Schedule of Events | Symposia

Investigating the flexible network architecture of intelligence

Poster Session E - Monday, March 31, 2025, 2:30 – 4:30 pm EDT, Back Bay Ballroom/Republic Ballroom

Ramsey R. Wilcox^1,2, Babak Hemmatian^1,3, Lav R. Varshney^3,4, Aron K. Barbey^1,2,3,5,6; ¹Decision Neuroscience Laboratory, Center for Brain, Biology & Behavior, University of Nebrask, ²Department of Psychology, University of Nebraska, ³Beckman Institute for Advanced Science & Technology, University of Illinois, ⁴Department of Electrical & Computer Engineering, University of Illinois, ⁵Department of Psychology, University of Illinois, ⁶Department of Bioengineering, University of Illinois

The network neuroscience theory posits that general intelligence (g) arises from the brain’s ability to flexibly recruit regions across networks in response to changing task demands. Central to this is the brain’s modular yet efficiently integrated global topology. Modularity allows for specialized processing within local networks, while efficient integration enables system-wide communication to enable complex cognition. This balance supports the brain’s ability to adapt to novel and shifting cognitive demands. Traditional theories, which focus on isolated regions or networks, fail to capture the adaptive nature of the human connectome, highlighting the need for a more integrative approach to understanding intelligence. To investigate how the brain’s flexible topology contributes to intelligence, we conducted a connectome-based predictive modeling study in 245 healthy adults. Participants completed a mental set-shifting task during functional MRI. Parametric modulators modeled changing task demands (e.g., stimulus complexity), where higher demand required greater functional flexibility. Diffusion-weighted MRI was used to map white matter connections, and advanced network analysis integrated functional and structural data to generate task-derived networks. Results demonstrate that g is: (i) better predicted by networks engaged during high-demand trials (R² = 0.19) compared to low-demand trials (R² = -0.02); (ii) supported by connections between multiple networks; and (iii) significantly associated with the modularity (β = -0.79; p = 0.01) and efficiency (β = 2518.00; p = 0.01) of these networks. These findings underscore the importance of neural flexibility and the brain’s modular yet integrated organization in supporting adaptive behavior, offering new insights into the dynamics of intelligence.

Topic Area: OTHER