The Experts below are selected from a list of 100563 Experts worldwide ranked by ideXlab platform
Yury Koush - One of the best experts on this subject based on the ideXlab platform.
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network based fmri Neurofeedback Training of sustained attention
NeuroImage, 2020Co-Authors: Frank Scharnowski, Gustavo S P Pamplona, Jennife Heldne, Robe Langne, Yury Koush, Lars Michels, Silvio Ionta, Carlos Ernesto Garrido SalmoAbstract:The brain regions supporting sustained attention (sustained attention network; SAN) and mind-wandering (default-mode network; DMN) have been extensively studied. Nevertheless, this knowledge has not yet been translated into advanced brain-based attention Training protocols. Here, we used network-based real-time functional magnetic resonance imaging (fMRI) to provide healthy individuals with information about current activity levels in SAN and DMN. Specifically, 15 participants trained to control the difference between SAN and DMN hemodynamic activity and completed behavioral attention tests before and after Neurofeedback Training. Through Training, participants improved controlling the differential SAN-DMN feedback signal, which was accomplished mainly through deactivating DMN. After Training, participants were able to apply learned self-regulation of the differential feedback signal even when feedback was no longer available (i.e., during transfer runs). The Neurofeedback group improved in sustained attention after Training, although this improvement was temporally limited and rarely exceeded mere practice effects that were controlled by a test-retest behavioral control group. The learned self-regulation and the behavioral outcomes suggest that Neurofeedback Training of differential SAN and DMN activity has the potential to become a non-invasive and non-pharmacological tool to enhance attention and mitigate specific attention deficits.
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opennft an open source python matlab framework for real time fmri Neurofeedback Training based on activity connectivity and multivariate pattern analysis
NeuroImage, 2017Co-Authors: Yury Koush, Joh Ashburne, Evgeny Prilepi, Ronald Sladky, Pete Zeidma, S A Ibikov, Frank ScharnowskiAbstract:Neurofeedback based on real-time functional magnetic resonance imaging (rt-fMRI) is a novel and rapidly developing research field. It allows for Training of voluntary control over localized brain activity and connectivity and has demonstrated promising clinical applications. Because of the rapid technical developments of MRI techniques and the availability of high-performance computing, new methodological advances in rt-fMRI Neurofeedback become possible. Here we outline the core components of a novel open-source Neurofeedback framework, termed Open Neurofeedback Training (OpenNFT), which efficiently integrates these new developments. This framework is implemented using Python and Matlab source code to allow for diverse functionality, high modularity, and rapid extendibility of the software depending on the user's needs. In addition, it provides an easy interface to the functionality of Statistical Parametric Mapping (SPM) that is also open-source and one of the most widely used fMRI data analysis software. We demonstrate the functionality of our new framework by describing case studies that include Neurofeedback protocols based on brain activity levels, effective connectivity models, and pattern classification approaches. This open-source initiative provides a suitable framework to actively engage in the development of novel Neurofeedback approaches, so that local methodological developments can be easily made accessible to a wider range of users.
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social reward improves the voluntary control over localized brain activity in fmri based Neurofeedback Training
Frontiers in Behavioral Neuroscience, 2015Co-Authors: Krystyna A Mathiak, Yury Koush, Eliza M Alawi, Miriam Dyck, Julia S Cordes, T J Gabe, Florian Daniel Zepf, Nicola Palomerogallaghe, Pegah SarkheilAbstract:Neurofeedback (NF) based on real-time functional magnetic resonance imaging (rt-fMRI) allows voluntary regulation of the activity in a selected brain region. For the Training of this regulation, a well-designed feedback system is required. Social reward may serve as an effective incentive in NF paradigms, but its efficiency has not yet been tested. Therefore, we developed a social reward NF paradigm and assessed it in comparison with a typical visual NF paradigm (moving bar). We trained twenty-four healthy participants, on three consecutive days, to control activation in dorsal anterior cingulate cortex (ACC) with fMRI-based NF. In the social feedback group, an avatar gradually smiled when ACC activity increased, whereas in the standard feedback group, a moving bar indicated the activation level. In order to assess a transfer of the NF Training both groups were asked to up-regulate their brain activity without receiving feedback immediately before and after the NF Training (pre- and post-test). Finally, the effect of the acquired NF Training on ACC function was evaluated in a cognitive interference task (Simon task) during the pre- and post-test. Social reward led to stronger activity in the ACC and reward-related areas during the NF Training when compared to standard feedback. After the Training, both groups were able to regulate ACC without receiving feedback, with a trend for stronger responses in the social feedback group. Moreover, despite a lack of behavioral differences, significant higher ACC activations emerged in the cognitive interference task, reflecting a stronger generalization of the NF Training on cognitive interference processing after social feedback. Social reward can increase self-regulation in fMRI-based NF and strengthen its effects on neural processing in related tasks, such as cognitive interference. A particular advantage of social feedback is that a direct external reward is provided as in natural social interactions, opening perspectives for implicit learning paradigms.
