TY - JOUR
T1 - Dimensionless, scale invariant, edge weight metric for the study of complex structural networks
AU - Colon-Perez, Luis M.
AU - Spindler, Caitlin
AU - Goicochea, Shelby
AU - Triplett, William
AU - Parekh, Mansi
AU - Montie, Eric
AU - Carney, Paul R.
AU - Price, Catherine
AU - Mareci, Thomas H.
N1 - Funding Information:
The authors would like to acknowledge insightful discussion with Dr. Garrett Astary, and the support of Dan Plant and the staff of Advanced Magnetic Resonance Imaging and Spectroscopy (AMRIS) Facility in the McKnight Brain Institute of the University of Florida (UF) for providing assistance with data acquisition. Additional acknowledgment is provided to Peter Nguyen for providing the human data. A portion of this work was performed in the McKnight Brain Institute at the National High Magnetic Field Laboratory’s AMRIS Facility, which is supported by National Science Foundation Cooperative Agreement No. DMR-1157490, the State of Florida, and the U.S. Department of Energy.
Publisher Copyright:
Copyright © 2015 Li et al.
PY - 2015/7/14
Y1 - 2015/7/14
N2 - High spatial and angular resolution diffusion weighted imaging (DWI) with network analysis provides a unique framework for the study of brain structure in vivo. DWI-derived brain connectivity patterns are best characterized with graph theory using an edge weight to quantify the strength of white matter connections between gray matter nodes. Here a dimensionless, scale-invariant edge weight is introduced to measure node connectivity. This edge weight metric provides reasonable and consistent values over any size scale (e.g. rodents to humans) used to quantify the strength of connection. Firstly, simulations were used to assess the effects of tractography seed point density and random errors in the estimated fiber orientations; with sufficient signal-to-noise ratio (SNR), edge weight estimates improve as the seed density increases. Secondly to evaluate the application of the edge weight in the human brain, ten repeated measures of DWI in the same healthy human subject were analyzed. Mean edge weight values within the cingulum and corpus callosum were consistent and showed low variability. Thirdly, using excised rat brains to study the effects of spatial resolution, the weight of edges connecting major structures in the temporal lobe were used to characterize connectivity in this local network. The results indicate that with adequate resolution and SNR, connections between network nodes are characterized well by this edge weight metric. Therefore this new dimensionless, scale-invariant edge weight metric provides a robust measure of network connectivity that can be applied in any size regime.
AB - High spatial and angular resolution diffusion weighted imaging (DWI) with network analysis provides a unique framework for the study of brain structure in vivo. DWI-derived brain connectivity patterns are best characterized with graph theory using an edge weight to quantify the strength of white matter connections between gray matter nodes. Here a dimensionless, scale-invariant edge weight is introduced to measure node connectivity. This edge weight metric provides reasonable and consistent values over any size scale (e.g. rodents to humans) used to quantify the strength of connection. Firstly, simulations were used to assess the effects of tractography seed point density and random errors in the estimated fiber orientations; with sufficient signal-to-noise ratio (SNR), edge weight estimates improve as the seed density increases. Secondly to evaluate the application of the edge weight in the human brain, ten repeated measures of DWI in the same healthy human subject were analyzed. Mean edge weight values within the cingulum and corpus callosum were consistent and showed low variability. Thirdly, using excised rat brains to study the effects of spatial resolution, the weight of edges connecting major structures in the temporal lobe were used to characterize connectivity in this local network. The results indicate that with adequate resolution and SNR, connections between network nodes are characterized well by this edge weight metric. Therefore this new dimensionless, scale-invariant edge weight metric provides a robust measure of network connectivity that can be applied in any size regime.
UR - http://www.scopus.com/inward/record.url?scp=84940529059&partnerID=8YFLogxK
U2 - 10.1371/journal.pone.0131493
DO - 10.1371/journal.pone.0131493
M3 - Article
C2 - 26173147
AN - SCOPUS:84940529059
SN - 1932-6203
VL - 10
JO - PLoS ONE
JF - PLoS ONE
IS - 7
M1 - e0131493
ER -