Radial Structure Scaffolds Convolution Patterns of Developing Cerebral Cortex

• Authors: Mir Jalil Razavi, University of Georgia; Tuo Zhang, University of Georgia; Hanbo Chen, University of Georgia; Yujie Li, University of Georgia; Simon Platt, University of Georgia; Yu Zhao, University of Georgia; Lei Guo, Northwestern Polytechnical University; Xiaoping Hu, Emory University; Xianqiao Wang, University of Georgia; Tianming Liu, University of Georgia
• Journal Title: Frontiers in Computational Neuroscience
• Volume: Volume 11
• Publisher: Frontiers Media | 2017-08-15, Pages 76-76
• Type of Work: Article | Final Publisher PDF
• Publisher DOI: http://doi.org/10.3389/fncom.2017.00076
• Subjects: Biology, Neuroscience
• Keywords: Science & Technology; Life Sciences & Biomedicine; Mathematical & Computational Biology; Neurosciences; Neurosciences & Neurology; neuroimaging; radial structure; radial convolution pattern; computational modeling; FETAL-BRAIN DEVELOPMENT; CORTICAL CONVOLUTIONS; MONOZYGOTIC TWINS; PRIMATE BRAINS; SOFT-TISSUES; GROWTH; SURFACE; EXPANSION; MORPHOGENESIS; MALFORMATION

Abstract

Commonly-preserved radial convolution is a prominent characteristic of the mammalian cerebral cortex. Endeavors from multiple disciplines have been devoted for decades to explore the causes for this enigmatic structure. However, the underlying mechanisms that lead to consistent cortical convolution patterns still remain poorly understood. In this work, inspired by prior studies, we propose and evaluate a plausible theory that radial convolution during the early development of the brain is sculptured by radial structures consisting of radial glial cells (RGCs) and maturing axons. Specifically, the regionally heterogeneous development and distribution of RGCs controlled by Trnp1 regulate the convex and concave convolution patterns (gyri and sulci) in the radial direction, while the interplay of RGCs’ effects on convolution and axons regulates the convex (gyral) convolution patterns. This theory is assessed by observations and measurements in literature from multiple disciplines such as neurobiology, genetics, biomechanics, etc., at multiple scales to date. Particularly, this theory is further validated by multimodal imaging data analysis and computational simulations in this study. We offer a versatile and descriptive study model that can provide reasonable explanations of observations, experiments, and simulations of the characteristic mammalian cortical folding.