The superoanterior fasciculus (SAF) is defined as a bilateral tract in the frontal lobe that resembles the structure of the anterior cingulum, but is located superior, anterior and lateral. In this work, we investigated the cortical projections of the structure utilizing the latest multi-shell multi tissue (MSMT) constrained spherical deconvolution (CSD) method to analyze diffusion MRI data from the Human Connectome Project (HCP). Our results show that the paracentral lobular, mid cingulate cortex and the orbital- and polar frontal cortex show high SAF termination prevalence, suggesting a novel connection in the frontal lobe.
[1] S. David et al., “The Superoanterior Fasciculus (SAF): A Novel White Matter Pathway in the Human Brain?,” Front. Neuroanat., vol. 13, no. March, pp. 1–18, Mar. 2019.
[2] S. Sarubbo et al., “The course and the anatomo-functional relationships of the optic radiation: a combined study with ‘post mortem’ dissections and ‘ in vivo ’ direct electrical mapping,” J. Anat., vol. 226, no. 1, pp. 47–59, Jan. 2015.
[3] H. Axer et al., “Microstructural analysis of human white matter architecture using polarized light imaging: views from neuroanatomy,” Front. Neuroinform., vol. 5, no. November, pp. 1–12, 2011.
[4] S. M. Smith et al., “Correspondence of the brain’s functional architecture during activation and rest,” Proc. Natl. Acad. Sci. U. S. A., vol. 106, no. 31, pp. 13040–13045, Aug. 2009.[5] L. H. Scholtens, M. A. de Reus, S. C. de Lange, R. Schmidt, and M. P. van den Heuvel, “An MRI Von Economo – Koskinas atlas,” Neuroimage, vol. 170, pp. 249–256, Apr. 2018.[6] M. F. Glasser et al., “A multi-modal parcellation of human cerebral cortex,” Nature, vol. 536, no. 7615, pp. 171–178, 2016.
[7] M. Catani and D. H. Ffytche, “The rises and falls of disconnection syndromes,” Brain, vol. 128, no. 10. Oxford University Press, pp. 2224–2239, 2005.
[8] M. Thiebaut de Schotten et al., “From Phineas Gage and Monsieur Leborgne to H.M.: Revisiting Disconnection Syndromes,” Cereb. Cortex, vol. 25, no. 12, pp. 4812–4827, Dec. 2015.
[9] P. N. Alves et al., “An improved neuroanatomical model of the default-mode network reconciles previous neuroimaging and neuropathological findings,” Commun. Biol. 2019 21, vol. 2, no. 1, pp. 1–14, 2019.
[10] F. Corrivetti et al., “Dissociating motor–speech from lexico-semantic systems in the left frontal lobe: insight from a series of 17 awake intraoperative mappings in glioma patients,” Brain Struct. Funct., vol. 224, no. 3, pp. 1151–1165, Apr. 2019.
[11] V. Pacella et al., “Anosognosia for hemiplegia as a tripartite disconnection syndrome,” Elife, vol. 8, Aug. 2019.[12] M. F. Glasser et al., “The minimal preprocessing pipelines for the Human Connectome Project,” Neuroimage, vol. 80, pp. 105–124, Oct. 2013.
[13] D. C. Van Essen, S. M. Smith, D. M. Barch, T. E. J. Behrens, E. Yacoub, and K. Ugurbil, “The WU-Minn Human Connectome Project: An overview,” Neuroimage, vol. 80, pp. 62–79, 2013.
[14] F. Guo, A. Leemans, M. A. Viergever, F. Dell’Acqua, and A. De Luca, “Generalized Richardson-Lucy (GRL) for analyzing multi-shell diffusion MRI data,” Oct. 2019.
[15] S. N. Sotiropoulos et al., “Advances in diffusion MRI acquisition and processing in the Human Connectome Project,” Neuroimage, vol. 80, pp. 125–143, 2013.
[16] R. Bammer et al., “Analysis and generalized correction of the effect of spatial gradient field distortions in diffusion-weighted imaging,” Magn. Reson. Med., vol. 50, no. 3, pp. 560–569, Sep. 2003.
[17] H. Y. Mesri, S. David, M. A. Viergever, and A. Leemans, “The adverse effect of gradient nonlinearities on diffusion MRI: From voxels to group studies,” Neuroimage, p. 116127, 2019.
[18] A. Leemans et al., “ExploreDTI: a graphical toolbox for processing, analyzing, and visualizing diffusion MR data,” Proc. Int. Soc. Magn. Reson. Med., vol. 17, no. 2, p. 3537, 2009.
[19] C. Lebel, L. Walker, A. Leemans, L. Phillips, and C. Beaulieu, “Microstructural maturation of the human brain from childhood to adulthood,” Neuroimage, vol. 40, no. 3, pp. 1044–1055, Apr. 2008.
[20] B. Fischl, “FreeSurfer,” NeuroImage, vol. 62, no. 2. Academic Press, pp. 774–781, 15-Aug-2012.
[21] C. Gaser and R. Dahnke, “CAT-a computational anatomy toolbox for the analysis of structural MRI data,” Hbm, vol. 2016, no. 7, pp. 336–48, 2016.
[22] R. S. Desikan et al., “An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest,” Neuroimage, vol. 31, no. 3, pp. 968–980, Jul. 2006.
[23] C. Destrieux, B. Fischl, A. Dale, and E. Halgren, “Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature,” Neuroimage, vol. 53, no. 1, pp. 1–15, Oct. 2010.
[24] E. Mandonnet, S. Sarubbo, and L. Petit, “The Nomenclature of Human White Matter Association Pathways: Proposal for a Systematic Taxonomic Anatomical Classification,” Front. Neuroanat., vol. 12, no. November, p. 94, Nov. 2018.
[25] M. Catani et al., “Short frontal lobe connections of the human brain,” Cortex, vol. 48, no. 2, pp. 273–291, 2012.
[26] J. D. Schmahmann and D. N. Pandya, Fiber Pathways of the Brain. 2009.
[27] I. N. C. Lawes et al., “Atlas-based segmentation of white matter tracts of the human brain using diffusion tensor tractography and comparison with classical dissection,” Neuroimage, 2008.