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The ability to temporarily store and manipulate information in working memory is a hallmark of human intelligence and differs considerably across individuals, but the structural brain correlates underlying these differences in working memory capacity (WMC) are only poorly understood. In two separate studies, diffusion MRI data and WMC scores were collected for 70 and 109 healthy individuals. Using a combination of probabilistic tractography and network analysis of the white matter tracts, we examined whether structural brain network properties were predictive of individual WMC. Converging evidence from both studies showed that lateral prefrontal cortex and posterior parietal cortex of high-capacity individuals are more densely connected compared with low-capacity individuals. Importantly, our network approach was further able to dissociate putative functional roles associated with two different pathways connecting frontal and parietal regions: a corticocortical pathway and a subcortical pathway. In Study 1, where participants were required to maintain and update working memory items, the connectivity of the direct and indirect pathway was predictive of WMC. In contrast, in Study 2, where participants were required to maintain working memory items without updating, only the connectivity of the direct pathway was predictive of individual WMC. Our results suggest an important dissociation in the circuitry connecting frontal and parietal regions, where direct frontoparietal connections might support storage and maintenance, whereas subcortically mediated connections support the flexible updating of working memory content.Key words: basal ganglia; diffusion MRI; network analysis; prefrontal cortex; working memory capacity IntroductionHumans and nonhuman primates have developed a remarkable capacity to store and manipulate information "online." This process, referred to as working memory, is fundamental for many perceptual and cognitive abilities (Fukuda et al., 2010;Johnson et al., 2013) and is closely linked to general intelligence (Conway et al., 2003). The capacity of working memory (WMC) varies substantially across individuals (Cowan, 2001;Luck and Vogel, 2013), but the neural correlates of individual differences in WMC are at present only poorly understood.Recent studies using fMRI and EEG suggest that differences in WMC can be partly attributed to differences in attentional processes involved in the selection of relevant and filtering of irrelevant information (Vogel and Machizawa, 2004;McNab and Klingberg, 2008;Luck and Vogel, 2013). Irrelevant information consumes unnecessary capacity, and it has been shown that lowcapacity individuals tend to encode irrelevant information to a greater extent than high-capacity individuals (Vogel et al., 2005;Luck and Vogel, 2013 Significance StatementUsing diffusion MRI and network analysis, we found that the capacity of healthy individuals to temporally maintain information in working memory was related to a cortical pathway connecting frontal and parietal regions. The up...

One of the most promising avenues for compiling connectivity data originates from the notion that individual brain regions maintain individual connectivity profiles; the functional repertoire of a cortical area (“the functional fingerprint”) is closely related to its anatomical connections (“the connectional fingerprint”) and, hence, a segregated cortical area may be characterized by a highly coherent connectivity pattern. Diffusion tractography can be used to identify borders between such cortical areas. Each cortical area is defined based upon a unique probabilistic tractogram and such a tractogram is representative of a group of tractograms, thereby forming the cortical area. The underlying methodology is called connectivity-based cortex parcellation and requires clustering or grouping of similar diffusion tractograms. Despite the relative success of this technique in producing anatomically sensible results, existing clustering techniques in the context of connectivity-based parcellation typically depend on several non-trivial assumptions. In this paper, we embody an unsupervised hierarchical information-based framework to clustering probabilistic tractograms that avoids many drawbacks offered by previous methods. Cortex parcellation of the inferior frontal gyrus together with the precentral gyrus demonstrates a proof of concept of the proposed method: The automatic parcellation reveals cortical subunits consistent with cytoarchitectonic maps and previous studies including connectivity-based parcellation. Further insight into the hierarchically modular architecture of cortical subunits is given by revealing coarser cortical structures that differentiate between primary as well as premotoric areas and those associated with pre-frontal areas.