MR Imaging of Human Brain Mechanics In Vivo: New Measurements to Facilitate the Development of Computational Models of Brain Injury

• Published in: Annals of biomedical engineering 2021-10, Vol.49 (10), p.2677-2692
• Main Authors: Bayly, Philip V., Alshareef, Ahmed, Knutsen, Andrew K., Upadhyay, Kshitiz, Okamoto, Ruth J., Carass, Aaron, Butman, John A., Pham, Dzung L., Prince, Jerry L., Ramesh, K. T., Johnson, Curtis L.
• Format: Article
• Language: English
• Subjects: Animal models; Animals; Biochemistry; Biological and Medical Physics; Biomechanical Phenomena; Biomechanics; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedicine; Biophysics; Brain; Brain - anatomy & histology; Brain - diagnostic imaging; Brain - physiology; Brain Injuries - diagnostic imaging; Brain Injuries - physiopathology; Cadavers; Classical Mechanics; Computational neuroscience; Head injuries; Humans; Magnetic Resonance Imaging; Mechanical properties; Models, Biological; Neuroimaging; Neurological diseases; Older people; Review; Skull; Traumatic brain injury
• ISSN: 0090-6964; 1573-9686
• DOI: 10.1007/s10439-021-02820-0

Computational models of the brain and its biomechanical response to skull accelerations are important tools for understanding and predicting traumatic brain injuries (TBIs). However, most models have been developed using experimental data collected on animal models and cadaveric specimens, both of which differ from the living human brain. Here we describe efforts to noninvasively measure the biomechanical response of the human brain with MRI—at non-injurious strain levels—and generate data that can be used to develop, calibrate, and evaluate computational brain biomechanics models. Specifically, this paper reports on a project supported by the National Institute of Neurological Disorders and Stroke to comprehensively image brain anatomy and geometry, mechanical properties, and brain deformations that arise from impulsive and harmonic skull loadings. The outcome of this work will be a publicly available dataset ( http://www.nitrc.org/projects/bbir ) that includes measurements on both males and females across an age range from adolescence to older adulthood. This article describes the rationale and approach for this study, the data available, and how these data may be used to develop new computational models and augment existing approaches; it will serve as a reference to researchers interested in using these data.