1 Akil, H., Martone, M. E. & Van Essen, D. C. Challenges and opportunities in mining neuroscience data. Science 331, 708-712, doi:331/6018/708 [pii] 10.1126/science.1199305 (2011).
2 Glasser, M. F. et al. The Human Connectome Project's neuroimaging approach. Nat Neurosci 19, 1175-1187, doi:10.1038/nn.4361 (2016).
3 Sporns, O., Tononi, G. & Kotter, R. The human connectome: A structural description of the human brain. PLoS Comput Biol 1, e42, doi:10.1371/journal.pcbi.0010042 (2005).
4 Biswal, B. B. et al. Toward discovery science of human brain function. Proc Natl Acad Sci U S A 107, 4734-4739, doi:10.1073/pnas.0911855107 (2010).
5 Bullmore, E. & Sporns, O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci 10, 186-198, doi:10.1038/nrn2575 (2009).
6 O’Donnell, L. J. et al. Automated white matter fiber tract identification in patients with brain tumors. NeuroImage: Clinical 13, 138-153 (2017).
7 Garyfallidis, E. et al. Recognition of white matter bundles using local and global streamline-based registration and clustering. Neuroimage 170, 283-295, doi:10.1016/j.neuroimage.2017.07.015 (2018).
8 Rheault, F. et al. Bundle-specific tractography with incorporated anatomical and orientational priors. NeuroImage 186, 382-398 (2019).
9 Zollei, L., Jaimes, C., Saliba, E., Grant, P. E. & Yendiki, A. TRActs constrained by UnderLying INfant anatomy (TRACULInA): An automated probabilistic tractography tool with anatomical priors for use in the newborn brain. Neuroimage 199, 1-17, doi:10.1016/j.neuroimage.2019.05.051 (2019).
10 Zhang, F. et al. An anatomically curated fiber clustering white matter atlas for consistent white matter tract parcellation across the lifespan. Neuroimage 179, 429-447, doi:10.1016/j.neuroimage.2018.06.027 (2018).
11 Yeh, F. C. et al. Population-averaged atlas of the macroscale human structural connectome and its network topology. Neuroimage 178, 57-68, doi:10.1016/j.neuroimage.2018.05.027 (2018).
12 Yeh, F. C. Shape analysis of the human association pathways. Neuroimage 223, 117329, doi:10.1016/j.neuroimage.2020.117329 (2020).
13 Pijnenburg, R. et al. Myelo- and cytoarchitectonic microstructural and functional human cortical atlases reconstructed in common MRI space. Neuroimage 239, 118274, doi:10.1016/j.neuroimage.2021.118274 (2021).
14 Glasser, M. F. et al. A multi-modal parcellation of human cerebral cortex. Nature 536, 171-178, doi:10.1038/nature18933 (2016).
15 Catani, M. & de Schotten, M. T. Atlas of human brain connections. (Oxford University Press, 2012).
16 Thiebaut de Schotten, M. et al. Atlasing location, asymmetry and inter-subject variability of white matter tracts in the human brain with MR diffusion tractography. Neuroimage 54, 49-59, doi:10.1016/j.neuroimage.2010.07.055 (2011).
17 Hansen, C. B. et al. Pandora: 4-D White Matter Bundle Population-Based Atlases Derived from Diffusion MRI Fiber Tractography. Neuroinformatics, doi:10.1007/s12021-020-09497-1 (2020).
18 Bernal, B. & Ardila, A. The role of the arcuate fasciculus in conduction aphasia. Brain 132, 2309-2316 (2009).
19 Catani, M., Jones, D. K. & ffytche, D. H. Perisylvian language networks of the human brain. Ann Neurol 57, 8-16, doi:10.1002/ana.20319 (2005).
20 Glasser, M. F. & Rilling, J. K. DTI tractography of the human brain's language pathways. Cerebral cortex 18, 2471-2482 (2008).
21 Rilling, J. K. et al. The evolution of the arcuate fasciculus revealed with comparative DTI. Nat Neurosci 11, 426-428, doi:10.1038/nn2072 (2008).
22 Saur, D. et al. Ventral and dorsal pathways for language. Proc Natl Acad Sci U S A 105, 18035-18040, doi:10.1073/pnas.0805234105 (2008).
23 Bernal, B. & Ardila, A. The role of the arcuate fasciculus in conduction aphasia. Brain 132, 2309-2316, doi:10.1093/brain/awp206 (2009).
24 Fridriksson, J. et al. Revealing the dual streams of speech processing. Proceedings of the National Academy of Sciences 113, 15108-15113 (2016).
25 Milner, A. D. & Goodale, M. A. Two visual systems re-viewed. Neuropsychologia 46, 774-785 (2008).
26 Rauschecker, J. P. & Tian, B. Mechanisms and streams for processing of “what” and “where” in auditory cortex. Proceedings of the National Academy of Sciences 97, 11800-11806 (2000).
27 Mishkin, M., Ungerleider, L. G. & Macko, K. A. Object vision and spatial vision: two cortical pathways. Trends in neurosciences 6, 414-417 (1983).
28 Siless, V., Chang, K., Fischl, B. & Yendiki, A. AnatomiCuts: Hierarchical clustering of tractography streamlines based on anatomical similarity. Neuroimage 166, 32-45, doi:10.1016/j.neuroimage.2017.10.058 (2018).
