LEAP-HI: Tackling Brain Diseases with Mechanics: A Data-Driven Approach to Merge Advanced Neuroimaging and Multi-Physics Modeling

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The human brain has been studied extensively for centuries, but the role of biomechanics remains mostly unknown. Recent advances in medical imaging, experimentation, and computational modeling, however, have led to a growing body of evidence linking biomechanics of the human brain with major processes in brain development, disease, and damage. Brain biomechanics establishes a relationship between the brain’s structure, function, and motion using the methods of applied mechanics. This Leading Engineering for America's Prosperity, Health, and Infrastructure (LEAP-HI) project combines novel medical imaging methods, image analysis, computational modeling, and mechanical testing to determine the fundamental mechanical properties of living brain tissues and the differences in properties between healthy and diseased tissues, and may enable the early diagnosis and prevention of neurological disorders, such as stroke, traumatic brain injury, and dementia. As such, the project has the potential to reduce the financial burden on society and increase the quality of life for millions of people. Outreach activities in brain mechanics will be provided for underrepresented groups in science and engineering, as well as training opportunities for undergraduate and graduate students, and postdoctoral researchers.The research will provide a novel platform for investigating the mechanobiology of the human brain in health and disease. The research team will develop a novel approach to merge advanced neuroimaging tools and multi-physics brain modeling into a semi-automated pipeline for the in vivo investigation of brain mechanics. Ultrahigh field magnetic resonance imaging technology merged with automated imaging-modeling integration will be utilized to enable the subject-specific investigation of brain mechanics across disparate spatio-temporal scales. Specifically, ultrahigh resolution mechanical, structural, and connectomic neuroimaging tools will be developed and integrated with automatic brain segmentation and mesh generation for finite element and isogeometric analysis to create multi-scale brain mechanics computer models. These tools will then be utilized to provide an in-depth characterization of the mechanobiochemical response of traumatic brain injury, in decompressive craniectomies for stroke patients, and the coupling between prion-like protein progression and cerebral atrophy in dementia. By developing a pipeline for the creation of personalized, data-driven brain models, the research team will demonstrate the transformative power of combined imaging, modeling, and machine learning techniques towards better understanding, improved treatment, and ultimately preventive medicine for neurological disorders.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.