Perceptual Learning Incepted by Decoded fMRI Neurofeedback Without Stimulus Presentation

Abstract

It is controversial whether the adult early visual cortex is sufficiently plastic to cause visual perceptual learning (VPL). The controversy occurs partially because most VPL studies have examined correlations between behavioral and neural activity changes rather than cause-and-effect relationships. Using an online-feedback method that utilizes decoded functional magnetic resonance imaging signals, we induced activity patterns only in early visual cortex corresponding to an orientation without stimulus presentation or subjects’ awareness of what was to be learned. The induced activation caused VPL specific to the orientation. These results suggest that early visual areas are so plastic that mere inductions of activity patterns are sufficient to cause VPL. This technique can induce plasticity in a highly selective manner, potentially leading to powerful training and rehabilitative protocols.

Creating the brain and interacting with the brain: an integrated approach to understanding the brain

“…The lack of experimental tools for examining cause-and-effect relationships between brain activity and the mind in systems neuroscience has severely constrained its progress and applicability to such practical problems as robotics or clinical issues [36]. To bridge this gap between major concepts and current technology, we developed a new technique that manipulates neural codes [38] by applying a novel onlinefeedback method that uses decoded fMRI signals. We call this new technique DecNef.…”

“…rsif.royalsocietypublishing.org J. R. Soc. Interface 12: 20141250 activity patterns in a specific brain area that can be non-invasively controlled by real-time fMRI neurofeedback [38]. We induced visual perceptual learning without visual stimulus presentation or conscious awareness of tasks.…”

“…The future of computational neuroscience in a broader sense is the integration of all three disciplines. Integration could take the concrete form of a human volunteer wearing a humanoid exoskeleton controlled by brain activity measured, and rich information fedback to the volunteer's body and brain while covering a wide range of sensory modalities based on DecNef [38] from a computational modelling point of view. To derive an optimal control policy for nonlinear dynamics, dynamic programming [45] and reinforcement learning [11] are used as the standard policy-learning algorithms.…”

“…The decoded neurofeedback (DecNef) method is another concrete example of the integration of the previously divergent way of understanding brains, because it integrates computational neuroscience and BMI disciplines to experimentally induce spatial voxel patterns in a limited brain region, facial preference and colour. DecNef has already realized the perceptual learning of specific orientation gratings [38], the associative learning for the manipulation of facial preference [39] and the creation of the phenomenal consciousness of colour [40].…”

Multi-modal Virtual Scenario Enhances Neurofeedback Learning

“…During NF blocks participants were instructed to downregulate amyg-EFP signal amplitude by practicing mental strategies. Instructions were intentionally unspecific, allowing individuals to adopt the mental strategy that they subjectively found most efficient (Shibata et al, 2011). During washout blocks, participants were instructed to tap their thumb to their fingers according to a 3 digit number that appeared on their screen.…”