Network neuroscience theory best predictor of intelligence

Scientists have labored for decades to understand how brain structure and functional connectivity drive intelligence. A new analysis offers the clearest picture yet of how various brain regions and neural networks contribute to a person’s problem-solving ability in a variety of contexts, a trait known as general intelligence, researchers report.

They detail their findings in the journal Human Brain Mapping.

The study used “connectome-based predictive modeling” to compare five theories about how the brain gives rise to intelligence, said Aron Barbey, a professor of psychology, bioengineering and neuroscience at the University of Illinois Urbana-Champaign who led the new work with first author Evan Anderson, now a researcher for Ball Aerospace and Technologies Corp. working at the Air Force Research Laboratory.

“To understand the remarkable cognitive abilities that underlie intelligence, neuroscientists look to their biological foundations in the brain,” Barbey said. “Modern theories attempt to explain how our capacity for problem-solving is enabled by the brain’s information-processing architecture.”

A biological understanding of these cognitive abilities requires “characterizing how individual differences in intelligence and problem-solving ability relate to the underlying architecture and neural mechanisms of brain networks,” Anderson said.

Historically, theories of intelligence focused on localized brain regions such as the prefrontal cortex, which plays a key role in cognitive processes such as planning, problem-solving and decision-making. More recent theories emphasize specific brain networks, while others examine how different networks overlap and interact with one another, Barbey said. He and Anderson tested these established theories against their own “network neuroscience theory,” which posits that intelligence emerges from the global architecture of the brain, including both strong and weak connections.

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