The effects of SIFT on the reproducibility and biological accuracy of the structural connectome
Abstract
Diffusion MRI streamlines tractography is increasingly being used to characterise and assess the structural connectome of the human brain. However, issues pertaining to quantification of structural connectivity using streamlines reconstructions are well-established in the field, and therefore the validity of any conclusions that may be drawn from these analyses remains ambiguous. We recently proposed a post-processing method entitled "SIFT: Spherical-deconvolution Informed Filtering of Tractograms" as a mechanism for reducing the biases in quantitative measures of connectivity introduced by the streamlines reconstruction method. Here, we demonstrate the advantage of this approach in the context of connectomics in three steps. Firstly, we carefully consider the model imposed by the SIFT method, and the implications this has for connectivity quantification. Secondly, we investigate the effects of SIFT on the reproducibility of structural connectome construction. Thirdly, we compare quantitative measures extracted from structural connectomes derived from streamlines tractography, with and without the application of SIFT, to published estimates drawn from post-mortem brain dissection. The combination of these sources of evidence demonstrates the important role the SIFT methodology has for the robust quantification of structural connectivity of the brain using diffusion MRI.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Streamline filtering techniques can optimize the signal prediction error in order to reduce tractography biases.
“Streamline filtering techniques can optimize the signal prediction error in order to reduce tractography biases14, 16, 51.”
From Tractography-based connectomes are dominated by false-positive connections (2016) · cites this paper as [51]
SIFT reduces local streamline density bias by filtering the tractogram to improve the fit between the overall distribution of streamlines and the FOD in every voxel, which may improve accuracy of connectome reconstruction.
“Recently, a new method, called spherical-deconvolution informed filtering of tractograms (SIFT) has been proposed to reduce local streamline density bias by filtering the tractogram (i.e., selectively removing streamlines) to improve the fit between the overall distribution of streamlines and the FOD in every voxel, estimated from the diffusion-weighted image, which may improve accuracy of connect”
From Diffusion MRI-based cortical connectome reconstruction: dependency on tractography procedures and neuroanatomical characteristics (2018) · cites this paper as Smith et al. 2015a
Cites
Advances in functional and structural MR image analysis and implementation as FSL
Smith, S. M., Jenkinson, M., Woolrich, M. W., Beckmann, C. F., Behrens, T. E. J., Johansen-Berg, H., Bannister, P. R., De Luca, M., Drobnjak, I., Flitney, D. E., et al. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.07.051 · Cited by 13,630 · see citing works
Desikan, R. S., Ségonne, F., Fischl, B., Quinn, B. T., Dickerson, B. C., Blacker, D., Buckner, R. L., Dale, A. M., Maguire, R. P., Hyman, B. T., et al. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.01.021 · Cited by 13,324 · see citing works
Complex brain networks: graph theoretical analysis of structural and functional systems
Bullmore, E., & Sporns, O. (2009) · Nature Reviews Neuroscience · doi:10.1038/nrn2575 · Cited by 11,820 · see citing works
Complex network measures of brain connectivity: Uses and interpretations
Rubinov, M., & Sporns, O. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2009.10.003 · Cited by 11,522 · see citing works
Cortical Surface-Based Analysis
Dale, A. M., Fischl, B., & Sereno, M. I. (1999) · NeuroImage · doi:10.1006/nimg.1998.0395 · Cited by 10,910 · see citing works
N4ITK: Improved N3 Bias Correction
Tustison, N. J., Avants, B., Cook, P. A., Zheng, Y., Egan, A., Yushkevich, P. A., & Gee, J. C. (2010) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2010.2046908 · Cited by 6,119 · see citing works
Mapping the structural core of human cerebral cortex
Hagmann, P., Cammoun, L., Gigandet, X., Meuli, R., Honey, C. J., Wedeen, V. J., & Sporns, O. (2008) · PLoS Biology · doi:10.1371/journal.pbio.0060159 · Cited by 4,173 · see citing works
Andersson, J. L. R., Skare, S., & Ashburner, J. (2003) · NeuroImage · doi:10.1016/s1053-8119(03)00336-7 · Cited by 3,469 · see citing works
The Human Connectome: A Structural Description of the Human Brain
Sporns, O., Tononi, G., & Kötter, R. (2005) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0010042 · Cited by 3,260 · see citing works
White matter integrity, fiber count, and other fallacies: The do's and don'ts of diffusion MRI
Jones, D. K., Knösche, T. R., & Turner, R. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2012.06.081 · Cited by 2,479 · cited 1× · see citing works
“This is however contrary to a fundamental limitation of streamlines tractography approaches that is well-established in thefield: streamline count is not a validmarker of axonalfibre count (Jones et al., 2013).”
