Predicting future learning from baseline network architecture

Marcelo G. Mattar, Nicholas F. Wymbs, Andrew S. Bock, Geoffrey K. Aguirre, Scott T. Grafton, Danielle S. Bassett

Research output: Contribution to journalArticlepeer-review


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 analyzed functional MRI data from 19 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 suggest that individual differences in motor skill learning can be predicted from sensorimotor autonomy at baseline prior to task execution.

Original languageEnglish (US)
Pages (from-to)107-117
Number of pages11
StatePublished - May 15 2018


  • Brain networks
  • Functional connectivity
  • Human learning
  • Motor learning
  • Network science
  • fMRI

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience


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