Abstract
The archetypical folded shape of the human cortex has been a long-standing topic for neuroscientific research. Nevertheless, the accurate neuroanatomical segmentation of sulci remains a challenge. Part of the problem is the uncertainty of where a sulcus transitions into a gyrus and vice versa. This problem can be avoided by focusing on sulcal fundi and gyral crowns, which represent the topological opposites of cortical folding. We present Automated Brain Lines Extraction (ABLE), a method based on Laplacian surface collapse to reliably segment sulcal fundi and gyral crown lines. ABLE is built to work on standard FreeSurfer outputs and eludes the delineation of anastomotic sulci while maintaining sulcal fundi lines that traverse the regions with the highest depth and curvature. First, it segments the cortex into gyral and sulcal surfaces; then, each surface is spatially filtered. A Laplacian-collapse-based algorithm is applied to obtain a thinned representation of the surfaces. This surface is then used for careful detection of the endpoints of the lines. Finally, sulcal fundi and gyral crown lines are obtained by eroding the surfaces while preserving the connectivity between the endpoints. The method is validated by comparing ABLE with three other sulcal extraction methods using the Human Connectome Project (HCP) test-retest database to assess the reproducibility of the different tools. The results confirm ABLE as a reliable method for obtaining sulcal lines with an accurate representation of the sulcal topology while ignoring anastomotic branches and the overestimation of the sulcal fundi lines. ABLE is publicly available via https://github.com/HGGM-LIM/ABLE.
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Data Availability
The datasets generated during the current study are available from the corresponding author on reasonable request.
Code Availability
The source code of the application is available in https://github.com/HGGM-LIM/ABLE.
References
Aleman-Gomez, Y., Janssen, J., Schnack, H., Balaban, E., Pina-Camacho, L., Alfaro-Almagro, F., Castro-Fornieles, J., Otero, S., Baeza, I., Moreno, D., Bargallo, N., Parellada, M., Arango, C., & Desco, M. (2013). The Human Cerebral Cortex Flattens during Adolescence. Journal of Neuroscience, 33, 15004–15010. http://doi.org/10.1523/JNEUROSCI.1459-13.2013, https://www.jneurosci.org/lookup/doi/10.1523/JNEUROSCI.1459-13.2013
Alexander-Bloch, A. F., Raznahan, A., Vandekar, S. N., Seidlitz, J., Lu, Z., Mathias, S. R., Knowles, E., Mollon, J., Rodrigue, A., Curran, J. E., Görring, H. H. H., Satterthwaite, T. D., Gur, R. E., Bassett, D. S., Hoftman, G. D., Pearlson, G., Shinohara, R. T., Liu, S., Fox, P. T., Glahn, D. C. (2020). Imaging local genetic influences on cortical folding. Proceedings of the National Academy of Sciences, 117, 7430–7436. http://doi.org/10.1073/pnas.1912064117, http://www.pnas.org/lookup/doi/10.1073/pnas.1912064117
Amiez, C., Wilson, C. R. E., & Procyk, E. (2018). Variations of cingulate sulcal organization and link with cognitive performance. Scientific Reports, 8, 13988. http://doi.org/10.1038/s41598-018-32088-9, http://www.nature.com/articles/s41598-018-32088-9
Au, O. K.-C., Tai, C.-L., Chu, H.-K., Cohen-Or, D., & Lee, T.-Y. (2008). Skeleton extraction by mesh contraction. In ACM SIGGRAPH 2008 papers on - SIGGRAPH ’08 (p. 1). New York, New York, USA: ACM Press. http://doi.org/10.1145/1399504.1360643, http://portal.acm.org/citation.cfm?doid=1399504.1360643
Besl, P., & McKay, N. D. (1992). A method for registration of 3-D shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14, 239–256. http://doi.org/10.1109/34.121791, http://ieeexplore.ieee.org/document/121791/