A Bayesian model of shape and appearance for subcortical brain segmentation
Patenaude, B., Smith, S. M., Kennedy, D. N., & Jenkinson, M. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.02.046 · Cited by 2,287 · see citing works
Tournier, J., Calamante, F., & Connelly, A. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.02.016 · Cited by 2,191 · see citing works
Self-citationTournier, J.-D., Calamante, F., Gadian, D. G., & Connelly, A. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.07.037 · Cited by 1,635 · see citing works
Self-citationFiber composition of the human corpus callosum
Aboitiz, F., Scheibel, A. B., Fisher, R. S., & Eran Zaidel, B. (1992) · Brain Research · doi:10.1016/0006-8993(92)90178-c · Cited by 1,467 · see citing works
MRtrix: Diffusion tractography in crossing fiber regions
Tournier, J., Calamante, F., & Connelly, A. (2012) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.22005 · Cited by 1,370 · see citing works
Self-citationThree‐dimensional magnetization‐prepared rapid gradient‐echo imaging (3D MP RAGE)
Mugler, J. P., & Brookeman, J. R. (1990) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910150117 · Cited by 1,276 · see citing works
Reduction of eddy‐current‐induced distortion in diffusion MRI using a twice‐refocused spin echo
Reese, T. G., Heid, O., Weisskoff, R. M., & Wedeen, V. J. (2002) · Magnetic Resonance in Medicine · doi:10.1002/mrm.10308 · Cited by 1,229 · see citing works
Smith, R. E., Tournier, J.-D., Calamante, F., & Connelly, A. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.06.005 · Cited by 1,216 · see citing works
Self-citationWhole-brain anatomical networks: does the choice of nodes matter?
Zalesky, A., Fornito, A., Harding, I. H., Cocchi, L., Yücel, M., Pantelis, C., & Bullmore, E. T. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2009.12.027 · Cited by 1,169 · see citing works
Human brain networks in health and disease
Bassett, D. S., & Bullmore, E. T. (2009) · Current Opinion in Neurology · doi:10.1097/wco.0b013e32832d93dd · Cited by 894 · see citing works
Advances in diffusion MRI acquisition and processing in the Human Connectome Project
Sotiropoulos, S. N., Jbabdi, S., Xu, J., Andersson, J., Moeller, S., Auerbach, E. J., Glasser, M. F., Hernandez, M., Sapiro, G., Jenkinson, M., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.057 · Cited by 872 · see citing works
Uğurbil, K., Xu, J., Auerbach, E. J., Moeller, S., Vu, A. T., Duarte-Carvajalino, J. M., Lenglet, C., Wu, X., Schmitter, S., Van de Moortele, P. F., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.012 · Cited by 812 · see citing works
Mapping Human Whole-Brain Structural Networks with Diffusion MRI
Hagmann, P., Kurant, M., Gigandet, X., Thiran, P., Wedeen, V. J., Meuli, R., & Thiran, J.-P. (2007) · PLoS ONE · doi:10.1371/journal.pone.0000597 · Cited by 788 · see citing works
SIFT: Spherical-deconvolution informed filtering of tractograms
Smith, R. E., Tournier, J.-D., Calamante, F., & Connelly, A. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2012.11.049 · Cited by 752 · see citing works
Self-citationWhite matter maturation reshapes structural connectivity in the late developing human brain
Hagmann, P., Sporns, O., Madan, N., Cammoun, L., Pienaar, R., Wedeen, V. J., Meuli, R., Thiran, J.-P., & Grant, P. E. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1009073107 · Cited by 700 · see citing works
Kaiser, M., & Hilgetag, C. C. (2006) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0020095 · Cited by 638 · see citing works
Determinants of conduction velocity in myelinated nerve fibers
Waxman, S. G. (1980) · Muscle & Nerve · doi:10.1002/mus.880030207 · Cited by 629 · see citing works
Raffelt, D., Tournier, J.-D., Rose, S., Ridgway, G., Henderson, R., Crozier, S., Salvado, O., & Connelly, A. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.10.045 · Cited by 616 · see citing works
Self-citationLo, C. Z., Wang, P.-N., Chou, K., Wang, J., Yong He, A., & Lin, C. (2010) · Journal of Neuroscience · doi:10.1523/jneurosci.4136-10.2010 · Cited by 538 · see citing works
Wiring Optimization in Cortical Circuits
Chklovskii, D. B., Schikorski, T., & Stevens, C. F. (2002) · Neuron · doi:10.1016/s0896-6273(02)00679-7 · Cited by 499 · see citing works
AXON DIAMETERS IN RELATION TO THE SPIKE DIMENSIONS AND THE CONDUCTION VELOCITY IN MAMMALIAN A FIBERS
Gasser, H. S., & Grundfest, H. (1939) · American Journal of Physiology-Legacy Content · doi:10.1152/ajplegacy.1939.127.2.393 · Cited by 462 · see citing works
Mapping the human connectome at multiple scales with diffusion spectrum MRI
