Real-time fMRI brain computer interfaces: Self-regulation of single brain regions to networks

Ruíz, Sergio; Buyukturkoglu, Korhan; Rana, Mohit; Birbaumer, Niels; Sitaram, Ranganatha

doi:10.1016/j.biopsycho.2013.04.010

Cited by 156 publications

(140 citation statements)

References 81 publications

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“…This is consistent with the results of a few other studies using real-time fMRI, which also reported successful transfer runs (Scharnowski et al, 2012;Sitaram et al, 2012). Several other neurofeedback studies have however failed to show a transfer (Hamilton et al, 2011;Lee et al, 2012;Ruiz et al, 2014;Sulzer et al, 2013b), and none have reported increases in performance during transfer runs across sessions . We therefore speculate that including the multiple transfer runs within the neurofeedback training protocol might be beneficial, a feature especially critical in the context of rehabilitation training.…”

Section: Self-regulation In the Absence Of Neurofeedbacksupporting

confidence: 90%

“…Previous real-time fMRI neurofeedback studies either did not include transfer runs, or tested the transfer once the neurofeedback training had been completed (deCharms et al, 2004;deCharms et al, 2005;Hamilton et al, 2011;Lee et al, 2012;Ruiz et al, 2014;Scharnowski et al, 2012;Sitaram et al, 2012). Here we found that participants were able to control the differential visual cortex activity during transfer runs, and that this control increased over sessions (Fig.…”

Section: Self-regulation In the Absence Of Neurofeedbackmentioning

confidence: 99%

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Self-regulation of inter-hemispheric visual cortex balance through real-time fMRI neurofeedback training

Robineau¹,

Rieger²,

Mermoud³

et al. 2014

NeuroImage

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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. Realtime 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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Section: Self-regulation In the Absence Of Neurofeedbacksupporting

confidence: 90%

Section: Self-regulation In the Absence Of Neurofeedbackmentioning

confidence: 99%

Self-regulation of inter-hemispheric visual cortex balance through real-time fMRI neurofeedback training

Robineau¹,

Rieger²,

Mermoud³

et al. 2014

NeuroImage

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“…Disabilities and brain strokes have been subject for many studies interested in solutions involving brain signals. It has been pointed out in [42] that brain structures associated with stroke injuries could be reorganized and the damaged motor functions could be restored via neuroplasticity [43,44].…”

Section: Rehabilitation and Restorationmentioning

confidence: 99%

Brain computer interfacing: Applications and challenges

Abdulkader

Atia

Mostafa

2015

Egyptian Informatics Journal

445

247

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Brain computer interface technology represents a highly growing field of research with application systems. Its contributions in medical fields range from prevention to neuronal rehabilitation for serious injuries. Mind reading and remote communication have their unique fingerprint in numerous fields such as educational, self-regulation, production, marketing, security as well as games and entertainment. It creates a mutual understanding between users and the surrounding systems. This paper shows the application areas that could benefit from brain waves in facilitating or achieving their goals. We also discuss major usability and technical challenges that face brain signals utilization in various components of BCI system. Different solutions that aim to limit and decrease their effects have also been reviewed.Ó 2015 Production and hosting by Elsevier B.V. on behalf

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“…dopaminergic mid-brain regions, rt-fMRI may become an effective non-invasive tool to study the role of sub-cortical brain structures in the context of stroke recovery (Sulzer et al, 2013). Also, multisite rt-fMRI BMI feedback could be used to increase the connectivity between functionally associated brain regions (Ruiz et al, 2014).…”

Section: Bmis In Stroke Neurorehabilitationmentioning

confidence: 99%

Brain–machine interfaces in neurorehabilitation of stroke

Soekadar

Birbaumer

Slutzky

et al. 2015

Neurobiology of Disease

Self Cite

270

180

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Stroke is among the leading causes of long-term disabilities leaving an increasing number of people with cognitive, affective and motor impairments depending on assistance in their daily life. While function after stroke can significantly improve in the first weeks and months, further recovery is often slow or non-existent in the more severe cases encompassing 30-50% of all stroke victims. The neurobiological mechanisms underlying recovery in those patients are incompletely understood. However, recent studies demonstrated the brain's remarkable capacity for functional and structural plasticity and recovery even in severe chronic stroke. As all established rehabilitation strategies require some remaining motor function, there is currently no standardized and accepted treatment for patients with complete chronic muscle paralysis. The development of brain-machine interfaces (BMIs) that translate brain activity into control signals of computers or external devices provides two new strategies to overcome stroke-related motor paralysis. First, BMIs can establish continuous high-dimensional brain-control of robotic devices or functional electric stimulation (FES) to assist in daily life activities (assistive BMI). Second, BMIs could facilitate neuroplasticity, thus enhancing motor learning and motor recovery (rehabilitative BMI). Advances in sensor technology, development of non-invasive and implantable wireless BMI-systems and their combination with brain stimulation, along with evidence for BMI systems' clinical efficacy suggest that BMI-related strategies will play an increasing role in neurorehabilitation of stroke.

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Real-time fMRI brain computer interfaces: Self-regulation of single brain regions to networks

Cited by 156 publications

References 81 publications

Self-regulation of inter-hemispheric visual cortex balance through real-time fMRI neurofeedback training

Self-regulation of inter-hemispheric visual cortex balance through real-time fMRI neurofeedback training

Brain computer interfacing: Applications and challenges

Brain–machine interfaces in neurorehabilitation of stroke

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