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Cited by 43Advanced Neuroimaging Techniques and ApplicationsFunctional Brain Connectivity StudiesCerebrovascular and genetic disorders

A comparison of three fiber tract delineation methods and their impact on white matter analysis

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Sydnor, Valerie J., Rivas-Grajales, Ana María, Lyall, Amanda E., Zhang, Fan, Bouix, Sylvain, Karmacharya, Sarina, Shenton, Martha E., Westin, Carl-Fredrik +4 more·2018·NeuroImage
DOI 10.1016/j.neuroimage.2018.05.044PMC PMC6481642
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Abstract

Diffusion magnetic resonance imaging (dMRI) is an important method for studying white matter connectivity in the brain in vivo in both healthy and clinical populations. Improvements in dMRI tractography algorithms, which reconstruct macroscopic three-dimensional white matter fiber pathways, have allowed for methodological advances in the study of white matter; however, insufficient attention has been paid to comparing post-tractography methods that extract white matter fiber tracts of interest from whole-brain tractography. Here we conduct a comparison of three representative and conceptually distinct approaches to fiber tract delineation: 1) a manual multiple region of interest-based approach, 2) an atlas-based approach, and 3) a groupwise fiber clustering approach, by employing methods that exemplify these approaches to delineate the arcuate fasciculus, the middle longitudinal fasciculus, and the uncinate fasciculus in 10 healthy male subjects. We enable qualitative comparisons across methods, conduct quantitative evaluations of tract volume, tract length, mean fractional anisotropy, and true positive and true negative rates, and report measures of intra-method and inter-method agreement. We discuss methodological similarities and differences between the three approaches and the major advantages and drawbacks of each, and review research and clinical contexts for which each method may be most apposite. Emphasis is given to the means by which different white matter fiber tract delineation approaches may systematically produce variable results, despite utilizing the same input tractography and reliance on similar anatomical knowledge.

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  • Several studies have demonstrated advantages of fiber clustering methods over cortical-parcellation-based methods, including more consistent parcellation across subjects and higher reproducibility between test-retest scans.

    “Due to these factors, several studies have demonstrated advantages of fiber clustering methods over the cortical-parcellation-based methods, including more consistent parcellation across subjects (Sydnor et al., 2018; Zhang et al., 2017) and a higher reproducibility between test-retest scans (Zhang et al., 2019).”

    From Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review (2022) · cites this paper as Sydnor et al., 2018

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  1. Avants BB, Epstein CL, Grossman M, Gee JC, 2008 Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med Image Anal 12, 26– 41. 10.1016/j.media.2007.06.004 17659998 PMC2276735 · DOI
  2. Baumgartner C, Michailovich O, Levitt J, Pasternak O, Bouix S, Westin CF, Rathi Y, 2012 A unified tractography framework for comparing diffusion models on clinical scans, in: Computational Diffusion MRI Workshop. Presented at the MICCAI, Nice, pp 27– 32.
  3. Behrens TEJ, Berg HJ, Jbabdi S, Rushworth MFS, Woolrich MW, 2007 Probabilistic diffusion tractography with multiple fibre orientations: What can we gain? Neuroimage 34, 144– 155. 10.1016/j.neuroimage.2006.09.018 17070705 PMC7116582 · DOI · Paper page
  4. Behrens TEJ, Woolrich MW, Jenkinson M, Johansen-Berg H, Nunes RG, Clare S, Matthews PM, Brady JM, Smith SM, 2003 Characterization and propagation of uncertainty in diffusion-weighted MR imaging. Magn Reson Med 50, 1077– 1088. 10.1002/mrm.10609 14587019 · DOI · Paper page
  5. Berman J, 2009 Diffusion MR Tractography As a Tool for Surgical Planning. Magnetic Resonance Imaging Clinics of North America, Clinical Applications of MR Diffusion and Perfusion Imaging 17, 205– 214. 10.1016/j.mric.2009.02.002 19406354 · DOI
