TY - GEN
T1 - Genetics of path lengths in brain connectivity networks
T2 - 2nd International Workshop on Multimodal Brain Image Analysis, MBIA 2012, Held in Conjunction with the 15th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2012
AU - Jahanshad, Neda
AU - Prasad, Gautam
AU - Toga, Arthur W.
AU - McMahon, Katie L.
AU - De Zubicaray, Greig I.
AU - Martin, Nicholas G.
AU - Wright, Margaret J.
AU - Thompson, Paul M.
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
KW - Dijkstra's algorithm
KW - HARDI tractography
KW - Structural connectivity
KW - neuroimaging genetics
KW - path length
UR - http://www.scopus.com/inward/record.url?scp=84868221081&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-33530-3_3
DO - 10.1007/978-3-642-33530-3_3
M3 - Conference contribution
AN - SCOPUS:84868221081
SN - 9783642335297
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 29
EP - 40
BT - Multimodal Brain Image Analysis - Second International Workshop, MBIA 2012, Held in Conjunction with MICCAI 2012, Proceedings
Y2 - 1 October 2012 through 5 October 2012
ER -