Neural substrates of motor engrams in the human brain are hard to identify because their dormant states are difficult to discriminate. We utilized eigenvector centrality (EC) to measure the network information that accumulates as an engram during learning. To discriminate engrams formed by emphasis on speed or accuracy, we conducted functional MRI on 58 normal volunteers as they performed a sequential finger-tapping task with the non-dominant left hand. Participants alternated between performing a tapping sequence as quickly as possible (maximum mode) or at a constant speed of 2 Hz, paced by a sequence-specifying visual cue (constant mode). We depicted the formation of the motor engram by characterizing the dormant state as the increase in EC of the resting epoch throughout the training course, and the ecphory, or activated state, as the increment in EC during the task epoch relative to the alternated resting epoch. We found that a network covering the left anterior intraparietal sulcus and inferior parietal lobule represented the engram for the speed of execution, whereas bilateral premotor cortex and right primary motor cortex represented the sequential order of movements. This constitutes the first demonstration of learning-mode specific motor engrams formed by only 30 min of training.
Keywords: Eigenvector centrality; Motor engram; Sequential finger tapping; Short-term training; fMRI; motor learning.
Copyright © 2019. Published by Elsevier B.V.