  6. Brun A, Knutsson H, Park H-J, Shenton ME, Westin C-F, 2004 Clustering Fiber Traces Using Normalized Cuts, in: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2004, Lecture Notes in Computer Science Presented at the International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, Berlin, Heidelberg, pp. 368– 375. 10.1007/978-3-540-30135-6_45 PMC3296487 20209048 · DOI
  7. Bürgel U, Mädler B, Honey CR, Thron A, Gilsbach J, Coenen VA, 2009 Fiber Tracking with Distinct Software Tools Results in a Clear Diversity in Anatomical Fiber Tract Portrayal. Cen Eur Neurosurg 70, 27– 35. 10.1055/s-0028-1087212 19191204 · DOI
  8. Catani M, Howard RJ, Pajevic S, Jones DK, 2002 Virtual in vivo interactive dissection of white matter fasciculi in the human brain. Neuroimage 17, 77– 94. 10.1006/nimg.2002.1136 12482069 · DOI · Paper page
  9. Catani M, Thiebaut de Schotten M, 2008a A diffusion tensor imaging tractography atlas for virtual in vivo dissections. Cortex 44, 1105– 1132. 10.1016/j.cortex.2008.05.004 18619589 · DOI · Paper page
  10. Catani M, Thiebaut de Schotten M, 2008b A diffusion tensor imaging tractography atlas for virtual in vivo dissections. Cortex 44, 1105– 1132. 10.1016/j.cortex.2008.05.004 18619589 · DOI · Paper page
  11. Chen X, Weigel D, Ganslandt O, Buchfelder M, Nimsky C, 2009 Prediction of visual field deficits by diffusion tensor imaging in temporal lobe epilepsy surgery. Neuroimage 45, 286– 297. 10.1016/j.neuroimage.2008.11.038 19135156 · DOI
  12. Conturo TE, Lori NF, Cull TS, Akbudak E, Snyder AZ, Shimony JS, McKinstry RC, Burton H, Raichle ME, 1999 Tracking neuronal fiber pathways in the living human brain. Proc Natl Acad Sci U S A 96, 10422– 10427. 10468624 PMC17904
  13. Dauguet J, Peled S, Berezovskii V, Delzescaux T, Warfield SK, Born R, Westin C-F, 2007. Comparison of fiber tracts derived from in-vivo DTI tractography with 3D histological neural tract tracer reconstruction on a macaque brain. Neuroimage 37, 530– 538. 10.1016/j.neuroimage.2007.04.067 17604650 · DOI · Paper page
  14. Desikan RS, Segonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, Buckner RL, Dale AM, Maguire RP, Hyman BT, Albert MS, Killiany RJ, 2006 An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31, 968– 980. 10.1016/j.neuroimage.2006.01.021 16530430 · DOI · Paper page
  15. Ding Z, Gore JC, Anderson AW, 2003 Classification and quantification of neuronal fiber pathways using diffusion tensor MRI. Magn Reson Med 49, 716– 721. 10.1002/mrm.10415 12652543 · DOI
  16. Donahue CJ, Sotiropoulos SN, Jbabdi S, Hernandez-Fernandez M, Behrens TE, Dyrby TB, Coalson T, Kennedy H, Knoblauch K, Van Essen DC, Glasser MF, 2016 Using Diffusion Tractography to Predict Cortical Connection Strength and Distance: A Quantitative Comparison with Tracers in the Monkey. J. Neurosci. 36, 6758– 6770. 10.1523/JNEUROSCI.0493-16.2016 27335406 PMC4916250 · DOI · Paper page
  17. Essayed WI, zhang F, Unadkat P, Cosgrove GR, Golby AJ, O’Donnell LJ, 2017 White matter tractography for neurosurgical planning: A topography-based review of the current state of the art. Neuroimage: Clinical 15, 659– 672. 10.1016/j.nicl.2017.06.011 28664037 PMC5480983 · DOI
  18. Fillard P, Descoteaux M, Goh A, Gouttard S, Jeurissen B, Malcolm J, Ramirez-Manzanares A, Reisert M, Sakaie K, Tensaouti F, Yo T, Mangin J-F, Poupon C, 2011 Quantitative evaluation of 10 tractography algorithms on a realistic diffusion MR phantom. Neuroimage 56, 220– 234. 10.1016/j.neuroimage.2011.01.032 21256221 · DOI · Paper page
  19. Fischl B, 2012 FreeSurfer. Neuroimage, 20 YEARS OF fMRI 62, 774– 781. 10.1016/j.neuroimage.2012.01.021 PMC3685476 22248573 · DOI · Paper page
  20. Fischl B, Salat DH, Busa E, Albert M, Dieterich M, Haselgrove C, Van Der Kouwe A, Killiany R, Kennedy D, Klaveness S, Montillo A, Makris N, Rosen B, Dale AM, 2002 Whole brain segmentation: Automated labeling of neuroanatomical structures in the human brain. Neuron 33, 341– 355. 10.1016/S0896-6273(02)00569-X 11832223 · DOI · Paper page
  21. Fischl B, van der Kouwe A, Destrieux C, Halgren E, Segonne F, Salat DH, Busa E, Seidman LJ, Goldstein J, Kennedy D, Caviness V, Makris N, Rosen B, Dale AM, 2004 Automatically parcellating the human cerebral cortex. Cereb. Cortex 14, 11– 22. https://doi-org.ezproxyhost.library.tmc.edu/10.1093/cercor/bhg087 14654453 · DOI
  22. Fritzsche KH, Laun FB, Meinzer H-P, Stieltjes B, 2010 Opportunities and pitfalls in the quantification of fiber integrity: what can we gain from Q-ball imaging? Neuroimage 51, 242– 251. 10.1016/j.neuroimage.2010.02.007 20149879 · DOI
