The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance

Abstract

Higher brain function relies upon the ability to flexibly integrate information across specialized communities of brain regions; however, it is unclear how this mechanism manifests over time. In this study, we used time-resolved network analysis of fMRI data to demonstrate that the human brain traverses between functional states that maximize either segregation into tight-knit communities or integration across otherwise disparate neural regions. Integrated states enable faster and more accurate performance on a cognitive task, and are associated with dilations in pupil diameter, suggesting that ascending neuromodulatory systems may govern the transition between these alternative modes of brain function. Together, our results confirm a direct link between cognitive performance and the dynamic reorganization of the network structure of the brain.

Figure 1. Dynamic fluctuations in cartography

a) upper: a representative time series of the mean B_T for a single individual from the Discovery cohort (HCP #100307); lower: each temporal window was partitioned into one of two topological ‘states’ using k-means clustering (red: ‘Segregated’ and blue: ‘Integrated’); b) the mean cartographic profile of both the Segregated and Integrated states (HCP Discovery cohort; n = 92); c) regions with greater W_T in the Integrated than Segregated state; and d) regions with greater B_T in the Integrated than Segregated state.

Figure 2. Alteration of cartographic profile during task performance

a) time series plot demonstrating the close temporal relationship between mean B_T across 100 subjects (thick black line; individual subject data plotted in grey) and task-block regressors (blue line) – Pearson’s correlation between regressor and group mean B_T: r = 0.521); b) regions of the 2-dimensional joint histogram that were significantly different between N-back task blocks and the resting state (paired-samples t-test) – colored points indicate regions that survived false discovery correction (FDR α < 0.05): red/yellow – increased frequency during N-back task blocks; blue/light blue – increased frequency during resting state (FDR α < 0.05); c) surface projections of parcels associated with higher W_T (left) or B_T (right) during the N-back task, when compared the resting state – frontoparietal and subcortical ‘hub’ regions showed elevated B_T during task, whereas W_T was elevated in primary systems and decreased in default mode regions; d) a plot quantifying the shift away from the cartographic profile in the resting state (along the between-module (B_T) connectivity axis) across the six tasks in the HCP dataset.

Figure 3. Relationship between task performance and the cartographic profile

a) a graphical depiction of the drift-diffusion model, which uses the mean and standard deviation of a subjects reaction time and performance accuracy to estimate the ‘drift rate’, or rate of evidence accumulation (v), the length of non-decision time (t) and the response boundary (a); b) left – group-level correlation between drift rate on the N-back task and each bin of the mean cartographic profile during the N-back task in the Discovery cohort; right – parcels showing a positive correlation between mean B_T and drift rate; and c) left – group-level correlation between non-decision time on the N-back task and each bin of the mean cartographic profile during the N-back task in the Discovery cohort; right – parcels showing a negative correlation between mean B_T and non-decision time. False discovery rate, alpha = 0.05. No bins of the cartographic profile showed a consistent response with the response boundary. Similarly, no parcels showed a significant correlation between W_T and any of the three diffusion model fits.

Figure 4. Relationship between cartography and pupillometery

a) an example time series (subject #1) showing the covariance between the pupil diameter (after convolution with a hemodynamic response function; blue) and mean between-module connectivity (B_T; red); b) mean Pearson correlation between each bin of the cartographic profile and the convolved pupil diameter. Across the cohort of 14 subjects, we observed a positive relationship between pupil diameter and network-level integration (FDR α = 0.05); c) results from a conjunction analysis (FDR α < 0.05) that compared relationships between WT (red) or BT (blue) and drift-rate (positive correlation), non-decision time (inverse correlation) and pupillometery (positive correlation). There were no cerebellar parcels above threshold in all three contrasts.