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Knowledge was assessed by 3 separate instruments administered via telephone interview, including an 8‐item measure assessing knowledge concerning methotrexate (which is often first‐line therapy for RA) ( 36), a 20‐item measure assessing knowledge concerning biologic treatment options ( 35), and an 8‐item measure assessing knowledge of RA and RA treatment options more generally ( 37). Correct answers were summed across all 3 measures and transformed to a 100‐point scale, reflecting the percentage of questions answered correctly. Dotz, Warren; Morton, Jim (1996). What a Character! 20th Century American Advertising Icons. Chronicle Books. p.56. ISBN 0-8118-0936-6. The improvements in informed decision‐making in this study were driven by increases in knowledge, which was the only component of informed decision‐making that differed between the SMART and no SMART groups at the 6‐month follow‐up. This finding is noteworthy because the SMART program did not provide any content that would have increased patient knowledge concerning RA treatment options directly. Rather, the program is designed to enhance gist reasoning ability, which we view as an essential health literacy skill ( 24). We observed transient improvements in our measures of gist reasoning ability (i.e., complex abstraction and lesson quality) at the 3‐month follow‐up. Although these differences were not sustained at the 6‐month follow‐up, they may have been sufficient to facilitate uptake of medication information at earlier time points and facilitate decision‐making.

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Introduction

Gallen CL, Turner GR, Adnan A, D'Esposito M. Reconfiguration of brain network architecture to support executive control in aging. Neurobiology of Aging. 2016;44: 42–52. pmid:27318132 Chan MY, Park DC, Savalia NK, Petersen SE, Wig GS. Decreased segregation of brain systems across the healthy adult lifespan. Proceedings of the National Academy of Sciences. 2014;111: E4997–E5006.

where e ii is the fraction of connections that connect two nodes within module i, a i is the fraction of connections connecting a node in module i to any other node, and m is the total number of modules in the network [ 4]. Modularity is a measure that compares the number of connections within modules to the number of connections between modules across the network. Modularity will be close to 1 if all connections fall within modules and it will be 0 if there are no more connections within modules than would be expected by chance. As there are multiple methods for grouping nodes into modules, we also repeated these analyses using spectral clustering [ 32] to confirm that our results could generalize across other clustering algorithms and were not driven by imposing the specific Power et al. (2011) module assignments across all subjects. Importantly, the spectral method groups ROIs into subject-specific modules to generate the modular organization with the highest modularity value for this algorithm. It should be noted, however, that exhaustively searching through all possible ROI groupings to identify the ‘true’ modular organization with the highest modularity value is a computationally intensive problem [ 33]. Spectral clustering is one commonly used heuristic used to approximate the organization with the highest modularity value. Unless otherwise noted, modularity values are presented as the average across connection density thresholds. Although we confirm that our results are similar across commonly used connection density thresholds and clustering algorithms, the optimal methods for uncovering modular network organization remain an open question [ 34].Betzel RF, Byrge L, He Y, Goñi J, Zuo XN, Sporns O. Changes in structural and functional connectivity among resting-state networks across the human lifespan. NeuroImage. 2014;102: 345–357. pmid:25109530 Subjects’ T1-weighted anatomical scans were warped to MNI space and parcellated into 264 regions of interest (ROIs) [ 29]. Time-series from EPI data were averaged over the voxels in each ROI. Nine ROIs were excluded from subsequent analyses because they were missing coverage in at least one subject. Correlation matrices were created for each subject by correlating the time-series between each pair of ROIs using Pearson’s correlation coefficient and applying a Fisher z-transform. Adjacency matrices were created by thresholding each correlation matrix over a range of thresholds (the top 2–10% of connections in 2% increments), resulting in unweighted and undirected graphs comprised of nodes, or ROIs, and edges, or the connections between them. While this range of connection density thresholds is similar to that used in the creation of the Power et al. (2011) atlas and an approach we have taken previously [ 30], it should be noted that other thresholds may be equally valid (e.g., [ 31]). We then assigned each ROI to a module as defined in Power et al. (2011) and quantified each subject’s network modularity, defined as: Our results also suggest that the modular organization of association cortex sub-networks may be more informative in predicting training-related gains than the modular organization of sensory-motor sub-networks. We have previously reported that SMART is associated with changes in functional connectivity of association cortex sub-networks, such as the default mode sub-network, and that these changes are associated with training-related cognitive gains [ 16]. This suggests that sub-networks that exhibit alterations with training may be more predictive of cognitive gains than those that do not exhibit training-related changes. Previous studies have also shown that individuals with greater segregation of association cortex modules have greater episodic memory performance [ 9]. In addition, association cortex modules, such as the default mode sub-network, reconfigure during working memory task performance [ 45– 47] and, importantly, these changes are related to higher task accuracy [ 45]. Finally, in normal aging, association cortex modules exhibit more pronounced changes in functional connectivity compared with sensory-motor modules [ 9], such that association cortex modules become less ‘segregated’, or modular, with advancing age. Thus, the modular organization of association cortex sub-networks may be particularly sensitive to the aging process and important in supporting complex behaviors. Medaglia JD, Lynall ME, Bassett DS. Cognitive Network Neuroscience. Journal of Cognitive Neuroscience. 2015;27: 1471–1491. pmid:25803596