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self regulation of inter hemispheric visual cortex balance through real time fmri Neurofeedback Training
NeuroImage, 2014Co-Authors: Fabie Robineau, Yury Koush, Sebastian Walte Riege, C Mermoud, Swa Picho, D Van De Ville, Patrik Vuilleumie, Frank ScharnowskiAbstract:Recent advances in Neurofeedback based on real-time functional magnetic resonance imaging (fMRI) allow for learning to control spatially localized brain activity in the range of millimeters across the entire brain. Real-time fMRI Neurofeedback studies have demonstrated the feasibility of self-regulating activation in specific areas that are involved in a variety of functions, such as perception, motor control, language, and emotional processing. In most of these previous studies, participants trained to control activity within one region of interest (ROI). In the present study, we extended the Neurofeedback approach by now Training healthy participants to control the interhemispheric balance between their left and right visual cortices. This was accomplished by providing feedback based on the difference in activity between a target visual ROI and the corresponding homologue region in the opposite hemisphere. Eight out of 14 participants learned to control the differential feedback signal over the course of 3 Neurofeedback Training sessions spread over 3 days, i.e., they produced consistent increases in the visual target ROI relative to the opposite visual cortex. Those who learned to control the differential feedback signal were subsequently also able to exert that control in the absence of Neurofeedback. Such learning to voluntarily control the balance between cortical areas of the two hemispheres might offer promising rehabilitation approaches for neurological or psychiatric conditions associated with pathological asymmetries in brain activity patterns, such as hemispatial neglect, dyslexia, or mood disorders.
Jerzy Odurka - One of the best experts on this subject based on the ideXlab platform.
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altered task based and resting state amygdala functional connectivity following real time fmri amygdala Neurofeedback Training in major depressive disorder
NeuroImage: Clinical, 2018Co-Authors: Kymberly D Young, Raquel Phillips, Vadim Zotev, Masaya Misaki, Greg J Siegle, Wayne C Drevets, Jerzy OdurkaAbstract:Abstract Background We have previously shown that in participants with major depressive disorder (MDD) trained to upregulate their amygdala hemodynamic response during positive autobiographical memory (AM) recall with real-time fMRI Neurofeedback (rtfMRI-nf) Training, depressive symptoms diminish. Here, we assessed the effect of rtfMRI-nf on amygdala functional connectivity during both positive AM recall and rest. Method The current manuscript consists of a secondary analysis on data from our published clinical trial of Neurofeedback. Patients with MDD completed two rtfMRI-nf sessions (18 received amygdala rtfMRI-nf, 16 received control parietal rtfMRI-nf). One-week prior-to and following Training participants also completed a resting-state fMRI scan. A GLM-based functional connectivity analysis was applied using a seed ROI in the left amygdala. We compared amygdala functional connectivity changes while recalling positive AMs from the baseline run to the final transfer run during rtfMRI-nf Training, as well during rest from the baseline to the one-week follow-up visit. Finally, we assessed the correlation between change in depression scores and change in amygdala connectivity, as well as correlations between amygdala regulation success and connectivity changes. Results Following Training, amygdala connectivity during positive AM recall increased with widespread regions in the frontal and limbic network. During rest, amygdala connectivity increased following Training within the fronto-temporal-limbic network. During both task and resting-state analyses, amygdala-temporal pole connectivity decreased. We identified increased amygdala-precuneus and amygdala-inferior frontal gyrus connectivity during positive memory recall and increased amygdala-precuneus and amygdala-thalamus connectivity during rest as functional connectivity changes that explained significant variance in symptom improvement. Amygdala-precuneus connectivity changes also explain a significant amount of variance in Neurofeedback regulation success. Conclusions Neurofeedback Training to increase amygdala hemodynamic activity during positive AM recall increased amygdala connectivity with regions involved in self-referential, salience, and reward processing. Results suggest future targets for Neurofeedback interventions, particularly interventions involving the precuneus.
