Abstract
Tractography is a powerful technique capable of non-invasively reconstructing the structural connections in the brain using diffusion MRI images, but the validation of tractograms is challenging due to lack of ground truth. Owing to recent developments in mapping the mouse brain connectome, high-resolution tracer injection-based axonal projection maps have been created and quickly adopted for the validation of tractography. Previous studies using tracer injections mainly focused on investigating the match in projections and optimal tractography protocols. Being a complicated technique, however, tractography relies on multiple stages of operations and parameters. These factors introduce large variabilities in tractograms, hindering the optimization of protocols and making the interpretation of results difficult. Based on this observation, in contrast to previous studies, in this work we focused on quantifying and ranking the amount of performance variation introduced by these factors. For this purpose, we performed over a million tractography experiments and studied the variability across different subjects, injections, anatomical constraints and tractography parameters. By using N-way ANOVA analysis, we show that all tractography parameters are significant and importantly performance variations with respect to the differences in subjects are comparable to the variations due to tractography parameters, which strongly underlines the importance of fully documenting the tractography protocols in scientific experiments. We also quantitatively show that inclusion of anatomical constraints is the most significant factor for improving tractography performance. Although this critical factor helps reduce false positives, our analysis indicates that anatomy-informed tractography still fails to capture a large portion of axonal projections.
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References
Aganj I, Lenglet C, Sapiro G, Yacoub E, Ugurbil K, Harel N (2010) Reconstruction of the orientation distribution function in single-and multiple-shell q-ball imaging within constant solid angle. Magn Reson Med 64(2):554–566
Albi A, Meola A, Zhang F, Kahali P, Rigolo L, Tax CMW, Ciris PA, Essayed WI, Unadkat P, Norton I, Rathi Y, Olubiyi O, Golby AJ, O’Donnell LJ (2018) Image registration to compensate for EPI distortion in patients with brain tumors: an evaluation of tract-specific effects. J Neuroimag. https://doi.org/10.1111/jon.12485
Avants BB, Tustison NJ, Song G, Cook PA, Klein A, Gee JC (2011) A reproducible evaluation of ANTs similarity metric performance in brain image registration. Neuroimage 54(3):2033–2044
Aydogan DB, Shi Y (2016) Probabilistic tractography for topographically organized connectomes. In: Ourselin S, Joskowicz L, Sabuncu MR, Unal G, Wells W (eds) Medical image computing and computer-assisted intervention—MICCAI 2016: 19th international conference, Athens, Greece, October 17–21, 2016, Proceedings, Part I. Springer International Publishing, Cham, pp 201–209. https://doi.org/10.1007/978-3-319-46720-7_24
Azadbakht H, Parkes LM, Haroon HA, Augath M, Logothetis NK, de Crespigny A, D’Arceuil HE, Parker GJ (2015) Validation of high-resolution tractography against in vivo tracing in the macaque visual cortex. Cereb Cortex 25(11):4299–4309. https://doi.org/10.1093/cercor/bhu326
Bach M, Fritzsche KH, Stieltjes B, Laun FB (2014) Investigation of resolution effects using a specialized diffusion tensor phantom. Magn Reson Med 71(3):1108–1116
