Avena-Koenigsberger A, Mišić B, Hawkins RXD et al (2017) Path ensembles and a tradeoff between communication efficiency and resilience in the human connectome. Brain Struct Funct 222:603–618. https://doi.org/10.1007/s00429-016-1238-5

Article  PubMed  Google Scholar 

Bajada CJ, Schreiber J, Caspers S (2019) Fiber length profiling: A novel approach to structural brain organization. NeuroImage 186:164–173. https://doi.org/10.1016/j.neuroimage.2018.10.070

Article  PubMed  Google Scholar 

Betzel RF, Griffa A, Hagmann P, Mišić B (2019) Distance-dependent consensus thresholds for generating group-representative structural brain networks. Netw Neurosci 3:475–496. https://doi.org/10.1162/netn_a_00075

Article  PubMed  PubMed Central  Google Scholar 

Bullmore E, Sporns O (2009) Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci 10:186–198. https://doi.org/10.1038/nrn2575

Article  PubMed  CAS  Google Scholar 

Chamberland M, Tax CMW, Jones DK (2018) Meyer’s loop tractography for image-guided surgery depends on imaging protocol and hardware. NeuroImage Clin 20:458–465. https://doi.org/10.1016/j.nicl.2018.08.021

Article  PubMed  PubMed Central  Google Scholar 

Cieslak M, Cook PA, He X et al (2021) QSIPrep: an integrative platform for preprocessing and reconstructing diffusion MRI data. Nat Methods 18:775–778. https://doi.org/10.1038/s41592-021-01185-5

Article  PubMed  PubMed Central  CAS  Google Scholar 

Daducci A (2024) Chap. 16 - Global Tractography. Handbook of Diffusion MR Tractography: Imaging Methods, Biophysical Models, Algorithms and Applications. Academic Press. https://doi.org/10.1016/B978-0-12-818894-1.00014-8

de Lange SC, van den Scholtens LH et al (2019) Shared vulnerability for connectome alterations across psychiatric and neurological brain disorders. Nat Hum Behav 3:988–998. https://doi.org/10.1038/s41562-019-0659-6

Article  PubMed  Google Scholar 

Dell’Acqua F, Descoteaux M, Leemans A (2024) Handbook of Diffusion MR Tractography: Imaging Methods, Biophysical Models, Algorithms and Applications. Academic Press. ISBN 978-0-12-818895-8

Dhollander T, Clemente A, Singh M et al (2021) Fixel-based analysis of diffusion MRI: methods, applications, challenges and opportunities. NeuroImage 241:118417. https://doi.org/10.1016/j.neuroimage.2021.118417

Article  PubMed  Google Scholar 

Donahue CJ, Sotiropoulos SN, Jbabdi S et al (2016) Using diffusion tractography to predict cortical connection strength and distance: A quantitative comparison with tracers in the monkey. J Neurosci 36:6758–6770. https://doi.org/10.1523/JNEUROSCI.0493-16.2016

Article  PubMed  PubMed Central  CAS  Google Scholar 

Fotiadis P, Parkes L, Davis KA et al (2024) Structure–function coupling in macroscale human brain networks. Nat Rev Neurosci 25:688–704. https://doi.org/10.1038/s41583-024-00846-6

Article  PubMed  CAS  Google Scholar 

Gast H, Assaf Y (2024) Weighting the structural connectome: exploring its impact on network properties and predicting cognitive performance in the human brain. Netw Neurosci 8:119–137. https://doi.org/10.1162/netn_a_00342

Article  PubMed  PubMed Central  Google Scholar 

Giampiccolo D, Herbet G, Duffau H (2025) The inferior fronto-occipital fasciculus: bridging phylogeny, ontogeny and functional anatomy. Brain 148:1507–1525. https://doi.org/10.1093/brain/awaf055

Article  PubMed  PubMed Central  Google Scholar 

Girard G, Whittingstall K, Deriche R, Descoteaux M (2014) Towards quantitative connectivity analysis: reducing tractography biases. NeuroImage 98:266–278. https://doi.org/10.1016/j.neuroimage.2014.04.074

Article  PubMed  Google Scholar 

Girard G, Caminiti R, Battaglia-Mayer A et al (2020) On the cortical connectivity in the macaque brain: A comparison of diffusion tractography and histological tracing data. NeuroImage 221:117201. https://doi.org/10.1016/j.neuroimage.2020.117201