29 Guevara, P. et al. Robust clustering of massive tractography datasets. Neuroimage 54, 1975-1993, doi:S1053-8119(10)01320-0 [pii]
10.1016/j.neuroimage.2010.10.028 (2011).
30 Jin, Y. et al. Automatic clustering of white matter fibers in brain diffusion MRI with an application to genetics. Neuroimage 100, 75-90, doi:10.1016/j.neuroimage.2014.04.048 (2014).
31 Maddah, M., Mewes, A. U., Haker, S., Grimson, W. E. & Warfield, S. K. Automated atlas-based clustering of white matter fiber tracts from DTMRI. Med Image Comput Comput Assist Interv 8, 188-195 (2005).
32 Wang, Q., Yap, P. T., Wu, G. & Shen, D. Application of neuroanatomical features to tractography clustering. Hum Brain Mapp, doi:10.1002/hbm.22051 (2012).
33 Dick, A. S. & Tremblay, P. Beyond the arcuate fasciculus: consensus and controversy in the connectional anatomy of language. Brain 135, 3529-3550, doi:10.1093/brain/aws222 (2012).
34 Wang, X. et al. Subcomponents and connectivity of the superior longitudinal fasciculus in the human brain. Brain Struct Funct 221, 2075-2092, doi:10.1007/s00429-015-1028-5 (2016).
35 Petrides, M. & Pandya, D. N. Projections to the frontal cortex from the posterior parietal region in the rhesus monkey. J Comp Neurol 228, 105-116, doi:10.1002/cne.902280110 (1984).
36 Ghulam-Jelani, Z. et al. Redundancy circuits of the commissural pathways in human and rhesus macaque brains. Hum Brain Mapp 42, 2250-2261, doi:10.1002/hbm.25363 (2021).
37 Sohn, Y., Choi, M. K., Ahn, Y. Y., Lee, J. & Jeong, J. Topological cluster analysis reveals the systemic organization of the Caenorhabditis elegans connectome. PLoS Comput Biol 7, e1001139, doi:10.1371/journal.pcbi.1001139 (2011).
38 Akiki, T. J. & Abdallah, C. G. Determining the Hierarchical Architecture of the Human Brain Using Subject-Level Clustering of Functional Networks. Sci Rep 9, 19290, doi:10.1038/s41598-019-55738-y (2019).
39 Raut, R. V., Snyder, A. Z. & Raichle, M. E. Hierarchical dynamics as a macroscopic organizing principle of the human brain. Proc Natl Acad Sci U S A 117, 20890-20897, doi:10.1073/pnas.2003383117 (2020).
40 Gajardo-Vidal, A. et al. Damage to Broca’s area does not contribute to long-term speech production outcome after stroke. Brain 144, 817-832 (2021).
41 Ardila, A., Bernal, B. & Rosselli, M. Why Broca's area damage does not result in classical Broca's aphasia. Frontiers in human neuroscience 10, 249 (2016).
42 Fridriksson, J., Guo, D., Fillmore, P., Holland, A. & Rorden, C. Damage to the anterior arcuate fasciculus predicts non-fluent speech production in aphasia. Brain 136, 3451-3460, doi:10.1093/brain/awt267 (2013).
43 Thiebaut de Schotten, M., Foulon, C. & Nachev, P. Brain disconnections link structural connectivity with function and behaviour. Nat Commun 11, 5094, doi:10.1038/s41467-020-18920-9 (2020).
44 Griffis, J. C., Metcalf, N. V., Corbetta, M. & Shulman, G. L. Lesion Quantification Toolkit: A MATLAB software tool for estimating grey matter damage and white matter disconnections in patients with focal brain lesions. Neuroimage Clin 30, 102639, doi:10.1016/j.nicl.2021.102639 (2021).
45 Greene, C. et al. Finding maximally disconnected subnetworks with shortest path tractography. Neuroimage Clin 23, 101903, doi:10.1016/j.nicl.2019.101903 (2019).
46 Salvalaggio, A., De Filippo De Grazia, M., Zorzi, M., Thiebaut de Schotten, M. & Corbetta, M. Post-stroke deficit prediction from lesion and indirect structural and functional disconnection. Brain 143, 2173-2188, doi:10.1093/brain/awaa156 (2020).
47 Yeh, F. C., Wedeen, V. J. & Tseng, W. Y. Generalized q-sampling imaging. IEEE Trans Med Imaging 29, 1626-1635, doi:10.1109/TMI.2010.2045126 (2010).
48 Schilling, K. G. et al. A fiber coherence index for quality control of B-table orientation in diffusion MRI scans. Magn Reson Imaging 58, 82-89, doi:10.1016/j.mri.2019.01.018 (2019).
49 Yeh, F. C., Verstynen, T. D., Wang, Y., Fernandez-Miranda, J. C. & Tseng, W. Y. Deterministic diffusion fiber tracking improved by quantitative anisotropy. PLoS ONE 8, e80713, doi:10.1371/journal.pone.0080713
PONE-D-13-26801 [pii] (2013).
50 Yeh, F. C. et al. Automatic Removal of False Connections in Diffusion MRI Tractography Using Topology-Informed Pruning (TIP). Neurotherapeutics 16, 52-58, doi:10.1007/s13311-018-0663-y (2019).
51 Towns, J. et al. XSEDE: accelerating scientific discovery. Computing in science & engineering 16, 62-74 (2014).
52 Sokal, R. R. A statistical method for evaluating systematic relationships. Univ. Kansas, Sci. Bull. 38, 1409-1438 (1958).