Functional Brain Network Modularity Captures Inter- and Intra-Individual Variation in Working Memory Capacity

Cognitive abilities, such as working memory, differ among people; however, individuals also vary in their own day-to-day cognitive performance. One potential source of cognitive variability may be fluctuations in the functional organization of neural systems. The degree to which the organization of these functional networks is optimized may relate to the effective cognitive functioning of the individual. Here we specifically examine how changes in the organization of large-scale networks measured via resting state functional connectivity MRI and graph theory track changes in working memory capacity.Twenty-two participants performed a test of working memory capacity and then underwent resting-state fMRI. Seventeen subjects repeated the protocol three weeks later. We applied graph theoretic techniques to measure network organization on 34 brain regions of interest (ROI). Network modularity, which measures the level of integration and segregation across sub-networks, and small-worldness, which measures global network connection efficiency, both predicted individual differences in memory capacity; however, only modularity predicted intra-individual variation across the two sessions. Partial correlations controlling for the component of working memory that was stable across sessions revealed that modularity was almost entirely associated with the variability of working memory at each session. Analyses of specific sub-networks and individual circuits were unable to consistently account for working memory capacity variability.The results suggest that the intrinsic functional organization of an a priori defined cognitive control network measured at rest provides substantial information about actual cognitive performance. The association of network modularity to the variability in an individual's working memory capacity suggests that the organization of this network into high connectivity within modules and sparse connections between modules may reflect effective signaling across brain regions, perhaps through the modulation of signal or the suppression of the propagation of noise

The correlation matrix for connection weights between nodes and VSTM.

<p>Capacity <b>a</b>) in Session 1 (lower triangle) and Session 2 (upper triangle). <b>b</b>) For clarity, the same correlation matrix with non-significant connections eliminated (threshold r = 0.47, p≤0.05, uncorrected for multiple correlations).</p

Correlations between VSTM capacity and intra-module connection density, and between module connectivity.

<p>*P<0.05, uncorrected;</p><p>**P<0.01, uncorrected.</p

Connectivity matrix for the six regions of interest from Xu & Chun (2006).

<p>The matrices indicate a) the intercorrelations among node-pairs for Session 1 (lower triangle) and Session 2 (upper triangle), b) the correlation between VSTM capacity and the strength of correlation for each node pair at Session 1 and Session 2, and c) the same matrix as in b but set to a threshold of r = 0.47, p = 0.05, uncorrected for multiple comparisons. Abbreviations of anatomical locations: sIPS.R - Right superior intra-parietal sulcus, sIPS.L - Left superior intra-parietal sulcus, iIPS.R - Right inferior intra-parietal sulcus, iIPS.L - Left inferior intra-parietal sulcus, LOC.R - Right lateral occipital cortex, LOC.L - Left lateral occipital cortex.</p

Regions of interest used as nodes in the network network analysis, drawn from [22], [23].

<p>Regions of interest used as nodes in the network network analysis, drawn from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0030468#pone.0030468-Dosenbach2" target="_blank">[22]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0030468#pone.0030468-Fair1" target="_blank">[23]</a>.</p