Castellano, G., Lotufo, R., Falcao, A., & Cendes, F. (2003). Characterization of the human cortex in MR images through the image foresting transform. In Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429) (pp. I–357–60). IEEE volume 1. http://doi.org/10.1109/ICIP.2003.1246972, http://ieeexplore.ieee.org/document/1246972/
Caviness, V. S., Meyer, J., Makris, N., & Kennedy, D. N. (1996). MRI-Based Topographic Parcellation of Human Neocortex: An Anatomically Specified Method with Estimate of Reliability. Journal of Cognitive Neuroscience, 8, 566–587. http://doi.org/10.1162/jocn.1996.8.6.566, https://direct.mit.edu/jocn/article/8/6/566-587/3232
Dale, A. M., Fischl, B., & Sereno, M. I. (1999). Cortical Surface-Based Analysis. NeuroImage, 9, 179–194. http://doi.org/10.1006/nimg.1998.0395, http://linkinghub.elsevier.com/retrieve/pii/S1053811998903950
De Guio, F., Germanaud, D., Lefèvre, J., Fischer, C., Mangin, J. F., Chabriat, H., & Jouvent, E. (2019). Alteration of the Cortex Shape as a Proxy of White Matter Swelling in Severe Cerebral Small Vessel Disease. Frontiers in Neurology, 10, 753. http://doi.org/10.3389/FNEUR.2019.00753, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6635831/
Dijkstra, E. W. (1959). A note on two problems in connexion with graphs. Numerische Mathematik, 1, 269–271. http://doi.org/10.1007/BF01386390, http://link.springer.com/10.1007/BF01386390
Durrleman, S., Pennec, X., Trouvé, A., & Ayache, N. (2007). Measuring brain variability via sulcal lines registration: A diffeomorphic approach. In N. Ayache, S. Ourselin, & A. Maeder (Eds.), Medical Image Computing and Computer-Assisted Intervention - MICCAI 2007 (pp. 675–682). Berlin, Heidelberg: Springer, Berlin Heidelberg.
Fischer, C., Operto, G., Laguitton, S., Perrot, M., Denghien, I., Rivière, D., & Mangin, J.-F. (2012). Morphologist 2012: the new morphological pipeline of brainvisa. Proc. HBM.
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. (2013). The minimal preprocessing pipelines for the Human Connectome Project. NeuroImage, 80, 105–124. http://doi.org/10.1016/j.neuroimage.2013.04.127, https://linkinghub.elsevier.com/retrieve/pii/S1053811913005053
Hopkins, W. D., Meguerditchian, A., Coulon, O., Bogart, S., Mangin, J. F., Sherwood, C. C., Grabowski, M. W., Bennett, A. J., Pierre, P. J., Fears, S., Woods, R., Hof, P. R., & Vauclair, J. (2014). Evolution of the Central Sulcus Morphology in Primates. Brain, Behavior and Evolution, 84, 19–30. http://doi.org/10.1159/000362431, https://www.karger.com/Article/FullText/362431, https://www.karger.com/Article/Abstract/362431
Im, K., Choi, Y. Y., Yang, J. J., Lee, K. H., Kim, S. I., Grant, P. E., & Lee, J. M. (2011). The relationship between the presence of sulcal pits and intelligence in human brains. NeuroImage, 55, 1490–1496. http://dx.doi.org/10.1016/j.neuroimage.2010.12.080
Im, K., Lee, J.-M., Yoon, U., Shin, Y.-W., Hong, S. B., Kim, I. Y., Kwon, J. S., & Kim, S. I. (2006). Fractal dimension in human cortical surface: Multiple regression analysis with cortical thickness, sulcal depth, and folding area. Human Brain Mapping, 27, 994–1003. http://doi.org/10.1002/hbm.20238, https://onlinelibrary.wiley.com/doi/full/10.1002/hbm.20238, https://onlinelibrary.wiley.com/doi/abs/10.1002/hbm.20238, https://onlinelibrary.wiley.com/doi/10.1002/hbm.20238
Jacobson, A. et al. (2018). gptoolbox: Geometry processing toolbox. http://github.com/alecjacobson/gptoolbox
Janssen, J., Alemán-Gómez, Y., Schnack, H., Balaban, E., Pina-Camacho, L., Alfaro-Almagro, F., Castro-Fornieles, J., Otero, S., Baeza, I., Moreno, D., Bargalló, N., Parellada, M., Arango, C., & Desco, M. (2014). Cortical morphology of adolescents with bipolar disorder and with schizophrenia. Schizophrenia Research, 158, 91–99. http://doi.org/10.1016/j.schres.2014.06.040, https://linkinghub.elsevier.com/retrieve/pii/S0920996414003545
