The Seven Deadly Sins of Measuring Brain Structural Connectivity Using Diffusion MRI Streamlines Fibre-Tracking
Abstract
:1. Introduction
2. Seven Deadly Sins
2.1. Model to Estimate Local Fibre Directions—Deadly Sin 1
2.2. Consistent Results Do Not Mean Accurate Results—Deadly Sin 2
2.3. Acquisition Protocol for Fibre-Tracking Applications—Deadly Sin 3
2.4. Criteria for Streamline Termination—Deadly Sin 4
2.5. Image Distortions—Deadly Sin 5
2.6. Quantification of Connectivity—Deadly Sin 6
2.7. Binarisation of The Connectome—Deadly Sin 7
3. Conclusions
Funding
Acknowledgments
Conflicts of Interest
Abbreviations
ACT | Anatomically Constrained Tractography |
CSD | Constrained Spherical Deconvolution |
CMC | Continuous Map Criterion |
DTI | Diffusion Tensor Imaging |
FOD | Fibre Orientation Distribution |
fODF | Fibre Orientation Distribution Function |
HARDI | High Angular Resolution Diffusion Imaging |
LiFE | Linear Fascicle Evaluation |
ODF | Orientation Distribution Function |
SIFT2 | Spherical-deconvolution Informed Filtering of Tractograms 2 |
References
- Jones, D.K. Diffusion MRI; Oxford University Press: New York, NY, USA, 2010; ISBN 978-0-19-970870-3. [Google Scholar]
- Basser, P.J. Inferring microstructural features and the physiological state of tissues from diffusion-weighted images. NMR Biomed. 1995, 8, 333–344. [Google Scholar] [CrossRef]
- Basser, P.J.; Mattiello, J.; LeBihan, D. MR diffusion tensor spectroscopy and imaging. Biophys. J. 1994, 66, 259–267. [Google Scholar] [CrossRef] [Green Version]
- Jeurissen, B.; Descoteaux, M.; Mori, S.; Leemans, A. Diffusion MRI fiber tractography of the brain. NMR Biomed. 2019, 32, e3785. [Google Scholar] [CrossRef]
- Conturo, T.E.; Lori, N.F.; Cull, T.S.; Akbudak, E.; Snyder, A.Z.; Shimony, J.S.; McKinstry, R.C.; Burton, H.; Raichle, M.E. Tracking neuronal fiber pathways in the living human brain. Proc. Natl. Acad. Sci. U.S.A. 1999, 96, 10422–10427. [Google Scholar] [CrossRef] [Green Version]
- Mori, S.; Crain, B.J.; Chacko, V.P.; van Zijl, P.C. Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging. Ann. Neurol. 1999, 45, 265–269. [Google Scholar] [CrossRef]
- Jones, D.K.; Simmons, A.; Williams, S.C.; Horsfield, M.A. Non-invasive assessment of axonal fiber connectivity in the human brain via diffusion tensor MRI. Magn. Reson. Med. 1999, 42, 37–41. [Google Scholar] [CrossRef]
- Parker, G.J.M.; Haroon, H.A.; Wheeler-Kingshott, C.A.M. A framework for a streamline-based probabilistic index of connectivity (PICo) using a structural interpretation of MRI diffusion measurements. J. Magn. Reson. Imaging 2003, 18, 242–254. [Google Scholar] [CrossRef]
- Behrens, T.E.J.; Woolrich, M.W.; Jenkinson, M.; Johansen-Berg, H.; Nunes, R.G.; Clare, S.; Matthews, P.M.; Brady, J.M.; Smith, S.M. Characterization and propagation of uncertainty in diffusion-weighted MR imaging. Magn. Reson. Med. 2003, 50, 1077–1088. [Google Scholar] [CrossRef]
- Jones, D.K. Tractography gone wild: Probabilistic fibre tracking using the wild bootstrap with diffusion tensor MRI. IEEE Trans. Med. Imaging 2008, 27, 1268–1274. [Google Scholar] [CrossRef]
- Lazar, M.; Alexander, A.L. Bootstrap white matter tractography (BOOT-TRAC). Neuroimage 2005, 24, 524–532. [Google Scholar] [CrossRef]
