CRII: SCH: A Computational Toolbox for Analysis of Big Brain Data

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The brain is our most complex and least understood organ. Due to the recent proliferation of large public neuroimaging data repositories, researchers have access to an unprecedented amount of data, but in many ways the amount of data has surpassed our abilities to analyze it. With pressing health questions attracting the interest of scientists, clinicians, and engineers, we have an urgent need for computational tools that integrate the methods and expertise of different fields. This project seeks to advance the analysis of big brain data by developing, using, and sharing novel open-source computational tools for the modeling and analysis of cortical thickness, an indicator of healthy brain development. Through the analysis of two large data sets containing over 500 individual scans, a baseline for cortical thickness variation throughout healthy development will be generated. Numerical simulations will also shed light on the effect of the mechanical forces that give rise to the brain?s unique shape. The computational tools developed will be made available for use by other researchers to further leverage existing open access databases of MRI scans. Beyond that, this project has the potential to produce new insights with clinical applications in the analysis of neurological disorders such as Autism Spectrum Disorder, Alzheimer's Disease, and Parkinson's Disease. Alongside this trans-disciplinary project, the team will also develop a student-written blog, intended for the general public, on interesting investigations in the field of biomechanics.Gyrification, or the process by which the brain develops its characteristic wrinkles and folds, is the result of both biological processes and mechanical forces. These elements, tightly coupled and affecting each other, affect both the form and function of the brain. This project will increase the biological fidelity of the finite element simulations used to model gyrification by representing cerebrospinal fluid pressure, neuronal apoptosis, and synaptic pruning through the development of new material models that describe heterogeneous, anisotropic, growing and remodeling tissue. These computational simulations will generate a deeper understanding of the role of mechanical forces in the evolution of cortical thickness, which varies regionally both within and between individuals. By introducing a new metric of interest that allows for the characterization of thickness variations on arbitrarily small regions, this project will develop new computational tools for the analysis of within-subject and between-subject variations of cortical thickness and the characterization of these patterns in healthy development. The successful completion of this research will result in novel computational tools for neurological imaging analysis of big brain data in the many public neuroimaging databases, generating additional value out of existing resources.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.