A simulation-based approach to improve decoded neurofeedback performance

The neural correlates of specific brain functions such as visual orientation tuning and individual finger movements can be revealed using multivoxel pattern analysis (MVPA) of fMRI data. Neurofeedback based on these distributed patterns of brain activity presents a unique ability for precise neuromo...

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Veröffentlicht in:NeuroImage (Orlando, Fla.) Fla.), 2019-07, Vol.195, p.300-310
Hauptverfasser: Oblak, Ethan F., Sulzer, James S., Lewis-Peacock, Jarrod A.
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Sulzer, James S.
Lewis-Peacock, Jarrod A.
description The neural correlates of specific brain functions such as visual orientation tuning and individual finger movements can be revealed using multivoxel pattern analysis (MVPA) of fMRI data. Neurofeedback based on these distributed patterns of brain activity presents a unique ability for precise neuromodulation. Recent applications of this technique, known as decoded neurofeedback, have manipulated fear conditioning, visual perception, confidence judgements and facial preference. However, there has yet to be an empirical justification of the timing and data processing parameters of these experiments. Suboptimal parameter settings could impact the efficacy of neurofeedback learning and contribute to the ‘non-responder’ effect. The goal of this study was to investigate how design parameters of decoded neurofeedback experiments affect decoding accuracy and neurofeedback performance. Subjects participated in three fMRI sessions: two ‘finger localizer’ sessions to identify the fMRI patterns associated with each of the four fingers of the right hand, and one ‘finger finding’ neurofeedback session to assess neurofeedback performance. Using only the localizer data, we show that real-time decoding can be degraded by poor experiment timing or ROI selection. To set key parameters for the neurofeedback session, we used offline simulations of decoded neurofeedback using data from the localizer sessions to predict neurofeedback performance. We show that these predictions align with real neurofeedback performance at the group level and can also explain individual differences in neurofeedback success. Overall, this work demonstrates the usefulness of offline simulation to improve the success of real-time decoded neurofeedback experiments. •Real-time fMRI decoding accuracy relies most on experiment timing and ROI selection.•Parameters of decoded neurofeedback experiments can be designed using simulations.•Offline simulations of decoded neurofeedback can predict neurofeedback performance.
doi_str_mv 10.1016/j.neuroimage.2019.03.062
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subjects Accuracy
Adult
Biofeedback
Brain mapping
Brain Mapping - methods
Brain research
Electroencephalography
Experiments
Explicit knowledge
Fear conditioning
Feedback
Female
Finger
fMRI
Functional magnetic resonance imaging
Human performance
Humans
Image Processing, Computer-Assisted
Information processing
Machine Learning
Magnetic Resonance Imaging
Male
Medical imaging
Medical research
Multi-voxel pattern analysis
Neurofeedback
Neurofeedback - methods
Neuromodulation
Neurosciences
Orientation behavior
Research Design
Sensorimotor cortex
Sensorimotor Cortex - physiology
Simulation
Visual perception
title A simulation-based approach to improve decoded neurofeedback performance
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