Data-driven Brain Folding Exploration: A Tale of Variability and Regularity

During brain development, the cerebral cortex experiences a noticeable expansion in volume and surface area accompanied by tremendous tissue folding. Many studies attest that knowledge of cortical folding is key to interpreting the normal development of human brains during the early stages of growth. Primary cortical folding demonstrates remarkable consistency among individuals, while secondary and tertiary folding, which evolve after primary folding, exhibit substantial inter-individual variability. Notwithstanding this variability, certain distinct folding patterns are preserved within individuals or across species. The origin and formation mechanism of variable or regular folding patterns in the human brain yet remains to be thoroughly explored. There is a pressing need to discover the role of axonal fibers of the brain’s connectivity on the formation and modulation of folding patterns in the developing human brain. This unmet need creates an important problem, as the absence of understanding the physical interplay between cortical folding and neural wiring poses a critical barrier to comprehending the intricate relationships between cortical folding, brain connectivity, and brain function across various neurodevelopmental stages. In this talk, I will share the research experience on the exploration of human brain folding variability and regularity using neuroimaging data and computational modeling in the past few years. Our research helps address important questions: how the interplay of different influential factors, including differential tangential growth and neural wiring, can explain the emergence of variable and regular folding patterns in a human brain, and what is the hierarchy in the modulation of folding patterns? I will also introduce the opportunities and challenges in creating a unified mechanistic frame to explain the intrinsic relationships between cortical folding and structural connection patterns in the human brain as well its implication in brain disorder study.

Dr. XQ Wang now is an Associate Professor of Mechanical Engineering and Co-Director UGA Center for Brain-inspired Artificial Intelligence. He obtained his Ph.D. degree in Mechanical Engineering in 2011 from the George Washington University, and later he joined the University of Georgia as an Assistant Professor. He has published more than 140 peer-reviewed papers in top international journals such as Advanced Materials, Science Advances, ACS Nano, Advanced Functional Materials, Brain Structure and Functions, Cerebral Cortex, Human Brain Mapping, etc. He was the recipient of 2017 International Conferences on Computational Methods (ICCM) Young Investigator Award and the recipient of 2018 College of Engineering Excellence in Instruction. His research interests focus on data-driven brain mechanics, bio-inorganic interfaces, materials design by AI, and soft matters. His work is funded by multiple NSF and NIH grants.