Sensitivity analysis of human brain structural network construction

Kuang Wei, Matthew Cieslak, Clint Greene, Scott T. Grafton, Jean M. Carlson
2017 Network Neuroscience  
Network neuroscience leverages diffusion-weighted magnetic resonance imaging and tractography to quantify structural connectivity of the human brain. However, scientists and practitioners lack a clear understanding of the effects of varying tractography parameters on the constructed structural networks. With diffusion images from the Human Connectome Project (HCP), we characterize how structural networks are impacted by the spatial resolution of brain atlases, total number of tractography
more » ... lines, and grey matter dilation with various graph metrics. We demonstrate how injudicious combinations of highly refined brain parcellations and low numbers of streamlines may inadvertently lead to disconnected network models with isolated nodes. Furthermore, we provide solutions to significantly reduce the likelihood of generating disconnected networks. In addition, for different tractography parameters, we investigate the distributions of values taken by various graph metrics across the population of HCP subjects. Analyzing the ranks of individual subjects within the graph metric distributions, we find that the ranks of individuals are affected differently by atlas scale changes. Our work serves as a guideline for researchers to optimize the selection of tractography parameters and illustrates how biological characteristics of the brain derived in network neuroscience studies can be affected by the choice of atlas parcellation schemes. AUTHOR SUMMARY Diffusion tractography has been proven to be a promising noninvasive technique to study the network properties of the human brain. However, how various tractography and network construction parameters affect network properties has not been studied using a large cohort of high-quality data. We utilize data provided by the Human Connectome Project to characterize the changes to network properties induced by varying the brain parcellation atlas scales, the number of reconstructed tractography tracks, and the degree of grey matter dilation with graph metrics. We illustrate the importance of increasing the reconstructed track sampling rate when higher atlas scales are used. In addition to changing the raw values of graph metrics, we find that the ranks of individuals relative to the population metric distributions are altered. We further discuss how the dependency of graph metric ranks can affect the brain characteristics derived in group comparison studies using network neuroscience techniques.
doi:10.1162/netn_a_00025 pmid:30090874 pmcid:PMC6063716 fatcat:yfaxwjs4hzgntggnkcfu77kl3m