Abstract
The central sulcus is probably one of the most studied folds in the human brain, owing to its clear relationship with primary sensory-motor functional areas. However, due to the difficulty of estimating the trajectories of the U-shape fibres from diffusion MRI, the short structural connectivity of this sulcus remains relatively unknown. In this context, we studied the spatial organization of these U-shape fibres along the central sulcus. Based on high quality diffusion MRI data of 100 right-handed subjects and state-of-the-art pre-processing pipeline, we first define a connectivity space that provides a comprehensive and continuous description of the short-range anatomical connectivity around the central sulcus at both the individual and group levels. We then infer the presence of five major U-shape fibre bundles at the group level in both hemispheres by applying unsupervised clustering in the connectivity space. We propose a quantitative investigation of their position and number of streamlines as a function of hemisphere, sex and functional scores such as handedness and manual dexterity. Main findings of this study are twofold: a description of U-shape short-range connectivity along the central sulcus at group level and the evidence of a significant relationship between the position of three hand related U-shape fibre bundles and the handedness score of subjects.
Similar content being viewed by others
Availability of data and material
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.
References
Assaf Y, Johansen-Berg H, Thiebaut de Schotten M (2019) The role of diffusion MRI in neuroscience. NMR Biomed 32:e3762. https://doi.org/10.1002/nbm.3762
Boling W, Olivier A, Bittar RG, Reutens D (1999) Localization of hand motor activation in Broca’s pli de passage moyen. J Neurosurg 91(6):903–910
Broca P (1888) Mémoires d’anthropologie. Reiwald
Brun L, Pron A, Sein J et al (2019) Diffusion MRI: assessment of the impact of acquisition and preprocessing methods using the BrainVisa-diffuse toolbox. Front Neurosci 13:536. https://doi.org/10.3389/fnins.2019.00536
Caruyer E, Lenglet C, Sapiro G, Deriche R (2013) Design of multishell sampling schemes with uniform coverage in diffusion MRI: design of multishell sampling schemes. Magn Reson Med 69:1534–1540. https://doi.org/10.1002/mrm.24736
Catani M, Dell’Acqua F, Vergani F et al (2012) Short frontal lobe connections of the human brain. Cortex 48:273–291. https://doi.org/10.1016/j.cortex.2011.12.001
Coulon O, Lefevre J, Kloppel S et al (2015) Quasi-isometric length parameterization of cortical sulci: application to handedness and the central sulcus morphology. 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI). IEEE, Brooklyn, NY, USA, pp 1268–1271
Croxson PL (2005) Quantitative investigation of connections of the prefrontal cortex in the human and macaque using probabilistic diffusion tractography. J Neurosci 25:8854–8866. https://doi.org/10.1523/JNEUROSCI.1311-05.2005
Daducci A, Dal Palu A, Lemkaddem A, Thiran J-P (2013) A convex optimization framework for global tractography. 2013 IEEE 10th International Symposium on Biomedical Imaging. IEEE, San Francisco, CA, USA, pp 524–527
Daducci A, Dal Palu A, Lemkaddem A, Thiran J-P (2015) COMMIT: convex optimization modeling for microstructure informed tractography. IEEE Trans Med Imaging 34:246–257. https://doi.org/10.1109/TMI.2014.2352414
Dawson-Haggerty et al (2019) Trimesh. Version 3.2.0. https://trimsh.org/. Accessed 5 May 2020
Dejerine J, Déjerine-Klumpke A (1895) Anatomie des centres nerveux. Rueff, Paris
Dubois J, Dehaene-Lambertz G, Kulikova S et al (2014) The early development of brain white matter: a review of imaging studies in fetuses, newborns and infants. Neuroscience 276:48–71. https://doi.org/10.1016/j.neuroscience.2013.12.044