Ellefsen KO, Mouret JB, Clune J. Neural Modularity Helps Organisms Evolve to Learn New Skills without Forgetting Old Skills. PLoS Comput Biol. 2015;11: e1004128. pmid:25837826The inclusion criteria were clearly stated in terms of participants, intervention and outcomes. The search, covering a number of relevant sources, was likely to have reduced the risk of publication bias. Studies not written in English were excluded and this may have resulted in language bias. However, the authors' tests suggested that it was unlikely that missing studies would have significantly effected overall results. The methods of study selection and data extraction were aimed at reducing reviewer error or bias. The modularity-TOSL gain correlations were significantly different between the Control and SMART groups ( Fig 2A; p = 0.03). Further, while baseline performance on the TOSL was negatively related to TOSL gains in both groups (Control: rho(12) = -0.83, p < 0.001; SMART: rho(13) = -0.80, p < 0.001), there was no relationship between baseline TOSL and modularity in either group (Control: rho(12) = 0.20, p = 0.49; SMART: rho(13) = -0.33, p = 0.24). We also confirmed that, when controlling for baseline TOSL performance, the modularity-TOSL gain relationship remained significant in the SMART group (r p(12) = 0.57, p = 0.03), but was not significant in the Control group (r p(11) = 0.36, p = 0.22). Finally, as previous studies have integrated network measures over connection density thresholds rather than averaging (e.g., [ 22]), we confirmed that integrated baseline modularity was correlated with training-related gains on the TOSL in the SMART but not Control groups (Control: rho(12) = -0.15, p = 0.60; SMART: rho(13) = 0.68, p = 0.01). Vatansever D, Menon DK, Manktelow AE, Sahakian BJ, Stamatakis EA. Default Mode Dynamics for Global Functional Integration. Journal of Neuroscience. 2015;35: 15254–15262. pmid:26586814 Newman ME. Modularity and community structure in networks. Proceedings of the National Academy of Sciences. 2006;103: 8577–8582. Our findings demonstrate that older adults with more modular brain networks at baseline showed greater improvements after cognitive training. Critically, this relationship was not present in a control group and remained significant when accounting for baseline performance on the cognitive measures that improved with training. These results are directly in line with our previous work demonstrating that TBI patients with higher brain network modularity at baseline exhibited greater improvements on executive function tasks after cognitive training [ 22]. We expand on these findings by demonstrating that the relationship between brain network modularity and training-related cognitive gains in healthy older adults was stronger for association cortex modules compared with sensory-motor modules. Together, these findings suggest that individuals with a more modular brain network organization measured during a task-free ‘resting-state’ prior to training are more likely to benefit from cognitive training.