Escarcega Jordan D, Okamoto Ruth J, Alshareef Ahmed A, Johnson Curtis L, Bayly Philip V
Department of Mechanical Engineering and Materials Science, Washington University in St. Louis, 1 Brookings Drive, MSC 1185-208-125, St. Louis, MO, 63130, USA.
Department of Mechanical Engineering, University of South Carolina, Columbia, SC, 29208, USA.
Ann Biomed Eng. 2025 Apr;53(4):867-880. doi: 10.1007/s10439-024-03671-1. Epub 2024 Dec 31.
To determine how the biomechanical vulnerability of the human brain is affected by features of individual anatomy and loading.
To identify the features that contribute most to brain vulnerability, we imparted mild harmonic acceleration to the head and measured the resulting brain motion and deformation using magnetic resonance elastography (MRE). Oscillatory motion was imparted to the heads of adult participants using a lateral actuator (n = 24) or occipital actuator (n = 24) at 20 Hz, 30 Hz, and 50 Hz. Displacement vector fields and strain tensor fields in the brain were obtained from MRE measurements. Anatomical images, as well as displacement and strain fields from each participant were rigidly and deformably aligned to a common atlas (MNI-152). Vulnerability of the brain to deformation was quantified by the ratio of strain energy (SE) to kinetic energy (KE) for each participant. Similarity of deformation patterns between participants was quantified using strain field correlation (C). Linear regression models were used to identify the effect of similarity of brain size, shape, and age, as well as similarity of loading, on C.
The SE/KE ratio decreased with frequency and was larger for participants undergoing lateral, rather than occipital, actuation. Head rotation about the inferior-superior axis was correlated with larger SE/KE ratio. Strain field correlations were primarily affected by the similarity of rigid-body motion.
The motion applied to the skull is the most important factor in determining both the vulnerability of the brain to deformation and the similarity between strain fields in different individuals.
确定个体解剖结构特征和负荷如何影响人类大脑的生物力学易损性。
为了识别对大脑易损性贡献最大的特征，我们对头施加轻度谐波加速度，并使用磁共振弹性成像（MRE）测量由此产生的大脑运动和变形。使用横向致动器（n = 24）或枕部致动器（n = 24）对成年参与者的头部施加20Hz、30Hz和50Hz的振荡运动。从MRE测量中获得大脑中的位移矢量场和应变张量场。将每个参与者的解剖图像以及位移和应变场进行刚体和变形配准，使其与一个通用图谱（MNI - 152）对齐。通过计算每个参与者的应变能（SE）与动能（KE）之比来量化大脑对变形的易损性。使用应变场相关性（C）来量化参与者之间变形模式的相似性。使用线性回归模型来确定大脑大小、形状和年龄的相似性以及负荷的相似性对C的影响。
SE/KE比值随频率降低，并且对于接受横向而非枕部致动的参与者更大。绕上下轴的头部旋转与更大的SE/KE比值相关。应变场相关性主要受刚体运动相似性的影响。
施加于颅骨的运动是决定大脑对变形的易损性以及不同个体应变场之间相似性的最重要因素。
