRI-EEG neurofeedback produce different clinical effects in motor rehabilitation, which confirms the prospect of the closed-loop stroke treatment.

[Execution, assessment and improvement methods of motor imagery for brain-computer interface]

Motor imagery (MI) is an important paradigm of driving brain computer interface (BCI). However, MI is not easy to control or acquire, and the performance of MI-BCI depends heavily on the performance of the subjects' MI. Therefore, the correct execution of MI mental activities, ability evaluation and improvement methods play important and even critical roles in the improvement and application of MI-BCI system's performance. However, in the research and development of MI-BCI, the existing researches mainly focus on the decoding algorithm of MI, but do not pay enough attention to the above three aspects of MI mental activities. In this paper, these problems of MI-BCI are discussed in detail, and it is pointed out that the subjects tend to use visual motor imagery as kinesthetic motor imagery. In the future, we need to develop some objective, quantitatively visualized MI ability evaluation methods, and develop some effective and less time-consumption training methods to improve MI ability. It is also necessary to solve the differences and commonness of MI problems between and within individuals and MI-BCI illiteracy to a certain extent.

Gradients of connectivity as graph Fourier bases of brain activity

The application of graph theory to model the complex structure and function of the brain has shed new light on its organization, prompting the emergence of network neuroscience. Despite the tremendous progress that has been achieved in this field, still relatively few methods exploit the topology of brain networks to analyze brain activity. Recent attempts in this direction have leveraged on the one hand graph spectral analysis (to decompose brain connectivity into eigenmodes or gradients) and the other graph signal processing (to decompose brain activity "coupled to" an underlying network in graph Fourier modes). These studies have used a variety of imaging techniques (e.g., fMRI, electroencephalography, diffusion-weighted and myelin-sensitive imaging) and connectivity estimators to model brain networks. Results are promising in terms of interpretability and functional relevance, but methodologies and terminology are variable. The goals of this paper are twofold. First, we summarize recent contributions related to connectivity gradients and graph signal processing, and attempt a clarification of the terminology and methods used in the field, while pointing out current methodological limitations. Second, we discuss the perspective that the functional relevance of connectivity gradients could be fruitfully exploited by considering them as graph Fourier bases of brain activity.

Towards a common template for neural reinforcement of finger individuation

The inability to individuate finger movements is a common impairment following stroke. Conventional physical therapy ignores underlying neural changes with recovery, leaving it unclear why sensorimotor function often remains impaired. Functional MRI neurofeedback can monitor neural activity and reinforce it towards a healthy template to restore function. However, identifying an individualized training template may not be possible depending on the severity of impairment. In this study, we investigated the use of functional alignment of brain data across healthy participants to create an idealized neural template to be used as a training target for new participants. We employed multi-voxel pattern analyses to assess the prediction accuracy and robustness to missing data of pre-trained functional templates corresponding to individual finger presses. We found a significant improvement in classification accuracy (p < 0.001) of individual finger presses when group data was aligned based on function (88%) rather than anatomy (46%). Importantly, we found no significant drop in performance when aligning a new participant to a pre-established template as compared to including this new participant in the creation of a new template. These results indicate that functionally aligned templates could provide an effective surrogate training target for patients following neurological injury.

Modulating the interhemispheric activity balance in the intraparietal sulcus using real-time fMRI neurofeedback: Development and proof-of-concept

The intraparietal sulcus (IPS) plays a key role in the distribution of attention across the visual field. In stroke patients, an imbalance between left and right IPS activity has been related to a spatial bias in visual attention characteristic of hemispatial neglect. In this study, we describe the development and implementation of a real-time functional magnetic resonance imaging neurofeedback protocol to noninvasively and volitionally control the interhemispheric IPS activity balance in neurologically healthy participants. Six participants performed three neurofeedback training sessions across three weeks. Half of them trained to voluntarily increase brain activity in left relative to right IPS, while the other half trained to regulate the IPS activity balance in the opposite direction. Before and after the training, we estimated the distribution of attention across the visual field using a whole and partial report task. Over the course of the training, two of the three participants in the left-IPS group increased the activity in the left relative to the right IPS, while the participants in the right-IPS group were not able to regulate the interhemispheric IPS activity balance. We found no evidence for a decrease in resting-state functional connectivity between left and right IPS, and the spatial distribution of attention did not change over the course of the experiment. This study indicates the possibility to voluntarily modulate the interhemispheric IPS activity balance. Further research is warranted to examine the effectiveness of this technique in the rehabilitation of post-stroke hemispatial neglect.

