Article Figures & Data
Figures
- Figure 1.
Experimental paradigm and behavioral results. Fractal n-back task. Fractals were displayed under three conditions: 0-back, 1-back, and 2-back. Each condition consisted of a 20-trial block (60 s); each level was repeated over three blocks.
- Figure 2.
Relationship between motion and variables of interest in the full study sample and selected subsamples. A, In the full (n = 931) sample, both age and task performance (d′) were strongly negatively correlated with in-scanner motion: both older children and those who have better WM performance tend to move less during the scanning session. B, Following the application of the greedy matching algorithm to each variable, this relationship was no longer present in the matched subsamples.
- Figure 3.
Behavioral results. A, B, As expected, at higher levels of working memory load, subjects responded less accurately, with fewer correct responses (A), more false positives (B), and slower response times. Each plot displays the mean percentage of correct responses and false positives for each level of load; error bars indicate SEM. C, Overall task performance was summarized using d′, which improved with age; blue data points indicate male subjects; red data points indicate female subjects. Correct responses, false positives, and response times similarly improved with age.
- Figure 4.
WM load in the fractal n-back task robustly recruits the executive network. The parametric contrast evaluating the effect of working memory load (2-back > 0-back) robustly recruited the entire executive network; image was thresholded at z > 3.09; cluster corrected, p < 0.01.
- Figure 5.
Brain response to WM load is more significantly related to WM performance than subject age. A, When the effect of age is investigated without including WM performance in the model, older age is associated with greater activation of the executive system, as well as increased deactivation of DMN regions. B, When WM performance (summarized as d′) is analogously modeled without age, substantially more significant effects are seen. C, D, However, when age and WM performance are both included in the model, while some age effects remain significant, they are diminished (C), whereas the relationship between WM performance and both executive network activation and DMN deactivation remains quite robust (D). All models include subject sex and motion as covariates; images were thresholded at z > 3.09; corrected, p < 0.01.
- Figure 6.
Relationship between WM performance and activation is driven by effects at high levels of WM load. At each level of WM load (0-back, 1-back, 2-back > baseline) a voxelwise group-level model investigated age and performance effects. Each model included both age and performance; sex and motion were included as covariates. Image was thresholded using cluster correction as elsewhere (z > 3.09, p < 0.01). Results revealed load-dependent, performance-related activation of the executive network and deactivation of DMN regions. Additionally, load-independent age effects were seen in higher-order visual regions; load-independent performance effects were also seen in medial visual cortex.
- Figure 7.
Relationship between performance and activation in functional ROIs within the executive network and DMN. A, Regions of interest: the executive network (warm colors) was parsed into 21 functional regions of interest by applying a watershed algorithm to the map of the 2-back > 0-back contrast using an initial threshold of z > 20. Six regions of interest in the default mode network were similarly constructed from the contrast of 0-back > 2-back. B, Activation in each of these regions within the executive network (and deactivation of certain default mode regions) was significantly associated with better performance mainly at the highest (2-back) levels of WM load. In contrast, associations with age in these regions did not survive multiple comparison correction at any level of WM load. The y-axis depicts partial correlation at each level of WM load (0-back, 1-back, 2-back) from each of the 21 executive network regions (red) and 6 default mode regions (blue), displayed in A. Partial correlations were calculated between region percentage signal change and performance while accounting for age, sex, and in-scanner motion.
- Figure 8.
Analysis of categorically defined samples. A, B, To further illustrate the differential effects of age and performance, we constructed tightly matched, categorically defined samples of old and young subjects (A), as well as high and low performers (B). Groups were stratified by subject age and matched on performance, or alternately stratified by performance and matched on age. All images cluster corrected at z > 3.09 (p < 0.01), as elsewhere.
- Figure 9.
Out-of-scanner executive functioning relates to executive network activation and DMN deactivation. Here, instead of in-scanner n-back performance, we assessed the relationship between out-of-scanner executive function on the Penn CNB and brain activation to the parametric (2-back > 0-back) contrast of WM load. As prior, the model included age, sex, and motion as covariates; image thresholded using cluster correction (z > 3.09, p < 0.01).
- Figure 10.
Group-level motion control strategies have a minimal impact on results. Including motion in the group-level design matrix (middle column) or using motion-matched samples (right column; see Fig. 2) produced nearly identical results. Age-related effects were only minimally stronger when motion was not accounted for at the group level (left column). In all cases, brain activation to WM load was much more strongly related to performance than age. The right lateral cortical surface is displayed; similar effects were seen elsewhere.
- Figure 11.
Activation in 2-back > 0-back contrast predicts WM performance. A, A cross-validated multivariate relevance vector regression model predicted task performance (as summarized by d′) from WM activation map with a high degree of accuracy (r = 0.48) in a sample of 841 subjects where performance and motion were uncorrelated (Fig. 2). B, Significantly weighted features in this model included regions in the executive network as well as task-deactivated default-mode regions. C, Notably, when examined separately, load-deactivated voxels (i.e., 0-back > 2-back; blue bar), including the DMN, predicted task performance with equivalent accuracy as the load-activated voxels (2-back > 0-back; red bar) of the executive network. However, maximal accuracy was only achieved when both activated and deactivated regions were considered together.
- Figure 12.
Activation in the WM task mediates the observed age–performance relationship. In a sample where motion and performance were uncorrelated (n = 841), the performance-related multivariate pattern of activation to WM load (2-back > 0-back) significantly mediated the observed relationship between age and WM performance.