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resting state functional connectivity modulation and sustained changes after real time functional magnetic resonance imaging Neurofeedback Training in depression
Brain, 2014Co-Authors: Ha Yua, Kymberly D Young, Raquel Phillips, Vadim Zotev, Masaya Misaki, Jerzy OdurkaAbstract:Amygdala hemodynamic responses to positive stimuli are attenuated in major depressive disorder (MDD) and normalize with remission. Real-time functional magnetic resonance imaging Neurofeedback (rtfMRI-nf) Training with the goal of upregulating amygdala activity during recall of happy autobiographical memories (AMs) has been suggested, and recently explored, as a novel therapeutic approach that resulted in improvement in self-reported mood in depressed subjects. In this study, we assessed the possibility of sustained brain changes as well as the neuromodulatory effects of rtfMRI-nf Training of the amygdala during recall of positive AMs in MDD and matched healthy subjects. MDD and healthy subjects went through one visit of rtfMRI-nf Training. Subjects were assigned to receive active Neurofeedback from the left amygdale (LA) or from a control region putatively not modulated by AM recall or emotion regulation, that is, the left horizontal segment of the intraparietal sulcus. To assess lasting effects of Neurofeedback in MDD, the resting-state functional connectivity before and after rtfMRI-nf in 27 depressed subjects, as well as in 27 matched healthy subjects before rtfMRI-nf was measured. Results show that abnormal hypo-connectivity with LA in MDD is reversed after rtfMRI-nf Training by recalling positive AMs. Although such neuromodulatory changes are observed in both MDD groups receiving feedback from respective active and control brain regions, only in the active group are larger decreases of depression severity associated with larger increases of amygdala connectivity and a significant, positive correlation is found between the connectivity changes and the days after Neurofeedback. In addition, active Neurofeedback Training of the amygdala enhances connectivity with temporal cortical regions, including the hippocampus. These results demonstrate lasting brain changes induced by amygdala rtfMRI-nf Training and suggest the importance of reinforcement learning in rehabilitating emotion regulation in depression.
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real time fmri Neurofeedback Training of amygdala activity in patients with major depressive disorder
PLOS ONE, 2014Co-Authors: Kymberly D Young, Ha Yua, Raquel Phillips, Vadim Zotev, Masaya Misaki, Wayne C Drevets, Jerzy OdurkaAbstract:Background Amygdala hemodynamic responses to positive stimuli are attenuated in major depressive disorder (MDD), and normalize with remission. Real-time functional MRI Neurofeedback (rtfMRI-nf) offers a non-invasive method to modulate this regional activity. We examined whether depressed participants can use rtfMRI-nf to enhance amygdala responses to positive autobiographical memories, and whether this ability alters symptom severity.
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prefrontal control of the amygdala during real time fmri Neurofeedback Training of emotion regulation
PLOS ONE, 2013Co-Authors: Vadim Zotev, Kymberly D Young, Raquel Phillips, Wayne C Drevets, Jerzy OdurkaAbstract:We observed in a previous study (PLoS ONE 6:e24522) that the self-regulation of amygdala activity via real-time fMRI Neurofeedback (rtfMRI-nf) with positive emotion induction was associated, in healthy participants, with an enhancement in the functional connectivity between the left amygdala (LA) and six regions of the prefrontal cortex. These regions included the left rostral anterior cingulate cortex (rACC), bilateral dorsomedial prefrontal cortex (DMPFC), bilateral superior frontal gyrus (SFG), and right medial frontopolar cortex (MFPC). Together with the LA, these six prefrontal regions thus formed the functional neuroanatomical network engaged during the rtfMRI-nf procedure. Here we perform a structural vector autoregression (SVAR) analysis of the effective connectivity for this network. The SVAR analysis demonstrates that the left rACC plays an important role during the rtfMRI-nf Training, modulating the LA and the other network regions. According to the analysis, the rtfMRI-nf Training leads to a significant enhancement in the time-lagged effect of the left rACC on the LA, potentially consistent with the ipsilateral distribution of the monosynaptic projections between these regions. The Training is also accompanied by significant increases in the instantaneous (contemporaneous) effects of the left rACC on four other regions – the bilateral DMPFC, the right MFPC, and the left SFG. The instantaneous effects of the LA on the bilateral DMPFC are also significantly enhanced. Our results are consistent with a broad literature supporting the role of the rACC in emotion processing and regulation. Our exploratory analysis provides, for the first time, insights into the causal relationships within the network of regions engaged during the rtfMRI-nf procedure targeting the amygdala. It suggests that the rACC may constitute a promising target for rtfMRI-nf Training along with the amygdala in patients with affective disorders, particularly posttraumatic stress disorder (PTSD).
