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New Test of ‘Network Neuroscience Theory’ Outperforms Other Theories in Predicting General Intelligence

New study lends credence to the idea that the G Factor arises from global brain architecture, not specific regions

Holden Galusha

Holden Galusha is the associate editor for Lab Manager. He was a freelance contributing writer for Lab Manager before being invited to join the team full-time. Previously, he was the...

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A new study published in Human Brain Mapping explores the relationship between brain structure and a person’s problem-solving ability, otherwise known as general intelligence. The researchers compared five theories with connectome-based predictive modeling, finding that the “network neuroscience theory” posited by lead authors Evan Anderson and Aron Barbey is the best predictor of general intelligence.

To conduct the research, the team recruited 297 undergraduate students and had each undergo a battery of tests that measured their problem-solving skills and adaptability in a range of scenarios. Such tests are commonly used by psychologists to measure general intelligence. The researchers then gathered the resting-state functional MRI (fMRI) scans of every participant. With the test results and fMRI data, the researchers could determine which of the five theories best predicted how the participants performed on the intelligence tests. They found that considering the features of the entire brain and the interconnectivity between them, rather than just certain brain regions, returned the most accurate predictions of a participant’s general intelligence (that is, problem-solving aptitude and adaptability). According to the news release, it held true “even when accounting for the number of brain regions included in the analysis.”

Ultimately, the authors’ own network neuroscience theory produced the most accurate predictions. The theory posits that “intelligence emerges from the global architecture of the brain, including both strong and weak connections.” Explaining the theory, Anderson said, “Strong connections involve highly connected hubs of information-processing that are established when we learn about the world and become adept at solving familiar problems. Weak connections have fewer neural linkages but enable flexibility and adaptive problem-solving.” Together, both types of connections “provide the network architecture that is necessary for solving the diverse problems we encounter in life.”

While the other theories, which were limited to select brain regions or networks, also predicted intelligence with some accuracy, they were all outperformed by the network neuroscience theory.

“Rather than originate from a specific region or network, intelligence appears to emerge from the global architecture of the brain and to reflect the efficiency and flexibility of systemwide network function,” Barbey said.