Ester M, Kriegel H-P, Sander J, Xu X (1996) A Density-based Algorithm for Discovering Clusters a Density-based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. In: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining. AAAI Press, pp 226–231
Fischer C, Operto G, Laguitton S et al (2012) Morphologist 2012: the new morphological pipeline of BrainVISA. Brain Struct Funct 221:36–71
Fischl B (2012) FreeSurfer neuroImage 62:774–781. https://doi.org/10.1016/j.neuroimage.2012.01.021
Geffroy D, Rivière D, Denghien I et al (2011) BrainVISA: a complete software platform for neuroimaging. Python in Neuroscience workshop, Paris
Germann J, Chakravarty MM, Collins LD, Petrides M (2019) Tight Coupling between morphological features of the central sulcus and somatomotor body representations: a combined anatomical and functional MRI study. Cereb Cortex. https://doi.org/10.1093/cercor/bhz208
Glasser MF, Sotiropoulos SN, Wilson JA et al (2013) The minimal preprocessing pipelines for the human connectome project. NeuroImage 80:105–124. https://doi.org/10.1016/j.neuroimage.2013.04.127
Griffa A, Baumann PS, Thiran J-P, Hagmann P (2013) Structural connectomics in brain diseases. NeuroImage 80:515–526. https://doi.org/10.1016/j.neuroimage.2013.04.056
Guevara M, Román C, Houenou J et al (2017) Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography. NeuroImage 147:703–725. https://doi.org/10.1016/j.neuroimage.2016.11.066
Guevara M, Sun ZY, Guevara P et al (2018) Effect of the central sulcus morphology on the underlying U-bundle organization. 1
Hikosaka O, Tanaka M, Sakamoto M, Iwamura Y (1985) Deficits in manipulative behaviors induced by local injections of muscimol in the first somatosensory cortex of the conscious monkey. Brain Res 325:375–380. https://doi.org/10.1016/0006-8993(85)90344-0
Jenkinson M, Beckmann CF, Behrens TEJ et al (2012) FSL NeuroImage 62:782–790. https://doi.org/10.1016/j.neuroimage.2011.09.015
Jeurissen B, Tournier J-D, Dhollander T et al (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
Jones DK, Knösche TR, Turner R (2013) White matter integrity, fiber count, and other fallacies: the do’s and don’ts of diffusion MRI. NeuroImage 73:239–254. https://doi.org/10.1016/j.neuroimage.2012.06.081
Karolis VR, Corbetta M, Thiebaut de Schotten M (2018) The architecture of functional lateralisation and its relationship to callosal connectivity in the human brain. BioRxiv. https://doi.org/10.1101/372300
Le Troter A, Rivière D, Coulon O, Troter ALe, Team M (2011) An interactive sulcal fundi editor in Brainvisa. In: International conference on human brain mapping. Québec, Canada, pp 8–9
Le Troter A, Auzias G, Coulon O (2012) Automatic sulcal line extraction on cortical surfaces using geodesic path density maps. NeuroImage 61(4):941–949. https://doi.org/10.1016/j.neuroimage.2012.04.021
Magro E, Moreau T, Seizeur R et al (2012) Characterization of short white matter fiber bundles in the central area from diffusion tensor MRI. Neuroradiology 54:1275–1285. https://doi.org/10.1007/s00234-012-1073-1
Maier-Hein KH, Neher PF, Houde J-C et al (2017) The challenge of mapping the human connectome based on diffusion tractography. Nat Commun. https://doi.org/10.1038/s41467-017-01285-x
Mandonnet E, Sarubbo S, Petit L (2018) The nomenclature of human white matter association pathways: proposal for a systematic taxonomic anatomical classification. Front Neuroanat 12:94. https://doi.org/10.3389/fnana.2018.00094
Marcus DS, Harms MP, Snyder AZ et al (2013) Human connectome project informatics: quality control, database services, and data visualization. NeuroImage 80:202–219. https://doi.org/10.1016/j.neuroimage.2013.05.077
