fMRI Brain-Computer Interface: A Tool for Neuroscientific Research and Treatment

Sitaram, Ranganatha; Caria, Andrea; Veit, Ralf; Gaber, Tilman J.; Rota, Giuseppina; Kuebler, Andrea; Birbaumer, Niels

doi:10.1155/2007/25487

Cited by 180 publications

(108 citation statements)

References 47 publications

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“…For example, healthy individuals showed the ability to self-regulate brain activity in neuroanatomical structures often associated with affect (e.g., insula, amygdala, prefrontal cortex (PFC), and anterior cingulated cortex (ACC)) (Hamilton, Glover, Hsu, Johnson, & Gotlib, 2011;Johnston et al, 2011;Posse et al, 2003;Sitaram, Caria, et al, 2007;Zotev et al, 2011 Subramanian et al, 2011) and depression (e.g., Linden et al, 2012;Young et al, 2014). Although these clinical studies represent mostly nascent efforts in line with pilot data and usually draw on small samples and largely unreplicated assays, the emerging tenor from these preliminary findings seems to speak favorably to the clinical potential of rtfMRI-nf.…”

Section: Fmrimentioning

confidence: 99%

The self-regulating brain and neurofeedback: Experimental science and clinical promise

Thibault

Lifshitz

Raz

2016

Cortex

218

198

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Neurofeedback, one of the primary examples of self-regulation, designates a collection of techniques that train the brain and help to improve its function. Since coming on the scene in the 1960s, electroencephalography-neurofeedback has become a treatment vehicle for a host of mental disorders; however, its clinical effectiveness remains controversial. Modern imaging technologies of the living human brain (e.g., real-time functional magnetic resonance imaging) and increasingly rigorous research protocols that utilize such methodologies begin to shed light on the underlying mechanisms that may facilitate more effective clinical applications. In this paper we focus on recent technological advances in the field of human brain imaging and discuss how these modern methods may influence the field of neurofeedback. Toward this end, we outline the state of the evidence and sketch out future directions to further explore the potential merits of this contentious therapeutic prospect.

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Section: Fmrimentioning

confidence: 99%

The self-regulating brain and neurofeedback: Experimental science and clinical promise

Thibault

Lifshitz

Raz

2016

Cortex

218

198

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“…Besides research that has explicitly dealt with the development of fMRI-based BCI techniques for communication and control purposes (see below), there is another stream of BCI researchfocusing on neurofeedback training effects and exploiting fMRI-based BCI as a tool for neuroscientific research and treatment (deCharms, 2007(deCharms, , 2008Sitaram et al, 2007a;Weiskopf et al, 2004bWeiskopf et al, , 2007, e.g., to learn more about and enhance cognitive functioning in healthy humans (e.g., Rota et al, 2009;Scharnowski et al, 2004). Moreover, fMRI-based neurofeedback training may help to understand and ultimately treat certain pathological conditions as recently shown by deCharms et al (2005): Chronic pain patients were trained to control BOLD activation in the rostral anterior cingulate cortex -a region putatively involved in pain perception and regulation -and reported accordant decreases in the ongoing level of chronic pain.…”

Section: Fmri-based Bci Researchmentioning

confidence: 99%

Another kind of ‘BOLD Response’: answering multiple-choice questions via online decoded single-trial brain signals

Sorger

Dahmen

Reithler

et al. 2009

Progress in Brain Research

111

101

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Abstract:The term 'locked-in' syndrome (LIS) describes a medical condition in which persons concerned are severely paralyzed and at the same time fully conscious and awake. The resulting anarthria makes it impossible for these patients to naturally communicate, which results in diagnostic as well as serious practical and ethical problems. Therefore, developing alternative, muscle-independent communication means is of prime importance. Such communication means can be realized via brain-computer interfaces (BCIs) circumventing the muscular system by using brain signals associated with preserved cognitive, sensory, and emotional brain functions. Primarily, BCIs based on electrophysiological measures have been developed and applied with remarkable success. Recently, also blood flow-based neuroimaging methods, such as functional magnetic resonance imaging (fMRI) and functional near-infrared spectroscopy (fNIRS), have been explored in this context.After reviewing recent literature on the development of especially hemodynamically based BCIs, we introduce a highly reliable and easy-to-apply communication procedure that enables untrained participants to motor-independently and relatively effortlessly answer multiple-choice questions based on intentionally generated single-trial fMRI signals that can be decoded online. Our technique takes advantage of the participants' capability to voluntarily influence certain spatio-temporal aspects of the blood oxygenation level-dependent (BOLD) signal: source location (by using different mental tasks), signal onset and offset. We show that healthy participants are capable of hemodynamically encoding at least four distinct information units on a single-trial level without extensive pretraining and with little effort. Moreover, realtime data analysis based on simple multi-filter correlations allows for automated answer decoding with a high accuracy (94.9%) demonstrating the robustness of the presented method. Following our 'proof of concept', the next step will involve clinical trials with LIS patients, undertaken in close collaboration with their relatives and caretakers in order to elaborate individually tailored communication protocols.� Corresponding author. As our procedure can be easily transferred to MRI-equipped clinical sites, it may constitute a simple and effective possibility for online detection of residual consciousness and for LIS patients to communicate basic thoughts and needs in case no other alternative communication means are available (yet) -especially in the acute phase of the LIS. Future research may focus on further increasing the efficiency and accuracy of fMRI-based BCIs by implementing sophisticated data analysis methods (e.g., multivariate and independent component analysis) and neurofeedback training techniques. Finally, the presented BCI approach could be transferred to portable fNIRS systems as only this would enable hemodynamically based communication in daily life situations.

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“…Weiskopf et al [8] use real-time fMRI for self-regulation of local brain activity. An overview of fMRI brain computer interfaces is given by Sitaram et al [9].…”

Section: Figmentioning

confidence: 99%

Using Real-Time fMRI to Control a Dynamical System by Brain Activity Classification

Eklund

Andersson

Rydell

et al. 2009

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009

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Abstract. We present a method for controlling a dynamical system using real-time fMRI. The objective for the subject in the MR scanner is to balance an inverted pendulum by activating the left or right hand or resting. The brain activity is classified each second by a neural network and the classification is sent to a pendulum simulator to change the force applied to the pendulum. The state of the inverted pendulum is shown to the subject in a pair of VR goggles. The subject was able to balance the inverted pendulum during several minutes, both with real activity and imagined activity. In each classification 9000 brain voxels were used and the response time for the system to detect a change of activity was on average 2-4 seconds. The developments here have a potential to aid people with communication disabilities, such as locked in people. Another future potential application can be to serve as a tool for stroke and Parkinson patients to be able to train the damaged brain area and get real-time feedback for more efficient training.

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fMRI Brain-Computer Interface: A Tool for Neuroscientific Research and Treatment

Cited by 180 publications

References 47 publications

The self-regulating brain and neurofeedback: Experimental science and clinical promise

The self-regulating brain and neurofeedback: Experimental science and clinical promise

Another kind of ‘BOLD Response’: answering multiple-choice questions via online decoded single-trial brain signals

Using Real-Time fMRI to Control a Dynamical System by Brain Activity Classification

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