  23. Garyfallidis E, Brett M, Correia MM, Williams GB, Nimmo-Smith I, 2012 QuickBundles, a Method for Tractography Simplification. Front Neurosci 6 10.3389/fnins.2012.00175 PMC3518823 23248578 · DOI
  24. Golby AJ, Kindlmann G, Norton I, Yarmarkovich A, Pieper S, Kikinis R, 2011 Interactive Diffusion Tensor Tractography Visualization for Neurosurgical Planning. Neurosurgery 68, 496– 505. 10.1227/NEU.0b013e3182061ebb 21135713 PMC3112275 · DOI
  25. Gong G, He Y, Concha L, Lebel C, Gross DW, Evans AC, Beaulieu C, 2009 Mapping anatomical connectivity patterns of human cerebral cortex using in vivo diffusion tensor imaging tractography. Cereb. Cortex 19, 524– 536. 10.1093/cercor/bhn102 18567609 PMC2722790 · DOI · Paper page
  26. Guevara P, Duclap D, Poupon C, Marrakchi-Kacem L, Fillard P, Le Bihan D, Leboyer M, Houenou J, Mangin J-F, 2012 Automatic fiber bundle segmentation in massive tractography datasets using a multi-subject bundle atlas. Neuroimage 61, 1083– 1099. 10.1016/j.neuroimage.2012.02.071 22414992 · DOI
  27. Hua K, Zhang J, Wakana S, Jiang H, Li X, Reich DS, Calabresi PA, Pekar JJ, van Zijl PCM, Mori S, 2008 Tract Probability Maps in Stereotaxic Spaces: Analyses of White Matter Anatomy and Tract-Specific Quantification. Neuroimage 39, 336– 347. 10.1016/j.neuroimage.2007.07.053 17931890 PMC2724595 · DOI
  28. Huang H, Zhang J, van Zijl PCM, Mori S, 2004 Analysis of noise effects on DTI-based tractography using the brute-force and multi-ROI approach. Magn Reson Med 52, 559– 565. 10.1002/mrm.20147 15334575 · DOI
  29. Jbabdi S, Woolrich MW, Andersson JLR, Behrens TEJ, 2007 A Bayesian framework for global tractography. Neuroimage 37, 116– 129. 10.1016/j.neuroimage.2007.04.039 17543543 · DOI
  30. Jolles D, Wassermann D, Chokhani R, Richardson J, Tenison C, Bammer R, Fuchs L, Supekar K, Menon V, 2016 Plasticity of left perisylvian white-matter tracts is associated with individual differences in math learning. Brain Struct Funct 221, 1337– 1351. 10.1007/s00429-014-0975-6 25604464 PMC4819785 · DOI
  31. Jonasson L, Bresson X, Thiran JP, Wedeen VJ, Hagmann P, 2007 Representing Diffusion MRI in 5-D Simplifies Regularization and Segmentation of White Matter Tracts. IEEE Transactions on Medical Imaging 26, 1547– 1554. 10.1109/TMI.2007.899168 18041269 · DOI
  32. Jones DK, 2008 Studying connections in the living human brain with diffusion MRI. Cortex 44, 936– 952. 10.1016/j.cortex.2008.05.002 18635164 · DOI
  33. Jones DK, Simmons A, Williams SC, Horsfield MA, 1999 Non-invasive assessment of axonal fiber connectivity in the human brain via diffusion tensor MRI. Magn Reson Med 42, 37– 41. https://doi-org.ezproxyhost.library.tmc.edu/10.1002/(SICI)1522-2594(199907)42:1<37::AID-MRM7>3.0.CO;2-O 10398948 · DOI
  34. Khalsa S, Mayhew SD, Chechlacz M, Bagary M, Bagshaw AP, 2014 The structural and functional connectivity of the posterior cingulate cortex: comparison between deterministic and probabilistic tractography for the investigation of structure-function relationships. Neuroimage 102 Pt 1, 118– 127. 10.1016/j.neuroimage.2013.12.022 24365673 · DOI
  35. Knösche TR, Anwander A, Liptrot M, Dyrby TB, 2015 Validation of tractography: Comparison with manganese tracing. Hum Brain Mapp 36, 4116– 4134. 10.1002/hbm.22902 26178765 PMC5034837 · DOI · Paper page
  36. Kubicki M, McCarley R, Westin C-F, Park H-J, Maier S, Kikinis R, Jolesz FA, Shenton ME, 2007 A review of diffusion tensor imaging studies in schizophrenia. J Psychiatr Res 41, 15– 30. 10.1016/j.jpsychires.2005.05.005 16023676 PMC2768134 · DOI · Paper page
  37. Landis JR, Koch GG, 1977 The measurement of observer agreement for categorical data. Biometrics 33, 159– 174. https://10.1148/radiol.2282011860 843571 · DOI
  38. Lawes INC, Barrick TR, Murugam V, Spierings N, Evans DR, Song M, Clark CA, 2008 Atlas-based segmentation of white matter tracts of the human brain using diffusion tensor tractography and comparison with classical dissection. Neuroimage 39, 62– 79. 10.1016/j.neuroimage.2007.06.041 17919935 · DOI
  39. Lazar M, Alexander AL, Thottakara PJ, Badie B, Field AS, 2006 White Matter Reorganization After Surgical Resection of Brain Tumors and Vascular Malformations. American Journal of Neuroradiology 27, 1258– 1271. 16775277 PMC8133916
  40. Lazar M, Weinstein DM, Tsuruda JS, Hasan KM, Arfanakis K, Meyerand ME, Badie B, Rowley HA, Haughton V, Field A, Alexander AL, 2003 White matter tractography using diffusion tensor deflection. Hum Brain Mapp 18, 306– 321. 10.1002/hbm.10102 12632468 PMC6871932 · DOI
