7 research outputs found
Brain Modularity Mediates the Relation between Task Complexity and Performance
Recent work in cognitive neuroscience has focused on analyzing the brain as a
network, rather than as a collection of independent regions. Prior studies
taking this approach have found that individual differences in the degree of
modularity of the brain network relate to performance on cognitive tasks.
However, inconsistent results concerning the direction of this relationship
have been obtained, with some tasks showing better performance as modularity
increases and other tasks showing worse performance. A recent theoretical model
(Chen & Deem, 2015) suggests that these inconsistencies may be explained on the
grounds that high-modularity networks favor performance on simple tasks whereas
low-modularity networks favor performance on more complex tasks. The current
study tests these predictions by relating modularity from resting-state fMRI to
performance on a set of simple and complex behavioral tasks. Complex and simple
tasks were defined on the basis of whether they did or did not draw on
executive attention. Consistent with predictions, we found a negative
correlation between individuals' modularity and their performance on a
composite measure combining scores from the complex tasks but a positive
correlation with performance on a composite measure combining scores from the
simple tasks. These results and theory presented here provide a framework for
linking measures of whole brain organization from network neuroscience to
cognitive processing.Comment: 47 pages; 4 figure
Static and dynamic measures of human brain connectivity predict complementary aspects of human cognitive performance
In cognitive network neuroscience, the connectivity and community structure
of the brain network is related to cognition. Much of this research has focused
on two measures of connectivity - modularity and flexibility - which frequently
have been examined in isolation. By using resting state fMRI data from 52 young
adults, we investigate the relationship between modularity, flexibility and
performance on cognitive tasks. We show that flexibility and modularity are
highly negatively correlated. However, we also demonstrate that flexibility and
modularity make unique contributions to explain task performance, with
modularity predicting performance for simple tasks and flexibility 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.Comment: 37 pages; 7 figure
RSA and verbal STM
This project includes data which were reported in Yue & Martin (2021) "Maintaining verbal short-term memory representations in non-perceptual parietal regions". Corte
Static and Dynamic Measures of Human Brain Connectivity Predict Complementary Aspects of Human Cognitive Performance
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