Optimization and validation of diffusion MRI-based fiber tracking with neural tracer data as a reference.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
18 12 2020
18 12 2020
Historique:
received:
16
05
2020
accepted:
24
11
2020
entrez:
19
12
2020
pubmed:
20
12
2020
medline:
28
4
2021
Statut:
epublish
Résumé
Diffusion-weighted magnetic resonance imaging (dMRI) allows non-invasive investigation of whole-brain connectivity, which can reveal the brain's global network architecture and also abnormalities involved in neurological and mental disorders. However, the reliability of connection inferences from dMRI-based fiber tracking is still debated, due to low sensitivity, dominance of false positives, and inaccurate and incomplete reconstruction of long-range connections. Furthermore, parameters of tracking algorithms are typically tuned in a heuristic way, which leaves room for manipulation of an intended result. Here we propose a general data-driven framework to optimize and validate parameters of dMRI-based fiber tracking algorithms using neural tracer data as a reference. Japan's Brain/MINDS Project provides invaluable datasets containing both dMRI and neural tracer data from the same primates. A fundamental difference when comparing dMRI-based tractography and neural tracer data is that the former cannot specify the direction of connectivity; therefore, evaluating the fitting of dMRI-based tractography becomes challenging. The framework implements multi-objective optimization based on the non-dominated sorting genetic algorithm II. Its performance is examined in two experiments using data from ten subjects for optimization and six for testing generalization. The first uses a seed-based tracking algorithm, iFOD2, and objectives for sensitivity and specificity of region-level connectivity. The second uses a global tracking algorithm and a more refined set of objectives: distance-weighted coverage, true/false positive ratio, projection coincidence, and commissural passage. In both experiments, with optimized parameters compared to default parameters, fiber tracking performance was significantly improved in coverage and fiber length. Improvements were more prominent using global tracking with refined objectives, achieving an average fiber length from 10 to 17 mm, voxel-wise coverage of axonal tracts from 0.9 to 15%, and the correlation of target areas from 40 to 68%, while minimizing false positives and impossible cross-hemisphere connections. Optimized parameters showed good generalization capability for test brain samples in both experiments, demonstrating the flexible applicability of our framework to different tracking algorithms and objectives. These results indicate the importance of data-driven adjustment of fiber tracking algorithms and support the validity of dMRI-based tractography, if appropriate adjustments are employed.
Identifiants
pubmed: 33339834
doi: 10.1038/s41598-020-78284-4
pii: 10.1038/s41598-020-78284-4
pmc: PMC7749185
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Validation Study
Langues
eng
Sous-ensembles de citation
IM
Pagination
21285Références
Moseley, M. et al. Diffusion-weighted MR imaging of acute stroke: correlation with t2-weighted and magnetic susceptibility-enhanced MR imaging in cats. Am. J. Neuroradiol. 11, 423–429 (1990).
pubmed: 2161612
pmcid: 8367476
Conturo, T. E. et al. Tracking neuronal fiber pathways in the living human brain. Proc. Natl. Acad. Sci. 96, 10422–10427 (1999).
pubmed: 10468624
pmcid: 17904
doi: 10.1073/pnas.96.18.10422
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. 45, 265–269 (1999).
pubmed: 9989633
doi: 10.1002/1531-8249(199902)45:2<265::AID-ANA21>3.0.CO;2-3
Basser, P. J., Pajevic, S., Pierpaoli, C., Duda, J. & Aldroubi, A. In vivo fiber tractography using DT-MRI data. Magn. Reson. Med. 44, 625–632 (2000).
pubmed: 11025519
doi: 10.1002/1522-2594(200010)44:4<625::AID-MRM17>3.0.CO;2-O
Tuch, D. S. et al. High angular resolution diffusion imaging reveals intravoxel white matter fiber heterogeneity. Magn. Reson. Med. 48, 577–582 (2002).
pubmed: 12353272
doi: 10.1002/mrm.10268
Sporns, O. Networks of the Brain (MIT Press, Cambridge, 2010).
doi: 10.7551/mitpress/8476.001.0001
Sporns, O. Structure and function of complex brain networks. Dialog. Clin. Neurosci. 15, 247 (2013).
doi: 10.31887/DCNS.2013.15.3/osporns
Rubinov, M. & Sporns, O. Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52, 1059–1069 (2010).
pubmed: 19819337
doi: 10.1016/j.neuroimage.2009.10.003
Passingham, R. E., Stephan, K. E. & Kötter, R. The anatomical basis of functional localization in the cortex. Nat. Rev. Neurosci. 3, 606 (2002).
pubmed: 12154362
doi: 10.1038/nrn893
Schmahmann, J. D. & Pandya, D. N. The complex history of the fronto-occipital fasciculus. J. Hist. Neurosci. 16, 362–377 (2007).
pubmed: 17966054
doi: 10.1080/09647040600620468
Chédotal, A. & Richards, L. J. Wiring the brain: the biology of neuronal guidance. Cold Spring Harb. Perspect. Biol. 2, a001917 (2010).