  41. Maffei C, Soria G, Prats-Galino A, Catani M, 2015 Imaging white-matter pathways of the auditory system with diffusion imaging tractography. Handbook of Clinical Neurology, The Human Auditory System 129, 277– 288. 10.1016/B978-0-444-62630-1.00016-0 25726275 · DOI
  42. Maier-Hein KH, Neher PF, Houde J-C, Côté M-A, Garyfallidis E, Zhong J, Chamberland M, Yeh F-C, Lin Y-C, Ji Q, Reddick WE, Glass JO, Chen DQ, Feng Y, Gao C, Wu Y, Ma J, Renjie H, Li Q, Westin C-F, Deslauriers-Gauthier S, González JOO, Paquette M, St-Jean S, Girard G, Rheault F, Sidhu J, Tax CMW, Guo F, Mesri HY, Dávid S, Froeling M, Heemskerk AM, Leemans A, Boré A, Pinsard B, Bedetti C, Desrosiers M, Brambati S, Doyon J, Sarica A, Vasta R, Cerasa A, Quattrone A, Yeatman J, Khan AR, Hodges W, Alexander S, Romascano D, Barakovic M, Auría A, Esteban O, Lemkaddem A, Thiran J-P, Cetingul HE, Odry BL, Mailhe B, Nadar MS, Pizzagalli F, Prasad G, Villalon-Reina JE, Galvis J, Thompson PM, Requejo FDS, Laguna PL, Lacerda LM, Barrett R, Dell’Acqua F, Catani M, Petit L, Caruyer E, Daducci A, Dyrby TB, Holland-Letz T, Hilgetag CC, Stieltjes B, Descoteaux M, 2017 The challenge of mapping the human connectome based on diffusion tractography. Nature Communications 8, 1349 10.1038/s41467-017-01285-x PMC5677006 29116093 · DOI
  43. Makris N, Kennedy DN, Mclnerney S, Sorensen AG, Wang R, Caviness VS, Pandya DN, 2005 Segmentation of subcomponents within the superior longitudinal fascicle in humans: a quantitative, in vivo, DT-MRI study. Cereb. Cortex 15, 854– 869. 10.1093/cercor/bhh186 15590909 · DOI · Paper page
  44. Makris N, Pandya DN, 2009 The extreme capsule in humans and rethinking of the language circuitry. Brain Struct Funct 213, 343– 358. 10.1007/s00429-008-0199-8 19104833 PMC3777634 · DOI
  45. Makris N, Pandya DN, Normandin JJ, Papadimitriou GM, Rauch SL, Caviness VS, Kennedy DN, 2002 Quantitative DT-MRI Investigations of the Human Cingulum Bundle. CNS Spectrums 7, 522– 528. 10.1017/S1092852900018071 · DOI
  46. Makris N, Papadimitriou GM, Sorg S, Kennedy DN, Caviness VS, Pandya DN, 2007 The occipitofrontal fascicle in humans: a quantitative, in vivo, DT-MRI study. Neuroimage 37, 1100– 1111. 10.1016/j.neuroimage.2007.05.042 17681797 PMC3769215 · DOI
  47. Makris N, Preti MG, Asami T, Pelavin P, Campbell B, Papadimitriou GM, Kaiser J, Baselli G, Westin CF, Shenton ME, Kubicki M, 2013a Human middle longitudinal fascicle: variations in patterns of anatomical connections. Brain Struct Funct 218, 951– 968. 10.1007/s00429-012-0441-2 22782432 PMC3500586 · DOI
  48. Makris N, Preti MG, Wassermann D, Rathi Y, Papadimitriou GM, Yergatian C, Dickerson BC, Shenton ME, Kubicki M, 2013b Human middle longitudinal fascicle: segregation and behavioral-clinical implications of two distinct fiber connections linking temporal pole and superior temporal gyrus with the angular gyrus or superior parietal lobule using multi-tensor tractography. Brain Imaging Behav 7, 335– 352. 10.1007/s11682-013-9235-2 23686576 PMC3830590 · DOI
  49. Makris N, Worth AJ, Sorensen AG, Papadimitriou GM, Wu O, Reese TG, Wedeen VJ, Davis TL, Stakes JW, Caviness VS, Kaplan E, Rosen BR, Pandya DN, Kennedy DN, 1997 Morphometry of in vivo human white matter association pathways with diffusion-weighted magnetic resonance imaging. Ann. Neurol. 42, 951– 962. 10.1002/ana.410420617 9403488 · DOI
  50. Malcolm JG, Shenton ME, Rathi Y, 2010 Filtered multi-tensor tractography. IEEE Transactions on Medical Imaging 29, 1664– 1675. 10.1109/TMI.2010.2048121. Filtered 20805043 PMC3045040 · DOI · Paper page
  51. Malcolm JG, Shenton ME, Rathi Y, 2009 Neural tractography using an unscented Kalman filter. Inf Process Med Imaging 21, 126– 138. 19694258 PMC2768602
  52. Malykhin N, Concha L, Seres P, Beaulieu C, Coupland NJ, 2008 Diffusion tensor imaging tractography and reliability analysis for limbic and paralimbic white matter tracts. Psychiatry Res 164, 132– 142. 10.1016/j.pscychresns.2007.11.007 18945599 · DOI
  53. Mori S, Crain BJ, Chacko VP, van Zijl PC, 1999 Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging. Ann. Neurol. 45, 265– 269. 9989633
  54. Mori S, Kaufmann WE, Davatzikos C, Stieltjes B, Amodei L, Fredericksen K, Pearlson GD, Melhem ER, Solaiyappan M, Raymond GV, Moser HW, van Zijl PCM, 2002 Imaging cortical association tracts in the human brain using diffusion-tensor-based axonal tracking. Magn Reson Med 47, 215– 223. https://doi-org.ezproxyhost.library.tmc.edu/10.1002/mrm.10074 11810663 · DOI
