Abstract
Meaningful division of the human cortex into distinct regions is a longstanding goal in neuroscience. Many of the most widely cited parcellations utilize anatomical priors or depend on functional magnetic resonance imaging (MRI) data while there exists a relative dearth of parcellations that use only structural data based on diffusion MRI. In light of this, and the fact that structural connectivity represents the underlying substrates of functional connectivity, we employ a novel high-resolution, vertex-level graph model of the whole-brain structural connectome and show that the harmonic modes of this graph can be used to achieve parcellations that qualitatively agree with the widely accepted atlases in the literature. Further, we detail a multi-layer formulation of the structural connectome graph and demonstrate that hierarchical clustering of its harmonic modes yields subject-specific parcellations at varying resolutions with ensured and tunable group-level correspondence.
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References
Tittgemeyer, M., Rigoux, L., Knösche, T.R.: Cortical Parcellation Based on Structural Connectivity: A Case for Generative Models (2018)
Atasoy, S., Donnelly, I., Pearson, J.: Human brain networks function in connectome-specific harmonic waves. Nat. Commun. 7 (2016). Article number 10340
Lévy, B.: Laplace-beltrami eigenfunctions towards an algorithm that “understands” geometry. In: Proceedings - IEEE International Conference on Shape Modeling and Applications 2006, SMI 2006 (2006)
Lefèvre, J., et al.: SPANOL (SPectral ANalysis of Lobes): a spectral clustering framework for individual and group parcellation of cortical surfaces in lobes. Front. Neurosci. 12, 354 (2018)
Glasser, M.F., et al.: The minimal preprocessing pipelines for the human connectome project. NeuroImage 80, 105–124 (2013)
Van Essen, D.C., Smith, S.M., Barch, D.M., Behrens, T.E., Yacoub, E., Ugurbil, K.: The WU-Minn human connectome project: an overview. NeuroImage 80, 62–79 (2013)
Tournier, J.D., Calamante, F., Connelly, A.: MRtrix: diffusion tractography in crossing fiber regions. Int. J. Imaging Syst. Technol. 22, 53–66 (2012)
Singer, A.: From Graph to Manifold Laplacian: The Convergence Rate (2006)
Varoquaux, G., Buitinck, L., Grisel, O., Louppe, G., Mueller, A., Pedregosa, F.: Scikit-learn: machine learning without learning the machinery. GetMobile Mob. Comput. Commun. 19, 29–33 (2017)
Knösche, T.R., Tittgemeyer, M.: The role of long-range connectivity for the characterization of the functional-anatomical organization of the cortex. Front. Syst. Neurosci. 5, 58 (2011)
Zhang, H., Stanley, N., Mucha, P.J., Yin, W., Lin, W., Shen, D.: Multi-layer large-scale functional connectome reveals infant brain developmental patterns. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11072, pp. 136–144. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00931-1_16
Buckner, R.L., Krienen, F.M., Castellanos, A., Diaz, J.C., Yeo, B.T.T.: The organization of the human cerebellum estimated by intrinsic functional connectivity. J. Neurophysiol. 106, 2322–2345 (2011)
Power, J.D., et al.: Functional network organization of the human brain. Neuron 72, 665–678 (2011)
Garcia, K.E., et al.: Dynamic patterns of cortical expansion during folding of the preterm human brain. Proc. Natl. Acad. Sci. 115, 3156–3161 (2018)
Glasser, M.F., et al.: A multi-modal parcellation of human cerebral cortex. Nature 536, 171–178 (2016)
Wig, G.S., Laumann, T.O., Petersen, S.E.: An Approach for Parcellating Human Cortical Areas Using Resting-state Correlations (2014)
Acknowledgment
This work was supported in part by NIH grants (NS093842, EB022880, MH108914, AG042599, and AG041721).
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Taylor IV, H.P., Wu, Z., Wu, Y., Shen, D., Zhang, H., Yap, PT. (2019). Automated Parcellation of the Cortex Using Structural Connectome Harmonics. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11766. Springer, Cham. https://doi.org/10.1007/978-3-030-32248-9_53
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DOI: https://doi.org/10.1007/978-3-030-32248-9_53
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