Barazany D, Basser PJ, Assaf Y (2009) In vivo measurement of axon diameter distribution in the corpus callosum of rat brain. Brain 132(Pt 5):1210–1220. https://doi.org/10.1093/brain/awp042
Basser PJ, Mattiello J, LeBihan D (1994) MR diffusion tensor spectroscopy and imaging. Biophys J 66(1):259–267. https://doi.org/10.1016/s0006-3495(94)80775-1
Basser PJ, Pajevic S, Pierpaoli C, Duda J, Aldroubi A (2000) In vivo fiber tractography using DT-MRI data. Magn Reson Med 44(4):625–632
Benson NC, Butt OH, Datta R, Radoeva PD, Brainard DH, Aguirre GK (2012) The retinotopic organization of striate cortex is well predicted by surface topology. Curr Biol 22(21):2081–2085. https://doi.org/10.1016/j.cub.2012.09.014
Besseling RM, Jansen JF, Overvliet GM, Vaessen MJ, Braakman HM, Hofman PA, Aldenkamp AP, Backes WH (2012) Tract specific reproducibility of tractography based morphology and diffusion metrics. PloS one 7(4):e34125
Budde MD, Janes L, Gold E, Turtzo LC, Frank JA (2011) The contribution of gliosis to diffusion tensor anisotropy and tractography following traumatic brain injury: validation in the rat using Fourier analysis of stained tissue sections. Brain 134(Pt 8):2248–2260. https://doi.org/10.1093/brain/awr161
Calabrese E, Badea A, Cofer G, Qi Y, Johnson GA (2015) A diffusion MRI tractography connectome of the mouse brain and comparison with neuronal tracer data. Cereb Cortex 25:bhv121
Calamante F, Tournier J-D, Kurniawan ND, Yang Z, Gyengesi E, Galloway GJ, Reutens DC, Connelly A (2012) Super-resolution track-density imaging studies of mouse brain: comparison to histology. Neuroimage 59(1):286–296
Campbell JS, Savadjiev P, Siddiqi K, Pike GB (2006) Validation and regularization in diffusion MRI tractography. In: 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2006. IEEE, pp 351–354
Caruyer E, Lenglet C, Sapiro G, Deriche R (2013) Design of multishell sampling schemes with uniform coverage in diffusion MRI. Magn Reson Med 69(6):1534–1540. https://doi.org/10.1002/mrm.24736
Chen H, Liu T, Zhao Y, Zhang T, Li Y, Li M, Zhang H, Kuang H, Guo L, Tsien JZ (2015a) Optimization of large-scale mouse brain connectome via joint evaluation of DTI and neuron tracing data. NeuroImage 115:202–213
Chen H, Liu T, Zhao Y, Zhang T, Li Y, Li M, Zhang H, Kuang H, Guo L, Tsien JZ, Liu T (2015b) Optimization of large-scale mouse brain connectome via joint evaluation of DTI and neuron tracing data. NeuroImage 115:202–213. https://doi.org/10.1016/j.neuroimage.2015.04.050
Cheng J, Deriche R, Jiang T, Shen D, Yap P-T (2014) Non-negative spherical deconvolution (NNSD) for estimation of fiber orientation distribution function in single-/multi-shell diffusion MRI. NeuroImage 101:750–764. https://doi.org/10.1016/j.neuroimage.2014.07.062
Cote MA, Girard G, Bore A, Garyfallidis E, Houde JC, Descoteaux M (2013) Tractometer: towards validation of tractography pipelines. Med Image Anal 17(7):844–857. https://doi.org/10.1016/j.media.2013.03.009
Daducci A, Dal Palù A, Lemkaddem A, Thiran J-P (2015) COMMIT: convex optimization modeling for microstructure informed tractography. IEEE Trans Med Imaging 34(1):246–257
Daianu M, Jahanshad N, Villalon-Reina JE, Prasad G, Jacobs RE, Barnes S, Zlokovic BV, Montagne A, Thompson PM (2015) 7T multi-shell hybrid diffusion imaging (HYDI) for mapping brain connectivity in mice. Proc SPIE Int Soc Opt Eng 9413. https://doi.org/10.1117/12.2081491
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(2):530–538
Dice LR (1945) Measures of the Amount of Ecologic Association between Species. Ecology 26(3):297–302. https://doi.org/10.2307/1932409
