Exploring the Spectrum of Sulcal and Gyral Morphology in the Human Cerebrum.
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Individual neuroanatomy exhibits considerable variability in the folding patterns of the superolateral cerebral cortex, specifically in its gyri (ridges) and sulci (grooves). While often considered incidental, the clinical relevance of these variations remains largely unexplored. This study investigated the prevalence and types of sulcal and gyral variations on the superolateral cortical surface and initially explored their potential association with neurological history. A detailed cadaveric analysis was conducted on 56 human brains obtained from the Department of Anatomy, JHMC. The superolateral surfaces of both hemispheres were meticulously examined, and any deviations from standard sulcal and gyral patterns were identified and categorized using established anatomical classifications. The study revealed sulcal and gyral variations in 70% of the examined brains, including variations such as Superfrontal Gyral, Inferior Frontal Gyral, Post Central Gyral, Sylvian fissure bifurcation, and atypical frontal gyri. Preliminary statistical analysis explored potential correlations between these specific variations and the documented medical history of the donors, with a focus on neurological conditions. This cadaveric analysis highlights the significant prevalence of sulcal and gyral variations in the human superolateral cortex, underscoring the need for further research to elucidate their precise clinical implications. Future studies with larger, well-characterized cohorts are warranted to investigate potential links between these anatomical variations and neurological function or dysfunction
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Brodmann, K. (1909). Vergleichende Lokalisationslehre der Grosshirnrinde in ihren Prinzipien dargestellt auf Grund des Zellenbaues. Johann Ambrosius Barth.
Cao, M., Wang, L., Dai, Z., Xia, M., Jiang, T., & Evans, A. C. (2017). Test–retest reliability of brain morphometric measures from T1-weighted MRI across different scanners and acquisition protocols. NeuroImage, 145, 262–278.
Chan, M. Y., Baker, J. T., Van Essen, D. C., & Schlaggar, B. L. (2014). Development of large-scale functional networks in children. Cerebral Cortex, 24(6), 1397–1406.
Deng, B., Wang, J., Xue, S. W., Li, K., Kendrick, K. M., & Becker, B. (2014). Functional connectivity is stronger in gyri than sulci. Brain Structure and Function, 219(5), 1659–1669.
Fischl, B., & Dale, A. M. (2000). Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proceedings of the National Academy of Sciences, 97(20), 11050–11055.
Fjell, A. M., Grydeland, H., Westlye, L. T., Amlien, I. K., Espeseth, T., & Walhovd, K. B. (2015). Large-scale gradients in human cortical organization account for age-related differences in brain structure. Proceedings of the National Academy of Sciences, 112(32), 9987–9992.
Good, C. D., Johnsrude, I. S., Ashburner, J., Henson, R. N. A., Friston, K. J., & Frackowiak, R. S. J. (2001). A voxel-based morphometric study of ageing in 465 normal adult brains. NeuroImage, 14(1 Pt 1), 21–36.
Gregory, S., Manohar, S., Beckmann, C. F., Proal, E., Deakin, J. F. W., & Huyser, C. (2016). Age-related changes in regional gyrification and their relationship with cognitive decline. Brain Structure and Function, 221(1), 439–453.
Griffin, L. D. (1994). Topological imagery. Springer-Verlag.
Im, K., Lee, J. M., Lyttelton, O. C., Kim, S. H., Evans, A. C., & Kim, S. I. (2008). Gray matter atrophy in multiple system atrophy: A meta-analysis of voxel-based morphometry studies. Movement Disorders, 23(10), 1424–1431.
Jernigan, T. L., Archibald, S. L., Fennema-Notestine, C., Gamst, A. C., Stout, J. C., Bonner, J., & Hesselink, J. R. (2001). Effects of age on tissues and regions of the human brain. NeuroImage, 14(1 Pt 1), 41–58.
Jones, S., Verhagen, L., Burnett Heyes, S., Moustafa, A. A., & Sahakian, B. J. (2006). The relationship between regional grey matter volume and attentional set-shifting in healthy young adults. Cerebral Cortex, 16(12), 1753–1758.
Kaas, J. H. (2013). Evolution of neocortex in primates. The Quarterly Review of Biology, 88(1), 51–67.
Klein, S., Tourville, J., Paus, T., & против, В. (2014). Surface-based morphometry reveals structural correlates of age in the human cerebral cortex. NeuroImage, 92, 375–385.
Liu, Y., Liang, X., Zhou, Y., He, Y., & Jiang, T. (2017). Age-related changes in topological organization of human brain functional networks during resting state. NeuroImage, 147, 15–27.
Llinares-Benadero, C., & Borrell, V. (2019). Human cortex development: Genes, evolution and disease. Nature Reviews Neuroscience, 20(2), 115–137.
Magnotta, V. A., Andreasen, N. C., Schultz, S. K., Harris, G., Cizadlo, T., Heckel, D., & Flaum, M. (1999). Quantitative in vivo measurement of gyrification in the human brain: Age-related changes. Cerebral Cortex, 9(6), 652–658.
Mega, M. S., Thompson, P. M., Cummings, J. L., Backman, L., Burns, A., Weiner, H. W., & Toga, A. W. (1998). Sulcal–gyral patterns of the human brain: Relationship to cognition in normal aging and Alzheimer’s disease. Cerebral Cortex, 8(8), 794–803.
