Neurofeedback Technology Research Articles

LORETA Z-score Neurofeedback in Patients with Medically Refractory Epilepsy

Rationale: Published studies suggest that augmentation of the sensorimotor rhythm (SMR), a commonly-used neurofeedback protocol for patients with epilepsy, can be an effective means of reducing seizure frequency, even in patients with medically-refractory seizures. However, SMR protocols are limited to training a few frequency bands over sensorimotor cortex. Newer neurofeedback technology allows for the selection of multiple frequency bands in multiple head regions for training purposes and thus allows for training of neural networks.Functional MRI studies have shown abnormal connectivity within the default mode network (DMN) in patients with both focal-onset and primary generalized epilepsy syndromes. The DMN has also been shown to have altered activity concurrent with interictalepileptiform discharges. The effectiveness of newer neurofeedback techniques in reducing seizure frequency for patients with medically-refractory seizures has not yet been established. This case series explores the potential effectiveness of using LORETA z-score training within the DMN in reducing seizure frequency in patients with medically-refractory seizures. Methods: The records for all consecutive patients seen in the Neurofeedback Clinic at a single academic medical center over a one year period (n=6) were retrospectively reviewed. All patients had medically-refractory epilepsy and were either not candidates for epilepsy surgery (based on consensus decision of the center’s faculty) or had refused to consider surgery for personal reasons. Data on patient demographics, duration of epilepsy prior to training, seizure types and frequencies, antiepileptic drugs (AEDs), psychiatric and medical comorbidities, imaging results, neurophysiological results, and the duration of neurofeedback training were abstracted and analyzed. Patient-reported seizure frequency was also analyzed. Results: 125 total training sessions were reviewed. Mean patient age was 33 +/- 6.1 years with mean duration of epilepsy prior to training of 17.2 +/- 3.2 years. Five out of six patients had focal onset epilepsy. None of the patients had a structural lesion on MRI that correlated with their seizure focus. Five out of 6 patients had a history of comorbid mood disorder. No patient had ever been seizure free for more than 1 year. Patients had been trained using LORETA z-score training within the DMN for an average of 20.8 +/- 5.2weeks (1-2 sessions per weekfor 20-30 minutes per session) at the time of analysis. Five out of 6 patients trained had a subjective reduction in reported weekly seizure frequency after LORETA z-score neurofeedback training began. Conclusions: In this small case series, DMN training using LORETA z-score neurofeedback techniques resulted in subjective improvement in seizure frequency from reported baseline for five out of the six patients in this series.Larger studies are needed to more definitively assess the effectiveness of these techniques for reducing seizure frequency in patients with medically-refractory seizures who are not, for either medical or personal reasons, candidates for surgical intervention.

A decoded neurofeedback method for MEG/SSVEF

Event Abstract Back to Event A decoded neurofeedback method for MEG/SSVEF Hiroki Ora1, 2 and Kenji Kansaku1, 2* 1 Research Institute of National Rehabilitation Center for Persons with Disabilities, Department of Rehabilitation for Brain Function, Japan 2 The University of Electro-Communications, Brain Science Inspired Life Support Research Center, Japan Decoded neurofeedback method was recently proposed to lead brain activity to a target state (Shibata, et al., 2011). While researchers utilized functional magnetic resonance imaging in the former studies, little is focused on magnetoencephalography (MEG). Real-time magnetoencephalography (rtMEG) is an emerging neurofeedback technology that could potentially benefit multiple areas of basic and clinical neuroscience, and in this study, we used an rtMEG system to perform decoded neurofeedback training for MEG/steady-state visual evoked field (SSVEF). Five able-bodied participants (age 33.2 years old, 5 females) participated in this study. The visual stimuli were displayed on a screen in front of the participant, and consisted of a circular checkerboard patch on the left and a circular checkerboard patch on the right. The checkerboard patches flickered at 5 (left) or 6 (right) Hz. They were asked to attend to the left, right, or middle of the screen in a SSVEF task. The used MEG scanner was a 306-channel Elekta Neuromag system (Elekta Oy, Helsinki, Finland). We constructed a sparse multinomial logistic regression (SMLR) decoder from MEG signals during the SSVEF task in each participant. We then conducted 3-day MEG neurofeedback training. In a trial, a white fixation cross was turned green for 5 second, then a green solid circle, whose radius indicated a score of the SMLR decoder, was presented. Participants were asked to “somehow regulate your brain activity to make the green solid circle bigger while the fixation cross is green.” During the trainings, no flickering visual stimuli were presented. After the trainings, the SSVEF task was used again to evaluate the training effects. Furthermore, we analyzed weights of the SMLR decoders to determine a subset of MEG sensors that contributed to decode the attended orientation. The SMLR decoder was able to classify the MEG signals into 3 attentional directions (left, right or middle) (88.2 %). The participants were able to increase decoder scores through the trainings (p<0.05). In the post-training SSVEF tasks, accuracy for the target orientation was higher than that for the non-target orientation (p<0.05). Analysis of weights of the decoders demonstrated that sensors on the occipital region contributed to decode the attended orientation (p<0.05, Bonferroni corrected). The results suggest that the decoded neurofeedback training was effective for MEG/SSVEF, and the method may enhance robustness of steady-state visual evoked potential (SSVEP)-based Brain-Computer Interface (BCI). Keywords: Magnetic Resonance Imaging, decoded neurofeedback, Magnetoencephalography (MEG), Real-time magnetoencephalography, Steady-state visual evoked field Conference: 2015 International Workshop on Clinical Brain-Machine Interfaces (CBMI2015), Tokyo, Japan, 13 Mar - 15 Mar, 2015. Presentation Type: Poster 1-6 Topic: Clinical Brain-Machine Interfaces Citation: Ora H and Kansaku K (2015). A decoded neurofeedback method for MEG/SSVEF. Conference Abstract: 2015 International Workshop on Clinical Brain-Machine Interfaces (CBMI2015). doi: 10.3389/conf.fnhum.2015.218.00007 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 23 Apr 2015; Published Online: 29 Apr 2015. * Correspondence: Dr. Kenji Kansaku, Research Institute of National Rehabilitation Center for Persons with Disabilities, Department of Rehabilitation for Brain Function, Tokorozawa, Saitama, Japan, kansakuk@dokkyomed.ac.jp Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Hiroki Ora Kenji Kansaku Google Hiroki Ora Kenji Kansaku Google Scholar Hiroki Ora Kenji Kansaku PubMed Hiroki Ora Kenji Kansaku Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Neurofeedback technology for cognitive ability training