Mathiowetz V, Weber K, Kashman N, Volland G (1985) Adult norms for the nine hole peg test of finger dexterity. Occup Ther J Res 5:24–38. https://doi.org/10.1177/153944928500500102
Maximov II, Alnæs D, Westlye LT (2019) Towards an optimised processing pipeline for diffusion magnetic resonance imaging data: Effects of artefact corrections on diffusion metrics and their age associations in UK Biobank. Hum Brain Mapp. https://doi.org/10.1002/hbm.24691
Meynert TD (1885) A Clinical Treatise on Diseases of the Fore-brain Based upon a Study of its Structure, Functions, and Nutrition. GP Putnam’s Sons, New York
Michio OMD, Abernathey CD, Kubik S (1990) Atlas of the cerebral sulci. In: Stuttgart G (ed) Thieme Verlag. Thieme Medical Publishers, Newyork
Oldfield RC (1971) The assessment and analysis of handedness: the edinburgh inventory. Neuropsychologia 9:97–113. https://doi.org/10.1016/0028-3932(71)90067-4
Panagiotaki E, Schneider T, Siow B et al (2012) Compartment models of the diffusion MR signal in brain white matter: a taxonomy and comparison. NeuroImage 59:2241–2254. https://doi.org/10.1016/j.neuroimage.2011.09.081
Pedregosa F, Varoquaux G, Gramfort A et al (2011) Scikit-learn: machine learning in python. J Mach Learn Res 12:2825–2830
Pron A, Brun L, Deruelle C, Coulon O (2018) Dense and structured representations of U-Shape fibers connectivity in the central sulcus. C., United States, IEEE, Washington D
Reveley C, Seth AK, Pierpaoli C et al (2015) Superficial white matter fiber systems impede detection of long-range cortical connections in diffusion MR tractography. Proc Natl Acad Sci 112:E2820–E2828. https://doi.org/10.1073/pnas.1418198112
Rivière D, Geoffroy D, Denghien I, Souedet N, Cointepas Y (2011) Anatomist: a python framework for interactive 3D visualization of neuroimaging data. Python in Neuroscience Workshop. pp 3–4
Rojkova K, Volle E, Urbanski M et al (2016) Atlasing the frontal lobe connections and their variability due to age and education: a spherical deconvolution tractography study. Brain Struct Funct 221:1751–1766. https://doi.org/10.1007/s00429-015-1001-3
Román C, Guevara M, Valenzuela R et al (2017) Clustering of whole-brain white matter short association bundles using HARDI data. Front Neuroinform. https://doi.org/10.3389/fninf.2017.00073
Saygin ZM, Osher DE, Koldewyn K et al (2011) Anatomical connectivity patterns predict face selectivity in the fusiform gyrus. Nat Neurosci 15:321–327. https://doi.org/10.1038/nn.3001
Schilling K, Gao Y, Janve V et al (2018) Confirmation of a gyral bias in diffusion MRI fiber tractography. Hum Brain Mapp 39:1449–1466. https://doi.org/10.1002/hbm.23936
Schilling KG, Nath V, Hansen C et al (2019) Limits to anatomical accuracy of diffusion tractography using modern approaches. NeuroImage 185:1–11. https://doi.org/10.1016/j.neuroimage.2018.10.029
Schmahmann JD, Pandya DN (2006) Fiber pathways of the brain. Oxford University Press, Oxford
Schuz A, Braitenberg V (2002) The human cortical white matter: quantitative aspects of cortico-cortical long-range connectivity. In: Miller R (ed) Schuz A. Cortical Areas, Unity and Diversity, pp 377–386
Sinke MRT, Otte WM, Christiaens D et al (2018) Diffusion MRI-based cortical connectome reconstruction: dependency on tractography procedures and neuroanatomical characteristics. Brain Struct Funct 223:2269–2285. https://doi.org/10.1007/s00429-018-1628-y
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 62:1924–1938. https://doi.org/10.1016/j.neuroimage.2012.06.005
Smith RE, Tournier J-D, Calamante F, Connelly A (2015) The effects of SIFT on the reproducibility and biological accuracy of the structural connectome. NeuroImage 104:253–265. https://doi.org/10.1016/j.neuroimage.2014.10.004
