The new study uses computational methods developed to analyze what the researchers call multilayer networks, in which each layer might represent a network at one snapshot in time, or a different set of connections between the same set of brain regions. These layers are combined into a larger mathematical object, which can contain a potentially huge amount of data and is difficult to analyze. Previous methods could only deal with each layer separately.
"Parts of the brain communicate with one another very strongly, so they form a sort of module of intercommunicating regions of the brain," said first author Danielle S. Bassett, postdoctoral fellow in physics at UC Santa Barbara. "In this way, brain activity can segregate into multiple functional modules. What we wanted to measure is how fluid those modules are."
"Parts of the brain communicate with one another very strongly, so they form a sort of module of intercommunicating regions of the brain," said first author Danielle S. Bassett, postdoctoral fellow in physics at UC Santa Barbara. "In this way, brain activity can segregate into multiple functional modules. What we wanted to measure is how fluid those modules are."
Bassett explained that there are flexible brain regions with allegiances that change through time. "That flexibility seems to be the factor that predicts learning," said Bassett. "So, if you are very flexible, then you will end up learning better on the second day, and if you are not very flexible, then you learn less."
The central finding that the better the flexibility, the better the learning, might be behind the studies indicating music making is helpful for overall cognition, because music making seems to be all about creating lots of subnetworks throughout the brain.