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Engvig A, Fjell AM, Westlye LT, Moberget T, Sundseth Ø, Larsen VA, et al. Memory training impacts short-term changes in aging white matter: A Longitudinal Diffusion Tensor Imaging Study. Hum Brain Mapp. 2012;33: 2390–2406. pmid:21823209 a b c "Keebler Brilliant Marketing Pte Ltd Keebler". Brilliant-marketing.com. Archived from the original on April 2, 2010 . Retrieved April 9, 2010. 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 Newman ME. Modularity and community structure in networks. Proceedings of the National Academy of Sciences. 2006;103: 8577–8582. As aging has been shown to have a more pronounced effect on the modularity of association cortex modules compared with sensory-motor modules [ 9], we also examined the differential contribution of the modularity of these sub-networks to predicting cognitive gains in older adults. As described previously, the whole-brain modularity metric is computed as the sum of the modularity values for each module [ 4]. Using the Power et al. (2011) module assignments, we computed the baseline modularity of each sub-network, or module. We then classified these modules as ‘sensory-motor’ or ‘association cortex’ according to the groupings described in Chan et al. (2014). Specifically, sensory-motor modules included the auditory, somatomotor (hand and mouth), and visual modules; association cortex modules included the cingulo-opercular, default mode, dorsal attention, fronto-parietal, salience, and ventral attention modules. To compute average baseline sensory-motor and association cortex modularity, we averaged the modularity values over the sub-networks, or modules, in each group.

Bassett DS, Yang M, Wymbs NF, Grafton ST. Learning-induced autonomy of sensorimotor systems. Nature Neuroscience. 2015;18: 744–751. pmid:25849989 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:20816877To 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]. 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: 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 Cognitive training interventions are a promising approach to mitigate cognitive deficits common in aging and, ultimately, to improve functioning in older adults. Baseline neural factors, such as properties of brain networks, may predict training outcomes and can be used to improve the effectiveness of interventions. Here, we investigated the relationship between baseline brain network modularity, a measure of the segregation of brain sub-networks, and training-related gains in cognition in older adults. We found that older adults with more segregated brain sub-networks (i.e., more modular networks) at baseline exhibited greater training improvements in the ability to synthesize complex information. Further, the relationship between modularity and training-related gains was more pronounced in sub-networks mediating “associative” functions compared with those involved in sensory-motor processing. These results suggest that assessments of brain networks can be used as a biomarker to guide the implementation of cognitive interventions and improve outcomes across individuals. More broadly, these findings also suggest that properties of brain networks may capture individual differences in learning and neuroplasticity.

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. Dotz, Warren; Morton, Jim (1996). What a Character! 20th Century American Advertising Icons. Chronicle Books. p.56. ISBN 0-8118-0936-6.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. 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. Kashtan N, Alon U. Spontaneous evolution of modularity and network motifs. Proceedings of the National Academy of Sciences. 2005;102: 13773–13778. 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

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:

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. 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. Schultz DH, Cole MW. Higher Intelligence Is Associated with Less Task-Related Brain Network Reconfiguration. Journal of Neuroscience. 2016;36: 8551–8561. pmid:27535904 Onoda K, Yamaguchi S. Small-worldness and modularity of the resting-state functional brain network decrease with aging. Neuroscience Letters. 2013;556: 104–108. pmid:24157850 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

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). Vas AK, Spence JS, Eschler B, Chapman SB. Sensitivity and Specificity of Abstraction using Gist Reasoning Measure in Adults with Traumatic Brain Injury. Journal of Applied Biobehavioral Research. 2016; in press.Cerny, JoBe (May 11, 2015). "Icons of Advertising". Screen Magazine. Archived from the original on June 7, 2015 . Retrieved August 17, 2019. 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 Despite previous work showing that cognitive training can alter network connectivity in older adults, there has been little focus on identifying baseline neural factors that can predict training-related improvements in cognition. In a study with traumatic brain injury (TBI) patients, we found that brain network organization assessed at baseline predicted training-related cognitive gains. Specifically, individuals with higher baseline brain network modularity showed greater improvements on tests of executive functioning after goal-oriented attention self-regulation training [ 22]. These findings suggest that brain network modularity can be used as a biomarker to guide cognitive interventions as well as provide insight into the neural mechanisms underlying these interventions. However, the utility of brain network modularity as a predictor of training outcomes has not yet been tested in healthy individuals.