@article{RDM,
      recid = {3310},
      author = {Trinkle, Scott},
      title = {The role of spatial embedding in mouse brain networks  constructed from diffusion tractography and tracer  injections},
      publisher = {Knowledge@UChicago},
      address = {2021},
      number = {RDM},
      abstract = {Diffusion MRI tractography is the only noninvasive method  to measure the structural connectome in humans. However,  recent validation studies have revealed limitations of  modern tractography approaches, which lead to significant  mistracking caused in part by local uncertainties in fiber  orientations that accumulate to produce larger errors for  longer streamlines. Characterizing the role of this length  bias in tractography is complicated by the true underlying  contribution of spatial embedding to brain topology. In  this work, we compare graphs constructed with ex vivo  tractography data in mice and neural tracer data from the  Allen Mouse Brain Connectivity Atlas to random geometric  surrogate graphs which preserve the low-order distance  effects from each modality in order to quantify the role of  geometry in various network properties. We find that  geometry plays a substantially larger role in determining  the topology of graphs produced by tractography than graphs  produced by tracers. Tractography underestimates weights at  long distances compared  to neural tracers, which leads  tractography to place network hubs close to the geometric  center of the brain, as do corresponding  tractography-derived random geometric surrogates, while  tracer graphs place hubs further into peripheral areas of  the cortex. We also explore the role of spatial embedding  in modular structure, network efficiency and other  topological measures in both modalities. Throughout, we  compare the use of two different tractography streamline  node assignment strategies and find that the overall  differences between tractography approaches are small  relative to the differences between tractography- and  tracer-derived graphs. These analyses help quantify  geometric biases inherent to tractography and promote the  use of geometric benchmarking in future tractography  validation efforts.},
      url = {http://knowledge.uchicago.edu/record/3310},
      doi = {https://doi.org/10.6082/uchicago.3310},
}