Anatomical accuracy of brain connections derived from diffusion MRI tractography is inherently limited

By Cibu Thomas, F. Ye, M. Irfanoglu, Pooja Modi, Kadharbatcha S. Saleem, David A. Leopold, Carlo Pierpaoli. Proceedings of the National Academy of Sciences of the United States of America, 2014

751 citations recorded by the source corpus.

Subfields: Orthopedics and Sports Medicine, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/d2b9b7fd1d2ca758ac4243f984cf7f7dfaebb109

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Citations in the corpus

The challenge of mapping the human connectome based on diffusion tractography (2017, positive): “This ﬁ nding is in line with previous reports, which found termination of tracts in the gray matter (GM) to be inaccurate 5 and highlights an important limitation of approaches that use a voxel-wise de ﬁ nition of parcellations on the T1 image for selecting relevant streamlines.”
Gradients of structure–function tethering across neocortex (2019, positive): “To mitigate concerns about inconsistencies in reconstruction of individual participant connectomes (89, 90), as well as the sensitive dependence of network measures on false positives and false negatives (81), we adopted a group-consensus approach (25, 63, 80).”
Simple, scalable proteomic imaging for high-dimensional profiling of intact systems (2015, positive): “Visualizing and analyzing neural fibers with high-resolution light microscopy can provide valuable insights into many studies (Thomas et al., 2014; Wedeen et al., 2012; Zuccaro and Arlotta, 2013), such as validating diffusion tensor imaging (DTI) and understanding the organizing principles of brain…”
Population-Averaged Atlas of the Macroscale Human Structural Connectome and Its Network Topology (2018, neutral): “Nonetheless, there are several caveats to the success of diffusion MRI fiber tracking, including the identification of false tracts and suboptimal coverage of small pathways or those with complex geometry (Reveley et al., 2015; Thomas et al., 2014).”
Closures and Cavities in the Human Connectome (2016, background): “Tractography algorithms trade off specificity and sensitivity, making it challenging to simultaneously detect true connections while avoiding false connections [61], fail to detect superficial connections (i.”
Network-Level Structure-Function Relationships in Human Neocortex (2016, background): “However, diffusion-based tractography has also been shown to be susceptible to both false positives and false negatives (Jones et al. 2013; Thomas et al. 2014).”
Network neuroscience (2017, background): “Although subject to many experimental and statistical limitations 23 and interpretational pitfalls 24 , comparative studies have suggested that representations of long-distance connectivity derived from invasive histological methods and noninvasive imaging of neuroanatomical structure are…”
Development of large-scale functional networks from birth to adulthood: a guide to neuroimaging literature (2017, methodology): “Unfortunately, follow-up work on this topic in humans has been relatively scant, and this work was prior to new information regarding tractography artifacts (Calabrese et al., 2015; Donahue et al., 2016; Reveley et al., 2015; Thomas et al., 2014), and prior to systematic motion artifacts being demon”
Cliques and cavities in the human connectome (2016, background): “Tractography algorithms trade off specificity and sensitivity, making it challenging to simultaneously detect true connections while avoiding false connections (Thomas et al. 2014), fail to detect superficial connections (i.”
Morphometric Similarity Networks Detect Microscale Cortical Organisation And Predict Inter-Individual Cognitive Variation (2017, positive): “, 2012) and by a large number of false positive connections (Maier-Hein et al., 2016; Thomas et al., 2014).”