Complexity in Relational Processing Predicts Changes in Functional Brain Network Dynamics. (5th April 2013)

• Record Type: Journal Article
• Authors: Cocchi, Luca; Halford, Graeme S.; Zalesky, Andrew; Harding, Ian H.; Ramm, Brentyn J.; Cutmore, Tim; Shum, David H. K.; Mattingley, Jason B.
• Is Part Of: Cerebral cortex. Volume 24:Number 9(2014:Sep.)
• Page Start: 2283
• Page End: 2296
• Publication Date: 2013-04-05
• Subjects: cognitive control -- reasoning -- connectivity -- fMRI -- prefrontal cortex; Cerebral cortex -- Periodicals; Brain -- Periodicals; 612.825
• DOI: 10.1093/cercor/bht075
• Languages: English
• ISSNs: 1047-3211

Abstract

Abstract : The ability to link variables is critical to many high-order cognitive functions, including reasoning. It has been proposed that limits in relating variables depend critically on relational complexity, defined formally as the number of variables to be related in solving a problem. In humans, the prefrontal cortex is known to be important for reasoning, but recent studies have suggested that such processes are likely to involve widespread functional brain networks. To test this hypothesis, we used functional magnetic resonance imaging and a classic measure of deductive reasoning to examine changes in brain networks as a function of relational complexity. As expected, behavioral performance declined as the number of variables to be related increased. Likewise, increments in relational complexity were associated with proportional enhancements in brain activity and task-based connectivity within and between 2 cognitive control networks: A cingulo-opercular network for maintaining task set, and a fronto-parietal network for implementing trial-by-trial control. Changes in effective connectivity as a function of increased relational complexity suggested a key role for the left dorsolateral prefrontal cortex in integrating and implementing task set in a trial-by-trial manner. Our findings show that limits in relational processing are manifested in the brain as complexity-dependent modulations of large-scale networks.