Cammoun, L., Gigandet, X., Meskaldji, D. E., Thiran, J., Sporns, O., Kim, Q., Maeder, P. P., Meuli, R. A., & Hagmann, P. (2011) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2011.09.031 · Cited by 443 · see citing works
Bassett, D. S., Greenfield, D. L., Meyer‐Lindenberg, A., Weinberger, D. R., Moore, S. W., & Bullmore, E. T. (2010) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000748 · Cited by 401 · see citing works
Calamante, F., Tournier, J., Jackson, G. D., & Connelly, A. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.07.024 · Cited by 399 · see citing works
Self-citationWeight consistency specifies regularities of macaque cortical networks
Markov, N. T., Misery, P., Falchier, A., Lamy, C., Vezoli, J., Quilodran, R., Gariel, M. A., Giroud, P., Ercsey-Ravasz, M., Pilaz, L. J., et al. (2011) · Cerebral Cortex · doi:10.1093/cercor/bhq201 · Cited by 386 · see citing works
Conserved and variable architecture of human white matter connectivity
Bassett, D. S., Brown, J. A., Deshpande, V. S., Carlson, J. M., & Grafton, S. T. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.09.006 · Cited by 356 · see citing works
Structural connectomics in brain diseases
Griffa, A., Baumann, P., Thiran, J., & Hagmann, P. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.04.056 · Cited by 306 · see citing works
Conduction velocity and myelin thickness in regenerating nerve fibres
SANDERS, F. K., & Whitteridge, D. (1946) · The Journal of Physiology · doi:10.1113/jphysiol.1946.sp004160 · Cited by 221 · see citing works
Bastiani, M., Shah, N. J., Goebel, R., & Roebroeck, A. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.06.002 · Cited by 192 · see citing works
From diffusion MRI to brain connectomics
Hagmann, P. (2005) · reroDoc Digital Library · doi:10.5075/epfl-thesis-3230 · Cited by 151 · see citing works
Heidemann, R. M., Anwander, A., Feiweier, T., Knösche, T. R., & Turner, R. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.12.081 · Cited by 125 · see citing works
Vaessen, M. J., Hofman, P. A. M., Tijssen, H. N., Aldenkamp, A. P., Jansen, J. F. A., & Backes, W. H. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.03.011 · Cited by 123 · see citing works
Characteristics and variability of structural networks derived from diffusion tensor imaging
Cheng, H., Wang, Y., Sheng, J., Kronenberger, W. G., Mathews, V. P., Hummer, T. A., & Saykin, A. J. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.03.036 · Cited by 122 · see citing works
The Human Cortical White Matter
Braitenberg, V., & Sch√ºz, A. (2002) · Conceptual advances in brain research · doi:10.1201/9780203299296.ch16 · Cited by 112 · see citing works
Haug, H. (1986) · Journal of Neuroscience Methods · doi:10.1016/0165-0270(86)90110-x · Cited by 112 · see citing works
Schüz, A. (2002) · CRC Press eBooks · doi:10.1201/9780203299296 · Cited by 111 · see citing works
Waxman, H. (2012) · Scholarpedia · doi:10.4249/scholarpedia.1451 · Cited by 104 · see citing works
Owen, J. P., Ziv, E., Bukshpun, P., Pojman, N. J., Wakahiro, M. L. J., Berman, J., Roberts, T. P. L., Friedman, E. J., Sherr, E. H., & Mukherjee, P. (2013) · Brain Connectivity · doi:10.1089/brain.2012.0121 · Cited by 76 · see citing works
The Wiring Economy Principle: Connectivity Determines Anatomy in the Human Brain
Raj, A., & Chen, Y.-H. (2011) · PLoS ONE · doi:10.1371/journal.pone.0014832 · Cited by 74 · see citing works
Test-Retest Reliability of Graph Theory Measures of Structural Brain Connectivity
Dennis, E. L., Jahanshad, N., Toga, A. W., McMahon, K. L., de Zubicaray, G. I., Martin, N. G., Wright, M. J., & Thompson, P. M. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33454-2_38 · Cited by 49 · see citing works
Axon Diameter Mapping in Crossing Fibers with Diffusion MRI
Zhang, H. G., Dyrby, T. B., & Alexander, D. C. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-23629-7_11 · Cited by 38 · see citing works
Pannek, K., Hatzigeorgiou, X., Colditz, P. B., & Rose, S. (2013) · PLoS ONE · doi:10.1371/journal.pone.0068593 · Cited by 32 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Tractography-based connectomes are dominated by false-positive connections
Maier-Hein, K. H., Neher, P., Houde, J.-C., Côté, M.-A., Garyfallidis, E., Zhong, J., Chamberland, M., Yeh, F.-C., Lin, Y.-C., Ji, Q., et al. (2016) · doi:10.1101/084137 · Cited by 1,361 · see citing works
Grayson, D. S., & Fair, D. A. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.01.079 · Cited by 411 · see citing works
“Future work could probably improve these relationships substantially by imposing rigorous anatomical constraints in the tractography process (Smith et al., 2015), but such efforts have not seen extensive validation yet.”