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(25):6758–6770. https://doi.org/10.1523/JNEUROSCI.0493-16.2016
Dong HW (2007) Allen reference atlas: a digital color brain atlas of the C57BL/6J male mouse. Wiley, New York
Dyrby TB, Søgaard LV, Parker GJ, Alexander DC, Lind NM, Baaré WF, Hay-Schmidt A, Eriksen N, Pakkenberg B, Paulson OB (2007) Validation of in vitro probabilistic tractography. Neuroimage 37(4):1267–1277
Feinberg DA, Moeller S, Smith SM, Auerbach E, Ramanna S, Gunther M, Glasser MF, Miller KL, Ugurbil K, Yacoub E (2010) Multiplexed echo planar imaging for sub-second whole brain FMRI and fast diffusion imaging. PLoS One 5(12):e15710. https://doi.org/10.1371/journal.pone.0015710
Ferizi U, Schneider T, Panagiotaki E, Nedjati-Gilani G, Zhang H, Wheeler-Kingshott CA, Alexander DC (2014) A ranking of diffusion MRI compartment models with in vivo human brain data. Magn Reson Med 72(6):1785–1792. https://doi.org/10.1002/mrm.25080
Fieremans E, De Deene Y, Delputte S, Özdemir MS, Achten E, Lemahieu I (2008) The design of anisotropic diffusion phantoms for the validation of diffusion weighted magnetic resonance imaging. Phys Med Biol 53(19):5405
Fillard P, Descoteaux M, Goh A, Gouttard S, Jeurissen B, Malcolm J, Ramirez-Manzanares A, Reisert M, Sakaie K, Tensaouti F (2011) Quantitative evaluation of 10 tractography algorithms on a realistic diffusion MR phantom. Neuroimage 56(1):220–234
Girard G, Whittingstall K, Deriche R, Descoteaux M (2014) Towards quantitative connectivity analysis: reducing tractography biases. Neuroimage 98:266–278
Gyengesi E, Calabrese E, Sherrier MC, Johnson GA, Paxinos G, Watson C (2014) Semi-automated 3D segmentation of major tracts in the rat brain: comparing DTI with standard histological methods. Brain Struct Funct 219(2):539–550
Heiervang E, Behrens T, Mackay C, Robson M, Johansen-Berg H (2006) Between session reproducibility and between subject variability of diffusion MR and tractography measures. Neuroimage 33(3):867–877
Heilingoetter CL, Jensen MB (2016) Histological methods for ex vivo axon tracing: a systematic review. Neurol Res 38(7):561–569. https://doi.org/10.1080/01616412.2016.1153820
Jbabdi S, Johansen-Berg H (2011) Tractography: where do we go from here? Brain Connect 1(3):169–183. https://doi.org/10.1089/brain.2011.0033
Jbabdi S, Sotiropoulos SN, Savio AM, Grana M, Behrens TE (2012) Model-based analysis of multishell diffusion MR data for tractography: how to get over fitting problems. Magn Reson Med 68(6):1846–1855. https://doi.org/10.1002/mrm.24204
Jbabdi S, Lehman JF, Haber SN, Behrens TE (2013) Human and monkey ventral prefrontal fibers use the same organizational principles to reach their targets: tracing versus tractography. J Neurosci 33(7):3190–3201
Jbabdi S, Sotiropoulos SN, Haber SN, Van Essen DC, Behrens TE (2015) Measuring macroscopic brain connections in vivo. Nat Neurosci 18(11):1546–1555. https://doi.org/10.1038/nn.4134
Jenkinson M, Beckmann CF, Behrens TE, Woolrich MW, Smith SM (2012) Fsl. Neuroimage 62(2):782–790. https://doi.org/10.1016/j.neuroimage.2011.09.015
Jeurissen B, Tournier J-D, Dhollander T, Connelly A, Sijbers J (2014) Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data. NeuroImage 103:411–426. https://doi.org/10.1016/j.neuroimage.2014.07.061
Kammen A, Law M, Tjan BS, Toga AW, Shi Y (2016) Automated retinofugal visual pathway reconstruction with multi-shell HARDI and FOD-based analysis. Neuroimage 125:767–779. https://doi.org/10.1016/j.neuroimage.2015.11.005
