Static and Dynamic Measures of Human Brain Connectivity Predict Complementary Aspects of Human Cognitive Performance

Aurora I Ramos-Nuñez, Simon Fischer-Baum, Randi C Martin, Qiuhai Yue, Fengdan Ye, Michael W Deem, Aurora I Ramos-Nuñez, Simon Fischer-Baum, Randi C Martin, Qiuhai Yue, Fengdan Ye, Michael W Deem

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.

Keywords: brain network connectivity; flexibility; individual differences; modularity; resting-state fMRI; task complexity.

Figures

Figure 1
Figure 1
(A) Flexibility calculation using a sliding window method with 40 time points. (B) For each window a correlation matrix was obtained. (C) Computation of the record of which module the ith Brodmann areas was in C1(t) at time point window t, 1≤t≤165. (D) Relabeling of the allegiance of Brodmann areas was done to account for the fact that the labeling of the modules can change between time points. This relabeling process was done to ensure that the difference defined as the number of areas that have Ci(t+1) ≠ Ci(t) (i = 1–84) was minimized. (E) A detail of the relabeling process is shown here. The red star indicates that the permutation for window (t+1) “2 1 3 1” is the closest to the window t labeling “2 1 3 3”. The relabelling process will choose the starred permutation for window (t+1).
Figure 2
Figure 2
(A) The relationship between modularity and flexibility across 52 participants. r = −0.78 (p < 0.001). The y-axis shows the averaged flexibility values over all Brodmann areas across subjects. The x-axis shows modularity values, the excess probability of connections within the modules, relative to what is expected at random (B) Illustration of flexibility measures over cortical structures. Brodmann areas with higher flexibility are shown in red and those with lower flexibility are shown in yellow.
Figure 3
Figure 3
Modularity and Flexibility predict different performance during simple and complex tasks. The two graphs on the left and right illustrate the relationship between simple and complex composite scores (calculated by summing the z-scores for the performance measures for the simple and complex tasks) and modularity (green) and flexibility (red). This relationship is represented by the magnitude of the coefficient between modularity and task performance and flexibility and task performance for simple (left) and complex (right). The center of the figure depicts the theoretical prediction relating performance to task complexity for individuals with high and low modularity (green curve) and flexibility (red curve).
Figure 4
Figure 4
Observed correlation between modularity and flexibility with 52 participants (black bar) compared with the distribution of the correlation between modularity and flexibility from simulations of an artificial network in which modularity is matched to the modularity values from the human subjects (white bars). The observed correlation falls well outside of the distribution of correlations from the random data.

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