BackgroundGorgolewski, K. J., Alfaro-Almagro, F., Auer, T., Bellec, P., Capotă, M., Chakravarty, M. M., Churchill, N. W., Cohen, A. L., Craddock, R. C., Devenyi, G. A., et al. (2017) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1005209 · Cited by 311 · see citing works
Self-citationZhang, F., Daducci, A., He, Y., Schiavi, S., Seguin, C., Smith, R. E., Yeh, C.-H., Zhao, T., & O’Donnell, L. J. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2021.118870 · Cited by 302 · see citing works
“The most popular scalar value for connectivity matrix analysis is the NOS [112, 429, 211, 362, 182], where higher values are considered to indicate stronger connectivity between pairs of gray matter regions.”
InfluentialSelf-citationThe contribution of geometry to the human connectome
Roberts, J. A., Perry, A., Lord, A. R., Roberts, G., Mitchell, P. B., Smith, R. E., Calamante, F., & Breakspear, M. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2015.09.009 · Cited by 213 · see citing works
Self-citationMapping Structural Connectivity Using Diffusion <scp>MRI</scp> : Challenges and Opportunities
Yeh, C., Jones, D. K., Liang, X., Descoteaux, M., & Connelly, A. (2021) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.27188 · Cited by 187 · see citing works
Self-citationGirard, G., Caminiti, R., Battaglia-Mayer, A., St-Onge, E., Ambrosen, K. S., Eskildsen, S. F., Krug, K., Dyrby, T. B., Descoteaux, M., Thiran, J.-P., et al. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117201 · Cited by 83 · see citing works
Sinke, M. R. T., Otte, W. M., Christiaens, D., Schmitt, O., Leemans, A., van der Toorn, A., Sarabdjitsingh, R. A., Joëls, M., & Dijkhuizen, R. M. (2018) · Brain Structure and Function · doi:10.1007/s00429-018-1628-y · Cited by 70 · see citing works
- Authors
- Smith, R. E., Tournier, J.-D., Calamante, F., & Connelly, A.
- Affiliations
- Florey Institute of Neuroscience and Mental Health (AU)
- The University of Melbourne (AU)
- Corresponding author
- Smith, Robert E.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2015-01
- Volume
- 104
- Pages
- 253-265
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W1967159562
- Cited by
- 266
- References
- 52
- Citations by year
- 2026: 18 · 2025: 31 · 2024: 25 · 2023: 20 · 2022: 32 · 2021: 33 · 2020: 38 · 2019: 15 · 2018: 18 · 2017: 17 · 2016: 10 · 2015: 9
- FWCI
- 9.33
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- Research Portal (King's College London) (submittedVersion)
- Funders
- Victorian Life Sciences Computation Initiative (VLSCI) (VR0271)
- 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 › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Keywords
- SIFT, spherical deconvolution, tractography, structural connectome, diffusion MRI, reproducibility, connectomics
- MeSH terms
- Adult, Algorithms, Brain, Female, Humans, Image Processing, Computer-Assisted, Male, Software, Reproducibility of Results, Diffusion Magnetic Resonance Imaging, Diffusion Tensor Imaging, Connectome
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