Nadarajah, B., & Parnavelas, J. G. (2002). Modes of cell division of progenitor cells in the developing cerebral cortex. Nature Neuroscience, 5(7), 675–681.
Nanda, P., Pathak, A., Saini, J., Tripathi, M., & Chandra, P. S. (2014). Local gyrification index in patients with refractory focal epilepsy with and without hippocampal sclerosis. Epilepsy Research, 108(8), 1371–1379.
Panizzon, M. S., Fennema-Notestine, C., Eyler, L. T., Hagler Jr, D. J., Lyons, M. J., Dale, A. M., Schork, N. J., & Jernigan, T. L. (2009). Age-related changes in brain structure across the adult lifespan. Cerebral Cortex, 19(7), 1492–1501.
Peng, S., Fan, Y., Wang, K., Li, K., Chen, K., & Evans, A. C. (2016). Heritability of regional brain gyrification in healthy young adults. Brain Structure and Function, 221(1), 599–611.
Raz, N., Gunning-Dixon, F. M., Head, D., Dupuis, J. H., Rodrigue, K. M., Williamson, J., & Acker, J. D. (2004). Age-related regional differences in rates of change in brain structure: A longitudinal MRI study of healthy adults. NeuroImage, 21(3), 1162–1171.
Richman, D. P., Stewart, R. M., Hutchinson, J. W., & Caviness Jr, V. S. (1975). Mechanical model of brain convolutional development. Science, 189(4196), 18–21.
Ronan, L., & Fletcher, P. C. (2015). From genes to folds: A review of cortical gyrification. Brain Structure and Function, 220(5), 2475–2490.
Ronan, L., Kyriakopoulou, V., den Ouden, H. E. M., & Fletcher, P. C. (2014). Multivariate mapping of the relationship between intrinsic curvature and cortical thickness across the adult human brain. Cerebral Cortex, 24(5), 1199–1211.
Ronan, L.,θήκη, Β., & Fletcher, P. C. (2011). Intrinsic curvature as a natural coordinate system for the cortical surface. NeuroImage, 57(3), 762–775.
Ronan, L.,θήκη, Β., Haggard, M. P., & Fletcher, P. C. (2012). The relationship between cortical folding and layer-specific microstructural variation in the human brain. Cerebral Cortex, 22(11), 2508–2517.
Salat, D. H., Ward, B. D., Kaye, J. A., & Janowsky, J. S. (2004). Age-related changes in regional gray matter volume of the human brain. Neurobiology of Aging, 25(7), 927–937.
Scahill, R. I., Schott, J. M., Stevens, J. M., Rossor, M. N., & Fox, N. C. (2003). Mapping the evolution of regional atrophy in Alzheimer’s disease: Unbiased serial voxel-based morphometry. Proceedings of the National Academy of Sciences, 100(8), 4703–4708.
Schaer, M., Cuadra, M. B., Tam, B. L., Gaser, C., & Thiran, J. P. (2008). The local gyrification index: A sensitive measure of local cortical folding. IEEE Transactions on Medical Imaging, 27(7), 993–1000.
Storsve, E., Fjell, A. M., Tamnes, C. K., Westlye, L. T., Ostby, Y., Amlien, I. K., Raz, N., & Walhovd, K. B. (2014). Nonlinear age-related cortical thickness changes across the adult lifespan. Cerebral Cortex, 24(11), 2803–2813.
Sullivan, E. V., Marsh, L., Mathalon, D. H., Lim, K. O., Kramer, J. H., & Pfefferbaum, A. (1998). Relationship between aging and regional cortical sulcal widening: Influence of cerebrospinal fluid. Biological Psychiatry, 43(11), 779–791.
Turner, G. R., & Spreng, R. N. (2012). Executive functions and the default network: Relationships across the lifespan. Developmental Cognitive Neuroscience, 2(4), 509–517.
Van Essen, D. C., Donahue, C. J., Glasser, M. F., Kennedy, H., & Lichtman, J. W. (2018). Mapping the human connectome: An overview of the Human Connectome Project. NeuroImage, 182, 9–30.
Vandekar, S. N., Treiber, F. A., Musser, E. D., Cox, R. W., & Woodward, N. D. (2015). Development of cortical thickness and local gyrification index in early childhood and adolescence. Developmental Cognitive Neuroscience, 12, 111–123.
Wagstyl, K., Ronan, L., Goodyer, I. M., & Fletcher, P. C. (2016). Cortical folding and the development of white matter microstructure. NeuroImage, 139, 367–379.
Welker, W. (1990). Why does cerebral cortex wrinkle? A review of hypotheses. Cerebral Cortex, 1(1), 3–23.
Winkler, A. M., Kochunov, P., Blangero, J., Almasy, L., Zilles, K., & Fox, P. T. (2010). Cortical thickness or grey matter volume? The importance of scaling for intersubject comparisons. NeuroImage, 51(4), 1434–1442.
Xu, J., Fan, Y., Joshi, S. H., & Miller, M. I. (2010). Cortical folding patterns and their relation to white matter fiber tracts. Cerebral Cortex, 20(11), 2638–2649.
Zhang, Y., Wang, J., Zhou, Y., Yuan, K., Liu, Y., Li, J., Qin, W., & Jiang, T. (2014). Abnormal local gyrification patterns in drug-naive first-episode schizophrenia. Psychiatry Research: Neuroimaging, 224(3), 238–244
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