Frank Scharnowski - One of the best experts on this subject based on the ideXlab platform.
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network based fmri Neurofeedback Training of sustained attention
NeuroImage, 2020Co-Authors: Frank Scharnowski, Gustavo S P Pamplona, Jennife Heldne, Robe Langne, Yury Koush, Lars Michels, Silvio Ionta, Carlos Ernesto Garrido SalmoAbstract:The brain regions supporting sustained attention (sustained attention network; SAN) and mind-wandering (default-mode network; DMN) have been extensively studied. Nevertheless, this knowledge has not yet been translated into advanced brain-based attention Training protocols. Here, we used network-based real-time functional magnetic resonance imaging (fMRI) to provide healthy individuals with information about current activity levels in SAN and DMN. Specifically, 15 participants trained to control the difference between SAN and DMN hemodynamic activity and completed behavioral attention tests before and after Neurofeedback Training. Through Training, participants improved controlling the differential SAN-DMN feedback signal, which was accomplished mainly through deactivating DMN. After Training, participants were able to apply learned self-regulation of the differential feedback signal even when feedback was no longer available (i.e., during transfer runs). The Neurofeedback group improved in sustained attention after Training, although this improvement was temporally limited and rarely exceeded mere practice effects that were controlled by a test-retest behavioral control group. The learned self-regulation and the behavioral outcomes suggest that Neurofeedback Training of differential SAN and DMN activity has the potential to become a non-invasive and non-pharmacological tool to enhance attention and mitigate specific attention deficits.
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opennft an open source python matlab framework for real time fmri Neurofeedback Training based on activity connectivity and multivariate pattern analysis
NeuroImage, 2017Co-Authors: Yury Koush, Joh Ashburne, Evgeny Prilepi, Ronald Sladky, Pete Zeidma, S A Ibikov, Frank ScharnowskiAbstract:Neurofeedback based on real-time functional magnetic resonance imaging (rt-fMRI) is a novel and rapidly developing research field. It allows for Training of voluntary control over localized brain activity and connectivity and has demonstrated promising clinical applications. Because of the rapid technical developments of MRI techniques and the availability of high-performance computing, new methodological advances in rt-fMRI Neurofeedback become possible. Here we outline the core components of a novel open-source Neurofeedback framework, termed Open Neurofeedback Training (OpenNFT), which efficiently integrates these new developments. This framework is implemented using Python and Matlab source code to allow for diverse functionality, high modularity, and rapid extendibility of the software depending on the user's needs. In addition, it provides an easy interface to the functionality of Statistical Parametric Mapping (SPM) that is also open-source and one of the most widely used fMRI data analysis software. We demonstrate the functionality of our new framework by describing case studies that include Neurofeedback protocols based on brain activity levels, effective connectivity models, and pattern classification approaches. This open-source initiative provides a suitable framework to actively engage in the development of novel Neurofeedback approaches, so that local methodological developments can be easily made accessible to a wider range of users.
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self regulation of inter hemispheric visual cortex balance through real time fmri Neurofeedback Training
NeuroImage, 2014Co-Authors: Fabie Robineau, Yury Koush, Sebastian Walte Riege, C Mermoud, Swa Picho, D Van De Ville, Patrik Vuilleumie, Frank ScharnowskiAbstract:Recent advances in Neurofeedback based on real-time functional magnetic resonance imaging (fMRI) allow for learning to control spatially localized brain activity in the range of millimeters across the entire brain. Real-time fMRI Neurofeedback studies have demonstrated the feasibility of self-regulating activation in specific areas that are involved in a variety of functions, such as perception, motor control, language, and emotional processing. In most of these previous studies, participants trained to control activity within one region of interest (ROI). In the present study, we extended the Neurofeedback approach by now Training healthy participants to control the interhemispheric balance between their left and right visual cortices. This was accomplished by providing feedback based on the difference in activity between a target visual ROI and the corresponding homologue region in the opposite hemisphere. Eight out of 14 participants learned to control the differential feedback signal over the course of 3 Neurofeedback Training sessions spread over 3 days, i.e., they produced consistent increases in the visual target ROI relative to the opposite visual cortex. Those who learned to control the differential feedback signal were subsequently also able to exert that control in the absence of Neurofeedback. Such learning to voluntarily control the balance between cortical areas of the two hemispheres might offer promising rehabilitation approaches for neurological or psychiatric conditions associated with pathological asymmetries in brain activity patterns, such as hemispatial neglect, dyslexia, or mood disorders.
