A framework for a streamline-based probabilistic index of connectivity (pico) using a structural interpretation of MRI diffusion measurements
Abstract
Purpose To establish a general methodology for quantifying streamline-based diffusion fiber tracking methods in terms of probability of connection between points and/or regions. Materials and methods The commonly used streamline approach is adapted to exploit the uncertainty in the orientation of the principal direction of diffusion defined for each image voxel. Running the streamline process repeatedly using Monte Carlo methods to exploit this inherent uncertainty generates maps of connection probability. Uncertainty is defined by interpreting the shape of the diffusion orientation profile provided by the diffusion tensor in terms of the underlying microstructure. Results Two candidates for describing the uncertainty in the diffusion tensor are proposed and maps of probability of connection to chosen start points or regions are generated in a number of major tracts. Conclusion The methods presented provide a generic framework for utilizing streamline methods to generate probabilistic maps of connectivity.
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?
Probabilistic approaches have been used to create the first atlases of large-scale brain connections.
“Probabilistic approaches have also been used to create the first atlases of large-scale brain connections (Behrens et al., 2003; Hagmann et al., 2003; Parker et al., 2003).”
From 40 Years of diffusion MRI in the brain: From history to emerging frontiers (2026) · cites this paper as Parker et al., 2003
Cites
Diffusion Tensor Imaging and Axonal Tracking in the Human Brainstem
Stieltjes, B., Kaufmann, W. E., van Zijl, P. C. M., Fredericksen, K., Pearlson, G. D., Solaiyappan, M., & Mori, S. (2001) · NeuroImage · doi:10.1006/nimg.2001.0861 · Cited by 492 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
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
Diffusion MRI fiber tractography of the brain
Jeurissen, B., Descoteaux, M., Mori, S., & Leemans, A. (2019) · NMR in Biomedicine · doi:10.1002/nbm.3785 · Cited by 574 · see citing works
“Brain regions that contain higher densities of the resulting trajectories are then deemed to have a higher probability of “connection” with the seed point.(66,67) Probabilistic streamline results are, therefore, often quantified by generating visitation count maps of the number of trajectories that traverse each voxel…”
BackgroundQuantitative evaluation of 10 tractography algorithms on a realistic diffusion MR phantom
Fillard, P., Descoteaux, M., Goh, A., Gouttard, S., Jeurissen, B., Malcolm, J., Ramirez-Manzanares, A., Reisert, M., Sakaie, K., Tensaouti, F., et al. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.01.032 · Cited by 418 · see citing works
Challenges and limitations of quantifying brain connectivity <i>in vivo</i> with diffusion MRI
Jones, D. K. (2010) · Imaging in Medicine · doi:10.2217/iim.10.21 · Cited by 321 · see citing works
Global fiber reconstruction becomes practical
Reisert, M., Mader, I., Anastasopoulos, C., Weigel, M., Schnell, S., & Kiselev, V. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2010.09.016 · Cited by 312 · see citing works
Evaluation and statistical inference for human connectomes
Pestilli, F., Yeatman, J. D., Rokem, A., Kay, K. N., & Wandell, B. A. (2014) · Nature Methods · doi:10.1038/nmeth.3098 · Cited by 259 · see citing works
COMMIT: Convex Optimization Modeling for Microstructure Informed Tractography
Daducci, A., Dal Palu, A., Lemkaddem, A., & Thiran, J.-P. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2014.2352414 · Cited by 255 · see citing works
Validation of in vitro probabilistic tractography
Dyrby, T. B., Søgaard, L. V., Parker, G. J., Alexander, D. C., Lind, N. M., Baaré, W. F. C., Hay-Schmidt, A., Eriksen, N., Pakkenberg, B., Paulson, O. B., et al. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.06.022 · Cited by 236 · see citing works