pubmed: 20463002
pmcid: 2869517
doi: 10.1101/cshperspect.a001917
Bassett, D. S. & Bullmore, E. T. Human brain networks in health and disease. Curr. Opin. Neurol. 22, 340 (2009).
pubmed: 19494774
pmcid: 2902726
doi: 10.1097/WCO.0b013e32832d93dd
Stam, C. J. Modern network science of neurological disorders. Nat. Rev. Neurosci. 15, 683 (2014).
pubmed: 25186238
doi: 10.1038/nrn3801
Xue, K. et al. Diffusion tensor tractography reveals disrupted structural connectivity in childhood absence epilepsy. Epilepsy Res. 108, 125–138 (2014).
pubmed: 24246142
doi: 10.1016/j.eplepsyres.2013.10.002
Skudlarski, P. et al. Brain connectivity is not only lower but different in schizophrenia: a combined anatomical and functional approach. Biol. Psychiatry 68, 61–69 (2010).
pubmed: 20497901
pmcid: 2900394
doi: 10.1016/j.biopsych.2010.03.035
Izhikevich, E. M. & Edelman, G. M. Large-scale model of mammalian thalamocortical systems. Proc. Natl. Acad. Sci. 105, 3593–3598 (2008).
pubmed: 18292226
pmcid: 2265160
doi: 10.1073/pnas.0712231105
Fillard, P. et al. Quantitative evaluation of 10 tractography algorithms on a realistic diffusion MR phantom. Neuroimage 56, 220–234 (2011).
pubmed: 21256221
doi: 10.1016/j.neuroimage.2011.01.032
Schilling, K. et al. Can increased spatial resolution solve the crossing fiber problem for diffusion MRI?. NMR Biomed. 30, e3787 (2017).
doi: 10.1002/nbm.3787
Calabrese, E., Badea, A., Cofer, G., Qi, Y. & Johnson, G. A. A diffusion MRI tractography connectome of the mouse brain and comparison with neuronal tracer data. Cereb. Cortex 25, 4628–4637 (2015).
pubmed: 26048951
pmcid: 4715247
doi: 10.1093/cercor/bhv121
Thomas, C. et al. Anatomical accuracy of brain connections derived from diffusion MRI tractography is inherently limited. Proc. Natl. Acad. Sci. 111, 16574–16579 (2014).
pubmed: 25368179
pmcid: 4246325
doi: 10.1073/pnas.1405672111
Zalesky, A. et al. Connectome sensitivity or specificity: which is more important?. Neuroimage 142, 407–420 (2016).
pubmed: 27364472
doi: 10.1016/j.neuroimage.2016.06.035
Reveley, C. et al. Superficial white matter fiber systems impede detection of long-range cortical connections in diffusion MR tractography. Proc. Natl. Acad. Sci. 112, E2820–E2828 (2015).
pubmed: 25964365
pmcid: 4450402
doi: 10.1073/pnas.1418198112
Donahue, C. J. et al. Using diffusion tractography to predict cortical connection strength and distance: a quantitative comparison with tracers in the monkey. J. Neurosci. 36, 6758–6770 (2016).
pubmed: 27335406
pmcid: 4916250
doi: 10.1523/JNEUROSCI.0493-16.2016
Sinke, M. R. et al. Diffusion MRI-based cortical connectome reconstruction: dependency on tractography procedures and neuroanatomical characteristics. Brain Struct. Funct. 223, 2269–2285 (2018).
pubmed: 29464318
pmcid: 5968063
doi: 10.1007/s00429-018-1628-y
Drakesmith, M. et al. Overcoming the effects of false positives and threshold bias in graph theoretical analyses of neuroimaging data. Neuroimage 118, 313–333 (2015).
pubmed: 25982515
doi: 10.1016/j.neuroimage.2015.05.011
Maier-Hein, K. H. et al. The challenge of mapping the human connectome based on diffusion tractography. Nat. Commun. 8, 1–13 (2017).
doi: 10.1038/s41467-017-01285-x
Reisert, M. et al. Global fiber reconstruction becomes practical. Neuroimage 54, 955–962 (2011).
pubmed: 20854913
doi: 10.1016/j.neuroimage.2010.09.016
Mangin, J.-F. et al. Toward global tractography. Neuroimage 80, 290–296 (2013).
pubmed: 23587688
doi: 10.1016/j.neuroimage.2013.04.009
Christiaens, D. et al. Global tractography of multi-shell diffusion-weighted imaging data using a multi-tissue model. Neuroimage 123, 89–101 (2015).
pubmed: 26272729
doi: 10.1016/j.neuroimage.2015.08.008
Okano, H. et al. Brain/minds: a Japanese national brain project for marmoset neuroscience. Neuron 92, 582–590 (2016).
pubmed: 27809998
doi: 10.1016/j.neuron.2016.10.018
Woodward, A. et al. The brain/minds 3d digital marmoset brain atlas. Sci. Data 5, 180009 (2018).
pubmed: 29437168
pmcid: 5810420
doi: 10.1038/sdata.2018.9
Skibbe, H. et al. Marmonet: a pipeline for automated projection mapping of the common marmoset brain from whole-brain serial two-photon tomography. (2019). arXiv preprint arXiv:1908.00876 .
