Correlation Engine 2.0
Clear Search sequence regions

  • adult (1)
  • brain (8)
  • female (1)
  • fmri (2)
  • foreign (1)
  • humans (1)
  • young adult (1)
  • Sizes of these terms reflect their relevance to your search.

    Can learning capacity of the human brain be predicted from initial spontaneous functional connectivity (FC) between brain areas involved in a task? We combined task-related functional magnetic resonance imaging (fMRI) and resting-state fMRI (rs-fMRI) before and after training with a Hindi dental-retroflex nonnative contrast. Previous fMRI results were replicated, demonstrating that this learning recruited the left insula/frontal operculum and the left superior parietal lobe, among other areas of the brain. Crucially, resting-state FC (rs-FC) between these two areas at pretraining predicted individual differences in learning outcomes after distributed (Experiment 1) and intensive training (Experiment 2). Furthermore, this rs-FC was reduced at posttraining, a change that may also account for learning. Finally, resting-state network analyses showed that the mechanism underlying this reduction of rs-FC was mainly a transfer in intrinsic activity of the left frontal operculum/anterior insula from the left frontoparietal network to the salience network. Thus, rs-FC may contribute to predict learning ability and to understand how learning modifies the functioning of the brain. The discovery of this correspondence between initial spontaneous brain activity in task-related areas and posttraining performance opens new avenues to find predictors of learning capacities in the brain using task-related fMRI and rs-fMRI combined.


    Noelia Ventura-Campos, Ana Sanjuán, Julio González, María-Ángeles Palomar-García, Aina Rodríguez-Pujadas, Núria Sebastián-Gallés, Gustavo Deco, César Ávila. Spontaneous brain activity predicts learning ability of foreign sounds. The Journal of neuroscience : the official journal of the Society for Neuroscience. 2013 May 29;33(22):9295-305

    Expand section icon Mesh Tags

    PMID: 23719798

    View Full Text