  55. Mori S, C M van Zijl P, Oishi K, Faria AV, 2011 MRI Atlas of Human White Matter, 2nd ed Academic Press.
  56. Nazem-Zadeh M-R, Chapman CH, Lawrence TL, Tsien CI, Cao Y, 2012 Radiation therapy effects on white matter fiber tracts of the limbic circuit. Med Phys 39, 5603– 5613. 10.1118/1.4745560 22957626 PMC3436921 · DOI
  57. Nazem-Zadeh M-R, Davoodi-Bojd E, Soltanian-Zadeh H, 2011 Atlas-based fiber bundle segmentation using principal diffusion directions and spherical harmonic coefficients. Neuroimage 54 Suppl 1, S146– 164. 10.1016/j.neuroimage.2010.09.035 20869453 · DOI
  58. Norton I, Essayed WI, Zhang F, Pujol S, Yarmarkovich A, Golby AJ, Kindlmann G, Wassermann D, Estepar RSJ, Rathi Y, Pieper S, Kikinis R, Johnson HJ, Westin C-F, O’Donnell LJ, 2017 SlicerDMRI: Open Source Diffusion MRI Software for Brain Cancer Research. Cancer Res. 77, e101– e103. 10.1158/0008-5472.CAN-17-0332 29092950 PMC5679308 · DOI
  59. O’Donnell LJ, Golby AJ, Westin C-F, 2013 Fiber clustering versus the parcellation-based connectome. Neuroimage 80, 283– 289. 10.1016/j.neuroimage.2013.04.066 23631987 PMC3731058 · DOI · Paper page
  60. O’Donnell LJ, Suter Y, Rigolo L, Kahali P, Zhang F, Norton I, Albi A, Olubiyi O, Meola A, Essayed WI, Unadkat P, Ciris PA, Wells WM, Rathi Y, Westin C-F, Golby AJ, 2016 Automated white matter fiber tract identification in patients with brain tumors. Neuroimage Clin 13, 138– 153. 10.1016/j.nicl.2016.11.023 27981029 PMC5144756 · DOI
  61. O’Donnell LJ, Wells WM, Golby AJ, Westin C-F, 2012 Unbiased groupwise registration of white matter tractography. Med Image Comput Comput Assist Interv 15, 123– 130. 23286122 PMC3638882
  62. O’Donnell LJ, Westin CF, 2007 Automatic Tractography Segmentation Using a High-Dimensional White Matter Atlas. IEEE Transactions on Medical Imaging 26, 1562– 1575. 10.1109/TMI.2007.906785 18041271 · DOI
  63. Oishi K, Faria A, Jiang H, Li X, Akhter K, Zhang J, Hsu JT, Miller MI, van Zijl PCM, Albert M, Lyketsos CG, Woods R, Toga AW, Pike GB, Rosa-Neto P, Evans A, Mazziotta J, Mori S, 2009 Atlas-based whole brain white matter analysis using large deformation diffeomorphic metric mapping: application to normal elderly and Alzheimer’s disease participants. Neuroimage 46, 486– 499. 19385016 PMC2885858
  64. Parker GJ., Alexander DC, 2005 Probabilistic anatomical connectivity derived from the microscopic persistent angular structure of cerebral tissue. Philos Trans R Soc Lond B Biol Sci 360, 893– 902. 10.1098/rstb.2005.1639 16087434 PMC1854923 · DOI
  65. Parker GJM, Haroon HA, Wheeler-Kingshott CAM, 2003 A framework for a streamline-based probabilistic index of connectivity (PICo) using a structural interpretation of MRI diffusion measurements. J Magn Reson Imaging 18, 242– 254. 10.1002/jmri.10350 12884338 · DOI · Paper page
  66. Poupon C, Rieul B, Kezele I, Perrin M, Poupon F, Mangin J-F, 2008 New diffusion phantoms dedicated to the study and validation of high-angular-resolution diffusion imaging (HARDI) models. Magn Reson Med 60, 1276– 1283. 10.1002/mrm.21789 19030160 · DOI
  67. Psomiades M, Fonteneau C, Mondino M, Luck D, Haesebaert F, Suaud-Chagny M-F, Brunelin J, 2016 Integrity of the arcuate fasciculus in patients with schizophrenia with auditory verbal hallucinations: A DTI-tractography study. NeuroImage: Clinical 12, 970– 975. 10.1016/j.nicl.2016.04.013 27995063 PMC5153606 · DOI
  68. Pujol S, Wells W, Pierpaoli C, Brun C, Gee J, Cheng G, Vemuri B, Commowick O, Prima S, Stamm A, Goubran M, Khan A, Peters T, Neher P, Maier-Hein KH, Shi Y, Tristan-Vega A, Veni G, Whitaker R, Styner M, Westin C-F, Gouttard S, Norton I, Chauvin L, Mamata H, Gerig G, Nabavi A, Golby A, Kikinis R, 2015 The DTI Challenge: Toward Standardized Evaluation of Diffusion Tensor Imaging Tractography for Neurosurgery. J Neuroimaging 25, 875– 882. https://doi.org/10.nn/jon.12283 26259925 PMC4641305
