Intensive Working Memory Training Produces Functional Changes in Large-scale Frontoparietal Networks

Abstract

Working memory is central to human cognition, and intensive cognitive training has been shown to expand working memory capacity in a given domain. It remains unknown, however, how the neural systems that support working memory are altered through intensive training to enable the expansion of working memory capacity. We used fMRI to measure plasticity in activations associated with complex working memory before and after 20 days of training. Healthy young adults were randomly assigned to train on either a dual n-back working memory task or a demanding visuospatial attention task. Training resulted in substantial and task-specific expansion of dual n-back abilities accompanied by changes in the relationship between working memory load and activation. Training differentially affected activations in two large-scale frontoparietal networks thought to underlie working memory: the executive control network and the dorsal attention network. Activations in both networks linearly scaled with working memory load before training, but training dissociated the role of the two networks and eliminated this relationship in the executive control network. Load-dependent functional connectivity both within and between these two networks increased following training, and the magnitudes of increased connectivity were positively correlated with improvements in task performance. These results provide insight into the adaptive neural systems that underlie large gains in working memory capacity through training.

Figure 1

Adaptive dual n-back training selectively facilitates performance on the n-back task. (A) Corrected hit rates for in-scanner n-back performance for each training group. (B) Reaction times in-scanner performance for each training group. Error bars represent standard error of the mean.

Figure 2

Brain regions exhibiting significant increases in activation as a function of WM load in all participants (n = 39). Statistical inferences derived from volume-based analysis, but projected onto Freesurfer average cortical surface mesh for visualization.

Figure 3

Baseline load-dependent WM activation occurs in executive control network (ECN) and dorsal attention network (DAN). ECN (orange) and DAN (green) defined anatomically and independently from resting-state networks (Yeo et al., 2011). Regions of load-dependent activation are outlined in black and substantially overlap ECN and DAN networks bilaterally.

Figure 4

Dual n-back training reduces load-dependent activation. Results are shown for a group x time interaction analysis on the parametric effect of WM load. Black outlines indicate the extent of load-modulated regions in the pre-training analysis (Figure 2). Image presentation is otherwise identical to Figure 2.

Figure 5

Only the N-Back Group exhibited reductions of activation after training in the 2-back and 3-back conditions. (A) Mean BOLD signal, relative to baseline. (B) The mean difference between pre- and post-training BOLD signal (changes from implicit baseline) is shown for each n-back level for N-Back Group (top row) and MOT Group (bottom row). In both panels, the outlines correspond to the extent of activations identified using a parametric analysis as shown in Figure 3.

Figure 6

N-back training dissociates the contribution of ECN and DAN to working memory. Mean activation coefficients for each n-back load relative to rest blocks were extracted from the ECN and DAN and plotted separately for each session and training group. Error bars represent standard error of the mean.

Figure 7

Performance-weighted analysis of relationship between WM load and BOLD activation. Black circles show mean BOLD signal, along with standard errors, as in Figure 6. Grey squares show the predictions of a performance-weighted model fit to the BOLD data from before training. Gray X’s show the predictions of a performance-weighted model fit to the BOLD data after training. Results are shown only for the N-Back training group.

Figure 8

Training increased fronto-parietal functional connectivity in N-Back group. (A) Group by session interaction measures, showing group differences in the change of functional connectivity strength. Positive values indicate greater connectivity after training for the N-Back Group relative to the MOT Group. Each bar shows the change in connectivity for a pair of frontal and parietal ROIs. (B) Group by session interaction measures during 2-back blocks for ROIs defined using functional activations within and between resting-state networks. Error bars in (A) and (B) represent standard error of the mean. (C) Change in functional connectivity strength for each pair of ROIs, as shown in (B). The weight of the edge indicates the Bonferroni-corrected p value for the group by session interaction. (D) Scatterplot showing the relationship between the change in behavioral performance and the change in functional connectivity in 2-back blocks. The functional connectivity measure is averaged across the four edges shown in panel A.