- Berman, J.I.; Chung, S.; Mukherjee, P.; Hess, C.P.; Han, E.T.; Henry, R.G. Probabilistic streamline q-ball tractography using the residual bootstrap. Neuroimage 2008, 39, 215–222. [Google Scholar] [CrossRef]
- Tournier, J.-D.; Calamante, F.; Connelly, A. MRtrix: Diffusion tractography in crossing fiber regions. Int. J. Imaging Syst. Technol. 2012, 22, 53–66. [Google Scholar] [CrossRef]
- Calamante, F. Track-weighted imaging methods: Extracting information from a streamlines tractogram. Magn. Reson. Mater. Phy. 2017. [Google Scholar] [CrossRef]
- Fornito, A.; Zalesky, A.; Bullmore, E. Fundamentals of Brain Network Analysis; Elsevier Science: San Diego, CA, USA, 2016; ISBN 978-0-12-407908-3. [Google Scholar]
- Sporns, O. Networks of the Brain; MIT Press: Cambridge, MA, USA, 2010; ISBN 978-0-262-28892-7. [Google Scholar]
- Jones, D.K.; Knösche, T.R.; Turner, R. White matter integrity, fiber count, and other fallacies: The do’s and don’ts of diffusion MRI. Neuroimage 2013, 73, 239–254. [Google Scholar] [CrossRef]
- Schilling, K.G.; Daducci, A.; Maier-Hein, K.; Poupon, C.; Houde, J.-C.; Nath, V.; Anderson, A.W.; Landman, B.A.; Descoteaux, M. Challenges in diffusion MRI tractography–Lessons learned from international benchmark competitions. Magn. Reson. Imaging 2019, 57, 194–209. [Google Scholar] [CrossRef]
- Sotiropoulos, S.N.; Zalesky, A. Building connectomes using diffusion MRI: Why, how and but. NMR Biomed. 2019, 32, e3752. [Google Scholar] [CrossRef]
- Basser, P.J.; Pajevic, S.; Pierpaoli, C.; Duda, J.; Aldroubi, A. In vivo fiber tractography using DT-MRI data. Magn. Reson. Med. 2000, 44, 625–632. [Google Scholar] [CrossRef]
- Dell’Acqua, F.; Tournier, J.-D. Modelling white matter with spherical deconvolution: How and why? NMR Biomed. 2019, 32, e3945. [Google Scholar] [CrossRef]
- Tournier, J.-D.; Mori, S.; Leemans, A. Diffusion tensor imaging and beyond. Magn. Reson. Med. 2011, 65, 1532–1556. [Google Scholar] [CrossRef] [Green Version]
- Tuch, D.S.; Reese, T.G.; Wiegell, M.R.; Makris, N.; Belliveau, J.W.; Wedeen, V.J. High angular resolution diffusion imaging reveals intravoxel white matter fiber heterogeneity. Magn. Reson. Med. 2002, 48, 577–582. [Google Scholar] [CrossRef]
- Tournier, J.-D.; Calamante, F.; Gadian, D.G.; Connelly, A. Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution. Neuroimage 2004, 23, 1176–1185. [Google Scholar] [CrossRef]
- Tuch, D.S. Q-ball imaging. Magn. Reson. Med. 2004, 52, 1358–1372. [Google Scholar] [CrossRef]
- Jansons, K.M.; Alexander, D.C. Persistent Angular Structure: New insights from diffusion mri data. dummy version. In Information Processing in Medical Imaging, Proceedings of the Biennial International Conference on Information Processing in Medical Imaging, Ambleside, UK, 20–25 July 2003; Springer: Berlin/Heidelberg, Germany, 2003; Volume 18, pp. 672–683. [Google Scholar]
- Wedeen, V.J.; Hagmann, P.; Tseng, W.-Y.I.; Reese, T.G.; Weisskoff, R.M. Mapping complex tissue architecture with diffusion spectrum magnetic resonance imaging. Magn. Reson. Med. 2005, 54, 1377–1386. [Google Scholar] [CrossRef]
- Tournier, J.-D.; Calamante, F.; Connelly, A. Robust determination of the fibre orientation distribution in diffusion MRI: Non-negativity constrained super-resolved spherical deconvolution. Neuroimage 2007, 35, 1459–1472. [Google Scholar] [CrossRef]
