Self-regulation strategy, feedback timing and hemodynamic properties modulate learning in a simulated fMRI neurofeedback environment

• Published in: PLoS computational biology 2017-07, Vol.13 (7), p.e1005681-e1005681
• Main Authors: Oblak, Ethan F, Lewis-Peacock, Jarrod A, Sulzer, James S
• Format: Article
• Language: English
• Subjects: Activity patterns; Adolescent; Adult; Behavior; Biofeedback; Biology and Life Sciences; Brain; Brain - physiology; Brain mapping; Brain research; Buttons; Cognitive ability; Computational neuroscience; Computer simulation; Cortex (temporal); Electroencephalography; Engineering and Technology; Feedback; Female; Functional magnetic resonance imaging; Funding; Hemodynamics; Hemodynamics - physiology; Humans; Learning; Learning - physiology; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Mechanical engineering; Medical imaging; Medical research; Medicine and Health Sciences; Methods; Models, Neurological; Neural circuitry; Neurofeedback - physiology; Neuroimaging; Neurosciences; NMR; Nuclear magnetic resonance; Observations; Physiological aspects; Reinforcement; Research and Analysis Methods; Roles; Scanners; Simulation; Social Sciences; Stimulation; Strategy; Studies; Temporal lobe; Visual cortex; Visual Cortex - physiology; Visual perception; Young Adult
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1005681

Direct manipulation of brain activity can be used to investigate causal brain-behavior relationships. Current noninvasive neural stimulation techniques are too coarse to manipulate behaviors that correlate with fine-grained spatial patterns recorded by fMRI. However, these activity patterns can be manipulated by having people learn to self-regulate their own recorded neural activity. This technique, known as fMRI neurofeedback, faces challenges as many participants are unable to self-regulate. The causes of this non-responder effect are not well understood due to the cost and complexity of such investigation in the MRI scanner. Here, we investigated the temporal dynamics of the hemodynamic response measured by fMRI as a potential cause of the non-responder effect. Learning to self-regulate the hemodynamic response involves a difficult temporal credit-assignment problem because this signal is both delayed and blurred over time. Two factors critical to this problem are the prescribed self-regulation strategy (cognitive or automatic) and feedback timing (continuous or intermittent). Here, we sought to evaluate how these factors interact with the temporal dynamics of fMRI without using the MRI scanner. We first examined the role of cognitive strategies by having participants learn to regulate a simulated neurofeedback signal using a unidimensional strategy: pressing one of two buttons to rotate a visual grating that stimulates a model of visual cortex. Under these conditions, continuous feedback led to faster regulation compared to intermittent feedback. Yet, since many neurofeedback studies prescribe implicit self-regulation strategies, we created a computational model of automatic reward-based learning to examine whether this result held true for automatic processing. When feedback was delayed and blurred based on the hemodynamics of fMRI, this model learned more reliably from intermittent feedback compared to continuous feedback. These results suggest that different self-regulation mechanisms prefer different feedback timings, and that these factors can be effectively explored and optimized via simulation prior to deployment in the MRI scanner.