Neurofeedback technology for cognitive ability training

Mind over brain, brain over mind: cognitive causes and consequences of controlling brain activity.

Mind over brain, brain over mind: cognitive causes and consequences of controlling brain activity.

Neurofeedback and networks of depression

Recent advances in imaging technology and in the understanding of neural circuits relevant to emotion, motivation, and depression have boosted interest and experimental work in neuromodulation for affective disorders. Real-time functional magnetic resonance imaging (fMRI) can be used to train patients in the self regulation of these circuits, and thus complement existing neurofeedback technologies based on electroencephalography (EEG). EEG neurofeedback for depression has mainly been based on models of altered hemispheric asymmetry. fMRI-based neurofeedback (fMRI-NF) can utilize functional localizer scans that allow the dynamic adjustment of the target areas or networks for self-regulation training to individual patterns of emotion processing. An initial application of fMRI-NF in depression has produced promising clinical results, and further clinical trials are under way. Challenges lie in the design of appropriate control conditions for rigorous clinical trials, and in the transfer of neurofeedback protocols from the laboratory to mobile devices to enhance the sustainability of any clinical benefits.

Brain–computer interfaces for EEG neurofeedback: Peculiarities and solutions

Neurofeedback training procedures designed to alter a person's brain activity have been in use for nearly four decades now and represent one of the earliest applications of brain–computer interfaces (BCI). The majority of studies using neurofeedback technology relies on recordings of the electroencephalogram (EEG) and applies neurofeedback in clinical contexts, exploring its potential as treatment for psychopathological syndromes. This clinical focus significantly affects the technology behind neurofeedback BCIs. For example, in contrast to other BCI applications, neurofeedback BCIs usually rely on EEG-derived features with only a minimum of additional processing steps being employed. Here, we highlight the peculiarities of EEG-based neurofeedback BCIs and consider their relevance for software implementations. Having reviewed already existing packages for the implementation of BCIs, we introduce our own solution which specifically considers the relevance of multi-subject handling for experimental and clinical trials, for example by implementing ready-to-use solutions for pseudo-/sham-neurofeedback.

Implementation of a beam forming technique in real-time magnetoencephalography

Real-time magnetoencephalography (rtMEG) is an emerging neurofeedback technology that could potentially benefit multiple areas of basic and clinical neuroscience. In the present study, we implemented voxel-based real-time coherence measurements in a rtMEG system in which we employed a beamformer to localize signal sources in the anatomical space prior to computing imaginary coherence. Our rtMEG experiment showed that a healthy subject could increase coherence between the parietal cortex and visual cortex when attending to a flickering visual stimulus. This finding suggests that our system is suitable for neurofeedback training and can be useful for practical brain-machine interface applications or neurofeedback rehabilitation.

Brain imaging and psychotherapy: methodological considerations and practical implications

The development of psychotherapy has been based on psychological theories and clinical effects. However, an investigation of the neurobiological mechanisms of psychological interventions is also needed in order to improve indication and prognosis, inform the choice of parallel pharmacotherapy, provide outcome measures and potentially even aid the development of new treatment protocols. This neurobiological investigation can be informed by animal models, for example of learning and conditioning, but will essentially need the non-invasive techniques of functional neuroimaging in order to assess psychotherapy effects on patients' brains, which will be reviewed here. Most research so far has been conducted in obsessive compulsive disorder (OCD), anxiety disorders and depression. Effects in OCD were particularly exciting in that both cognitive behavioural therapy and medication with a selective serotonin inhibitor led to a reduction in blood flow in the caudate nucleus. In phobia, brief courses of behavioural therapy produced marked reductions of paralimbic responses to offensive stimuli in line with the clinical improvement. Findings in depression are less consistent, with both increases and decreases in prefrontal metabolism being reported. However, they are important in pointing to different mechanisms for the clinical effects of pharmacotherapy (more "bottom up") and psychotherapy (more "top down"). For the future it would be desirable if the findings of psychotherapy changes to brain activation patterns were confirmed in larger groups with homogenous imaging protocols. Functional imaging has already made great contributions to the understanding of the neural correlates of psychopathology. For example, evidence converges to suggest that the subgenual cingulate is crucial for mood regulation. One current clinical application of these findings is deep brain stimulation in areas highlighted by such imaging studies. I will discuss their initial application in depression and OCD, and suggest potential alternative options based on recent developments in neurofeedback technology.