Neural Connectivity Evolution during Adaptive Learning with and without Proprioception

Understanding brain connectivity patterns that may spontaneously emerge in response to biofeedback training remains of great interest to neuroscientists. Along those lines, Brain Computer Interfaces (BCI) mediated by EEG signals that dynamically evolve as the user attempts to control a cursor on the screen, has helped identify brain areas recruited during the learning process. There is an adaptive process that takes place between the computer algorithm and the solution that the brain arrives at to mentally control the instructed cursor direction through intentional thoughts. Using new personalized techniques, we here address how different participants learn during this co-adaptive process, in which bodily motions are curtailed in favor of mental motion. First, the person uses mental imagery of directional movements to attempt the cursor control, but as the computer algorithm and the brain work together to gain accuracy, this mental imagery reportedly reaches a different level of abstraction to the point when the participants are mentally controlling the external computer cursor, yet no longer imagining the movement direction. We compared the evolution of a participant without proprioception owing to neuronopathy, to that of participants with intact afferent nerves and found fundamentally different patterns of activation. In the former, the connectivity patterns were far higher and distributed across the entire brain during the initial stages of learning, along with the changes across the learning stages being more pronounced in contrast to the other participants. We infer from this result that in the absence of kinesthetic reafference, heavy reliance on other senses like vision and hearing, may endow the brain with higher capacity to handle the excess cognitive load.