  69. Pullens P, Roebroeck A, Goebel R, 2010 Ground truth hardware phantoms for validation of diffusion-weighted MRI applications. J Magn Reson Imaging 32, 482– 488. 10.1002/jmri.22243 20677281 · DOI
  70. Rijken BFM, Leemans A, Lucas Y, Montfort K van, Mathijssen IMJ, Lequin MH, 2015 Diffusion Tensor Imaging and Fiber Tractography in Children with Craniosynostosis Syndromes. American Journal of Neuroradiology 36, 1558– 1564. 10.3174/ajnr.A4301 25953762 PMC7964691 · DOI
  71. Rojkova K, Volle E, Urbanski M, Humbert F, Dell’Acqua F, Thiebaut de Schotten M, 2016 Atlasing the frontal lobe connections and their variability due to age and education: a spherical deconvolution tractography study. Brain Struct Funct 221, 1751– 1766. 10.1007/s00429-015-1001-3 25682261 · DOI · Paper page
  72. Ros C, Güllmar D, Stenzel M, Mentzel H-J, Reichenbach JR, 2013 Atlas-Guided Cluster Analysis of Large Tractography Datasets. PLOS ONE 8, e83847 10.1371/journal.pone.0083847 24386292 PMC3875498 · DOI
  73. Rousson M, Lenglet C, Deriche R, 2004 Level Set and Region Based Surface Propagation for Diffusion Tensor MRI Segmentation, in: Sonka M, Kakadiaris IA, Kybic J (Eds.), Computer Vision and Mathematical Methods in Medical and Biomedical Image Analysis. Springer Berlin Heidelberg, pp. 123– 134.
  74. Seehaus AK, Roebroeck A, Chiry O, Kim D-S, Ronen I, Bratzke H, Goebel R, Galuske RAW, 2013 Histological validation of DW-MRI tractography in human postmortem tissue. Cereb. Cortex 23, 442– 450. 10.1093/cercor/bhs036 22345356 PMC3584953 · DOI · Paper page
  75. Seitz J, Zuo JX, Lyall AE, Makris N, Kikinis Z, Bouix S, Pasternak O, Fredman E, Duskin J, Goldstein JM, Petryshen TL, Mesholam-Gately RI, Wojcik J, McCarley RW, Seidman LJ, Shenton ME, Koerte IK, Kubicki M, 2016 Tractography Analysis of 5 White Matter Bundles and Their Clinical and Cognitive Correlates in Early-Course Schizophrenia. Schizophr Bull 42, 762– 771. 10.1093/schbul/sbv171 27009248 PMC4838095 · DOI
  76. Shaffer JJ, Ghayoor A, Long JD, Kim RE-Y, Lourens S, O’Donnell LJ, Westin C-F, Rathi Y, Magnotta V, Paulsen JS, Johnson HJ, 2017 Longitudinal Diffusion Changes in Prodromal and Early HD: Evidence of White-Matter Tract Deterioration. Hum Brain Mapp 38, 1460– 1477. 10.1002/hbm.23465 28045213 PMC5400101 · DOI
  77. Shenton ME, Hamoda HM, Schneiderman JS, Bouix S, Pasternak O, Rathi Y, Vu M-A, Purohit MP, Helmer K, Koerte I, Lin AP, Westin C-F, Kikinis R, Kubicki M, Stern RA, Zafonte R, 2012 A review of magnetic resonance imaging and diffusion tensor imaging findings in mild traumatic brain injury. Brain Imaging Behav 6, 137– 192. 10.1007/s11682-012-9156-5 22438191 PMC3803157 · DOI
  78. Sherbondy A, Akers D, Mackenzie R, Dougherty R, Wandell B, 2005 Exploring connectivity of the brain’s white matter with dynamic queries. IEEE Transactions on Visualization and Computer Graphics 11, 419– 430. 10.1109/TVCG.2005.59 16138552 · DOI
  79. Sotiropoulos SN, Behrens TEJ, Jbabdi S, 2012 Ball and rackets: Inferring fiber fanning from diffusion-weighted MRI. NeuroImage 60, 1412– 1425. 10.1016/j.neuroimage.2012.01.056 22270351 PMC3304013 · DOI · Paper page
  80. Spena G, Nava A, Cassini F, Pepoli A, Bruno M, D’Agata F, Cauda F, Sacco K, Duca S, Barletta L, Versari P, 2010 Preoperative and intraoperative brain mapping for the resection of eloquent-area tumors. A prospective analysis of methodology, correlation, and usefulness based on clinical outcomes. Acta Neurochir (Wien) 152, 1835– 1846. 10.1007/s00701-010-0764-9 20730457 · DOI
  81. Suarez RO, Commowick O, Prabhu SP, Warfield SK, 2012 Automated delineation of white matter fiber tracts with a multiple region-of-interest approach. Neuroimage 59, 3690– 3700. 10.1016/j.neuroimage.2011.11.043 22155046 PMC3302580 · DOI