Tournier, J. D., Calamante, F. & Connelly, A. Improved probabilistic streamlines tractography by 2nd order integration over fibre orientation distributions. In Proceedings of the International Society for Magnetic Resonance in Medicine, vol. 1670 (Ismrm, 2010).
Tournier, J.-D. et al. MRtrix3: a fast, flexible and open software framework for medical image processing and visualisation. NeuroImage116137 (2019).
Deb, K., Pratap, A., Agarwal, S. & Meyarivan, T. A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Trans. Evol. Comput. 6, 182–197 (2002).
doi: 10.1109/4235.996017
Hansen, N. & Auger, A. Cma-es: evolution strategies and covariance matrix adaptation. In Proceedings of the 13th Annual Conference Companion on Genetic and Evolutionary Computation, 991–1010 (ACM, 2011).
Hansen, N. The CMA evolution strategy: a tutorial. arXiv preprint arXiv:1604.00772 (2016).
Oh, S. W. et al. A mesoscale connectome of the mouse brain. Nature 508, 207–214 (2014).
pubmed: 24695228
pmcid: 5102064
doi: 10.1038/nature13186
Bakker, R., Wachtler, T. & Diesmann, M. Cocomac 2.0 and the future of tract-tracing databases. Front. Neuroinform. 6, 30 (2012).
pubmed: 23293600
pmcid: 3530798
doi: 10.3389/fninf.2012.00030
Markram, H. The human brain project. Sci. Am. 306, 50–55 (2012).
doi: 10.1038/scientificamerican0612-50
pubmed: 22649994
Zhang, T. et al. Optimization of macaque brain DMRI connectome by neuron tracing and myelin stain data. Comput. Med. Imaging Graph. 69, 9–20 (2018).
pubmed: 30170273
pmcid: 6176488
doi: 10.1016/j.compmedimag.2018.06.001
Mikula, S., Trotts, I., Stone, J. M. & Jones, E. G. Internet-enabled high-resolution brain mapping and virtual microscopy. Neuroimage 35, 9–15 (2007).
pubmed: 17229579
doi: 10.1016/j.neuroimage.2006.11.053
Youden, W. J. Index for rating diagnostic tests. Cancer 3, 32–35 (1950).
pubmed: 15405679
doi: 10.1002/1097-0142(1950)3:1<32::AID-CNCR2820030106>3.0.CO;2-3
Yeh, F.-C., Wedeen, V. J. & Tseng, W.-Y.I. Generalized [Formula: see text]-sampling imaging. IEEE Trans. Med. Imaging 29, 1626–1635 (2010).
pubmed: 20304721
doi: 10.1109/TMI.2010.2045126
Colman, D. R. et al. Cell adhesion molecules. Basic Neurochemistry: Molecular, Cellular, and Medical Aspects 175–190 (1999).
Homma, R. et al. Wide-field and two-photon imaging of brain activity with voltage and calcium-sensitive dyes. In Dynamic Brain Imaging, 43–79 (Humana Press, 2009).
Agrawal, R. B., Deb, K. & Agrawal, R. Simulated binary crossover for continuous search space. Complex Syst. 9, 115–148 (1995).
Dalcin, L. D., Paz, R. R., Kler, P. A. & Cosimo, A. Parallel distributed computing using python. Adv. Water Resour. 34, 1124–1139 (2011).
doi: 10.1016/j.advwatres.2011.04.013
Ronneberger, O., Fischer, P. & Brox, T. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention, 234–241 (Springer, Berlin, 2015).
Veraart, J. et al. Denoising of diffusion MRI using random matrix theory. NeuroImage 142, 394–406 (2016).
pubmed: 27523449
doi: 10.1016/j.neuroimage.2016.08.016
Veraart, J., Fieremans, E. & Novikov, D. S. Diffusion MRI noise mapping using random matrix theory. Magn. Reson. Med. 76, 1582–1593 (2016).
pubmed: 26599599
doi: 10.1002/mrm.26059
Kellner, E., Dhital, B., Kiselev, V. G. & Reisert, M. Gibbs-ringing artifact removal based on local subvoxel-shifts. Magn. Reson. Med. 76, 1574–1581 (2016).
pubmed: 26745823
doi: 10.1002/mrm.26054
Avants, B. B. et al. A reproducible evaluation of ants similarity metric performance in brain image registration. Neuroimage 54, 2033–2044 (2011).
pubmed: 20851191
doi: 10.1016/j.neuroimage.2010.09.025