title: The influence of construction methodology on structural brain network measures: A review.
abstract: Structural brain networks based on diffusion MRI and tractography show robust attributes such as small-worldness, hierarchical modularity, and rich-club organization. However, there are large discrepancies in the reports about specific network measures. It is hypothesized that these discrepancies result from the influence of construction methodology. We surveyed the methodological options and their influences on network measures. It is found that most network measures are sensitive to the scale of brain parcellation, MRI gradient schemes and orientation model, and the tractography algorithm, which is in accordance with the theoretical analysis of the small-world network model. Different network weighting schemes represent different attributes of brain networks, which makes these schemes incomparable between studies. Methodology choice depends on the specific study objectives and a clear understanding of the pros and cons of a particular methodology. Because there is no way to eliminate these influences, it seems more practical to quantify them, optimize the methodologies, and construct structural brain networks with multiple spatial resolutions, multiple edge densities, and multiple weighting schemes.
authors.affiliation: Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang, China; Academic Center for Epileptology Kempenhaeghe & Maastricht UMC+, Heeze, The Netherlands; Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands. Electronic address: qisl@bmie.neu.edu.cn.
authors.forename: Shouliang
authors.initials: S
authors.lastname: Qi
authors.affiliation: Department of Mathematics & Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands; Academic Center for Epileptology Kempenhaeghe & Maastricht UMC+, Heeze, The Netherlands.
authors.forename: Stephan
authors.initials: S
authors.lastname: Meesters
authors.affiliation: Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
authors.forename: Klaas
authors.initials: K
authors.lastname: Nicolay
authors.affiliation: Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang, China; Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
authors.forename: Bart M Ter Haar
authors.initials: BM
authors.lastname: Romeny
authors.affiliation: Academic Center for Epileptology Kempenhaeghe & Maastricht UMC+, Heeze, The Netherlands; Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
authors.forename: Pauly
authors.initials: P
authors.lastname: Ossenblok
mesh_terms: D000818:Animals; D001921:Brain / Q000033:anatomy & histology*; D001931:Brain Mapping; D038524:Diffusion Magnetic Resonance Imaging; D006801:Humans; D007091:Image Processing, Computer-Assisted; D009434:Neural Pathways / Q000033:anatomy & histology*
keywords: Connectivity; Diffusion MRI; Methodology; Networks; Tractography