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Connectivity Changes Underlying Neurofeedback Training of Visual Cortex Activity
PLOS ONE, 2014Co-Authors: Frank Scharnowski, Maria J. Rosa, Oliver Josephs, Chloe Hutton, Narly Golestani, Nikolaus Weiskopf, Geraint ReesAbstract:Neurofeedback based on real-time functional magnetic resonance imaging (fMRI) is a new approach that allows Training of voluntary control over regionally specific brain activity. However, the neural basis of successful Neurofeedback learning remains poorly understood. Here, we assessed changes in effective brain connectivity associated with Neurofeedback Training of visual cortex activity. Using dynamic causal modeling (DCM), we found that Training participants to increase visual cortex activity was associated with increased effective connectivity between the visual cortex and the superior parietal lobe. Specifically, participants who learned to control activity in their visual cortex showed increased top-down control of the superior parietal lobe over the visual cortex, and at the same time reduced bottom-up processing. These results are consistent with efficient employment of top-down visual attention and imagery, which were the cognitive strategies used by participants to increase their visual cortex activity.
Kymberly D Young - One of the best experts on this subject based on the ideXlab platform.
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altered task based and resting state amygdala functional connectivity following real time fmri amygdala Neurofeedback Training in major depressive disorder
NeuroImage: Clinical, 2018Co-Authors: Kymberly D Young, Raquel Phillips, Vadim Zotev, Masaya Misaki, Greg J Siegle, Wayne C Drevets, Jerzy OdurkaAbstract:Abstract Background We have previously shown that in participants with major depressive disorder (MDD) trained to upregulate their amygdala hemodynamic response during positive autobiographical memory (AM) recall with real-time fMRI Neurofeedback (rtfMRI-nf) Training, depressive symptoms diminish. Here, we assessed the effect of rtfMRI-nf on amygdala functional connectivity during both positive AM recall and rest. Method The current manuscript consists of a secondary analysis on data from our published clinical trial of Neurofeedback. Patients with MDD completed two rtfMRI-nf sessions (18 received amygdala rtfMRI-nf, 16 received control parietal rtfMRI-nf). One-week prior-to and following Training participants also completed a resting-state fMRI scan. A GLM-based functional connectivity analysis was applied using a seed ROI in the left amygdala. We compared amygdala functional connectivity changes while recalling positive AMs from the baseline run to the final transfer run during rtfMRI-nf Training, as well during rest from the baseline to the one-week follow-up visit. Finally, we assessed the correlation between change in depression scores and change in amygdala connectivity, as well as correlations between amygdala regulation success and connectivity changes. Results Following Training, amygdala connectivity during positive AM recall increased with widespread regions in the frontal and limbic network. During rest, amygdala connectivity increased following Training within the fronto-temporal-limbic network. During both task and resting-state analyses, amygdala-temporal pole connectivity decreased. We identified increased amygdala-precuneus and amygdala-inferior frontal gyrus connectivity during positive memory recall and increased amygdala-precuneus and amygdala-thalamus connectivity during rest as functional connectivity changes that explained significant variance in symptom improvement. Amygdala-precuneus connectivity changes also explain a significant amount of variance in Neurofeedback regulation success. Conclusions Neurofeedback Training to increase amygdala hemodynamic activity during positive AM recall increased amygdala connectivity with regions involved in self-referential, salience, and reward processing. Results suggest future targets for Neurofeedback interventions, particularly interventions involving the precuneus.
