Aging is associated with declines in various cognitive functions, such as attention, cognitive control, and memory [ 1]. There is emerging evidence that characterization of large-scale brain network properties provides an important framework for understanding such complex behaviors [ 2, 3]. Previous work has shown that brain networks exhibit a modular organization, such that they are comprised of sub-networks, or modules. The extent of segregation of brain network modules can be quantified with a modularity metric [ 4], where highly modular networks have many connections within modules and fewer connections to other modules. Previous studies examining changes in modularity with aging have shown that older adults have less modular structural and functional brain networks than young adults [ 5– 8], particularly in sub-networks thought to mediate ‘associative’ functions, such as the fronto-parietal control and dorsal and ventral attention modules, compared to those involved in sensory-motor processing [ 9].

The 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. 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. Ernie Keebler was first voiced by Walker Edmiston, later by Parley Baer, then Frank Welker in 2007, then from 2016-2023 by Chicago actor Richard Henzel also known as the "Rise and Shine Campers" DJ Voice in the film Groundhog Day.

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Sridharan D, Levitin DJ, Menon V. A critical role for the right fronto-insular cortex in switching between central-executive and default-mode networks. Proceedings of the National Academy of Sciences. 2008;105: 12569–12574. Medaglia JD, Lynall ME, Bassett DS. Cognitive Network Neuroscience. Journal of Cognitive Neuroscience. 2015;27: 1471–1491. pmid:25803596

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 Arnemann KL, Chen AJW, Novakovic-Agopian T, Gratton C, Nomura EM, D'Esposito M. Functional brain network modularity predicts response to cognitive training after brain injury. Neurology. 2015;84: 1568–1574. pmid:25788557 Lustig C, Shah P, Seidler R, Reuter-Lorenz PA. Aging, Training, and the Brain: A Review and Future Directions. Neuropsychol Rev. 2009;19: 504–522. pmid:19876740 Kashtan N, Alon U. Spontaneous evolution of modularity and network motifs. Proceedings of the National Academy of Sciences. 2005;102: 13773–13778.

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Godfrey Keebler, of German descent, opened a bakery in Philadelphia, Pennsylvania, in 1853. His bakery networked with several other local bakeries and others around the country over the years, and in 1927 they merged into the United Biscuit Company of America. [6]

To confirm that the effects of SMART on the TOSL and Similarities were similar to the original report [ 16] in this reduced sample of subjects, we first conducted repeated measures ANOVAs on these neurocognitive measures with a within-subjects factor of time (pre- and post-training) and a between subjects factor of group (SMART and Control). We report effect sizes for these ANOVAs as partial eta-squared (η2p). 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.Among patients with knowledge deficits, the SMART program may facilitate informed decision‐making by helping them develop the skills needed to understand and use complex information concerning medication risks/benefits. 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.

More generally, the relationship between baseline brain network modularity and training-related cognitive gains also suggests that brain network properties may be related to learning, such that individuals with a more modular brain may have a greater learning capacity and ability to benefit from training. While previous studies have shown that neural factors (e.g., frontal alpha power and striatal volume) are related to skill learning [ 48– 51], the aspects of brain structure and function that predicted learning were variable across studies. Computational models examining the modularity of neural networks have demonstrated that more modular networks enable organisms to learn new skills without forgetting old ones [ 52]. Further, greater segregation of visual and motor sub-networks (i.e., more modular sub-networks) is predictive of motor learning [ 53] and the segregation of these sub-networks increases over the course of learning [ 54]. Our findings suggest that higher baseline modularity may also allow for more complex learning that is likely necessary for a cognitive intervention to be successful. Given that we have found that brain network modularity is predictive of cognitive training gains in two types of training paradigms and populations, baseline brain network modularity may provide a unifying framework that can not only be used to predict cognitive outcomes for other types of interventions, but also could be used for understanding the neural mechanisms that underlie training effects.

Coyle, John J.; Bardi, Edward J.; Langley, C. John (1996). "15". The management of business logistics (6thed.). Minneapolis/St. Paul: West Pub. Co. ISBN 9780314065070. OCLC 33280849. 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].