Joshi, A. A., Pantazis, D., Li, Q., Damasio, H., Shattuck, D. W., Toga, A. W., & Leahy, R. M. (2010). Sulcal set optimization for cortical surface registration. NeuroImage, 50, 950–9. http://doi.org/10.1016/j.neuroimage.2009.12.064, https://linkinghub.elsevier.com/retrieve/pii/S1053811909013536, http://www.ncbi.nlm.nih.gov/pubmed/20056160, http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC2832615
Kao, C.-Y., Hofer, M., Sapiro, G., Stern, J., Rehm, K., & Rottenberg, D. A. (2007). A Geometric Method for Automatic Extraction of Sulcal Fundi. IEEE Transactions on Medical Imaging, 26, 530–540. http://doi.org/10.1109/TMI.2006.886810, http://ieeexplore.ieee.org/document/4141194/
Kippenhan, J. S., Olsen, R. K., Mervis, C. B., Morris, C. A., & Kohn, P. (2005). Genetic Contributions to Human Gyrification: Sulcal Morphometry in Williams Syndrome. Journal of Neuroscience, 25, 7840–7846. http://doi.org/10.1523/JNEUROSCI.1722-05.2005, http://www.jneurosci.org/cgi/doi/10.1523/JNEUROSCI.1722-05.2005
Klein, A., Ghosh, S. S., Bao, F. S., Giard, J., Häme, Y., Stavsky, E., Lee, N., Rossa, B., Reuter, M., Chaibub Neto, E., & Keshavan, A. (2017). Mindboggling morphometry of human brains. PLOS Computational Biology, 13, e1005350. http://doi.org/10.1371/journal.pcbi.1005350, https://dx.plos.org/10.1371/journal.pcbi.1005350
Klein, A., & Tourville, J. (2012). 101 Labeled Brain Images and a Consistent Human Cortical Labeling Protocol. Frontiers in Neuroscience, 6, 171. http://doi.org/10.3389/fnins.2012.00171, http://www.ncbi.nlm.nih.gov/pubmed/23227001, http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC3514540, http://journal.frontiersin.org/article/10.3389/fnins.2012.00171/abstract
Kochunov, P., Glahn, D. C., Fox, P. T., Lancaster, J. L., Saleem, K., Shelledy, W., Zilles, K., Thompson, P. M., Coulon, O., Mangin, J. F., Blangero, J., & Rogers, J. (2010). Genetics of primary cerebral gyrification: Heritability of length, depth and area of primary sulci in an extended pedigree of Papio baboons. NeuroImage, 53, 1126–1134. http://doi.org/10.1002/hbm.20689, http://doi.wiley.com/10.1002/hbm.20689, https://onlinelibrary.wiley.com/doi/10.1002/hbm.20689
Kochunov, P., Robin, D. A., Royall, D. R., Coyle, T., Lancaster, J., Kochunov, V., Schlosser, A. E., & Fox, P. T. (2009). Can structural MRI indices of cerebral integrity track cognitive trends in executive control function during normal maturation and adulthood? Human Brain Mapping, 30, 2581–2594. http://doi.org/10.1016/j.neuroimage.2009.12.045, http://dx.doi.org/10.1016/j.neuroimage.2009.12.045
Kruskal, J. B. (1956). On the Shortest Spanning Subtree of a Graph and the Traveling Salesman Problem. Proceedings of the American Mathematical Society, 7, 48. http://doi.org/10.2307/2033241, https://www.jstor.org/stable/2033241?origin=crossref
Le Troter, A., Auzias, G., & Coulon, O. (2012). Automatic sulcal line extraction on cortical surfaces using geodesic path density maps. NeuroImage, 61, 941–949. http://doi.org/10.1016/j.neuroimage.2012.04.021, http://dx.doi.org/10.1016/j.neuroimage.2012.04.021
Leroy, F., Cai, Q., Bogart, S. L., Dubois, J., Coulon, O., Monzalvo, K., Fischer, C., Glasel, H., Van der Haegen, L., Bénézit, A., Lin, C.-P., Kennedy, D. N., Ihara, A. S., Hertz-Pannier, L., Moutard, M.-L., Poupon, C., Brysbaert, M., Roberts, N., Hopkins, W. D., Dehaene-Lambertz, G. (2015). New human-specific brain landmark: The depth asymmetry of superior temporal sulcus. Proceedings of the National Academy of Sciences, 112, 1208–1213. http://doi.org/10.1073/pnas.1412389112, http://www.pnas.org/lookup/doi/10.1073/pnas.1412389112
Lohmann, G., Kruggel, F., & von Cramon, D. Y. (1997). Automatic detection of sulcal bottom lines in mr images of the human brain. In J. Duncan & G. Gindi (Eds.), Information Processing in Medical Imaging (pp. 369–374). Berlin, Heidelberg: Springer, Berlin Heidelberg.
