Abstract
Determining cortical functional areas is an important goal for neurosciences and clinical neurosurgery. This paper presents a method for connectivity-based parcellation of the entire human cortical surface, exploiting the idea that each cortex region has a specific connection profile. The connectivity matrix of the cortex is computed using analytical Q-ball-based tractography. The parcellation is achieved independently for each subject and applied to the subset of the cortical surface endowed with enough connections to estimate safely a connectivity profile, namely the top of the cortical gyri. The key point of the method lies in a twofold reduction of the connectivity matrix dimension. First, parcellation amounts to iterating the clustering of Voronoï patches of the cortical surface into parcels endowed with homogeneous profiles. The parcels without intersection with the patch boundaries are selected for the final parcellation. Before clustering a patch, the complete profiles are collapsed into short profiles indicating connectivity with a set of putative cortical areas. These areas are supposed to correspond to the catchment basins of the watershed of the density of connection to the patch computed on the cortical surface. The results obtained for several brains are compared visually using a coordinate system.
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References
Bullmore, E., Sporns, O.: Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Neuroscience 10 (2009)
Iturria-Medina, Y., Canales-Rodriguez, E., Melie-Garcia, L.: Characterizing brain anatomical connections using diffusion weighted mri and graph theory. NeuroImage 36, 645–660 (2007)
Hagmann, P., Cammoun, L., Gigandet, X., et al.: Mapping the structural core of human cerebral cortex. PLOS Computational Biology 6(7), 1479–1493 (2008)
Sporns, O., Tononi, G., Kötter, R.: The human connectome: A structural description of the human brain. PLOS Comp. Biology 1(4), 245–251 (2005)
Passingham, R., Stephan, K., Kötter, R.: The anatomical basis of functional localization in the cortex. Nature 3, 606–616 (2002)
Honey, C.J., Sporns, O., et al.: Predicting human resting-state functional connectivity from structural connectivity. Proc. Natl. Acad. Sci. 106(6), 2035–2040 (2009)
Skudlarski, P., Jagannathan, K., Calhoun, V., et al.: Measuring brain connectivity: Diffusion tensor imaging validates resting state temporal correlations. NeuroImage 43(3), 554–561 (2008)
Behrens, T., Johansen-Berg, H., Woolrich, M.W., et al.: Non-invasive mapping of connections between human thalamus and cortex using diffusion imaging. Nature Neuroscience 6(7), 750–757 (2003)
Perrin, M., Cointepas, Y., Cachia, A.: Connectivity-based parcellation of the cortical mantle using q-ball diffusion imaging. Int. J. Biomed. Imaging (2008)
Guevara, P., Perrin, M., Cathier, P.: et al.: Connectivity-based parcellation of the cortical surface using Q-ball imaging. In: 5th Proc. IEEE ISBI, Paris, France, pp. 903–906 (2008)
Rushworth, M., Behrens, T., Johansen-Berg, H.: Connections patterns distinguish 3 regions of human parietal cortex. Cerebral Cortex 16, 1418–1430 (2005)
Anwander, A., Tittgemeyer, M., von Cramon, D., et al.: Connectivity-based parcellation of brocas area. Cerebral Cortex 17, 816–825 (2007)
Klein, J., Behrens, T., Robson, M., et al.: Connectivity-based parcellation of human cortex using diffusion mri: Establishing reproducibility, validity and observer independence in ba 44/45 and sma/pre-sma. NeuroImage 34, 204–211 (2007)
Tomassini, V.: Relating connectional architecture to grey matter function in the human lateral premotor cortex using functional and diffusion imaging. HBM (2008)
Clouchoux, C., Coulon, O., Rivière, D.: Anatomically constrained surface parameterization for cortical localization. In: Duncan, J.S., Gerig, G. (eds.) MICCAI 2005. LNCS, vol. 3750, pp. 344–351. Springer, Heidelberg (2005)
Poupon, C., Poupon, F., Allirol, L.: NMR: a free database dedicated to the anatomofunctional study of the human brain connectivity. In: HBM (2006)
Descoteaux, M., Deriche, R., Knösche, T.R., et al.: Deterministic and probabilistic tractography based on complex fibre orientation distributions. IEEE Transactions on Medical Imaging 28, 269–286 (2009)
Perrin, M., Poupon, C., Cointepas, Y.: Fiber tracking in q-ball fields using regularized particle trajectories. Inf. Process Med. Imaging 19, 52–63 (2005)
Cathier, P., Mangin, J.-F.: Registration of cortical connectivity matrices. In: Proc. MMBIA 2006, New York, USA (2006)
Kimmel, R., Sethian, J.A.: Computing geodesic paths on manifolds. Proc. Natl. Acad. Sci. 95, 8431–8435 (1998)
Du, Q., Faber, V., Gunzburger, M.: Centroidal voronoi tessellations: Applications and algorithms. Society for Industrial and Applied Mathematics Review 41(4), 637–676 (1999)
Van Essen, D.C., Dierker, D.L.: Surface-based and probabilistic atlases of primate cerebral cortex. Neuron 56, 209–225 (2007)
Kaufmann, L., Rousseeuw, P.: Finding groups in data: an introduction to cluster analysis. Wiley Interscience, Hoboken (1990)
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Roca, P., Rivière, D., Guevara, P., Poupon, C., Mangin, JF. (2009). Tractography-Based Parcellation of the Cortex Using a Spatially-Informed Dimension Reduction of the Connectivity Matrix. In: Yang, GZ., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009. MICCAI 2009. Lecture Notes in Computer Science, vol 5761. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04268-3_115
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DOI: https://doi.org/10.1007/978-3-642-04268-3_115
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