| Literature DB >> 25584366 |
Neda Jahanshad1, Gautam Prasad2, Arthur W Toga3, Katie L McMahon4, Greig I de Zubicaray, Nicholas G Martin, Margaret J Wright, Paul M Thompson.
Abstract
Brain connectivity analyses are increasingly popular for investigating organization. Many connectivity measures including path lengths are generally defined as the number of nodes traversed to connect a node in a graph to the others. Despite its name, path length is purely topological, and does not take into account the physical length of the connections. The distance of the trajectory may also be highly relevant, but is typically overlooked in connectivity analyses. Here we combined genotyping, anatomical MRI and HARDI to understand how our genes influence the cortical connections, using whole-brain tractography. We defined a new measure, based on Dijkstra's algorithm, to compute path lengths for tracts connecting pairs of cortical regions. We compiled these measures into matrices where elements represent the physical distance traveled along tracts. We then analyzed a large cohort of healthy twins and show that our path length measure is reliable, heritable, and influenced even in young adults by the Alzheimer's risk gene, CLU.Entities:
Keywords: Dijkstra’s algorithm; HARDI tractography; Structural connectivity; neuroimaging genetics; path length
Year: 2012 PMID: 25584366 PMCID: PMC4288784 DOI: 10.1007/978-3-642-33530-3_3
Source DB: PubMed Journal: Multimodal Brain Image Anal (2012)