Decoded fMRI neurofeedback can induce bidirectional confidence changes within single participants.
Neurofeedback studies using real-time functional magnetic resonance imaging (rt-fMRI) have recently incorporated the multi-voxel pattern decoding approach, allowing for fMRI to serve as a tool to manipulate fine-grained neural activity embedded in voxel patterns. Because of its tremendous potential for clinical applications, certain questions regarding decoded neurofeedback (DecNef) must be addressed. Specifically, can the same participants learn to induce neural patterns in opposite directions in different sessions? If so, how does previous learning affect subsequent induction effectiveness? These questions are critical because neurofeedback effects can last for months, but the short- to mid-term dynamics of such effects are unknown. Here we employed a within-subjects design, where participants underwent two DecNef training sessions to induce behavioural changes of opposing directionality (up or down regulation of perceptual confidence in a visual discrimination task), with the order of training counterbalanced across participants. Behavioral results indicated that the manipulation was strongly influenced by the order and the directionality of neurofeedback training. We applied nonlinear mathematical modeling to parametrize four main consequences of DecNef: main effect of change in confidence, strength of down-regulation of confidence relative to up-regulation, maintenance of learning effects, and anterograde learning interference. Modeling results revealed that DecNef successfully induced bidirectional confidence changes in different sessions within single participants. Furthermore, the effect of up- compared to down-regulation was more prominent, and confidence changes (regardless of the direction) were largely preserved even after a week-long interval. Lastly, the effect of the second session was markedly diminished as compared to the effect of the first session, indicating strong anterograde learning interference. These results are interpreted in the framework of reinforcement learning and provide important implications for its application to basic neuroscience, to occupational and sports training, and to therapy.
Department of Decoded Neurofeedback, ATR Computational Neuroscience Laboratories, Kyoto, Japan; Faculty of Information Science, Nara Institute of Science and Technology, Nara, Japan; Center for Information and Neural Networks (CiNet), NICT, Osaka, Japan; Department of Psychology, UCLA, Los Angeles, USA. Electronic address: cortese.aurelio@gmail.com.
Aurelio
A
Cortese
Center for Information and Neural Networks (CiNet), NICT, Osaka, Japan.
Kaoru
K
Amano
Department of Decoded Neurofeedback, ATR Computational Neuroscience Laboratories, Kyoto, Japan; Center for Information and Neural Networks (CiNet), NICT, Osaka, Japan.
Ai
A
Koizumi
Department of Psychology, UCLA, Los Angeles, USA; Brain Research Institute, UCLA, Los Angeles, USA. Electronic address: hakwan@gmail.com.
Hakwan
H
Lau
Department of Decoded Neurofeedback, ATR Computational Neuroscience Laboratories, Kyoto, Japan; Faculty of Information Science, Nara Institute of Science and Technology, Nara, Japan; Center for Information and Neural Networks (CiNet), NICT, Osaka, Japan. Electronic address: kawato@atr.jp.
Mitsuo
M
Kawato
D000465:Algorithms; D001931:Brain Mapping / Q000379:methods; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008959:Models, Neurological; D008962:Models, Theoretical; D058765:Neurofeedback / Q000379:methods*; D012054:Reinforcement, Psychology; D055815:Young Adult
Confidence; MVPA; Nonlinear modeling; fMRI neurofeedback