Effects of simultaneous real-time fMRI and EEG neurofeedback in major depressive disorder evaluated with brain electromagnetic tomography

Recently, we reported an emotion self-regulation study (Zotev et al., 2020), in which patients with major depressive disorder (MDD) used simultaneous real-time fMRI and EEG neurofeedback (rtfMRI-EEG-nf) to upregulate two fMRI and two EEG activity measures, relevant to MDD. The target measures included fMRI activities of the left amygdala and left rostral anterior cingulate cortex, and frontal EEG asymmetries in the alpha band (FAA) and high-beta band (FBA). Here we apply the exact low resolution brain electromagnetic tomography (eLORETA) to investigate EEG source activities during the rtfMRI-EEG-nf procedure. The exploratory analyses reveal significant changes in hemispheric lateralities of upper alpha and high-beta current source densities in the prefrontal regions, consistent with upregulation of the FAA and FBA during the rtfMRI-EEG-nf task. Similar laterality changes are observed for current source densities in the amygdala. Prefrontal upper alpha current density changes show significant negative correlations with anhedonia severity. Changes in prefrontal high-beta current density are consistent with reduction in comorbid anxiety. Comparisons with results of previous LORETA studies suggest that the rtfMRI-EEG-nf training is beneficial to MDD patients, and may have the ability to correct functional deficiencies associated with anhedonia and comorbid anxiety in MDD.

Real-Time Functional Magnetic Resonance Imaging Neurofeedback for the Relief of Distressing Auditory-Verbal Hallucinations: Methodological and Empirical Advances

Auditory-verbal hallucinations (AVH) are often associated with high levels of distress and disability in individuals with schizophrenia-spectrum disorders. In around 30% of individuals with distressing AVH and diagnosed with schizophrenia, traditional antipsychotic drugs have little or no effect. Thus, it is important to develop mechanistic models of AVH to inform new treatments. Recently a small number of studies have begun to explore the use of real-time functional magnetic resonance imaging neurofeedback (rtfMRI-NF) for the treatment of AVH in individuals with schizophrenia. rtfMRI-NF protocols have been developed to provide feedback about brain activation in real time to enable participants to progressively achieve voluntary control over their brain activity. We offer a conceptual review of the background and general features of neurofeedback procedures before summarizing and evaluating existing mechanistic models of AVH to identify feasible neural targets for the application of rtfMRI-NF as a potential treatment. We consider methodological issues, including the choice of localizers and practicalities in logistics when setting up neurofeedback procedures in a clinical setting. We discuss clinical considerations relating to the use of rtfMRI-NF for AVH in individuals distressed by their experiences and put forward a number of questions and recommendations about best practice. Lastly, we conclude by offering suggestions for new avenues for neurofeedback methodology and mechanistic targets in relation to the research and treatment of AVH.

Improving Real-Time Brain State Classification of Motor Imagery Tasks During Neurofeedback Training

In this study, we investigated the effect of the dynamic changes in brain activation during neurofeedback training in the classification of the different brain states associated with the target tasks. We hypothesized that ongoing activation patterns could change during neurofeedback session due to learning effects and, in the process, could affect the performance of brain state classifiers trained using data obtained prior to the session. Using a motor imagery paradigm, we then examined the application of an incremental training approach where classifiers were continuously updated in order to account for these activation changes. Our results confirmed our hypothesis that neurofeedback training could be associated with dynamic changes in brain activation characterized by an initially more widespread brain activation followed by a more focused and localized activation pattern. By continuously updating the trained classifiers after each feedback run, significant improvement in accurately classifying the different brain states associated with the target motor imagery tasks was achieved. These findings suggest the importance of taking into account brain activation changes during neurofeedback in order to provide more reliable and accurate feedback information to the participants, which is critical for an effective neurofeedback application.