Keifer OP, Gutman DA, Hecht EE, Keilholz SD, Ressler KJ (2015) A comparative analysis of mouse and human medial geniculate nucleus connectivity: a DTI and anterograde tracing study. NeuroImage 105:53–66
Knösche TR, Anwander A, Liptrot M, Dyrby TB (2015) Validation of tractography: comparison with manganese tracing. Hum Brain Map 36(10):4116–4134
Kuan L, Li Y, Lau C, Feng D, Bernard A, Sunkin SM, Zeng H, Dang C, Hawrylycz M, Ng L (2015) Neuroinformatics of the allen mouse brain connectivity atlas. Methods 73:4–17
Leemans A, Sijbers J, Verhoye M, Van der Linden A, Van Dyck D (2005) Mathematical framework for simulating diffusion tensor MR neural fiber bundles. Magn Reson Med 53(4):944–953
Maier-Hein KH, Neher PF, Houde JC, Côté MA, Garyfallidis E, Zhong J, Chamberland M, Yeh FC, Lin YC, Ji Q, Reddick WE (2017) The challenge of mapping the human connectome based on diffusion tractography. Nat Commun 8(1):1349. https://doi.org/10.1038/s41467-017-01285-x
Mangin J-F, Fillard P, Cointepas Y, Le Bihan D, Frouin V, Poupon C (2013) Toward global tractography. NeuroImage 80:290–296
MathWorks I (2012) MATLAB and statistics toolbox release 2012. The MathWorks. Inc, Natick
Moeller S, Yacoub E, Olman CA, Auerbach E, Strupp J, Harel N, Ugurbil K (2010) Multiband multislice GE-EPI at 7 T, with 16-fold acceleration using partial parallel imaging with application to high spatial and temporal whole-brain fMRI. Magn Reson Med 63(5):1144–1153. https://doi.org/10.1002/mrm.22361
Mollink J, Kleinnijenhuis M, Cappellen van Walsum AV, Sotiropoulos SN, Cottaar M, Mirfin C, Heinrich MP, Jenkinson M, Pallebage-Gamarallage M, Ansorge O, Jbabdi S, Miller KL (2017) Evaluating fibre orientation dispersion in white matter: Comparison of diffusion MRI, histology and polarized light imaging. Neuroimage 157:561–574. https://doi.org/10.1016/j.neuroimage.2017.06.001
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(2):265–269
Mukherjee P, Berman JI, Chung SW, Hess CP, Henry RG (2008) Diffusion tensor MR imaging and fiber tractography: theoretic underpinnings. AJNR 29(4):632–641. https://doi.org/10.3174/ajnr.A1051
Neher PF, Laun FB, Stieltjes B, Maier-Hein KH (2014) Fiberfox: facilitating the creation of realistic white matter software phantoms. Magn Reson Med 72(5):1460–1470
Neher PF, Descoteaux M, Houde JC, Stieltjes B, Maier-Hein KH (2015) Strengths and weaknesses of state of the art fiber tractography pipelines—a comprehensive in-vivo and phantom evaluation study using Tractometer. Med Image Anal 26(1):287–305. https://doi.org/10.1016/j.media.2015.10.011
Nolte J (2009) The human brain: an introduction to its functional anatomy, 6th edn. Mosby/Elsevier, Philadelphia
Novikov DS, Jespersen SN, Kiselev VG, Fieremans E (2016) Quantifying brain microstructure with diffusion MRI: Theory and parameter estimation (ArXiv e-prints 1612)
Nucifora PG, Verma R, Lee SK, Melhem ER (2007) Diffusion-tensor MR imaging and tractography: exploring brain microstructure and connectivity. Radiology 245(2):367–384. https://doi.org/10.1148/radiol.2452060445
Oh SW, Harris JA, Ng L, Winslow B, Cain N, Mihalas S, Wang Q, Lau C, Kuan L, Henry AM, Mortrud MT, Ouellette B, Nguyen TN, Sorensen SA, Slaughterbeck CR, Wakeman W, Li Y, Feng D, Ho A, Nicholas E, Hirokawa KE, Bohn P, Joines KM, Peng H, Hawrylycz MJ, Phillips JW, Hohmann JG, Wohnoutka P, Gerfen CR, Koch C, Bernard A, Dang C, Jones AR, Zeng H (2014) A mesoscale connectome of the mouse brain. Nature 508(7495):207–214. https://doi.org/10.1038/nature13186
Panagiotaki E, Schneider T, Siow B, Hall MG, Lythgoe MF, Alexander DC (2012) Compartment models of the diffusion MR signal in brain white matter: a taxonomy and comparison. NeuroImage 59(3):2241–2254. https://doi.org/10.1016/j.neuroimage.2011.09.081