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resting state functional connectivity modulation and sustained changes after real time functional magnetic resonance imaging Neurofeedback Training in depression
Brain, 2014Co-Authors: Ha Yua, Kymberly D Young, Raquel Phillips, Vadim Zotev, Masaya Misaki, Jerzy OdurkaAbstract:Amygdala hemodynamic responses to positive stimuli are attenuated in major depressive disorder (MDD) and normalize with remission. Real-time functional magnetic resonance imaging Neurofeedback (rtfMRI-nf) Training with the goal of upregulating amygdala activity during recall of happy autobiographical memories (AMs) has been suggested, and recently explored, as a novel therapeutic approach that resulted in improvement in self-reported mood in depressed subjects. In this study, we assessed the possibility of sustained brain changes as well as the neuromodulatory effects of rtfMRI-nf Training of the amygdala during recall of positive AMs in MDD and matched healthy subjects. MDD and healthy subjects went through one visit of rtfMRI-nf Training. Subjects were assigned to receive active Neurofeedback from the left amygdale (LA) or from a control region putatively not modulated by AM recall or emotion regulation, that is, the left horizontal segment of the intraparietal sulcus. To assess lasting effects of Neurofeedback in MDD, the resting-state functional connectivity before and after rtfMRI-nf in 27 depressed subjects, as well as in 27 matched healthy subjects before rtfMRI-nf was measured. Results show that abnormal hypo-connectivity with LA in MDD is reversed after rtfMRI-nf Training by recalling positive AMs. Although such neuromodulatory changes are observed in both MDD groups receiving feedback from respective active and control brain regions, only in the active group are larger decreases of depression severity associated with larger increases of amygdala connectivity and a significant, positive correlation is found between the connectivity changes and the days after Neurofeedback. In addition, active Neurofeedback Training of the amygdala enhances connectivity with temporal cortical regions, including the hippocampus. These results demonstrate lasting brain changes induced by amygdala rtfMRI-nf Training and suggest the importance of reinforcement learning in rehabilitating emotion regulation in depression.
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real time fmri Neurofeedback Training of amygdala activity in patients with major depressive disorder
PLOS ONE, 2014Co-Authors: Kymberly D Young, Ha Yua, Raquel Phillips, Vadim Zotev, Masaya Misaki, Wayne C Drevets, Jerzy OdurkaAbstract:Background Amygdala hemodynamic responses to positive stimuli are attenuated in major depressive disorder (MDD), and normalize with remission. Real-time functional MRI Neurofeedback (rtfMRI-nf) offers a non-invasive method to modulate this regional activity. We examined whether depressed participants can use rtfMRI-nf to enhance amygdala responses to positive autobiographical memories, and whether this ability alters symptom severity.
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prefrontal control of the amygdala during real time fmri Neurofeedback Training of emotion regulation
PLOS ONE, 2013Co-Authors: Vadim Zotev, Kymberly D Young, Raquel Phillips, Wayne C Drevets, Jerzy OdurkaAbstract:We observed in a previous study (PLoS ONE 6:e24522) that the self-regulation of amygdala activity via real-time fMRI Neurofeedback (rtfMRI-nf) with positive emotion induction was associated, in healthy participants, with an enhancement in the functional connectivity between the left amygdala (LA) and six regions of the prefrontal cortex. These regions included the left rostral anterior cingulate cortex (rACC), bilateral dorsomedial prefrontal cortex (DMPFC), bilateral superior frontal gyrus (SFG), and right medial frontopolar cortex (MFPC). Together with the LA, these six prefrontal regions thus formed the functional neuroanatomical network engaged during the rtfMRI-nf procedure. Here we perform a structural vector autoregression (SVAR) analysis of the effective connectivity for this network. The SVAR analysis demonstrates that the left rACC plays an important role during the rtfMRI-nf Training, modulating the LA and the other network regions. According to the analysis, the rtfMRI-nf Training leads to a significant enhancement in the time-lagged effect of the left rACC on the LA, potentially consistent with the ipsilateral distribution of the monosynaptic projections between these regions. The Training is also accompanied by significant increases in the instantaneous (contemporaneous) effects of the left rACC on four other regions – the bilateral DMPFC, the right MFPC, and the left SFG. The instantaneous effects of the LA on the bilateral DMPFC are also significantly enhanced. Our results are consistent with a broad literature supporting the role of the rACC in emotion processing and regulation. Our exploratory analysis provides, for the first time, insights into the causal relationships within the network of regions engaged during the rtfMRI-nf procedure targeting the amygdala. It suggests that the rACC may constitute a promising target for rtfMRI-nf Training along with the amygdala in patients with affective disorders, particularly posttraumatic stress disorder (PTSD).