Self-citationTractometer: Towards validation of tractography pipelines
Côté, M.-A., Girard, G., Boré, A., Garyfallidis, E., Houde, J.-C., & Descoteaux, M. (2013) · Medical Image Analysis · doi:10.1016/j.media.2013.03.009 · Cited by 228 · 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
In vivo definition of parieto-motor connections involved in planning of grasping movements
Koch, G., Cercignani, M., Pecchioli, C., Versace, V., Oliveri, M., Caltagirone, C., Rothwell, J., & Bozzali, M. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.02.022 · Cited by 143 · see citing works
A DTI-Derived Measure of Cortico-Cortical Connectivity
Zalesky, A., & Fornito, A. (2009) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2008.2012113 · Cited by 141 · see citing works
Microstructure informed tractography: pitfalls and open challenges
Daducci, A., Dal Palú, A., Descoteaux, M., & Thiran, J.-P. (2016) · Frontiers in Neuroscience · doi:10.3389/fnins.2016.00247 · Cited by 138 · see citing works
Bozzali, M., Giulietti, G., Basile, B., Serra, L., Spanò, B., Perri, R., Giubilei, F., Marra, C., Caltagirone, C., & Cercignani, M. (2012) · Human Brain Mapping · doi:10.1002/hbm.21287 · Cited by 107 · see citing works
Feigl, G. C., Hiergeist, W., Fellner, C., Schebesch, K.-M. M., Doenitz, C., Finkenzeller, T., Brawanski, A., & Schlaier, J. (2014) · World Neurosurgery · doi:10.1016/j.wneu.2013.01.004 · Cited by 106 · see citing works
Validation of High-Resolution Tractography Against<i>In Vivo</i>Tracing in the Macaque Visual Cortex
Azadbakht, H., Parkes, L. M., Haroon, H. A., Augath, M., Logothetis, N. K., de Crespigny, A., D'Arceuil, H. E., & Parker, G. J. M. (2015) · Cerebral Cortex · doi:10.1093/cercor/bhu326 · Cited by 103 · see citing works
Self-citationYang, J. Y.-M., Yeh, C.-H., Poupon, C., & Calamante, F. (2021) · Physics in Medicine & Biology · doi:10.1088/1361-6560/ac0d90 · Cited by 80 · see citing works
Extraction of prefronto-amygdalar pathways by combining probability maps
Bracht, T., Tüscher, O., Schnell, S., Kreher, B., Rüsch, N., Glauche, V., Lieb, K., Ebert, D., Il'yasov, K. A., Hennig, J., et al. (2009) · Psychiatry Research: Neuroimaging · doi:10.1016/j.pscychresns.2009.05.001 · Cited by 55 · see citing works
40 Years of diffusion MRI in the brain: From history to emerging frontiers
Le Bihan, D. (2026) · Imaging Neuroscience · doi:10.1162/imag.a.1365
“Probabilistic approaches have also been used to create the first atlases of large-scale brain connections ( Behrens et al., 2003; Hagmann et al., 2003; Parker et al., 2003).”
- Authors
- Parker, G. J. M., Haroon, H. A., & Wheeler‐Kingshott, C. A. M.
- Affiliations
- University of Manchester (GB)
- UCL Queen Square Institute of Neurology (GB)
- Venue
- Journal of Magnetic Resonance Imaging
- Publisher
- Wiley
- Published
- 2003-07-21
- Volume
- 18
- Issue
- 2
- Pages
- 242-254
- ISSN
- 1053-1807 (ISSN-L)
- 1522-2586
- OpenAlex
- W2085297965
- Cited by
- 527
- References
- 1
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 3 · 2023: 9 · 2022: 7 · 2021: 15 · 2020: 13 · 2019: 19 · 2018: 16 · 2017: 20 · 2016: 25 · 2015: 28 · 2014: 33 · 2013: 52 · 2012: 49
- FWCI
- 13.92
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Magnetic Resonance Imaging (publishedVersion)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Research Explorer (The University of Manchester) (submittedVersion)
- 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
- streamline tracking, diffusion tensor imaging, Monte Carlo simulation, white matter tracts
- MeSH terms
- Brain, Diffusion, Humans, Monte Carlo Method, Probability, Models, Statistical, Anisotropy, Echo-Planar Imaging, Uncertainty, Diffusion Magnetic Resonance Imaging
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
