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Optimization of graph construction can significantly increase the power of structural brain network studies
- Source :
- Neuroimage
- Publication Year :
- 2019
- Publisher :
- Cold Spring Harbor Laboratory Press, 2019.
-
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. The reproducibility of the graphs and their attributes depended heavily on the graph construction scheme. Graph reproducibility was higher for schemes that used thresholding to define the graph topology, while data-driven schemes performed better at topology reproducibility (mean similarities of 0.962 and 0.984 respectively, for graphs derived from diffusion images with b=2000 s/mm2). Additionally, schemes that used thresholding resulted in better reproducibility for local graph theoretical metrics (intra-class correlation coefficients (ICC) of the order of 0.8), compared to data-driven schemes. Thresholded and data-driven schemes resulted in high (0.86 or higher) ICCs only for schemes that use exclusively the number of streamlines to construct the graphs. Crucially, the number of participants required to detect a difference between populations or an effect of an intervention could change by a factor of two or more depending on the scheme used, affecting the power of studies to reveal the effects of interest.<br />Highlights • The reproducibility of graphs depends on the graph-construction scheme. • The reproducibility of graph theoretical metrics also depends on the scheme used. • The reproducibility of edge weights depends on the graph-construction scheme. • Structural network graphs are fairly consistent across diffusion weightings.
- Subjects :
- Adult
PS, percentage of streamlines
Computer science
Network topology
CM, connectivity matrix
Article
030218 nuclear medicine & medical imaging
NS, number of streamlines
03 medical and health sciences
0302 clinical medicine
GLM, general linear model
Fractional anisotropy
SLD, streamline density
Connectome
Humans
FA, fractional anisotropy
ICC, intra-class correlation coefficient
WM, white matter
MD, mean diffusivity
TV, tract volume
Human Connectome Project
Data Visualization
Brain
Reproducibility of Results
Graph theory
Thresholding
Magnetic Resonance Imaging
Graph
RD, radial diffusivity
TL, tract length
Diffusion Tensor Imaging
OMST, orthogonal minimal spanning tree
Topological graph theory
Nerve Net
SD, standard deviation
Algorithm
030217 neurology & neurosurgery
Subjects
Details
- Language :
- English
- ISSN :
- 10538119
- Database :
- OpenAIRE
- Journal :
- Neuroimage
- Accession number :
- edsair.doi.dedup.....4c32b543b72763b49a27c80f42f38923