Partadiredja G, Miller R, Oorschot DE (2003) The number, size, and type of axons in rat subcortical white matter on left and right sides: a stereological, ultrastructural study. J Neurocytol 32(9):1165–1179. https://doi.org/10.1023/B:NEUR.0000021910.65920.41
Pestilli F, Yeatman JD, Rokem A, Kay KN, Wandell BA (2014) Evaluation and statistical inference for human connectomes. Nat Methods 11(10):1058–1063
Pullens P, Roebroeck A, Goebel R (2010) Ground truth hardware phantoms for validation of diffusion-weighted MRI applications. J Magn Reson Imaging 32(2):482–488
Reisert M, Kiselev VG, Dihtal B, Kellner E, Novikov DS (2014) MesoFT: unifying diffusion modelling and fiber tracking. Med Image Comput Comput Assist Interv 17(Pt 3):201–208
Rojkova K, Volle E, Urbanski M, Humbert F, Dell’Acqua F, de Schotten MT (2016) Atlasing the frontal lobe connections and their variability due to age and education: a spherical deconvolution tractography study. Brain Struct Funct 221(3):1751–1766
Schmahmann JD, Pandya DN, Wang R, Dai G, D’Arceuil HE, de Crespigny AJ, Wedeen VJ (2007) Association fibre pathways of the brain: parallel observations from diffusion spectrum imaging and autoradiography. Brain 130(3):630–653
Seehaus AK, Roebroeck A, Chiry O, Kim D-S, Ronen I, Bratzke H, Goebel R, Galuske RA (2012) Histological validation of DW-MRI tractography in human postmortem tissue. Cereb Cortex 23:bhs036
Sergejeva M, Papp EA, Bakker R, Gaudnek MA, Okamura-Oho Y, Boline J, Bjaalie JG, Hess A (2015) Anatomical landmarks for registration of experimental image data to volumetric rodent brain atlasing templates. J Neurosci Methods 240:161–169. https://doi.org/10.1016/j.jneumeth.2014.11.005
Setsompop K, Gagoski BA, Polimeni JR, Witzel T, Wedeen VJ, Wald LL (2012) Blipped-controlled aliasing in parallel imaging for simultaneous multislice echo planar imaging with reduced g-factor penalty. Magn Reson Med 67(5):1210–1224. https://doi.org/10.1002/mrm.23097
Shattuck DW, Leahy RM (2002) BrainSuite: an automated cortical surface identification tool. Med Image Anal 6(2):129–142
Smith RE, Tournier J-D, Calamante F, Connelly A (2012) Anatomically-constrained tractography: improved diffusion MRI streamlines tractography through effective use of anatomical information. Neuroimage 62(3):1924–1938
Smith RE, Tournier J-D, Calamante F, Connelly A (2015) SIFT2: Enabling dense quantitative assessment of brain white matter connectivity using streamlines tractography. Neuroimage 119:338–351
Sotiropoulos SN, Hernandez-Fernandez M, Vu AT, Andersson JL, Moeller S, Yacoub E, Lenglet C, Ugurbil K, Behrens TEJ, Jbabdi S (2016) Fusion in diffusion MRI for improved fibre orientation estimation: an application to the 3T and 7T data of the human connectome project. Neuroimage 134:396–409. https://doi.org/10.1016/j.neuroimage.2016.04.014
Thomas C, Frank QY, Irfanoglu MO, Modi P, Saleem KS, Leopold DA, Pierpaoli C (2014) Anatomical accuracy of brain connections derived from diffusion MRI tractography is inherently limited. Proc Natl Acad Sci 111(46):16574–16579
Toga AW, Clark KA, Thompson PM, Shattuck DW, Van Horn JD (2012) Mapping the human connectome. Neurosurgery 71(1):1–5. https://doi.org/10.1227/neu.0b013e318258e9ff
Tournier J-D, Calamante F, Gadian DG, Connelly A (2004) Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution. NeuroImage 23(3):1176–1185