- Jeurissen, B.; Tournier, J.-D.; Dhollander, T.; Connelly, A.; Sijbers, J. Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data. Neuroimage 2014, 103, 411–426. [Google Scholar] [CrossRef]
- Farquharson, S.; Tournier, J.-D.; Calamante, F.; Fabinyi, G.; Schneider-Kolsky, M.; Jackson, G.D.; Connelly, A. White matter fiber tractography: Why we need to move beyond DTI. J. Neurosurg. 2013, 118, 1367–1377. [Google Scholar] [CrossRef]
- Bucci, M.; Mandelli, M.L.; Berman, J.I.; Amirbekian, B.; Nguyen, C.; Berger, M.S.; Henry, R.G. Quantifying diffusion MRI tractography of the corticospinal tract in brain tumors with deterministic and probabilistic methods. Neuroimage Clin. 2013, 3, 361–368. [Google Scholar] [CrossRef] [Green Version]
- Descoteaux, M.; Deriche, R.; Knösche, T.R.; Anwander, A. Deterministic and probabilistic tractography based on complex fibre orientation distributions. IEEE Trans. Med. Imaging 2009, 28, 269–286. [Google Scholar] [CrossRef]
- Jeurissen, B.; Leemans, A.; Jones, D.K.; Tournier, J.-D.; Sijbers, J. Probabilistic fiber tracking using the residual bootstrap with constrained spherical deconvolution. Hum. Brain Mapp. 2011, 32, 461–479. [Google Scholar] [CrossRef]
- Abhinav, K.; Yeh, F.-C.; Pathak, S.; Suski, V.; Lacomis, D.; Friedlander, R.M.; Fernandez-Miranda, J.C. Advanced diffusion MRI fiber tracking in neurosurgical and neurodegenerative disorders and neuroanatomical studies: A review. Biochim. Biophys. Acta 2014, 1842, 2286–2297. [Google Scholar] [CrossRef] [Green Version]
- Baird, A.E.; Warach, S. Magnetic resonance imaging of acute stroke. J. Cereb. Blood Flow Metab. 1998, 18, 583–609. [Google Scholar] [CrossRef]
- Kreher, B.W.; Mader, I.; Kiselev, V.G. Gibbs tracking: A novel approach for the reconstruction of neuronal pathways. Magn. Reson. Med. 2008, 60, 953–963. [Google Scholar] [CrossRef]
- Mandelli, M.L.; Berger, M.S.; Bucci, M.; Berman, J.I.; Amirbekian, B.; Henry, R.G. Quantifying accuracy and precision of diffusion MR tractography of the corticospinal tract in brain tumors. J. Neurosurg. 2014, 121, 349–358. [Google Scholar] [CrossRef] [Green Version]
- Jeurissen, B.; Leemans, A.; Tournier, J.-D.; Jones, D.K.; Sijbers, J. Investigating the prevalence of complex fiber configurations in white matter tissue with diffusion magnetic resonance imaging. Hum. Brain Mapp. 2013, 34, 2747–2766. [Google Scholar] [CrossRef]
- Nimsky, C.; Ganslandt, O.; Fahlbusch, R. Implementation of fiber tract navigation. Neurosurgery 2006, 58, ONS-292–ONS-304. [Google Scholar]
- Nath, V.; Schilling, K.; Parvathaneni, P.; Huo, Y.; Blaber, J.A.; Hainline, A.E.; Barakovic, M.; Romascano, D.; Rafael-Patino, J.; Frigo, M.; et al. Tractography Reproducibility Challenge with Empirical Data (TraCED): The 2017 ISMRM Diffusion Study Group Challenge. J. Magn. Reson. Imaging 2019. [Google Scholar] [CrossRef]
- Pestilli, F.; Yeatman, J.D.; Rokem, A.; Kay, K.N.; Wandell, B.A. Evaluation and statistical inference for living connectomes. Nat. Methods 2014, 11, 1058–1063. [Google Scholar] [CrossRef]
- Daducci, A.; Dal Palù, A.; Lemkaddem, A.; Thiran, J.-P. COMMIT: Convex optimization modeling for microstructure informed tractography. IEEE Trans. Med. Imaging 2015, 34, 246–257. [Google Scholar] [CrossRef]
