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. Kellogg gets out of cookie business by selling Keebler, Famous Amos brands". WXIN. April 1, 2019 . Retrieved October 13, 2019. 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. Mathewson KE, Basak C, Maclin EL, Low KA, Boot WR, Kramer AF, et al. Different slopes for different folks: Alpha and delta EEG power predict subsequent video game learning rate and improvements in cognitive control tasks. Psychophysiology. 2012;49: 1558–1570. pmid:23095124

Citation: Gallen CL, Baniqued PL, Chapman SB, Aslan S, Keebler M, Didehbani N, et al. (2016) Modular Brain Network Organization Predicts Response to Cognitive Training in Older Adults. PLoS ONE 11(12): In March 2001, The Keebler Company was acquired by the Kellogg Company. [1] At that time, headquarters were located in Elmhurst, Illinois. [23] Currently, Keebler has manufacturing plants in the United States, Thailand, Indonesia, and Malaysia. [ citation needed] Leo Burnett Worldwide, an advertising agency, created the elves in 1968, calling the bakery "The Hollow Tree Factory." [17] When comparing patients receiving dialysis with those not receiving dialysis with chronic kidney disease (glomerular filtration rate of less than 60ml/min/1.73m 2), the risk of death was similar for both groups (five studies; random-effects model). Kashtan N, Alon U. Spontaneous evolution of modularity and network motifs. Proceedings of the National Academy of Sciences. 2005;102: 13773–13778.Bherer L. Cognitive plasticity in older adults: effects of cognitive training and physical exercise. Annals of the New York Academy of Sciences. 2015;1337: 1–6. pmid:25773610 To quantify the relationship between baseline whole-brain modularity and training-related cognitive gains, we examined the correlation between baseline modularity and cognitive gains on the TOSL and Similarities. Cognitive gains were computed as the difference in post-training and pre-training (or baseline) scores, separately in the Control and SMART groups. Due to the relatively small sample size in each group, we conducted non-parametric Spearman correlations to reduce influence from extreme values, unless we were examining partial correlations that controlled for variables of non-interest (e.g., baseline TOSL or in-scanner motion). We denote Spearman correlations as ‘rho’ and partial correlations as ‘r p’. We also report 95% bias-corrected and accelerated (BCa) confidence intervals (CIs) based on 2000 bootstrap samples for main correlation analyses. We compared the magnitude of correlations between Control and SMART groups [ 35], after converting Spearman’s correlation coefficients to Pearson’s correlation coefficients using the formula described by Myers and Sirois [ 36]. Power JD, Barnes KA, Snyder AZ, Schlaggar BL, Petersen SE. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. NeuroImage. 2012;59: 2142–2154. pmid:22019881 Elliot, Stuart (August 20, 2008). "Those Shelved Brands Start to Look Tempting". The New York Times.

Cao W, Cao X, Hou C, Li T, Cheng Y, Jiang L, et al. Effects of Cognitive Training on Resting-State Functional Connectivity of Default Mode, Salience, and Central Executive Networks. Frontiers in Aging Neuroscience. 2016;8: 1–11.Seeley WW, Menon V, Schatzberg AF, Keller J, Glover GH, Kenna H, et al. Dissociable Intrinsic Connectivity Networks for Salience Processing and Executive Control. Journal of Neuroscience. 2007;27: 2349–2356. pmid:17329432 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]. Stevens AA, Tappon SC, Garg A, Fair DA. Functional Brain Network Modularity Captures Inter- and Intra-Individual Variation in Working Memory Capacity. PLoS ONE. 2012;7: e30468. pmid:22276205 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

Cerny, JoBe (May 11, 2015). "Icons of Advertising". Screen Magazine. Archived from the original on June 7, 2015 . Retrieved August 17, 2019. Depictions of within- (left) and between- (right) module connections for SMART subjects with low (top) and high (bottom) brain network modularity. The presence or absence of a connection was calculated for each connection density threshold (i.e., an adjacency matrix) for the top 2–10% of connections in 2% increments. For illustration purposes, we then averaged the adjacency matrices over thresholds for each subject, where edges represent the proportion of thresholds for which a connection was present between two regions (ranging from 0 to 1). Brain regions are colored according to their module assignments in Power et al. (2011) and are grouped into sensory-motor and association cortex modules as defined in Chan et al. (2014). The subject with high modularity has many connections within modules and fewer connections between modules compared to the subject with low modularity. 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). 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.Stanley ML, Dagenbach D, Lyday RG, Burdette JH, Laurienti PJ. Changes in global and regional modularity associated with increasing working memory load. Frontiers in Human Neuroscience. 2014;8: 954. pmid:25520639 Power JD, Cohen AL, Nelson SM, Wig GS, Barnes KA, Church JA, et al. Functional Network Organization of the Human Brain. Neuron. 2011;72: 665–678. pmid:22099467 a b c d "Advertising Mascots > Keebler Elves (Kellogg's)". Tv Acres. Archived from the original on September 13, 2012 . Retrieved April 9, 2010. a b c "Keebler Brilliant Marketing Pte Ltd Keebler". Brilliant-marketing.com. Archived from the original on April 2, 2010 . Retrieved April 9, 2010.