  82. Sullivan EV, Pfefferbaum A, 2006 Diffusion tensor imaging and aging. Neurosci Biobehav Rev 30, 749– 761. 10.1016/j.neubiorev.2006.06.002 16887187 · DOI · Paper page
  83. Tensaouti F, Lahlou I, Clarisse P, Lotterie JA, Berry I, 2011 Quantitative and reproducibility study of four tractography algorithms used in clinical routine. J Magn Reson Imaging 34, 165– 172. 10.1002/jmri.22584 21618329 · DOI
  84. Tournier J-Donald, Calamante Fernando, Connelly Alan, 2012 MRtrix: Diffusion tractography in crossing fiber regions. International Journal of Imaging Systems and Technology 22, 53– 66. 10.1002/ima.22005 · DOI · Paper page
  85. Truong T-K, Guidon A, Song AW, 2014 Cortical Depth Dependence of the Diffusion Anisotropy in the Human Cortical Gray Matter In Vivo. PLoS One 9 10.1371/journal.pone.0091424 PMC3946789 24608869 · DOI
  86. Tylee DS, Kikinis Z, Quinn TP, Antshel KM, Fremont W, Tahir MA, Zhu A, Gong X, Glatt SJ, Coman IL, Shenton ME, Kates WR, Makris N, 2017 Machine-learning classification of 22q11.2 deletion syndrome: A diffusion tensor imaging study. NeuroImage: Clinical 15, 832– 842. 10.1016/j.nicl.2017.04.029 28761808 PMC5522376 · DOI
  87. Varentsova A, Zhang S, Arfanakis K, 2014 Development of a high angular resolution diffusion imaging human brain template. Neuroimage 91, 177– 186. 10.1016/j.neuroimage.2014.01.009 24440528 PMC3965645 · DOI
  88. Voineskos AN, O’Donnell LJ, Lobaugh NJ, Markant D, Ameis SH, Niethammer M, Mulsant BH, Pollock BG, Kennedy JL, Westin CF, Shenton ME, 2009 Quantitative examination of a novel clustering method using magnetic resonance diffusion tensor tractography. Neuroimage 45, 370– 376. 10.1016/j.neuroimage.2008.12.028 19159690 PMC2646811 · DOI
  89. Wakana S, Caprihan A, Panzenboeck MM, Fallon JH, Perry M, Gollub RL, Hua K, Zhang J, Jiang H, Dubey P, Blitz A, van Zijl P, Mori S, 2007 Reproducibility of quantitative tractography methods applied to cerebral white matter. Neuroimage 36, 630– 644. 10.1016/j.neuroimage.2007.02.049 17481925 PMC2350213 · DOI · Paper page
  90. Wakana S, Jiang H, Nagae-Poetscher LM, van Zijl PCM, Mori S, 2004 Fiber tract-based atlas of human white matter anatomy. Radiology 230, 77– 87. 10.1148/radiol.2301021640 14645885 · DOI
  91. Wang F, Jackowski M, Kalmar JH, Chepenik LG, Tie K, Qiu M, Gong G, Pittman BP, Jones MM, Shah MP, Spencer L, Papademetris X, Constable RT, Blumberg HP, 2008 Abnormal anterior cingulum integrity in bipolar disorder determined through diffusion tensor imaging. Br J Psychiatry 193, 126– 129. 10.1192/bjp.bp.107.048793 18669996 PMC2732002 · DOI
  92. Wang Y, Fernández-Miranda JC, Verstynen T, Pathak S, Schneider W, Yeh F-C, 2013 Rethinking the role of the middle longitudinal fascicle in language and auditory pathways. Cereb. Cortex 23, 2347– 2356. 10.1093/cercor/bhs225 22875865 · DOI
  93. Warfield SK, Zou KH, Wells WM, 2004 Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation. IEEE Trans Med Imaging 23, 903– 921. 10.1109/TMI.2004.828354 15250643 PMC1283110 · DOI · Paper page
  94. Wassermann D, Bloy L, Kanterakis E, Verma R, Deriche R, 2010. Unsupervised White Matter Fiber Clustering and Tract Probability Map Generation: Applications of a Gaussian Process framework for white matter fibers. Neuroimage 51, 228– 241. 10.1016/j.neuroimage. 2010.01.004 20079439 PMC2847030 · DOI
  95. Wassermann D, Makris N, Rathi Y, Shenton M, Kikinis R, Kubicki M, Westin C-F, 2016 The white matter query language: a novel approach for describing human white matter anatomy. Brain Struct Funct 221, 4705– 4721. 10.1007/s00429-015-1179-4 26754839 PMC4940319 · DOI