- Smith, R.E.; Tournier, J.-D.; Calamante, F.; Connelly, A. SIFT2: Enabling dense quantitative assessment of brain white matter connectivity using streamlines tractography. Neuroimage 2015, 119, 338–351. [Google Scholar] [CrossRef]
- Tournier, J.-D.; Calamante, F.; Connelly, A. Determination of the appropriate b value and number of gradient directions for high-angular-resolution diffusion-weighted imaging. NMR Biomed. 2013, 26, 1775–1786. [Google Scholar] [CrossRef]
- Kuo, L.-W.; Chen, J.-H.; Wedeen, V.J.; Tseng, W.-Y.I. Optimization of diffusion spectrum imaging and q-ball imaging on clinical MRI system. Neuroimage 2008, 41, 7–18. [Google Scholar] [CrossRef]
- Scherrer, B.; Warfield, S.K. Parametric representation of multiple white matter fascicles from cube and sphere diffusion MRI. PLoS ONE 2012, 7, e48232. [Google Scholar] [CrossRef]
- Smith, R.E.; Tournier, J.-D.; Calamante, F.; Connelly, A. Anatomically-constrained tractography: Improved diffusion MRI streamlines tractography through effective use of anatomical information. Neuroimage 2012, 62, 1924–1938. [Google Scholar] [CrossRef]
- Girard, G.; Whittingstall, K.; Deriche, R.; Descoteaux, M. Towards quantitative connectivity analysis: Reducing tractography biases. Neuroimage 2014, 98, 266–278. [Google Scholar] [CrossRef]
- Lemkaddem, A.; Skiöldebrand, D.; Dal Palú, A.; Thiran, J.-P.; Daducci, A. Global Tractography with Embedded Anatomical Priors for Quantitative Connectivity Analysis. Front. Neurol. 2014, 5, 232. [Google Scholar] [CrossRef]
- St-Onge, E.; Daducci, A.; Girard, G.; Descoteaux, M. Surface-enhanced tractography (SET). Neuroimage 2018, 169, 524–539. [Google Scholar] [CrossRef]
- Yeh, C.-H.; Smith, R.; Dhollander, T.; Calamante, F.; Connelly, A. Connectomes from streamlines tractography: Assigning streamlines to brain parcellations is not trivial but highly consequential. Neuroimage 2019, 199, 160–171. [Google Scholar] [CrossRef]
- Andersson, J.L.R.; Skare, S.; Ashburner, J. How to correct susceptibility distortions in spin-echo echo-planar images: Application to diffusion tensor imaging. Neuroimage 2003, 20, 870–888. [Google Scholar] [CrossRef]
- Andersson, J.L.R.; Sotiropoulos, S.N. An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. Neuroimage 2016, 125, 1063–1078. [Google Scholar] [CrossRef]
- Jones, D.K.; Cercignani, M. Twenty-five pitfalls in the analysis of diffusion MRI data. NMR Biomed. 2010, 23, 803–820. [Google Scholar] [CrossRef]
- Irfanoglu, M.O.; Walker, L.; Sarlls, J.; Marenco, S.; Pierpaoli, C. Effects of image distortions originating from susceptibility variations and concomitant fields on diffusion MRI tractography results. Neuroimage 2012, 61, 275–288. [Google Scholar] [CrossRef] [Green Version]
- Smith, R.E.; Tournier, J.-D.; Calamante, F.; Connelly, A. SIFT: Spherical-deconvolution informed filtering of tractograms. Neuroimage 2013, 67, 298–312. [Google Scholar] [CrossRef]
- Calamante, F.; Smith, R.E.; Tournier, J.-D.; Raffelt, D.; Connelly, A. Quantification of voxel-wise total fibre density: Investigating the problems associated with track-count mapping. Neuroimage 2015, 117, 284–293. [Google Scholar] [CrossRef]
- Raffelt, D.; Tournier, J.-D.; Rose, S.; Ridgway, G.R.; Henderson, R.; Crozier, S.; Salvado, O.; Connelly, A. Apparent Fibre Density: A novel measure for the analysis of diffusion-weighted magnetic resonance images. Neuroimage 2012, 59, 3976–3994. [Google Scholar] [CrossRef]