Lyu, I., Kim, S. H., Woodward, N. D., Styner, M. A., & Landman, B. A. (2018). TRACE: A Topological Graph Representation for Automatic Sulcal Curve Extraction. IEEE Transactions on Medical Imaging, 37, 1653–1663. http://doi.org/10.1109/TMI.2017.2787589
Mitchell, J. S. B., Mount, D. M., & Papadimitriou, C. H. (1987). The Discrete Geodesic Problem. SIAM Journal on Computing, 16, 647–668. http://doi.org/10.1137/0216045, http://epubs.siam.org/doi/10.1137/0216045
Pantazis, D., Joshi, A., Jiang, J., Shattuck, D. W., Bernstein, L. E., Damasio, H., & Leahy, R. M. (2010). Comparison of landmark-based and automatic methods for cortical surface registration. NeuroImage, 49, 2479–93. http://doi.org/10.1016/j.neuroimage.2009.09.027, https://linkinghub.elsevier.com/retrieve/pii/S1053811909010064, http://www.ncbi.nlm.nih.gov/pubmed/19796696, http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC2818237
Pizzagalli, F., Auzias, G., Yang, Q., Mathias, S. R., Faskowitz, J., Boyd, J. D., Amini, A., Rivière, D., McMahon, K. L., de Zubicaray, G. I., Martin, N. G., Mangin, J.-F., Glahn, D. C., Blangero, J., Wright, M. J., Thompson, P. M., Kochunov, P., & Jahanshad, N. (2020). The reliability and heritability of cortical folds and their genetic correlations across hemispheres. Communications Biology, 3, 510. http://doi.org/10.1038/s42003-020-01163-1, http://www.nature.com/articles/s42003-020-01163-1
Pron, A., Deruelle, C., & Coulon, O. (2021). U-shape short-range extrinsic connectivity organisation around the human central sulcus. Brain Structure and Function, 226, 179–193. http://doi.org/10.1007/s00429-020-02177-5, https://doi.org/10.1007/s00429-020-02177-5http://link.springer.com/10.1007/s00429-020-02177-5
Renault, C., Desvignes, M., & Revenu, M. (2000). 3D curves tracking and its application to cortical sulci detection. In Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101) (pp. 491–494 vol.2). IEEE. http://doi.org/10.1109/ICIP.2000.899462, http://ieeexplore.ieee.org/document/899462/
Rettmann, M. E., Han, X., Xu, C., & Prince, J. L. (2002). Automated sulcal segmentation using watersheds on the cortical surface. NeuroImage, 15, 329–344. http://doi.org/10.1006/nimg.2001.0975
Seong, J. K., Im, K., Yoo, S. W., Seo, S. W., Na, D. L., & Lee, J. M. (2010). Automatic extraction of sulcal lines on cortical surfaces based on anisotropic geodesic distance. NeuroImage, 49, 293–302. http://doi.org/10.1016/J.NEUROIMAGE.2009.08.013, https://pubmed.ncbi.nlm.nih.gov/19683580/
Shattuck, D. W., Joshi, A. A., Pantazis, D., Kan, E., Dutton, R. A., Sowell, E. R., Thompson, P. M., Toga, A. W., & Leahy, R. M. (2009). Semi-automated method for delineation of landmarks on models of the cerebral cortex. Journal of Neuroscience Methods, 178, 385–392. http://doi.org/10.1016/j.jneumeth.2008.12.025, https://linkinghub.elsevier.com/retrieve/pii/S0165027008007401
Shattuck, D. W., & Leahy, R. M. (2000). BrainSuite: An Automated Cortical Surface Identification Tool. In S. L. Delp, A. M. DiGoia, & B. Jaramaz (Eds.), Medical Image Computing and Computer-Assisted Intervention – MICCAI 2000 (pp. 50–61). Berlin, Heidelberg: Springer Berlin Heidelberg. http://doi.org/10.1007/978-3-540-40899-4_6, http://link.springer.com/10.1007/978-3-540-40899-4_6
Shi, Yonggang, Thompson, P., Dinov, I., & Toga, A. (2008). Hamilton-Jacobi Skeleton on Cortical Surfaces. IEEE Transactions on Medical Imaging, 27, 664–673. http://doi.org/10.1109/TMI.2007.913279, http://ieeexplore.ieee.org/document/4389763/
Shokouhi, M., Williams, J. H., Waiter, G. D., & Condon, B. (2012). Changes in the Sulcal Size Associated With Autism Spectrum Disorder Revealed by Sulcal Morphometry. Autism Research, 5, 245–252. http://doi.org/10.1002/AUR.1232, https://onlinelibrary.wiley.com/doi/full/10.1002/aur.1232, https://onlinelibrary.wiley.com/doi/abs/10.1002/aur.1232, https://onlinelibrary.wiley.com/doi/10.1002/aur.1232
Vallat, R. (2018). Pingouin: statistics in python. The Journal of Open Source Software, 3, 1026.