  96. Xu Q, Anderson AW, Gore JC, Ding Z, 2013 Gray matter parcellation constrained full brain fiber bundling with diffusion tensor imaging. Med Phys 40, 072301 10.1118/1.4811155 23822449 PMC7003478 · DOI
  97. Yendiki A, Panneck P, Srinivasan P, Stevens A, Zöllei L, Augustinack J, Wang R, Salat D, Ehrlich S, Behrens T, Jbabdi S, Gollub R, Fischl B, 2011 Automated Probabilistic Reconstruction of White-Matter Pathways in Health and Disease Using an Atlas of the Underlying Anatomy. Front Neuroinform 5 10.3389/fninf.20n.00023 PMC3193073 22016733 · DOI
  98. Yogarajah M, Focke NK, Bonelli S, Cercignani M, Acheson J, Parker GJM, Alexander DC, McEvoy AW, Symms MR, Koepp MJ, Duncan JS, 2009 Defining Meyer’s loop–temporal lobe resections, visual field deficits and diffusion tensor tractography. Brain 132, 1656– 1668. 10.1093/brain/awp114 19460796 PMC2685925 · DOI
  99. Yoo SW, Guevara P, Jeong Y, Yoo K, Shin JS, Mangin J-F, Seong J-K, 2015 An Example-Based Multi-Atlas Approach to Automatic Labeling of White Matter Tracts. PLoS ONE 10, e0133337 10.1371/journal.pone.0133337 26225419 PMC4520495 · DOI
  100. Zhang F, Savadjiev P, Cai W, Song Y, Rathi Y, Tunç B, Parker D, Kapur T, Schultz RT, Makris N, Verma R, O’Donnell LJ, 2017 Whole brain white matter connectivity analysis using machine learning: An application to autism. Neuroimage. 10.1016/j.neuroimage.2017.10.029 PMC5910272 29079524 · DOI
  101. Zhang H, Awatea SP, Das SR, Woo JH, Melhem ER, Gee JC, Yushkevich PA, 2010 A tract-specific framework for white matter morphometry combining macroscopic and microscopic tract features. Med Image Anal 14, 666– 673. 10.1016/j.media.2010.05.002 20547469 PMC2910320 · DOI
  102. Zhang W, Olivi A, Hertig SJ, van Zijl P, Mori S, 2008 Automated fiber tracking of human brain white matter using diffusion tensor imaging. Neuroimage 42, 771– 777. 10.1016/j.neuroimage.2008.04.241 18554930 PMC2585359 · DOI
  103. Zhukov Leonid, Museth Ken, Breen David E., Whitaker Ross T., Barr Alan H., 2003 Level set modeling and segmentation of diffusion tensor magnetic resonance imaging brain data. Journal of Electronic Imaging 12 10.1117/1.1527628 · DOI

Cited by

Sorted by citations

Authors
Sydnor, V. J., Rivas-Grajales, A. M., Lyall, A. E., Zhang, F., Bouix, S., Karmacharya, S., Shenton, M. E., Westin, C.-F., Makris, N., Wassermann, D., O'Donnell, L. J., & Kubicki, M.
Affiliations
Brigham and Women's Hospital (US)
Corresponding author
Kubicki, Marek
ORCID
Venue
NeuroImage
Publisher
Elsevier BV
Published
2018-09
Volume
178
Pages
318-331
ISSN
1053-8119 (ISSN-L)
DOI
10.1016/j.neuroimage.2018.05.044
PMC
PMC6481642
OpenAlex
W2803568155
Cited by
43
References
10
Citations by year
2026: 2 · 2025: 6 · 2024: 4 · 2023: 6 · 2022: 6 · 2021: 6 · 2020: 5 · 2019: 6 · 2018: 2
FWCI
3.28
Type
journal article
Language
en
Open access
Licence
Locations
  • NeuroImage (publishedVersion)
  • PubMed (publishedVersion)
  • Europe PMC (PubMed Central) (acceptedVersion)
  • PubMed Central (acceptedVersion)
  • HAL (submittedVersion, other-oa)
  • Espace ÉTS (ETS) (submittedVersion)
Funders
  • European Research Council
  • Horizon 2020 Framework Programme (757672)
  • National Institutes of Health (R01MH112748, U01CA199459, K24MH110807, R01MH074794, P41EB015902, P41EB015898, R01MH102377, T32MH016259-35, R01AG042512)
Topics
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
  • Health Sciences › Medicine › Neurology › Cerebrovascular and genetic disorders
Keywords
white matter tracts, diffusion MRI tractography, arcuate fasciculus, middle longitudinal fasciculus, uncinate fasciculus, inter-method agreement, tract volume, fractional anisotropy, white matter connectivity
MeSH terms
Brain, Brain Mapping, Humans, Image Processing, Computer-Assisted, Male, Diffusion Tensor Imaging, White Matter

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