Abstract
In cognitive network neuroscience, the connectivity and community structure of the brain network is related to measures of cognitive performance, like attention and memory. Research in this emerging discipline has largely focused on two measures of connectivity—modularity and flexibility—which, for the most part, have been examined in isolation. The current project investigates the relationship between these two measures of connectivity and how they make separable contribution to predicting individual differences in performance on cognitive tasks. Using resting state fMRI data from 52 young adults, we show that flexibility and modularity are highly negatively correlated. We use a Brodmann parcellation of the fMRI data and a sliding window approach for calculation of the flexibility. We also demonstrate that flexibility and modularity make unique contributions to explain task performance, with a clear result showing that modularity, not flexibility, predicts performance for simple tasks and that flexibility plays a greater role in predicting performance on complex tasks that require cognitive control and executive functioning. The theory and results presented here allow for stronger links between measures of brain network connectivity and cognitive processes.
Highlights
Research in cognitive neuroscience has typically focused on identifying the function of individual brain regions
Previous theory suggests that high modularity should result in better performance on simple tasks while low modularity should result in better performance on complex tasks (Deem, 2013). This same theory suggests that flexibility should be negatively correlated with performance on simple tasks and positively correlated with performance on complex tasks, and so we investigated the relationship between flexibility, modularity and performance on a battery of simple and complex cognitive tasks
Network Re-construction, Modularity, and Flexibility Calculation Network re-construction The whole brain network was re-constructed based on different functional and anatomical brain parcellations including others used in the resting state literature (Power et al, 2011; Craddock et al, 2012; Glasser et al, 2016; Gordon et al, 2016)
Summary
Research in cognitive neuroscience has typically focused on identifying the function of individual brain regions. Recent advances have led to thinking about the brain as consisting of interacting subnetworks that can be identified by examining connectivity across the whole brain This emerging discipline of cognitive network neuroscience has been made possible by combining methods from functional neuroimaging and network science (Bullmore et al, 2009; Sporns, 2014; Medaglia et al, 2015; Mill et al, 2017). Functional and diffusion MRI methods provide a rich source of data for characterizing the connections—either functionally or structurally—between different brain regions. Using these data, network science provides mathematical tools for investigating the structure of the brain network, with brain regions serving as nodes, and the connections between brain regions serving as edges in the analysis.
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