Dynamic reconfiguration of human brain networks during learning

Abstract

Human learning is a complex phenomenon requiring flexibility to adapt existing brain function and precision in selecting new neurophysiological activities to drive desired behavior. These two attributes--flexibility and selection--must operate over multiple temporal scales as performance of a skill changes from being slow and challenging to being fast and automatic. Such selective adaptability is naturally provided by modular structure, which plays a critical role in evolution, development, and optimal network function. Using functional connectivity measurements of brain activity acquired from initial training through mastery of a simple motor skill, we investigate the role of modularity in human learning by identifying dynamic changes of modular organization spanning multiple temporal scales. Our results indicate that flexibility, which we measure by the allegiance of nodes to modules, in one experimental session predicts the relative amount of learning in a future session. We also develop a general statistical framework for the identification of modular architectures in evolving systems, which is broadly applicable to disciplines where network adaptability is crucial to the understanding of system performance.

Fig. 1.

Structure of the investigation. (A) To characterize the network structure of low-frequency functional connectivity (24) at each temporal scale, we partitioned the raw fMRI data (Upper Left) from each subject’s brain into signals originating from N = 112 cortical structures, which constitute the network’s nodes (Upper Right). The functional connectivity, constituting the network edges, between two cortical structures is given by a Pearson correlation between the mean regional activity signals (Lower Right). We then statistically corrected the resulting N × N correlation matrix using a false discovery rate correction (54) to construct a subject-specific weighted functional brain network (Lower Left). (B) Schematic of the investigation that was performed over the temporal scales of days, hours, and minutes. The complete experiment, which defines the largest scale, took place over the course of three days. At the intermediate scale, we conducted further investigations of the experimental sessions that occurred on each of those three days. Finally, to examine higher-frequency temporal structure, we cut each experimental session into 25 nonoverlapping windows, each of which was a few minutes in duration.

Fig. 2.

Multiscale modular architecture. (A) Results for the modular decomposition of functional connectivity across temporal scales. (Left) The network plots show the extracted modules; different colors indicate different modules and larger separation between modules is used to visualize weaker connections between them. (A) and (B) correspond to the entire experiment and individual sessions, respectively. Boxplots show the modularity index Q (Left) and the number of modules (Right) in the brain network compared to randomized networks. See Materials and Methods for a formal definition of Q. (C) Modularity index Q and the number of modules for the cortical (blue) compared to randomized networks (red) over the 75 time windows. Error bars indicate standard deviation in the mean over subjects.

Fig. 3.

Temporal dynamics of modular architecture. (A) Schematic of a toy network with four nodes and four edges in a single time window. (B) Multilayer network framework in which the networks from four time windows are linked by connecting nodes in one time window to themselves in the adjacent time windows (colored curves). (C) Statistical framework composed of a connectional null model (Top), a nodal null model (Middle), and a temporal null model (Bottom) in which intranetwork links, internetwork links, and time windows, respectively, in the real network are randomized in the permuted network. (We show all of the randomized links in red.) (D) Boxplots showing differences in modular architecture between the real and permuted networks for the connectional (Top), nodal (Middle), and temporal (Bottom) null models. We measured the structure of the network using the modularity index Q, the number of modules, the module size, and stationarity, which is defined as the mean similarity in the nodal composition of modules over consecutive time steps. Below each plot, we indicate by asterisks the significance of one-sample t-tests that assess whether the differences that we observed were significantly different from zero (gray lines): A single asterisk indicates p < .05, two asterisks indicate p < 1 × 10^-6, and three asterisks indicate p < 1 × 10^-20.

Fig. 4.

Flexibility and learning. (A) Boxplots showing that the increase in flexibility from experimental session 1 to session 2 was significantly greater than zero (a one-sample t-test gives the result t ≈ 6.00 with p ≈ 2 × 10^-8), and that the magnitude of the decrease in flexibility from session 2 to session 3 was significantly greater than zero (t ≈ 7.46, p ≈ 2 × 10^-11). (B) Significant predictive correlations between flexibility in session 1 and learning in session 2 (black curve, p ≈ 0.001) and between flexibility in session 2 and learning in session 3 (red curve, p ≈ 0.009). Note that relationships between learning and network flexibility in the same experimental sessions (1 and 2) were not significant; we obtained p > 0.13 using permutation tests. (C) Brain regions whose flexibility in session 1 predicted learning in session 2 (p < 0.05, uncorrected for multiple comparisons). Regions that also passed false-positive correction were the left anterior fusiform cortex and the right inferior frontal gyrus, thalamus, and nucleus accumbens. (D) Brain regions whose flexibility in session 2 predicted learning in session 3 (p < 0.05, uncorrected for multiple comparisons). Regions that also passed false-positive correction for multiple comparisons were the left intracalcarine cortex, paracingulate gyrus, precuneus, and lingual gyrus and the right superior frontal gyrus and precuneus cortex. In (C) and (D), colors indicate the Spearman correlation coefficient r between flexibility and learning.