Real-Time fMRI Pattern Decoding and Neurofeedback Using FRIEND: An FSL-Integrated BCI Toolbox

Sato, João Ricardo; Basílio, Rodrigo; Paiva, Fernando Fernandes; Garrido, Griselda J.; Bado, Patricia; Tovar‐Moll, Fernanda; Zahn, Roland; Moll, Jorge

doi:10.1371/journal.pone.0081658

Cited by 36 publications

(43 citation statements)

References 44 publications

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“…html; Weiskopf et al, 2004), a BioImageSuite extension (bisweb. yale.edu; Scheinost et al, 2013a), a FSL based toolbox, FRIEND (nitrc.org/projects/friend/; Sato et al, 2013;Basilio et al, 2015), and an SPM/Matlab based toolbox OpenNFT (opennft.org; Koush et al, 2017). Many groups use custom implementations or extensions of these toolboxes and plugins.…”

Section: Artifacts and Technical Problemsmentioning

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: Artifacts and Technical Problemsmentioning

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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“…[47][48][49] After this, the variation of O 2 Hb and HHb concentrations was calculated by the Beer-Lambert law, using the pretest resting block as the reference of concentration. Finally, inspired by voxel normalization approaches for fMRI-based neurofeedback, 50,51,52 for each channel, the moving O 2 Hb and HHb concentration values were corrected by the averaged concentrations from the same channel during the previous neutral condition. This approach was used for both positive and neutral conditions (although for the neutral condition this procedure is only possible from the second trial since it needs the first one for normalization).…”

Section: Classifier Training Blocks (No Feedback)mentioning

confidence: 99%

Functional near-infrared spectroscopy-based affective neurofeedback: feedback effect, illiteracy phenomena, and whole-connectivity profiles

et al. 2018

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Background: Affective neurofeedback constitutes a suitable approach to control abnormal neural activities associated with psychiatric disorders and might consequently relief symptom severity. However, different aspects of neurofeedback remain unclear, such as its neural basis, the performance variation, the feedback effect, among others. Aim: First, we aimed to propose a functional near-infrared spectroscopy (fNIRS)-based affective neurofeedback based on the self-regulation of frontal and occipital networks. Second, we evaluated three different feedback approaches on performance: real, fixed, and random feedback. Third, we investigated different demographic, psychological, and physiological predictors of performance. Approach: Thirty-three healthy participants performed a task whereby an amorphous figure changed its shape according to the elicited affect (positive or neutral). During the task, the participants randomly received three different feedback approaches: real feedback, with no change of the classifier output; fixed feedback, keeping the feedback figure unmodified; and random feedback, where the classifier output was multiplied by an arbitrary value, causing a feedback different than expected by the subject. Then, we applied a multivariate comparison of the whole-connectivity profiles according to the affective states and feedback approaches, as well as during a pretask resting-state block, to predict performance. Results: Participants were able to control this feedback system with 70.00% AE 24.43% (p < 0.01) of performance during the real feedback trials. No significant differences were found when comparing the average performances of the feedback approaches. However, the whole functional connectivity profiles presented significant Mahalanobis distances (p ≪ 0.001) when comparing both affective states and all feedback approaches. Finally, task performance was positively correlated to the pretask resting-state whole functional connectivity (r ¼ 0.512, p ¼ 0.009). Conclusions: Our results suggest that fNIRS might be a feasible tool to develop a neurofeedback system based on the self-regulation of affective networks. This finding enables future investigations using an fNIRS-based affective neurofeedback in psychiatric populations. Furthermore, functional connectivity profiles proved to be a good predictor of performance and suggested an increased effort to maintain task control in the presence of feedback distractors.

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“…For example, Shibata et al (2011) used neurofeedback from visual areas V1 and V2 to control the size of a circular stimulus displayed to subjects and Ward et al (2011) explored real-time mapping of the early visual field using Kalman filtering. Most recently, Sato et al (2013) have developed a toolbox (“FRIEND”) that implements neural feedback applications in fMRI, applying classification and connectivity analyses to study the encoding of emotion. These studies support the idea of incorporating real-time analysis and feedback into neuroimaging work to expanding fields, such as the study of object perception.…”

Section: Introductionmentioning

confidence: 99%

A method for real-time visual stimulus selection in the study of cortical object perception

Leeds

Tarr

2016

NeuroImage

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The properties utilized by visual object perception in the mid- and high-level ventral visual pathway are poorly understood. To better establish and explore possible models of these properties, we adopt a data-driven approach in which we repeatedly interrogate neural units using functional Magnetic Resonance Imaging (fMRI) to establish each unit’s image selectivity. This approach to imaging necessitates a search through a broad space of stimulus properties using a limited number of samples. To more quickly identify the complex visual features underlying human cortical object perception, we implemented a new functional magnetic resonance imaging protocol in which visual stimuli are selected in real-time based on BOLD responses to recently shown images. Two variations of this protocol were developed, one relying on natural object stimuli and a second based on synthetic object stimuli, both embedded in feature spaces based on the complex visual properties of the objects. During fMRI scanning, we continuously controlled stimulus selection in the context of a real-time search through these image spaces in order to maximize neural responses across predetermined 1 cm3 brain regions. Elsewhere we have reported the patterns of cortical selectivity revealed by this approach (Leeds 2014). In contrast, here our objective is to present more detailed methods and explore the technical and biological factors influencing the behavior of our real-time stimulus search. We observe that: 1) Searches converged more reliably when exploring a more precisely parameterized space of synthetic objects; 2) Real-time estimation of cortical responses to stimuli are reasonably consistent; 3) Search behavior was acceptably robust to delays in stimulus displays and subject motion effects. Overall, our results indicate that real-time fMRI methods may provide a valuable platform for continuing study of localized neural selectivity, both for visual object representation and beyond.

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Real-Time fMRI Pattern Decoding and Neurofeedback Using FRIEND: An FSL-Integrated BCI Toolbox

Cited by 36 publications

References 44 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

Functional near-infrared spectroscopy-based affective neurofeedback: feedback effect, illiteracy phenomena, and whole-connectivity profiles

A method for real-time visual stimulus selection in the study of cortical object perception

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