Kashtan N, Alon U. Spontaneous evolution of modularity and network motifs. Proceedings of the National Academy of Sciences. 2005;102: 13773–13778. 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:20816877 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 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?. 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.

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. 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): 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: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. 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 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. 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 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

Wen X, Zhang D, Liang B, Zhang R, Wang Z, Wang J, et al. Reconfiguration of the Brain Functional Network Associated with Visual Task Demands. PLoS ONE. 2015;10: e0132518. pmid:26146993As 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. 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. 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:

Elliot, Stuart (August 20, 2008). "Those Shelved Brands Start to Look Tempting". The New York Times. Gallen CL, Turner GR, Adnan A, D'Esposito M. Reconfiguration of brain network architecture to support executive control in aging. Neurobiology of Aging. 2016;44: 42–52. pmid:27318132 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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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:22276205a b c "Keebler Brilliant Marketing Pte Ltd Keebler". Brilliant-marketing.com. Archived from the original on April 2, 2010 . Retrieved April 9, 2010. Bassett DS, Yang M, Wymbs NF, Grafton ST. Learning-induced autonomy of sensorimotor systems. Nature Neuroscience. 2015;18: 744–751. pmid:25849989 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]. The quality of included studies did not appear to have been assessed, so it was not possible to comment on the validity of included data. The methods of synthesis overall appeared appropriate, although there was considerable heterogeneity. The authors investigated possible sources of heterogeneity. For patients with implantable cardioverter defibrillators, 49% of those receiving dialysis and 17.7% of those not receiving dialysis died (RR 2.67, 95% CI 1.68 to 4.25; seven studies; random-effects model). There was evidence of statistical heterogeneity (I 2=76.4%).