Mapping Human Whole-Brain Structural Networks with Diffusion MRI

Abstract

Understanding the large-scale structural network formed by neurons is a major challenge in system neuroscience. A detailed connectivity map covering the entire brain would therefore be of great value. Based on diffusion MRI, we propose an efficient methodology to generate large, comprehensive and individual white matter connectional datasets of the living or dead, human or animal brain. This non-invasive tool enables us to study the basic and potentially complex network properties of the entire brain. For two human subjects we find that their individual brain networks have an exponential node degree distribution and that their global organization is in the form of a small world.

MR connectomics: Principles and challenges

“…Small-world structures has been reported to exist in the cortex of non-human primates based on data derived by classical tract tracing (Sporns et al, 2000;Sporns and Zwi, 2004), as well as in networks derived from diffusion imaging of the human brain (Gong et al, 2009;Hagmann et al, , 2007Iturria-Medina et al, 2008), see Fig. 5.…”

“…Therefore principal directions of diffusion are coherently aligned along white matter fiber tracts defining coherence paths of maximal diffusion. A tractography algorithm is a tool to capture this coherence and to map it as a curve in three-dimensional space, hence representing a fiber tract trajectory (Hagmann, 2005;Hagmann et al, 2007;Wedeen et al, 2008).…”

“…Although this configuration is likely to be less widespread than a crossing bundle, fiber-kissing situations as well as areas of a mixture of kissing and crossing do occur in the brain. This problem again led to investigation of different algorithms in the HARDI setting Gigandet, 2009;Hagmann et al, 2007;Parker and Alexander, 2005;Perrin et al, 2005;Wedeen et al, 2008). Some groups have tried to relate some complex diffusion PDF shapes to either crossing or kissing fiber profiles (Alexander, 2008).…”

“…Other groups have used similar strategies to partition the cortex using different matching criteria between subjects, using volumetric registration of atlases, like MNI, resulting in smaller networks (Gong et al, 2009;Iturria-Medina et al, 2007;Li et al, 2009). In our early works we used Tailarach coordinates (Hagmann, 2005) or arbitrary subdivisions (Hagmann et al, 2007). In our experience, homogeneity in size and shape of ROIs as well precise anatomical localization is critical in many respects for quantitative analysis of weighted networks.…”

“…Color code: subject 1 (blue circles), subject 2 (green diamonds), random graph reference (black filled circles). Adapted from (Hagmann et al, 2007). (B) Lateral views of the right and left cerebral hemispheres showing ROI centrality, averaged across five.…”