Tractography density and network measures in Alzheimer's disease

Brain connectivity declines in Alzheimer’s disease (AD), both func-tionally and structurally. Connectivity maps and networks derived from diffusion-based tractography offer new ways to track disease progression and to understand how AD affects the brain. Here we set out to identify (1) which fiber network measures show greatest differences between AD patients and controls, and (2) how these ef-fects depend on the density of fibers extracted by the tractography algorithm. We computed brain networks from diffusion-weighted images (DWI) of the brain, in 110 subjects (28 normal elderly, 56 with early and 11 with late mild cognitive impairment, and 15 with AD). We derived connectivity matrices and network topology mea-sures, for each subject, from whole-brain tractography and cortical parcellations. We used an ODF lookup table to speed up fiber extrac-tion, and to exploit the full information in the orientation distribution function (ODF). This made it feasible to compute high density con-nectivity maps. We used accelerated tractography to compute a large number of fibers to understand what effect fiber density has on net-work measures and in distinguishing different disease groups in our data. We focused on global efficiency, transitivity, path length, mean degree, density, modularity, small world, and assortativity measures computed from weighted and binary undirected connectivity matri-ces. Of all these measures, the mean nodal degree best distinguished diagnostic groups. High-density fiber matrices were most helpful for picking up the more subtle clinical differences, e.g. between mild cognitively impaired (MCI) and normals, or for distinguish-ing subtypes of MCI (early versus late). Care is needed in clinical analyses of brain connectivity, as the density of extracted fibers may affect how well a network measure can pick up differences between patients and controls