TY - JOUR T1 - Predicting future learning from baseline network architecture JF - bioRxiv DO - 10.1101/056861 SP - 056861 AU - Marcelo G. Mattar AU - Nicholas F. Wymbs AU - Andrew S. Bock AU - Geoffrey K. Aguirre AU - Scott T. Grafton AU - Danielle S. Bassett Y1 - 2016/01/01 UR - http://biorxiv.org/content/early/2016/06/03/056861.abstract N2 - Human behavior and cognition result from a complex pattern of interactions between brain regions. The flexible reconfiguration of these patterns enables behavioral adaptation, such as the acquisition of a new motor skill. Yet, the degree to which these reconfigurations depend on the brain’s baseline sensorimotor integration is far from understood. Here, we asked whether spontaneous fluctuations in sensorimotor networks at baseline were predictive of individual differences in future learning. We collected functional MRI data from 22 participants prior to six weeks of training on a new motor skill. We found that visual-motor connectivity was inversely related to learning rate: sensorimotor autonomy at baseline corresponded to faster learning in the future. Using three additional scans, we found that visual-motor connectivity at baseline is a relatively stable individual trait. These results demonstrate that individual differences in motor skill learning can be reliably predicted from sensorimotor autonomy at baseline prior to task execution. ER -