Graded fMRI Neurofeedback Training of Motor Imagery in Middle Cerebral Artery Stroke Patients: A Preregistered Proof-of-Concept Study

Ischemic stroke of the middle cerebral artery (MCA), a major brain vessel that supplies the primary motor and premotor cortex, is one of the most common causes for severe upper limb impairment. Currently available motor rehabilitation training largely lacks satisfying efficacy with over 70% of stroke survivors showing residual upper limb dysfunction. Motor imagery-based functional magnetic resonance imaging neurofeedback (fMRI-NF) has been suggested as a potential therapeutic technique to improve motor impairment in stroke survivors. In this preregistered proof-of-concept study (https://osf.io/y69jc/), we translated graded fMRI-NF training, a new paradigm that we have previously studied in healthy participants, to first-time MCA stroke survivors with residual mild to severe impairment of upper limb motor function. Neurofeedback was provided from the supplementary motor area (SMA) targeting two different neurofeedback target levels (low and high). We hypothesized that MCA stroke survivors will show (1) sustained SMA-region of interest (ROI) activation and (2) a difference in SMA-ROI activation between low and high neurofeedback conditions during graded fMRI-NF training. At the group level, we found only anecdotal evidence for these preregistered hypotheses. At the individual level, we found anecdotal to moderate evidence for the absence of the hypothesized graded effect for most subjects. These null findings are relevant for future attempts to employ fMRI-NF training in stroke survivors. The study introduces a Bayesian sequential sampling plan, which incorporates prior knowledge, yielding higher sensitivity. The sampling plan was preregistered together with a priori hypotheses and all planned analysis before data collection to address potential publication/researcher biases. Unforeseen difficulties in the translation of our paradigm to a clinical setting required some deviations from the preregistered protocol. We explicitly detail these changes, discuss the accompanied additional challenges that can arise in clinical neurofeedback studies, and formulate recommendations for how these can be addressed. Taken together, this work provides new insights about the feasibility of motor imagery-based graded fMRI-NF training in MCA stroke survivors and serves as a first example for comprehensive study preregistration of an (fMRI) neurofeedback experiment.

A Survey on the Use of Haptic Feedback for Brain-Computer Interfaces and Neurofeedback

Neurofeedback (NF) and brain-computer interface (BCI) applications rely on the registration and real-time feedback of individual patterns of brain activity with the aim of achieving self-regulation of specific neural substrates or control of external devices. These approaches have historically employed visual stimuli. However, in some cases vision is unsuitable or inadequately engaging. Other sensory modalities, such as auditory or haptic feedback have been explored, and multisensory stimulation is expected to improve the quality of the interaction loop. Moreover, for motor imagery tasks, closing the sensorimotor loop through haptic feedback may be relevant for motor rehabilitation applications, as it can promote plasticity mechanisms. This survey reviews the various haptic technologies and describes their application to BCIs and NF. We identify major trends in the use of haptic interfaces for BCI and NF systems and discuss crucial aspects that could motivate further studies.

Simultaneous EEG-fMRI during a neurofeedback task, a brain imaging dataset for multimodal data integration

Combining EEG and fMRI allows for integration of fine spatial and accurate temporal resolution yet presents numerous challenges, noticeably if performed in real-time to implement a Neurofeedback (NF) loop. Here we describe a multimodal dataset of EEG and fMRI acquired simultaneously during a motor imagery NF task, supplemented with MRI structural data. The study involved 30 healthy volunteers undergoing five training sessions. We showed the potential and merit of simultaneous EEG-fMRI NF in previous work. Here we illustrate the type of information that can be extracted from this dataset and show its potential use. This represents one of the first simultaneous recording of EEG and fMRI for NF and here we present the first open access bi-modal NF dataset integrating EEG and fMRI. We believe that it will be a valuable tool to (1) advance and test methodologies for multi-modal data integration, (2) improve the quality of NF provided, (3) improve methodologies for de-noising EEG acquired under MRI and (4) investigate the neuromarkers of motor-imagery using multi-modal information.