Research on brain networks explains differences in learning rates October 12, 2016 0 Comments Share tweet Ricardo Peterson By: Ricardo Peterson In efforts to learn more about the dynamics of brain networks, researchers at Stanford have linked fluctuations of brain signals to the ability to rapidly learn new tasks. Using data from functional magnetic resonance imaging (fMRI), a standard tool capable of time-resolved imaging of active brain regions, researchers applied a novel analysis technique to demonstrate fluctuations of a human brain’s “interconnectedness.” “The brain is stunning in its complexity and I feel like, in a way, we’ve been able to describe some of its beauty in this story,” said postdoctoral researcher and lead author Mac Shine in a recent interview with Stanford News. “We’ve been able to say, ‘Here’s this underlying structure that you would never have guessed was there that might help us explain the mystery of why the brain is organized in the way that it is.’” The researchers observed that when participants performed mentally exhausting tasks, their brains were more “integrated” — which implies communication between separate brain regions — in comparison with the average resting brain. Although the dynamic nature of the brain has been shown before, the data from this research reveals that the brain’s inner-connectivity was strongest in participants who demonstrated quick and accurate thinking during the working phase of the experiment. “[Stories] about how the brain works that don’t relate back to behavior don’t really do much for me,” said psychology professor Russell Poldrack, a co-author. “But this research shows these really clear relationships between how the brain is functioning at a network level and how the person’s actually performing on these psychological tasks.” To supplement these findings, the authors highlighted that the diameter of the pupil is an accurate measure for task engagement and arousal, according to previous findings. Thus, they hypothesized that the participant’s pupil diameter would correlate with the connectivity of the brain. As predicted, a positive correlation was observed, which suggests that the fluctuations of brain signals are partly driven by the same neurological mechanisms that trigger pupil dilation. The demonstrated link between integration and improved cognitive performance may eventually lead to increased understanding of the nature of cognitive disorders or other psychological phenomena. Contact Ricardo Andre Peterson at rp3 ’at’ stanford.edu. brain networks fmri learning Psychology research research 2016-10-12 Ricardo Peterson October 12, 2016 0 Comments Share tweet Subscribe Click here to subscribe to our daily newsletter of top headlines.