Edge density imaging: Mapping the anatomic embedding of the structural connectome within the white matter of the human brain

Abstract

The structural connectome has emerged as a powerful tool to characterize the network architecture of the human brain and shows great potential for generating important new biomarkers for neurologic and psychiatric disorders. The edges of the cerebral graph traverse white matter to interconnect cortical and subcortical nodes, although the anatomic embedding of these edges is generally overlooked in the literature. Mapping the paths of the connectome edges could elucidate the relative importance of individual white matter tracts to the overall network topology of the brain and also lead to a better understanding of the effect of regionally-specific white matter pathology on cognition and behavior. In this work, we introduce edge density imaging (EDI), which maps the number of network edges that pass through every white matter voxel. Test-retest analysis shows good to excellent reliability for edge density (ED) measurements, with consistent results using different cortical and subcortical parcellation schemes and different diffusion MR imaging acquisition parameters. We also demonstrate that ED yields complementary information to both traditional and emerging voxel-wise metrics of white matter microstructure and connectivity, including fractional anisotropy, track density, fiber orientation dispersion and neurite density. Our results demonstrate spatially ordered variations of ED throughout the white matter, notably including greater ED in posterior than anterior cerebral white matter. The EDI framework is employed to map the white matter regions that are enriched with pathways connecting rich club nodes and also those with high densities of intra-modular and inter-modular edges. We show that periventricular white matter has particularly high ED and high densities of rich club edges, which is significant for diseases in which these areas are selectively affected, ranging from white matter injury of prematurity in infants to leukoaraiosis in the elderly. Using edge betweenness centrality, we identify specific white matter regions involved in a large number of shortest paths, some containing highly connected rich club edges while others are relatively isolated within individual modules. Overall, these findings reveal an intricate relationship between white matter anatomy and the structural connectome, motivating further exploration of EDI for biomarkers of cognition and behavior.

Test–retest reproducibility of white matter parcellation using diffusion MRI tractography fiber clustering

“…In the plots, each point represents the mean relative difference (RD) of FA versus the mean parcel volume across all subjects in one dataset. A low relative difference value represents a high test-retest reproducibility [Color figure can be viewed at wileyonlinelibrary.com] Ciccarelli et al, 2003;Cousineau et al, 2017;Duan et al, 2015;Kristo et al, 2013;Lin et al, 2013;Owen et al, 2015;Papinutto et al, 2013;Pfefferbaum et al, 2003;Smith et al, 2015;Vollmar et al, 2010;Wang et al, 2012;Yendiki et al, 2016;Zhao et al, 2015), one study performed volumetric-overlap-based experiments in a comparable way to the present study (Cousineau et al, 2017) Volumetric overlap of anatomical fiber tracts identified from the test-retest dMRI data using the fiber clustering (FC) and corticalparcellation-based (CPB) methods. Plots show the mean wDice score (averaged across subjects in each dataset) for each tract.…”

“…In comparison to related work on test–retest reproducibility of white matter parcellation, we found that both of the fiber clustering and cortical‐parcellation‐based methods performed relatively well. While existing studies applied different evaluation criteria using different testing datasets (Besseling et al, ; Cheng et al, ; Ciccarelli et al, ; Cousineau et al, ; Duan et al, ; Kristo et al, ; Lin et al, ; Owen et al, ; Papinutto et al, ; Pfefferbaum et al, ; Smith et al, ; Vollmar et al, ; Wang et al, ; Yendiki et al, ; Zhao et al, ), one study performed volumetric‐overlap‐based experiments in a comparable way to the present study (Cousineau et al, ). In this work, Cousineau et al studied test–retest reproducibility of a Freesurfer‐based cortical‐parcellation‐based anatomical tract parcellation on PPMI data and suggested a threshold for a good wDice score to be 0.72 (based on an analysis of the mean wDice score across data in a healthy population; Cousineau et al, ).…”

“…Test–retest reproducibility is considered to be a good indicator of the reliability of white matter parcellation for potential clinical applications (Besseling et al, ; Jovicich et al, ; Keihaninejad et al, ; Kristo et al, ; Lin et al, ). To measure test–retest reproducibility, previous works have used geometrical measures such as volume, volumetric overlap, fiber length and shape, and number of fibers (Cheng et al, ; Cousineau et al, ; Kristo et al, ; Lin et al, ; Owen, Chang, & Mukherjee, ; Smith, Tournier, Calamante, & Connelly, ; Wang et al, ; Zhao et al, ). Other groups have used diffusion measures such as fractional anisotropy (FA) and mean diffusivity (MD) computed from the voxels through which the parcellated fibers pass (Besseling et al, ; Ciccarelli et al, ; Duan, Zhao, He, & Shu, ; Kristo et al, ; Papinutto, Maule, & Jovicich, ; Pfefferbaum, Adalsteinsson, & Sullivan, ; Vollmar et al, ; Yendiki, Reuter, Paul, Diana Rosas, & Fischl, ).…”

Disruption of rich club organisation in cerebral small vessel disease

“…), affecting among others the long association fibres, and the most common sites of lacunes (among others) are the basal ganglia and thalamus [Benjamin et al, ]. These regions are commonly found to be involved in the rich club organisation in structural networks [Owen et al, ] and thus damage to these regions or white matter tracts among these regions may produce disproportionate disturbance in the rich club organisation. Although the number of rich club connections is small, the widespread nature of white matter damage in SVD may have a greater overall impact on these connections due to the spatial embedding of the connections.…”

Subcortical contributions to large-scale network communication

“…Although prior network-analytic work has largely focused on cortico-cortical topologypossibly in part due to technical limitations inherent in studying connectivity of subcortical nuclei (see Section 3.2.1)recent examples in the literature have incorporated subcortical nodes into their analysis of rich-club patterning in human structural tractography data (van den Heuvel and Sporns, 2011;McColgan et al, 2015;Owen et al, 2015). Results from these studies reveal that the striatum and thalamus form part of the neural "rich-club" (van den Heuvel and Sporns, 2011;McColgan et al, 2015;Owen et al, 2015) and are in-line with findings from tract-tracing work in Macaque monkeys demonstrating that striatal and thalamic nuclei belong to an integrated core circuit (Modha and Singh, 2010) (Figure 1c).…”