Van Essen, D. C. (1997). A tension-based theory of morphogenesis and compact wiring in the central nervous system. Nature, 385, 313–318. http://doi.org/10.1038/385313a0, http://www.nature.com/articles/385313a0
Van Essen, D. C., Ugurbil, K., Auerbach, E., Barch, D., Behrens, T. E., Bucholz, R., Chang, A., Chen, L., Corbetta, M., Curtiss, S. W., Della Penna, S., Feinberg, D., Glasser, M. F., Harel, N., Heath, A. C., Larson-Prior, L., Marcus, D., Michalareas, G., Moeller, S., Yacoub, E. (2012). The Human Connectome Project: A data acquisition perspective. NeuroImage, 62, 2222–2231. http://doi.org/10.1016/j.neuroimage.2012.02.018
Wagstyl, K., Ronan, L., Whitaker, K. J., Goodyer, I. M., Roberts, N., Crow, T. J., & Fletcher, P. C. (2016). Multiple markers of cortical morphology reveal evidence of supragranular thinning in schizophrenia. Translational Psychiatry, 6, e780. http://doi.org/10.1038/TP.2016.43, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4872401/
Welker, W. (1990). Why Does Cerebral Cortex Fissure and Fold? In Cerebral Cortex (pp. 3–136). Springer US. http://doi.org/10.1007/978-1-4615-3824-0_1, http://link.springer.com/10.1007/978-1-4615-3824-0_1
Yekutieli, D., & Benjamini, Y. (2001). The control of the false discovery rate in multiple testing under dependency. The Annals of Statistics, 29, 1165–1188. http://doi.org/10.1214/aos/1013699998, http://projecteuclid.org/euclid.aos/1013699998
Acknowledgements
Data were provided by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University.
Funding
This work was supported by the project exAScale ProgramIng models for extreme Data procEssing (ASPIDE), that has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 801091. This work has received funding from “la Caixa” Foundation under the project code LCF/PR/HR19/52160001. Susanna Carmona funded by Instituto de Salud Carlos III, co-funded by European Social Fund “Investing in your future” (Miguel Servet Type I research contract CP16/00096). The CNIC is supported by the Instituto de Salud Carlos III (ISCIII), the Ministerio de Ciencia e Innovación (MCIN) and the Pro CNIC Foundation, and is a Severo Ochoa Center of Excellence (SEV-2015-0505). Yasser Alemán-Gómez is supported by the Swiss National Science Foundation (185897) and the National Center of Competence in Research (NCCR) SYNAPSY - The Synaptic Bases of Mental Diseases, funded as well by the Swiss National Science Foundation (51AU40-1257).
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Yasser Alemán-Gómez, Alberto Fernández-Pena, Daniel Martín de Blas, and Javier Santonja conceived the method. Yasser Alemán-Gómez, Alberto Fernández-Pena, Luis Marcos-Vidal, Pedro M. Gordaliza, and Francisco J. Navas Sánchez conceived the evaluation experiments and analyzed the results. Alberto Fernández-Pena, Daniel Martín de Blas, Susanna Carmona, Joost Janssen, Yasser Alemán-Gómez, and Manuel Desco worked on the redaction of the manuscript. All authors reviewed the manuscript.
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Fernández-Pena, A., Martín de Blas, D., Navas-Sánchez, F.J. et al. ABLE: Automated Brain Lines Extraction Based on Laplacian Surface Collapse. Neuroinform 21, 145–162 (2023). https://doi.org/10.1007/s12021-022-09601-7
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DOI: https://doi.org/10.1007/s12021-022-09601-7