How feedback, motor imagery, and reward influence brain self-regulation using real-time fMRI.
The learning process involved in achieving brain self-regulation is presumed to be related to several factors, such as type of feedback, reward, mental imagery, duration of training, among others. Explicitly instructing participants to use mental imagery and monetary reward are common practices in real-time fMRI (rtfMRI) neurofeedback (NF), under the assumption that they will enhance and accelerate the learning process. However, it is still not clear what the optimal strategy is for improving volitional control. We investigated the differential effect of feedback, explicit instructions and monetary reward while training healthy individuals to up-regulate the blood-oxygen-level dependent (BOLD) signal in the supplementary motor area (SMA). Four groups were trained in a two-day rtfMRI-NF protocol: GF with NF only, GF,I with NF + explicit instructions (motor imagery), GF,R with NF + monetary reward, and GF,I,R with NF + explicit instructions (motor imagery) + monetary reward. Our results showed that GF increased significantly their BOLD self-regulation from day-1 to day-2 and GF,R showed the highest BOLD signal amplitude in SMA during the training. The two groups who were instructed to use motor imagery did not show a significant learning effect over the 2 days. The additional factors, namely motor imagery and reward, tended to increase the intersubject variability in the SMA during the course of training. Whole brain univariate and functional connectivity analyses showed common as well as distinct patterns in the four groups, representing the varied influences of feedback, reward, and instructions on the brain. Hum Brain Mapp 37:3153-3171, 2016. © 2016 Wiley Periodicals, Inc.
Biomedical Imaging Center, Pontificia Universidad Católica De Chile, Santiago, Chile.|Department of Electrical Engineering, Pontificia Universidad Católica De Chile, Santiago, Chile.|Laboratory of Brain-Machine Interfaces and Neuromodulation, Pontificia Universidad Católica De Chile, Santiago, Chile.
Pradyumna
P
Sepulveda
Laboratory of Brain-Machine Interfaces and Neuromodulation, Pontificia Universidad Católica De Chile, Santiago, Chile.|Institute for Biological and Medical Engineering, Pontificia Universidad Católica De Chile, Santiago, Chile.|Department of Psychiatry, Faculty of Medicine, Interdisciplinary Center for Neuroscience, Pontificia Universidad Católica De Chile, Santiago, Chile.|Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany.
Ranganatha
R
Sitaram
Laboratory of Brain-Machine Interfaces and Neuromodulation, Pontificia Universidad Católica De Chile, Santiago, Chile.|Department of Psychiatry, Faculty of Medicine, Interdisciplinary Center for Neuroscience, Pontificia Universidad Católica De Chile, Santiago, Chile.
Mohit
M
Rana
Biomedical Imaging Center, Pontificia Universidad Católica De Chile, Santiago, Chile.
Cristian
C
Montalba
Biomedical Imaging Center, Pontificia Universidad Católica De Chile, Santiago, Chile.|Department of Electrical Engineering, Pontificia Universidad Católica De Chile, Santiago, Chile.
Cristian
C
Tejos
Laboratory of Brain-Machine Interfaces and Neuromodulation, Pontificia Universidad Católica De Chile, Santiago, Chile.|Department of Psychiatry, Faculty of Medicine, Interdisciplinary Center for Neuroscience, Pontificia Universidad Católica De Chile, Santiago, Chile.|Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany.
Sergio
S
Ruiz
D000293:Adolescent; D000328:Adult; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping; D006801:Humans; D019018:Imagery, Psychotherapy / Q000379:methods*; D007858:Learning / Q000502:physiology*; D008279:Magnetic Resonance Imaging; D008297:Male; D058765:Neurofeedback / Q000379:methods*; D012201:Reward; D055815:Young Adult
learning; mental strategy; motor imagery; neurofeedback; real-time fMRI; reward