Guilherme Wood - One of the best experts on this subject based on the ideXlab platform.
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upper alpha based Neurofeedback Training in chronic stroke brain plasticity processes and cognitive effects
Applied Psychophysiology and Biofeedback, 2017Co-Authors: Silvia Erika Kobe, Christa Neupe, Daniela Schweige, Johanna Louise Reiche, Guilherme WoodAbstract:In the present study, we investigated the effects of upper alpha based Neurofeedback (NF) Training on electrical brain activity and cognitive functions in stroke survivors. Therefore, two single chronic stroke patients with memory deficits (subject A with a bilateral subarachnoid hemorrhage; subject B with an ischemic stroke in the left arteria cerebri media) and a healthy elderly control group (N = 24) received up to ten NF Training sessions. To evaluate NF Training effects, all participants performed multichannel electroencephalogram (EEG) resting measurements and a neuropsychological test battery assessing different cognitive functions before and after NF Training. Stroke patients showed improvements in memory functions after successful NF Training compared to the pre-assessment. Subject B had a pathological delta (0.5–4 Hz) and upper alpha (10–12 Hz) power maximum over the unaffected hemisphere before NF Training. After NF Training, he showed a more bilateral and “normalized” topographical distribution of these EEG frequencies. Healthy participants as well as subject A did not show any abnormalities in EEG topography before the start of NF Training. Consequently, no changes in the topographical distribution of EEG activity were observed in these participants when comparing the pre- and post-assessment. Hence, our results show that upper alpha based NF Training had on the one hand positive effects on memory functions, and on the other hand led to cortical “normalization” in a stroke patient with pathological brain activation patterns, which underlines the potential usefulness of NF as neurological rehabilitation tool.
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specific effects of eeg based Neurofeedback Training on memory functions in post stroke victims
Journal of Neuroengineering and Rehabilitation, 2015Co-Authors: Silvia Erika Kobe, Matthias Witte, Christa Neupe, Daniela Schweige, Johanna Louise Reiche, Pete Grieshofe, Guilherme WoodAbstract:Using EEG based Neurofeedback (NF), the activity of the brain is modulated directly and, therefore, the cortical substrates of cognitive functions themselves. In the present study, we investigated the ability of stroke patients to control their own brain activity via NF and evaluated specific effects of different NF protocols on cognition, in particular recovery of memory. N = 17 stroke patients received up to ten sessions of either SMR (N = 11, 12–15 Hz) or Upper Alpha (N = 6, e.g. 10–12 Hz) NF Training. N = 7 stroke patients received treatment as usual as control condition. Furthermore, N = 40 healthy controls performed NF Training as well. To evaluate the NF Training outcome, a test battery assessing different cognitive functions was performed before and after NF Training. About 70 % of both patients and controls achieved distinct gains in NF performance leading to improvements in verbal short- and long-term memory, independent of the used NF protocol. The SMR patient group showed specific improvements in visuo-spatial short-term memory performance, whereas the Upper Alpha patient group specifically improved their working memory performance. NF Training effects were even stronger than effects of traditional cognitive Training methods in stroke patients. NF Training showed no effects on other cognitive functions than memory. Post-stroke victims with memory deficits could benefit from NF Training as much as healthy controls. The used NF Training protocols (SMR, Upper Alpha) had specific as well as unspecific effects on memory. Hence, NF might offer an effective cognitive rehabilitation tool improving memory deficits of stroke survivors.
