Name
Title | ||
|---|---|---|
Optimization of graph construction can significantly increase the power of structural brain network studies |
Abstract | ||
|---|---|---|
Structural brain networks derived from diffusion magnetic resonance imaging data have been used extensively to describe the human brain, and graph theory has allowed quantification of their network properties. Schemes used to construct the graphs that represent the structural brain networks differ in the metrics they use as edge weights and the algorithms they use to define the network topologies. In this work, twenty graph construction schemes were considered. The schemes use the number of streamlines, the fractional anisotropy, the mean diffusivity or other attributes of the tracts to define the edge weights, and either an absolute threshold or a data-driven algorithm to define the graph topology. The test-retest data of the Human Connectome Project were used to compare the reproducibility of the graphs and their various attributes (edges, topologies, graph theoretical metrics) derived through those schemes, for diffusion images acquired with three different diffusion weightings. The impact of the scheme on the statistical power of the study and on the number of participants required to detect a difference between populations or an effect of an intervention was also calculated. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.neuroimage.2019.05.052 | NeuroImage |
Keywords | Field | DocType |
CM,FA,GLM,ICC,MD,NS,OMST,PS,RD,SD,SLD,TL,TV,WM | Graph theory,Human Connectome Project,Biology,Fractional anisotropy,Algorithm,Network topology,Thresholding,Genetics,Absolute threshold,Topological graph theory,Statistical power | Journal |
Volume | ISSN | Citations |
199 | 1053-8119 | 3 |
PageRank | References | Authors |
0.38 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Eirini Messaritaki | 1 | 4 | 0.73 |
| Stavros I. Dimitriadis | 2 | 32 | 5.99 |
| Derek K. Jones | 3 | 655 | 48.55 |