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Research Papers
Showing 6 of 11Balancing the brain of offenders with psychopathy? Resting state EEG and electrodermal activity after a pilot study of brain self-regulation training
Although investigation of the brains of criminals began quite early in the history of psychophysiological research, little is known about brain plasticity of offenders with psychopathy. Building on our preliminary study reporting successful brain self-regulation using slow cortical potential (SCP) neurofeedback in offenders with psychopathy, we investigated the central nervous and autonomic peripheral changes occurring after brain self-regulation in a group of severe male offenders with psychopathy. Regarding the central nervous system, an overall suppression of the psychopathic overrepresentation of slow frequency bands was found, such as delta and theta band activity, after EEG neurofeedback. In addition, an increase in alpha band activity could be observed after the SCP self-regulation training. Electrodermal activity adaptively changed according to the regulation task, and this flexibility improved over training time. The results of this study point towards a constructive learning process and plasticity in neural and peripheral measures of offenders with psychopathy.
View Full Paper →Deep brain electrical neurofeedback allows Parkinson patients to control pathological oscillations and quicken movements
Parkinsonian motor symptoms are linked to pathologically increased beta-oscillations in the basal ganglia. While pharmacological treatment and deep brain stimulation (DBS) reduce these pathological oscillations concomitantly with improving motor performance, we set out to explore neurofeedback as an endogenous modulatory method. We implemented real-time processing of pathological subthalamic beta oscillations through implanted DBS electrodes to provide deep brain electrical neurofeedback. Patients volitionally controlled ongoing beta-oscillatory activity by visual neurofeedback within minutes of training. During a single one-hour training session, the reduction of beta-oscillatory activity became gradually stronger and we observed improved motor performance. Lastly, endogenous control over deep brain activity was possible even after removing visual neurofeedback, suggesting that neurofeedback-acquired strategies were retained in the short-term. Moreover, we observed motor improvement when the learnt mental strategies were applied 2 days later without neurofeedback. Further training of deep brain neurofeedback might provide therapeutic benefits for Parkinson patients by improving symptom control using strategies optimized through neurofeedback.
View Full Paper →Resting-state brain activity can predict target-independent aptitude in fMRI-neurofeedback training
Neurofeedback (NF) aptitude, which refers to an individual's ability to change brain activity through NF training, has been reported to vary significantly from person to person. The prediction of individual NF aptitudes is critical in clinical applications to screen patients suitable for NF treatment. In the present study, we extracted the resting-state functional brain connectivity (FC) markers of NF aptitude, independent of NF-targeting brain regions. We combined the data from fMRI-NF studies targeting four different brain regions at two independent sites (obtained from 59 healthy adults and six patients with major depressive disorder) to collect resting-state fMRI data associated with aptitude scores in subsequent fMRI-NF training. We then trained the multiple regression models to predict the individual NF aptitude scores from the resting-state fMRI data using a discovery dataset from one site and identified six resting-state FCs that predicted NF aptitude. Subsequently, the reproducibility of the prediction model was validated using independent test data from another site. The identified FC model revealed that the posterior cingulate cortex was the functional hub among the brain regions and formed predictive resting-state FCs, suggesting that NF aptitude may be involved in the attentional mode-orientation modulation system's characteristics in task-free resting-state brain activity.
View Full Paper →Real-time fMRI neurofeedback reduces auditory hallucinations and modulates resting state connectivity of involved brain regions: Part 2: Default mode network -preliminary evidence
Auditory hallucinations (AHs) are one of the most distressing symptoms of schizophrenia (SZ) and are often resistant to medication. Imaging studies of individuals with SZ show hyperactivation of the default mode network (DMN) and the superior temporal gyrus (STG). Studies in SZ show DMN hyperconnectivity and reduced anticorrelation between DMN and the central executive network (CEN). DMN hyperconnectivity has been associated with positive symptoms such as AHs while reduced DMN anticorrelations with cognitive impairment. Using real-time fMRI neurofeedback (rt-fMRI-NFB) we trained SZ patients to modulate DMN and CEN networks. Meditation is effective in reducing AHs in SZ and to modulate brain network integration and increase DMN anticorrelations. Consequently, patients were provided with meditation strategies to enhance their abilities to modulate DMN/CEN. Results show a reduction of DMN hyperconnectivity and increase in DMNCEN anticorrelation. Furthermore, the change in individual DMN connectivity significantly correlated with reductions in AHs. This is the first time that meditation enhanced through rt-fMRI-NFB is used to reduce AHs in SZ. Moreover, it provides the first empirical evidence for a direct causal relation between meditation enhanced rt-fMRI-NFB modulation of DMNCEN activity and post-intervention modulation of resting state networks ensuing in reductions in frequency and severity of AHs.
View Full Paper →Brain dysfunction in chronic pain patients assessed by resting-state electroencephalography
Chronic pain is a common and severely disabling disease whose treatment is often unsatisfactory. Insights into the brain mechanisms of chronic pain promise to advance the understanding of the underlying pathophysiology and might help to develop disease markers and novel treatments. Here, we systematically exploited the potential of electroencephalography to determine abnormalities of brain function during the resting state in chronic pain. To this end, we performed state-of-the-art analyses of oscillatory brain activity, brain connectivity, and brain networks in 101 patients of either sex suffering from chronic pain. The results show that global and local measures of brain activity did not differ between chronic pain patients and a healthy control group. However, we observed significantly increased connectivity at theta (4-8 Hz) and gamma (>60 Hz) frequencies in frontal brain areas as well as global network reorganization at gamma frequencies in chronic pain patients. Furthermore, a machine learning algorithm could differentiate between patients and healthy controls with an above-chance accuracy of 57%, mostly based on frontal connectivity. These results suggest that increased theta and gamma synchrony in frontal brain areas are involved in the pathophysiology of chronic pain. Although substantial challenges concerning the reproducibility of the findings and the accuracy, specificity, and validity of potential electroencephalography-based disease markers remain to be overcome, our study indicates that abnormal frontal synchrony at theta and gamma frequencies might be promising targets for noninvasive brain stimulation and/or neurofeedback approaches.
View Full Paper →Low Motivational Incongruence Predicts Successful EEG Resting-state Neurofeedback Performance in Healthy Adults
Neurofeedback is becoming increasingly sophisticated and widespread, although predictors of successful performance still remain scarce. Here, we explored the possible predictive value of psychological factors and report the results obtained from a neurofeedback training study designed to enhance the self-regulation of spontaneous EEG microstates of a particular type (microstate class D). Specifically, we were interested in life satisfaction (including motivational incongruence), body awareness, personality and trait anxiety. These variables were quantified with questionnaires before neurofeedback. Individual neurofeedback success was established by means of linear mixed models that accounted for the amount of observed target state (microstate class D contribution) as a function of time and training condition: baseline, training and transfer (results shown in Diaz Hernandez et al.). We found a series of significant negative correlations between motivational incongruence and mean percentage increase of microstate D during the condition transfer, across-sessions (36% of common variance) and mean percentage increase of microstate D during the condition training, within-session (42% of common variance). There were no significant correlations related to other questionnaires, besides a trend in a sub-scale of the Life Satisfaction questionnaire. We conclude that motivational incongruence may be a potential predictor for neurofeedback success, at least in the current protocol. The finding may be explained by the interfering effect on neurofeedback performance produced by incompatible simultaneously active psychological processes, which are indirectly measured by the Motivational Incongruence questionnaire.
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