- Sherbondy, A.J.; Rowe, M.C.; Alexander, D.C. MicroTrack: An algorithm for concurrent projectome and microstructure estimation. In Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention—MICCAI 2010, Beijing, China, 20–24 September 2010; Springer: Berlin/Heidelberg, Germany, 2010; Volume 13, pp. 183–190. [Google Scholar]
- Aganj, I.; Lenglet, C.; Jahanshad, N.; Yacoub, E.; Harel, N.; Thompson, P.M.; Sapiro, G. A Hough transform global probabilistic approach to multiple-subject diffusion MRI tractography. Med. Image Anal. 2011, 15, 414–425. [Google Scholar] [CrossRef]
- Daducci, A.; Dal Palú, A.; Descoteaux, M.; Thiran, J.-P. Microstructure Informed Tractography: Pitfalls and Open Challenges. Front. Neurosci. 2016, 10, 247. [Google Scholar] [CrossRef] [Green Version]
- Smith, R.E.; Tournier, J.-D.; Calamante, F.; Connelly, A. The effects of SIFT on the reproducibility and biological accuracy of the structural connectome. Neuroimage 2015, 104, 253–265. [Google Scholar] [CrossRef]
- Hagmann, P.; Cammoun, L.; Gigandet, X.; Meuli, R.; Honey, C.J.; Wedeen, V.J.; Sporns, O. Mapping the structural core of human cerebral cortex. PLoS Biol. 2008, 6, e159. [Google Scholar] [CrossRef]
- Yeh, C.-H.; Smith, R.E.; Liang, X.; Calamante, F.; Connelly, A. Correction for diffusion MRI fibre tracking biases: The consequences for structural connectomic metrics. Neuroimage 2016, 142, 150–162. [Google Scholar] [CrossRef]
- Civier, O.; Smith, R.E.; Yeh, C.-H.; Connelly, A.; Calamante, F. Is removal of weak connections necessary for graph-theoretical analysis of dense weighted structural connectomes from diffusion MRI? Neuroimage 2019, 194, 68–81. [Google Scholar] [CrossRef]
- Sarwar, T.; Ramamohanarao, K.; Zalesky, A. Mapping connectomes with diffusion MRI: Deterministic or probabilistic tractography? Magn. Reson. Med. 2019, 81, 1368–1384. [Google Scholar] [CrossRef]
- Zalesky, A.; Fornito, A.; Cocchi, L.; Gollo, L.L.; van den Heuvel, M.P.; Breakspear, M. Connectome sensitivity or specificity: Which is more important? Neuroimage 2016, 142, 407–420. [Google Scholar] [CrossRef]
- Maier-Hein, K.H.; Neher, P.F.; Houde, J.-C.; Côté, M.-A.; Garyfallidis, E.; Zhong, J.; Chamberland, M.; Yeh, F.-C.; Lin, Y.-C.; Ji, Q.; et al. The challenge of mapping the human connectome based on diffusion tractography. Nat. Commun. 2017, 8, 1349. [Google Scholar] [CrossRef]
- Girard, G.; Daducci, A.; Petit, L.; Thiran, J.-P.; Whittingstall, K.; Deriche, R.; Wassermann, D.; Descoteaux, M. AxTract: Toward microstructure informed tractography. Hum. Brain Mapp. 2017, 38, 5485–5500. [Google Scholar] [CrossRef] [Green Version]
© 2019 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Calamante, F. The Seven Deadly Sins of Measuring Brain Structural Connectivity Using Diffusion MRI Streamlines Fibre-Tracking. Diagnostics 2019, 9, 115. https://doi.org/10.3390/diagnostics9030115
Calamante F. The Seven Deadly Sins of Measuring Brain Structural Connectivity Using Diffusion MRI Streamlines Fibre-Tracking. Diagnostics. 2019; 9(3):115. https://doi.org/10.3390/diagnostics9030115
Chicago/Turabian StyleCalamante, Fernando. 2019. "The Seven Deadly Sins of Measuring Brain Structural Connectivity Using Diffusion MRI Streamlines Fibre-Tracking" Diagnostics 9, no. 3: 115. https://doi.org/10.3390/diagnostics9030115