Using real-time fMRI brain-computer interfacing to treat eating disorders

Sokunbi, Moses O.

doi:10.1016/j.jns.2018.03.011

Cited by 14 publications

(10 citation statements)

References 58 publications

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“…There is much discussion of translations of the research using healthy samples to clinical applications, such as stroke (Wang et al, 2018), OCD (Goncalves et al, 2017), hallucinations (Fovet et al, 2016), motor rehabilitation (Linden and Turner, 2016), internet addiction (Becker and Montag, 2017), eating disorders (Sokunbi, 2018), and psychiatry in general (Arns et al, 2017). However, the studies that have been done comparing the efficacy of rt-fMRI neurofeedback in healthy vs. clinical samples suggests translation between the two samples is not that simple.…”

Section: Investigation Of Rt-fmri Neurofeedback Using Clinical Populamentioning

confidence: 99%

A Guide to Literature Informed Decisions in the Design of Real Time fMRI Neurofeedback Studies: A Systematic Review

Fede

Dean

Manuweera

et al. 2020

Front. Hum. Neurosci.

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Background: Although biofeedback using electrophysiology has been explored extensively, the approach of using neurofeedback corresponding to hemodynamic response is a relatively young field. Real time functional magnetic resonance imaging-based neurofeedback (rt-fMRI-NF) uses sensory feedback to operantly reinforce patterns of neural response. It can be used, for example, to alter visual perception, increase brain connectivity, and reduce depression symptoms. Within recent years, interest in rt-fMRI-NF in both research and clinical contexts has expanded considerably. As such, building a consensus regarding best practices is of great value. Objective: This systematic review is designed to describe and evaluate the variations in methodology used in previous rt-fMRI-NF studies to provide recommendations for rt-fMRI-NF study designs that are mostly likely to elicit reproducible and consistent effects of neurofeedback. Methods: We conducted a database search for fMRI neurofeedback papers published prior to September 26th, 2019. Of 558 studies identified, 146 met criteria for inclusion. The following information was collected from each study: sample size and type, task used, neurofeedback calculation, regulation procedure, feedback, whether feedback was explicitly related to changing brain activity, feedback timing, control group for active neurofeedback, how many runs and sessions of neurofeedback, if a follow-up was conducted, and the results of neurofeedback training. Results: rt-fMRI-NF is typically upregulation practice based on hemodynamic response from a specific region of the brain presented using a continually updating thermometer display. Most rt-fMRI-NF studies are conducted in healthy samples and half evaluate its effect on immediate changes in behavior or affect. The most popular control group method is to provide sham signal from another region; however, many studies do not compare use a comparison group. Conclusions: We make several suggestions for designs of future rt-fMRI-NF studies. Researchers should use feedback calculation methods that consider neural response across regions (i.e., SVM or connectivity), which should be conveyed as intermittent, Fede et al. Neurofeedback Guide auditory feedback. Participants should be given explicit instructions and should be assessed on individual differences. Future rt-fMRI-NF studies should use clinical samples; effectiveness of rt-fMRI-NF should be evaluated on clinical/behavioral outcomes at follow-up time points in comparison to both a sham and no feedback control group.

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Section: Investigation Of Rt-fmri Neurofeedback Using Clinical Populamentioning

confidence: 99%

A Guide to Literature Informed Decisions in the Design of Real Time fMRI Neurofeedback Studies: A Systematic Review

Fede

Dean

Manuweera

et al. 2020

Front. Hum. Neurosci.

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“…In other words, BCI systems can provide alternative communication channels for LIS patients [ 3 ]. BCIs can be realized using various neuroimaging modalities, such as electrocorticography (ECoG) [ 4 , 5 ], electroencephalography (EEG) [ 6 , 7 ], magnetoencephalography (MEG) [ 8 , 9 ], functional near-infrared spectroscopy (fNIRS) [ 10 , 11 ], and functional magnetic resonance imaging (fMRI) [ 12 , 13 ]. An invasive signal recording technique, such as ECoG, requires a surgical operation to place recording electrodes on the cortex.…”

Section: Introductionmentioning

confidence: 99%

On the Feasibility of Using an Ear-EEG to Develop an Endogenous Brain-Computer Interface

Choi

Han

Choi

et al. 2018

Sensors

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Brain-computer interface (BCI) studies based on electroencephalography (EEG) measured around the ears (ear-EEGs) have mostly used exogenous paradigms involving brain activity evoked by external stimuli. The objective of this study is to investigate the feasibility of ear-EEGs for development of an endogenous BCI system that uses self-modulated brain activity. We performed preliminary and main experiments where EEGs were measured on the scalp and behind the ears to check the reliability of ear-EEGs as compared to scalp-EEGs. In the preliminary and main experiments, subjects performed eyes-open and eyes-closed tasks, and they performed mental arithmetic (MA) and light cognitive (LC) tasks, respectively. For data analysis, the brain area was divided into four regions of interest (ROIs) (i.e., frontal, central, occipital, and ear area). The preliminary experiment showed that the degree of alpha activity increase of the ear area with eyes closed is comparable to those of other ROIs (occipital > ear > central > frontal). In the main experiment, similar event-related (de)synchronization (ERD/ERS) patterns were observed between the four ROIs during MA and LC, and all ROIs showed the mean classification accuracies above 70% required for effective binary communication (MA vs. LC) (occipital = ear = central = frontal). From the results, we demonstrated that ear-EEG can be used to develop an endogenous BCI system based on cognitive tasks without external stimuli, which allows the usability of ear-EEGs to be extended.

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“…Examples of BCI effectors include computer cursors, 29 robotic orthoses, [16][17][18]59,60 exoskeletons, [61][62][63] wheelchairs, [64][65][66] virtual reality environments (i.e., computer-generated simulations that allow users to interactively practice activities), [67][68][69] artificial voice, 70,71 flash spellers, [72][73][74][75][76] or reanimation of one's own limb. [77][78][79][80][81] Neurofeedback is a critical component of both restoration of function (closed-loop control) and rehabilitation of function. The most common neurofeedback strategy has been audiovisual (AV) feedback.…”

Section: Neural Decoding: Translating Neural Informationmentioning

confidence: 99%

Brain–Computer Interfaces in Neurorecovery and Neurorehabilitation

2021

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Recent advances in brain–computer interface technology to restore and rehabilitate neurologic function aim to enable persons with disabling neurologic conditions to communicate, interact with the environment, and achieve other key activities of daily living and personal goals. Here we evaluate the principles, benefits, challenges, and future directions of brain–computer interfaces in the context of neurorehabilitation. We then explore the clinical translation of these technologies and propose an approach to facilitate implementation of brain–computer interfaces for persons with neurologic disease.

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Using real-time fMRI brain-computer interfacing to treat eating disorders

Cited by 14 publications

References 58 publications

A Guide to Literature Informed Decisions in the Design of Real Time fMRI Neurofeedback Studies: A Systematic Review

A Guide to Literature Informed Decisions in the Design of Real Time fMRI Neurofeedback Studies: A Systematic Review

On the Feasibility of Using an Ear-EEG to Develop an Endogenous Brain-Computer Interface

Brain–Computer Interfaces in Neurorecovery and Neurorehabilitation

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