Abstract
Current theories hold that brain function is highly related with long-range physical connections through axonal bundles, namely extrinsic connectivity. However, obtaining a groupwise cortical parcellation based on extrinsic connectivity remains challenging. Current parcellation methods are computationally expensive; need tuning of several parameters or rely on ad-hoc constraints. Furthermore, none of these methods present a model for the cortical extrinsic connectivity. To tackle these problems, we propose a parsimonious model for the extrinsic connectivity and an efficient parcellation technique based on clustering of tractograms. Our technique allows the creation of single subject and groupwise parcellations of the whole cortex. The parcellations obtained with our technique are in agreement with anatomical and functional parcellations in the literature. In particular, the motor and sensory cortex are subdivided in agreement with the human homunculus of Penfield. We illustrate this by comparing our resulting parcels with an anatomical atlas and the motor strip mapping included in the Human Connectome Project data.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
For the sake of clarity we denote all random variables with a tilde, e.g. \(\tilde{C}\).
References
Anwander, A., Tittgemeyer, M., von Cramon, D., Friederici, A., Knosche, T.: Connectivity-based parcellation of Broca’s area. Cereb. Cortex 17 (4), 816–825 (2006)
Barch, D.M., Burgess, G.C., Harms, M.P., Petersen, S.E., Schlaggar, B.L., Corbetta, M., Glasser, M.F., Curtiss, S., Dixit, S., Feldt, C., Nolan, D., Bryant, E., Hartley, T., Footer, O., Bjork, J.M., Poldrack, R., Smith, S., Johansen-Berg, H., Snyder, A.Z., Van Essen, D.C.: Function in the human connectome: task-fMRI and individual differences in behavior. Neuroimage 80, 169–189 (2013)
Behrens, T., Woolrich, M., Jenkinson, M., Johansen-Berg, H., Nunes, R., Clare, S., Matthews, P., Brady, J., Smith, S.: Characterization and propagation of uncertainty in diffusion-weighted MR imaging. Magn. Reson. Med. 50 (5), 1077–1088 (2003)
Clarkson, M.J., Malone, I.B., Modat, M., Leung, K.K., Ryan, N., Alexander, D.C., Fox, N.C., Ourselin, S.: A Framework for Using Diffusion Weighted Imaging to Improve Cortical Parcellation. Lecture Notes in Computer Science, vol. 6362. Springer, Berlin, Heidelberg (2010)
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., Albert, M.S., Killiany, R.J.: An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31 (3), 968–980 (2006)
Garyfallidis, E., Brett, M., Amirbekian, B., Rokem, A., van der Walt, S., Descoteaux, M., Nimmo-Smith, I.: Dipy, a library for the analysis of diffusion MRI data. Front. Neuroinform. 8, 8 (2014). http://journal.frontiersin.org/article/10.3389/fninf.2014.00008/full
Glasser, M.F., Sotiropoulos, S.N., Wilson, J.A., Coalson, T.S., Fischl, B., Andersson, J.L., Xu, J., Jbabdi, S., Webster, M., Polimeni, J.R., Van Essen, D.C., Jenkinson, M.: The minimal preprocessing pipelines for the Human Connectome Project. Neuroimage 80, 105–124 (2013)
Hubert, L., Arabie, P.: Comparing partitions. J. Classif. 2 (1), 193–218 (1985)
Jbabdi, S., Behrens, T.E.: Long-range connectomics. Ann. N.Y. Acad. Sci. 1305 (1), 83–93 (2013)
Lefranc, S., Roca, P., Perrot, M., Poupon, C., Le Bihan, D., Mangin, J.F., Rivière, D.: Groupwise connectivity-based parcellation of the whole human cortical surface using watershed-driven dimension reduction. Med. Image Anal. 30, 11–29 (2016)
McCullagh, P., Nelder, J.A.: Generalized Linear Models, 2nd edn. Chapman & Hall/CRC, London (1989)
Moreno-Dominguez, D., Anwander, A., Knösche, T.R.: A hierarchical method for whole-brain connectivity-based parcellation. Hum. Brain Mapp. 35 (10), 5000–5025 (2014)
Murtagh, F., Contreras, P.: Methods of Hierarchical Clustering. Empir. Econ. 38 (1), 23–45 (2011)
Parisot, S., Arslan, S., Passerat-Palmbach, J., Wells, W.M., Rueckert, D.: Tractography-driven groupwise multi-scale parcellation of the cortex. Inf. Process. Med. Imaging 24, 600–612 (2015)
Passingham, R.E., Stephan, K.E., Kötter, R.: The anatomical basis of functional localization in the cortex. Nat. Rev. Neurosci. 3 (8), 606–616 (2002)
Pendergast, J.F., Gange, S.J., Newton, M.A., Lindstrom, M.J., Palta, M., Fisher, M.R.: A survey of methods for analyzing clustered binary response data. Int. Stat. Rev./Rev. Int. Stat. 64 (1), 89 (1996)
Pohl, K.M., Fisher, J., Bouix, S., Shenton, M., McCarley, R.W., Grimson, W.E.L., Kikinis, R., Wells, W.M.: Using the logarithm of odds to define a vector space on probabilistic atlases. Med. Image Anal. 11 (5), 465–477 (2007)
Reveley, C., Seth, A.K., Pierpaoli, C., Silva, A.C., Yu, D., Saunders, R.C., Leopold, D.A., Ye, F.Q.: Superficial white matter fiber systems impede detection of long-range cortical connections in diffusion MR tractography. Proc. Natl. Acad. Sci. 112 (21), E2820–E2828 (2015)
Schmahmann, J.D., Pandya, D.N.: Fiber Pathways of the Brain, vol. 1. Oxford University Press, Oxford (2006)
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 23 (3), 1176–1185 (2004)
Acknowledgements
This work has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (ERC Advanced Grant agreement No 694665: CoBCoM).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Gallardo, G., Fick, R., Wells, W., Deriche, R., Wassermann, D. (2017). Groupwise Structural Parcellation of the Cortex: A Sound Approach Based on Logistic Models. In: Fuster, A., Ghosh, A., Kaden, E., Rathi, Y., Reisert, M. (eds) Computational Diffusion MRI. MICCAI 2016. Mathematics and Visualization. Springer, Cham. https://doi.org/10.1007/978-3-319-54130-3_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-54130-3_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-54129-7
Online ISBN: 978-3-319-54130-3
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)