Article  PubMed  Google Scholar 

Griffa A, Baumann PS, Thiran J-P, Hagmann P (2013) Structural connectomics in brain diseases. NeuroImage 80:515–526. https://doi.org/10.1016/j.neuroimage.2013.04.056

Article  PubMed  Google Scholar 

Hau J, Sarubbo S, Perchey G et al (2016) Cortical terminations of the inferior Fronto-Occipital and uncinate fasciculi: anatomical Stem-Based virtual dissection. https://doi.org/10.3389/fnana.2016.00058. Front Neuroanat 10:

Lipp I, Parker GD, Tallantyre EC et al (2020) Tractography in the presence of multiple sclerosis lesions. NeuroImage 209:116471. https://doi.org/10.1016/j.neuroimage.2019.116471

Article  PubMed  Google Scholar 

Maier-Hein KH, Neher PF, Houde J-C et al (2017) The challenge of mapping the human connectome based on diffusion tractography. Nat Commun 8:1349. https://doi.org/10.1038/s41467-017-01285-x

Article  PubMed  PubMed Central  CAS  Google Scholar 

Mansour LS, Tian Y, Yeo BTT et al (2021) High-resolution connectomic fingerprints: mapping neural identity and behavior. NeuroImage 229:117695. https://doi.org/10.1016/j.neuroimage.2020.117695

Article  Google Scholar 

Rheault F, St-Onge E, Sidhu J et al (2019) Bundle-specific tractography with incorporated anatomical and orientational priors. NeuroImage 186:382–398. https://doi.org/10.1016/j.neuroimage.2018.11.018

Article  PubMed  Google Scholar 

Schiavi S, Ocampo-Pineda M, Barakovic M et al (2020) A new method for accurate in vivo mapping of human brain connections using microstructural and anatomical information. Sci Adv 6:eaba8245. https://doi.org/10.1126/sciadv.aba8245

Article  PubMed  PubMed Central  Google Scholar 

Schiavi S, Lu P-J, Weigel M et al (2022) Bundle Myelin fraction (BMF) mapping of different white matter connections using microstructure informed tractography. NeuroImage 249:118922. https://doi.org/10.1016/j.neuroimage.2022.118922

Article  PubMed  CAS  Google Scholar 

Schilling KG, Nath V, Hansen C et al (2019) Limits to anatomical accuracy of diffusion tractography using modern approaches. NeuroImage 185:1–11. https://doi.org/10.1016/j.neuroimage.2018.10.029

Article  PubMed  Google Scholar 

Schirmer MD, Arichi T, Chung AW (2023) Connectome Analysis: Characterization, Methods, and Analysis. Academic Press. ISBN 978-0-323-85281-4

Schurr R, Duan Y, Norcia AM et al (2018) Tractography optimization using quantitative T1 mapping in the human optic radiation. NeuroImage 181:645–658. https://doi.org/10.1016/j.neuroimage.2018.06.060

Article  PubMed  Google Scholar 

Shastin D, Genc S, Parker GD et al (2022) Surface-based tracking for short association fibre tractography. NeuroImage 260:119423. https://doi.org/10.1016/j.neuroimage.2022.119423

Article  PubMed  Google Scholar 

Smith RE, Raffelt D, Tournier J-D, Connelly A (2022) Quantitative streamlines tractography: methods and inter-subject normalisation | published in aperture neuro. Aperture Neuro 2:1–25. https://doi.org/10.52294/ApertureNeuro.2022.2.NEOD9565

Article  Google Scholar 

Tax CMW, Bastiani M, Veraart J et al (2022) What’s new and what’s next in diffusion MRI preprocessing. NeuroImage 249:118830. https://doi.org/10.1016/j.neuroimage.2021.118830

Article  PubMed  Google Scholar 

Tournier J-D, Calamante F, Connelly A (2012) MRtrix: diffusion tractography in crossing fiber regions. Int J Imaging Syst Technol 22:53–66. https://doi.org/10.1002/ima.22005

Article  Google Scholar 

Zalesky A, Fornito A, Harding IH et al (2010) Whole-brain anatomical networks: does the choice of nodes matter? NeuroImage. 50:970–983. https://doi.org/10.1016/j.neuroimage.2009.12.027

Zalesky A, Fornito A, Cocchi L et al (2016) Connectome sensitivity or specificity: which is more important? NeuroImage 142:407–420. https://doi.org/10.1016/j.neuroimage.2016.06.035

Article  PubMed  Google Scholar 

Zhang F, Daducci A, He Y et al (2022) Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: a review. https://doi.org/10.1016/j.neuroimage.2021.118870. NeuroImage 118870