Tournier J, Calamante F, Connelly A (2010) Improved probabilistic streamlines tractography by 2nd order integration over fibre orientation distributions. In: Proc. 18th annual meeting of the Intl. Soc. Mag. Reson. Med.(ISMRM), p 1670
Tournier JD, Calamante F, Connelly A (2012) MRtrix: diffusion tractography in crossing fiber regions. Int J Imaging Syst Technol 22(1):53–66. https://doi.org/10.1002/ima.22005
Tran G, Shi Y (2015) Fiber orientation and compartment parameter estimation from multi-shell diffusion imaging. IEEE Trans Med Imaging 34(11):2320–2332
Tuch DS (2004) Q-ball imaging. Magn Reson Med 52(6):1358–1372
Tuch DS, Reese TG, Wiegell MR, Makris N, Belliveau JW, Wedeen VJ (2002) High angular resolution diffusion imaging reveals intravoxel white matter fiber heterogeneity. Magn Reson Med 48(4):577–582. https://doi.org/10.1002/mrm.10268
Van Essen DC, Smith SM, Barch DM, Behrens TE, Yacoub E, Ugurbil K, Consortium WU-MH. (2013) The WU-Minn human connectome project: an overview. Neuroimage 80:62–79. https://doi.org/10.1016/j.neuroimage.2013.05.041
Wandell BA (2016) Clarifying human white matter. Annu Rev Neurosci 39:103–128. https://doi.org/10.1146/annurev-neuro-070815-013815
Wedeen VJ, Hagmann P, Tseng WYI, Reese TG, Weisskoff RM (2005) Mapping complex tissue architecture with diffusion spectrum magnetic resonance imaging. Magn Reson Med 54(6):1377–1386
Willats L, Raffelt D, Smith RE, Tournier JD, Connelly A, Calamante F (2014) Quantification of track-weighted imaging (TWI): characterisation of within-subject reproducibility and between-subject variability. Neuroimage 87:18–31. https://doi.org/10.1016/j.neuroimage.2013.11.016
Wu D, Xu J, McMahon MT, van Zijl PC, Mori S, Northington FJ, Zhang J (2013) In vivo high-resolution diffusion tensor imaging of the mouse brain. Neuroimage 83:18–26. https://doi.org/10.1016/j.neuroimage.2013.06.012
Wu D, Martin LJ, Northington FJ, Zhang J (2014) Oscillating gradient diffusion MRI reveals unique microstructural information in normal and hypoxia-ischemia injured mouse brains. Magn Reson Med 72(5):1366–1374. https://doi.org/10.1002/mrm.25441
Yamada K, Sakai K, Akazawa K, Yuen S, Nishimura T (2009) MR tractography: a review of its clinical applications. Magn Reson Med Sci 8(4):165–174
Yeh FC, Wedeen VJ, Tseng WY (2010) Generalized q-sampling imaging. IEEE Trans Med Imaging 29(9):1626–1635. https://doi.org/10.1109/TMI.2010.2045126
Yeh CH, Smith RE, Liang X, Calamante F, Connelly A (2016) Correction for diffusion MRI fibre tracking biases: the consequences for structural connectomic metrics. Neuroimage. https://doi.org/10.1016/j.neuroimage.2016.05.047
Zingg B, Hintiryan H, Gou L, Song MY, Bay M, Bienkowski MS, Foster NN, Yamashita S, Bowman I, Toga AW, Dong HW (2014) Neural networks of the mouse neocortex. Cell 156(5):1096–1111. https://doi.org/10.1016/j.cell.2014.02.023
Acknowledgements
This work was supported by the National Institute of Health (NIH) under Grants R01EB022744, K01EB013633, P41EB015922, P50AG005142, U01EY025864 and U01AG051218.
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The study was funded by the National Institute of Health (NIH) (Grant numbers: R01EB022744, K01EB013633, P41EB015922, P50AG005142, U01EY025864, U01AG051218).
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Aydogan, D.B., Jacobs, R., Dulawa, S. et al. When tractography meets tracer injections: a systematic study of trends and variation sources of diffusion-based connectivity. Brain Struct Funct 223, 2841–2858 (2018). https://doi.org/10.1007/s00429-018-1663-8
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DOI: https://doi.org/10.1007/s00429-018-1663-8