Keebler M&M Cookies (1.6Oz., 30 Ct.)

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Behzadi Y, Restom K, Liau J, Liu TT. A component based noise correction method (CompCor) for BOLD and perfusion based fMRI. NeuroImage. 2007;37: 90–101. pmid:17560126 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 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. 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). Dotz, Warren; Morton, Jim (1996). What a Character! 20th Century American Advertising Icons. Chronicle Books. p.56. ISBN 0-8118-0936-6.

Newman ME. Modularity and community structure in networks. Proceedings of the National Academy of Sciences. 2006;103: 8577–8582. Lövdén M, Bodammer NC, Kühn S, Kaufmann J, Schütze H, Tempelmann C, et al. Experience-dependent plasticity of white-matter microstructure extends into old age. Neuropsychologia. 2010;48: 3878–3883. pmid:20816877Our 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. Medaglia JD, Lynall ME, Bassett DS. Cognitive Network Neuroscience. Journal of Cognitive Neuroscience. 2015;27: 1471–1491. pmid:25803596 Cohen J, Cohen P, West SG, Aiken LS. Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences. Erlbaum, Mahwah, NJ; 2003. Finally, as weaker network connections that do not pass our connection density thresholds may also be informative in predicting training outcomes, we quantified the ‘segregation’ [ 9] of each module from the Power et al. (2011) assignments, defined as: 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.

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 Leo Burnett Worldwide, an advertising agency, created the elves in 1968, calling the bakery "The Hollow Tree Factory." [17]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. 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. 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): 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 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. Schultz DH, Cole MW. Higher Intelligence Is Associated with Less Task-Related Brain Network Reconfiguration. Journal of Neuroscience. 2016;36: 8551–8561. pmid:27535904 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. Schultz, Clark (July 29, 2019). "Kellogg closes on Keebler sale". Seeking Alpha . Retrieved August 12, 2019.

Vanilla Sugar Wafers, 2.75 oz

Subgroup analysis by primary and primary plus secondary prevention studies showed similar results to the main analysis. Meta-regression showed no apparent effect on results of mean follow-up time or study size. Sensitivity analyses excluding single studies showed no effect on the results. Liang X, Zou Q, He Y, Yang Y. Topologically Reorganized Connectivity Architecture of Default-Mode, Executive-Control, and Salience Networks across Working Memory Task Loads. Cerebral Cortex. 2016;26: 1501–1511. pmid:25596593 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 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). 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

Acknowledgments

Brehmer Y, Kalpouzos G, Wenger E, Lövdén M. Plasticity of brain and cognition in older adults. Psychological Research. 2014;78: 790–802. pmid:25261907