title: Quantitative assessment of a framework for creating anatomical brain networks via global tractography.
abstract: Interregional connections of the brain measured with diffusion tractography can be used to infer valuable information regarding both brain structure and function. However, different tractography algorithms can generate networks that exhibit different characteristics, resulting in poor reproducibility across studies. Therefore, it is important to benchmark different tractography algorithms to quantitatively assess their performance. Here we systematically evaluated a newly introduced tracking algorithm, global tractography, to derive anatomical brain networks in a fiber phantom, 2 post-mortem macaque brains, and 20 living humans, and compared the results with an established local tracking algorithm. Our results demonstrated that global tractography accurately characterized the phantom network in terms of graph-theoretic measures, and significantly outperformed the local tracking approach. Results in brain tissues (post-mortem macaques and in vivo humans), however, showed that although the performance of global tractography demonstrated a trend of improvement, the results were not vastly different than that of local tractography, possibly resulting from the increased fiber complexity of real tissues. When using macaque tracer-derived connections as the ground truth, we found that both global and local algorithms generated non-random patterns of false negative and false positive connections that were probably related to specific fiber systems and largely independent of the tractography algorithm or tissue type (post-mortem vs. in vivo) used in the current study. Moreover, a close examination of the transcallosal motor connections, reconstructed via either global or local tractography, demonstrated that the lateral transcallosal fibers in humans and macaques did not exhibit the denser homotopic connections found in primate tracer studies, indicating the need for more robust brain mapping techniques based on diffusion MRI data.
authors.affiliation: Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA 30322, USA.
authors.forename: Longchuan
authors.initials: L
authors.lastname: Li
authors.forename: James K
authors.initials: JK
authors.lastname: Rilling
authors.forename: Todd M
authors.initials: TM
authors.lastname: Preuss
authors.forename: Matthew F
authors.initials: MF
authors.lastname: Glasser
authors.forename: Frederick W
authors.initials: FW
authors.lastname: Damen
authors.forename: Xiaoping
authors.initials: X
authors.lastname: Hu
mesh_terms: D000465:Algorithms; D000818:Animals; D001921:Brain / Q000033:anatomy & histology*; D001931:Brain Mapping / Q000379:methods*; D056324:Diffusion Tensor Imaging / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008251:Macaca; D009434:Neural Pathways / Q000033:anatomy & histology*