Cohen J, Cohen P, West SG, Aiken LS. Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences. Erlbaum, Mahwah, NJ; 2003. 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.

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 A) Relationship between baseline whole-brain modularity and change in performance on the TOSL, calculated as the difference of post-training and pre-training (i.e., ‘baseline’), in Control (grey) and SMART (green) groups. Here, modularity values were calculated for each connection density threshold and averaged for each subject. (B) Relationship between baseline modularity and change in performance on the TOSL for each connection density threshold in each group. Dotz, Warren; Morton, Jim (1996). What a Character! 20th Century American Advertising Icons. Chronicle Books. p.56. ISBN 0-8118-0936-6. 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):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 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] 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. Newman ME. Modularity and community structure in networks. Proceedings of the National Academy of Sciences. 2006;103: 8577–8582.

Elmhurst, IL". Illinois.com. Archived from the original on September 3, 2009 . Retrieved April 9, 2010. The present study was based on the premise that interventions designed to educate patients about the risks and benefits associated with different therapeutic options require a 2‐pronged approach, including simplification of educational materials to convey the essential gist (i.e., “bottom‐line” meaning) and assistance to patients in developing the health literacy/numeracy skills needed to process complex information (e.g., scientific uncertainty concerning medication risks/benefits) to derive that gist ( 16, 17). Thus, we examined the effectiveness of 2 innovative communication strategies, including DrugFactsBoxes and the Strategic Memory Advanced Reasoning Training (SMART) program. DrugFactsBoxes were developed to enhance the usability of written consumer medication information (CMI), especially among individuals with limited health literacy/numeracy skills ( 18, 19, 20). The SMART program was developed to enhance patients' ability to understand and extract “bottom‐line” meaning (gist) from complex information, which we view as an essential health literacy skill ( 21, 22, 23, 24, 25, 26, 27). 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 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].There's a huge variety of M&M's flavours and products to try. Shop the full range of M&M's here and find your new favourite M&M's chocolate candy. Warnings: May contain peanuts and tree nuts. E102, E110, and E129 may have an adverse effect on activity and attention in children. Newman ME, Girvan M. Finding and evaluating community structure in networks. Phys Rev E. 2004;69: 026113. Characteristics of study participants are presented using means and percentages, depending on the measurement properties of the variables. We used logistic regression to assess the effects of the 2 interventions on our primary outcome: informed decision‐making at the 6‐month follow‐up. A separate regression model was performed at each follow‐up time point (i.e., 6‐week, 3‐month, and 6‐month). Each model controlled for informed decision‐making at baseline (0=did not meet criteria, 1=met criteria) and indicator variables for each intervention indexing assignment to the SMART program (0=no, 1=yes) and the DrugFactsBox group (0=no, 1=yes). We also included three two‐way interaction terms in each model. The first interaction term assessed whether the effects of the 2 interventions were dependent on one another. The other interaction terms assessed whether the effects of the interventions varied as a function of informed decision‐making at baseline. Interaction terms that were not statistically significant ( P<0.05) were dropped from the models and the models were re‐run to examine main effects.

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. 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. Geerligs L, Renken RJ, Saliasi E, Maurits NM, Lorist MM. A Brain-Wide Study of Age-Related Changes in Functional Connectivity. Cerebral Cortex. 2015;25:1987–1999. pmid:24532319 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. American candy doesn't get much bigger than M&M's! Invented in 1941, today M&M's, the iconic melt in your mouth not in your hand sweets are America's top selling candy. But how well do you know M&M's? Have some fun with our M&M's trivia quiz in our blog - Are M&M's the best American Candy?.Chapman SB, Aslan S, Spence JS, Hart JJ, Bartz EK, Didehbani N, et al. Neural Mechanisms of Brain Plasticity with Complex Cognitive Training in Healthy Seniors. Cerebral Cortex. 2015;25: 396–405. pmid:23985135 The animated Keebler Elves, led by "Ernest J. 'Ernie' Keebler", rank among the best-known characters from commercials. [ citation needed] Ernie is the head elf and the friendliest of the bunch. [27] The elves have appeared in countless television advertisements throughout the years (most of them animated at FilmFair), shown baking their unique products. [28] In the commercials, the Keebler tree logo is often turned into the tree in which the elves reside. 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 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

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].Brehmer Y, Kalpouzos G, Wenger E, Lövdén M. Plasticity of brain and cognition in older adults. Psychological Research. 2014;78: 790–802. pmid:25261907 Elliot, Stuart (August 20, 2008). "Those Shelved Brands Start to Look Tempting". The New York Times. Sadaghiani S, Poline JB, Kleinschmidt A, D'Esposito M. Ongoing dynamics in large-scale functional connectivity predict perception. Proceedings of the National Academy of Sciences. 2015;112: 8463–8468.