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Studies of working memory (WM) function have tended to adopt either a within-subject approach, focusing on effects of load manipulations, or a between-subjects approach, focusing on individual differences. This dichotomy extends to WM neuroimaging studies, with different neural correlates being identified for within- and between-subjects variation in WM. Here, we examined this issue in a systematic fashion, leveraging the large-sample Human Connectome Project dataset, to conduct a well-powered, whole-brain analysis of the N-back WM task. We first demonstrate the advantages of parcellation schemes for dimension reduction, in terms of load-related effect sizes. This parcel-based approach is then utilized to directly compare the relationship between load-related (within-subject) and behavioral individual differences (between-subject) effects through both correlational and predictive analyses. The results suggest a strong linkage of within-subject and between-subject variation, with larger load-effects linked to stronger brain-behavior correlations. In frontoparietal cortex no hemispheric biases were found towards one type of variation, but the Dorsal Attention Network did exhibit greater sensitivity to between over within-subjects variation, whereas in the Somatomotor network, the reverse pattern was observed. Cross-validated predictive modeling capitalizing on this tight relationship between the two effects indicated greater predictive power for load-activated than load-deactivated parcels, while also demonstrating that load-related effect size can serve as an effective guide to feature (i.e., parcel) selection, in maximizing predictive power while maintaining interpretability. Together, the findings demonstrate an important consistency across within- and between-subjects approaches to identifying the neural substrates of WM, which can be effectively harnessed to develop more powerful predictive models. [ABSTRACT FROM AUTHOR]

Abstract: The purpose of this study is to investigate whether specific patterns of correlation exist in diffusion tensor imaging (DTI) parameters across different white matter tracts in the normal human brain, and whether the relative strengths of these putative microstructural correlations might reflect phylogenetic and functional similarities between tracts. We performed quantitative DTI fiber tracking on 44 healthy adult volunteers to obtain tract-based measures of mean diffusivity (MD), fractional anisotropy (FA), axial diffusivity (AD), and radial diffusivity (RD) from four homologous pairs of neocortical association pathways (arcuate fasciculi, inferior fronto-occipital fasciculi, inferior longitudinal fasciculi, and uncinate fasciculi bilaterally), a homologous pair of limbic association pathways (left and right dorsal cingulum bundles), and a homologous pair of cortical–subcortical projection pathways (left and right corticospinal tracts). From the resulting inter-tract correlation matrices, we show that there are statistically significant correlations of DTI parameters between tracts, and that there are statistically significant variations among these inter-tract correlations. Furthermore, we observe that many, but by no means all, of the strongest correlations are between homologous tracts in the left and right hemispheres. Even among homologous pairs of tracts, there are wide variations in the degree of coupling. Finally, we generate a data-driven hierarchical clustering of the fiber pathways based on pairwise FA correlations to demonstrate that the neocortical association pathways tend to group separately from the limbic pathways at trend-level statistical significance, and that the projection pathways of the left and right corticospinal tracts comprise the most distant outgroup with high confidence (p <0.01). Hence, specific patterns of microstructural correlation exist between tracts and may reflect phylogenetic and functional similarities between tracts. The study of these microstructural relationships between white matter pathways might aid research on the genetic basis and on the behavioral effects of axonal connectivity, as well as provide a revealing new perspective with which to investigate neurological and psychiatric disorders. [Copyright &y& Elsevier]