Song AW, Chang H-C, Petty C et al (2014) Improved delineation of short cortical association fibers and gray/white matter boundary using whole-brain three-dimensional diffusion tensor imaging at submillimeter spatial resolution. Brain Connect 4:636–640. https://doi.org/10.1089/brain.2014.0270
Sporns O, Tononi G, Kötter R (2005) The human connectome: a structural description of the human brain. PLoS Comput Biol 1:e42. https://doi.org/10.1371/journal.pcbi.0010042
Sun ZY, Klöppel S, Rivière D et al (2012) The effect of handedness on the shape of the central sulcus. NeuroImage 60:332–339. https://doi.org/10.1016/j.neuroimage.2011.12.050
Sun B, Ge H, Tang Y, Hou Z, Xu J, Lin X, Liu S (2015) Asymmetries of the central sulcus in young adults: effects of gender, age and sulcal pattern. Int J Dev Neurosci. https://doi.org/10.1016/j.ijdevneu.2015.06.003
Sun ZY, Pinel P, Rivière D, Moreno A, Dehaene S, Mangin J-F (2016) Linking morphological and functional variability in hand movement and silent reading. Brain Struct Funct 221(7):3361–3371. https://doi.org/10.1007/s00429-015-1106-8
Thompson A, Murphy D, Dell’Acqua F et al (2017) Impaired communication between the motor and somatosensory homunculus is associated with poor manual dexterity in autism spectrum disorder. Biol Psychiatry 81:211–219. https://doi.org/10.1016/j.biopsych.2016.06.020
Tournier JD, Calamante F, Connelly A (2010) Improved probabilistic streamlines tractography by 2nd order integration over fibre orientation distributions. Int Soc Mag Reson Med (ISMRM) 1:1670
Tournier J-D, Smith R, Raffelt D et al (2019) MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation. NeuroImage 202:116137. https://doi.org/10.1016/j.neuroimage.2019.116137
Tustison NJ, Avants BB, Cook PA et al (2010) N4ITK: improved N3 bias correction. IEEE Trans Med Imaging 29:1310–1320. https://doi.org/10.1109/TMI.2010.2046908
Van Essen DC, Smith SM, Barch DM et al (2013) The WU-Minn human connectome project: an overview. NeuroImage 80:62–79. https://doi.org/10.1016/j.neuroimage.2013.05.041
Viganò L, Fornia L, Rossi M et al (2019) Anatomo-functional characterisation of the human “hand-knob”: A direct electrophysiological study. Cortex 113:239–254. https://doi.org/10.1016/j.cortex.2018.12.011
Wendelken C, Ferrer E, Ghetti S et al (2017) Frontoparietal structural connectivity in childhood predicts development of functional connectivity and reasoning ability: a large-scale longitudinal investigation. J Neurosci 37:8549–8558. https://doi.org/10.1523/JNEUROSCI.3726-16.2017
Acknowledgements
The authors would like to thank Pr. Alessandro Daducci for his precious advice and discussion about the COMMIT framework. 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
Alexandre Pron is supported by doctoral a grant from Aix-Marseille University.
Author information
Authors and Affiliations
Contributions
All authors contributed to the study conception and design. AP implemented the processing pipeline and performed meshes visual quality control. OC and AP manually drew the surface cortical landmarks of the central sulcus onto the grey matter/white matter interface meshes. All authors contributed to the statistical analyses, the preparation, the writing and the correction of the article. All authors read and approved the final manuscript.
Corresponding author
Ethics declarations
Conflicts of interest
The authors declare that they have no conflict of interest.
Code availability
The code used to carry out the analyses of the current study is publicly available at https://github.com/alexpron/article_central_sulcus_connectivity.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Pron, A., Deruelle, C. & Coulon, O. U-shape short-range extrinsic connectivity organisation around the human central sulcus. Brain Struct Funct 226, 179–193 (2021). https://doi.org/10.1007/s00429-020-02177-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00429-020-02177-5