Real-Time Functional Magnetic Resonance Imaging Neurofeedback for Treatment of Parkinson's Disease

Subramanian, Leena; Hindle, John V.; Johnston, Stephen; Roberts, Mark; Husain, Masud; Goebel, Rainer; Linden, David Edmund Johannes

doi:10.1523/jneurosci.3498-11.2011

Cited by 237 publications

(237 citation statements)

References 51 publications

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“…Similar to previous neurofeedback studies, we found that self-regulation engaged widespread brain networks (e.g. Chiew et al, 2012;Haller et al, 2013;Rota et al, 2011;Subramanian et al, 2011;Sulzer et al, 2013b;Veit et al, 2012;Zotev et al, 2011). In particular, in the learners, activations concomitant to feedback training arose not only in visual areas, but also in bilateral parietal and frontal areas that are commonly associated with top-down attentional control (Bressler et al, 2008;Greenberg et al, 2010;Hopfinger et al, 2000;Kelley et al, 2008;Lauritzen et al, 2009;Vossel et al, 2012;Yantis et al, 2002) (Figs.…”

Section: Concomitant Brain Activationssupporting

confidence: 88%

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: Concomitant Brain Activationssupporting

confidence: 88%

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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“…Beyond the ACC and insula, training motor areas can improve motor control in both healthy participants (Hui et al, 2014) and individuals diagnosed with Parkinson's disease (Subramanian et al, 2011); however, in the former study the control group also demonstrated improved finger-tapping speed while the latter experiment used no sham-feedback control condition. Altering prefrontal blood flow can improve detection of emotional prosodic intonations (Rota et al, 2009) and verbal working memory ; however, again, the latter experiment demonstrated that sham-neurofeedback enhanced performance on four of the five working memory tasks, even while impairing the ability of participants to modulate target brain regions.…”

Section: Fmrimentioning

confidence: 84%

“…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

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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“…Several studies have demonstrated the feasibility of self-regulating activity in specific brain areas using rt-fMRI neurofeedback (e.g., deCharms et al, 2004;Posse et al, 2003;Weiskopf et al, 2003Weiskopf et al, , 2004aYoo and Jolesz, 2002). Some studies have even shown that self-regulation results in clinical benefits for specific neurological conditions such as chronic pain (deCharms et al, 2005), tinnitus (Haller et al, 2010), and Parkinson's disease (Subramanian et al, 2011). Further, there is preliminary evidence that learning self-regulation of brain activity can lead to changes in functional connectivity (Horovitz et al, 2010;Lee et al, 2011;Rota et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Dynamic reconfiguration of human brain functional networks through neurofeedback

Haller¹,

Kopel

Jhooti

et al. 2013

NeuroImage

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a b s t r a c t a r t i c l e i n f oRecent fMRI studies demonstrated that functional connectivity is altered following cognitive tasks (e.g., learning) or due to various neurological disorders. We tested whether real-time fMRI-based neurofeedback can be a tool to voluntarily reconfigure brain network interactions. To disentangle learning-related from regulation-related effects, we first trained participants to voluntarily regulate activity in the auditory cortex (training phase) and subsequently asked participants to exert learned voluntary self-regulation in the absence of feedback (transfer phase without learning). Using independent component analysis (ICA), we found network reconfigurations (increases in functional network connectivity) during the neurofeedback training phase between the auditory target region and (1) the auditory pathway; (2) visual regions related to visual feedback processing; (3) insula related to introspection and self-regulation and (4) working memory and high-level visual attention areas related to cognitive effort. Interestingly, the auditory target region was identified as the hub of the reconfigured functional networks without a-priori assumptions. During the transfer phase, we again found specific functional connectivity reconfiguration between auditory and attention network confirming the specific effect of self-regulation on functional connectivity. Functional connectivity to working memory related networks was no longer altered consistent with the absent demand on working memory. We demonstrate that neurofeedback learning is mediated by widespread changes in functional connectivity. In contrast, applying learned self-regulation involves more limited and specific network changes in an auditory setup intended as a model for tinnitus. Hence, neurofeedback training might be used to promote recovery from neurological disorders that are linked to abnormal patterns of brain connectivity.

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Real-Time Functional Magnetic Resonance Imaging Neurofeedback for Treatment of Parkinson's Disease

Cited by 237 publications

References 51 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

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

Dynamic reconfiguration of human brain functional networks through neurofeedback

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