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shutting down sensorimotor interference unblocks the networks for stimulus processing an smr Neurofeedback Training study
Clinical Neurophysiology, 2015Co-Authors: Silvia Erika Kober, Matthias Witte, Matthias Stangl, Aleksander Valjamae, Christa Neuper, Guilherme WoodAbstract:highlights abstract Objective: In the present study, we investigated how the electrical activity in the sensorimotor cortex contributes to improved cognitive processing capabilities and how SMR (sensorimotor rhythm, 12-15 Hz) Neurofeedback Training modulates it. Previous evidence indicates that higher levels of SMR activity reduce sensorimotor interference and thereby promote cognitive processing. Methods: Participants were randomly assigned to two groups, one experimental (N = 10) group receiving SMR Neurofeedback Training, in which they learned to voluntarily increase SMR, and one control group (N = 10) receiving sham feedback. Multiple cognitive functions and electrophysiological correlates of cog- nitive processing were assessed before and after 10 Neurofeedback Training sessions. Results: The experimental group but not the control group showed linear increases in SMR power over Training runs, which was associated with behavioural improvements in memory and attentional perfor- mance. Additionally, increasing SMR led to a more salient stimulus processing as indicated by increased N1 and P3 event-related potential amplitudes after the Training as compared to the pre-test. Finally, func- tional brain connectivity between motor areas and visual processing areas was reduced after SMR train- ing indicating reduced sensorimotor interference. Conclusions: These results indicate that SMR Neurofeedback improves stimulus processing capabilities and consequently leads to improvements in cognitive performance. Significance: The present findings contribute to a better understanding of the mechanisms underlying SMR Neurofeedback Training and cognitive processing and implicate that SMR Neurofeedback might be an effective cognitive Training tool.
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neural substrates of cognitive control under the belief of getting Neurofeedback Training
Frontiers in Human Neuroscience, 2013Co-Authors: Manuel Ninaus, Silvia Erika Kober, Matthias Witte, Matthias Stangl, Christa Neuper, Karl Koschutnig, Guilherme WoodAbstract:Learning to modulate one’s own brain activity is the fundament of Neurofeedback (NF) applications. Besides the neural networks directly involved in the generation and modulation of the neurophysiological parameter being specifically trained, more general determinants of NF efficacy such as self-referential processes and cognitive control have been frequently disregarded. Nonetheless, deeper insight into these cognitive mechanisms and their neuronal underpinnings sheds light on various open NF related questions concerning individual differences, brain-computer interface (BCI) illiteracy as well as a more general model of NF learning. In this context, we investigated the neuronal substrate of these more general regulatory mechanisms that are engaged when participants believe that they are receiving NF. Twenty healthy participants (40-63 years, 10 female) performed a sham NF paradigm during fMRI scanning. All participants were novices to NF-experiments and were instructed to voluntarily modulate their own brain activity based on a visual display of moving color bars. However, the bar depicted a recording and not the actual brain activity of participants. Reports collected at the end of the experiment indicate that participants were unaware of the sham feedback. In comparison to a passive watching condition, bilateral insula, anterior cingulate cortex and supplementary motor and dorsomedial and lateral prefrontal area were activated when participants actively tried to control the bar. In contrast, when merely watching moving bars, increased activation in the left angular gyrus was observed. These results show that the intention to control a moving bar is sufficient to engage a broad frontoparietal and cingulo-opercular network involved in cognitive control. The results of the present study indicate that tasks such as those generally employed in NF Training recruit the neuronal correlates of cognitive control even when only sham NF is presented.
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control beliefs can predict the ability to up regulate sensorimotor rhythm during Neurofeedback Training
Frontiers in Human Neuroscience, 2013Co-Authors: Matthias Witte, Christa Neupe, Manuel Ninaus, Silvia Erika Kobe, Guilherme WoodAbstract:Technological progress in computer science and neuroimaging has resulted in many approaches that aim to detect brain states and translate them to an external output. Studies from the field of brain-computer interfaces and Neurofeedback have validated the coupling between brain signals and computer devices; however a cognitive model of the processes involved remains elusive. Psychological parameters usually play a moderate role in predicting the performance of brain-computer interface (BCI) and Neurofeedback (NF) users. The concept of a locus of control, i.e. whether one’s own action is determined by internal or external causes, may help to unravel inter-individual performance capacities. Here, we present data from twenty healthy participants who performed a feedback task based on EEG recordings of the sensorimotor rhythm (SMR). One group of ten participants underwent ten Training sessions where the amplitude of the SMR was coupled to a vertical feedback bar. The other group of ten participants participated in the same task but relied on sham feedback. Our analysis revealed that a locus of control score focusing on control beliefs with regard to technology negatively correlated with the power of SMR. These preliminary results suggest that participants whose confidence in control over technical devices is high might consume additional cognitive resources. This higher effort in turn may interfere with brain states of relaxation as reflected in the SMR. As a consequence, one way to improve control over brain signals in Neurofeedback paradigms may be to explicitly instruct users not to force mastery but instead to aim at a state of effortless relaxation.