Computational study of the contribution of the vasculature on the dynamic response of the brain.
Brain tissue architecture consists of a complex network of neurons and vasculature interspersed within a matrix of supporting cells. The role of the relatively stiffer blood vessels on the more compliant brain tissues during rapid loading has not been properly investigated. Two 2-D finite element models of the human head were developed. The basic model (Model I) consisted of the skull, dura matter, cerebral spinal fluid (CSF), tentorium, brain tissue and the parasagittal bridging veins. The pia mater was also included but in a simplified form which does not correspond to the convolutions of the brain. In Model II, major branches of the cerebral arteries were added to Model I. Material properties for the brain tissues and vasculature were taken from those reported in the literature. The model was first validated against intracranial pressure and brain/skull relative motion data from cadaveric tests. Two loading conditions, an anterior-posterior linear acceleration and a flexion-extension angular velocity pulse, were applied to both models. Resulting maximum principal strain, shear strain and intracranial pressure throughout the intracranial tissue were calculated and compared. Overall, the maximum principal strain/stress in the brain was lower in the model that included simulated blood vessels. The inclusion of the cerebral vessels added regional strength to the brain substance, and thereby contributed to the load bearing capacity of this composite brain model during head impact, analogous to reinforcing bars in a reinforced concrete structure. In addition to the neurovasculature, the pia membrane, which conforms to the numerous gyri and sulci not modeled in this study, may add to the structural strength of the brain. Results from this investigation suggest that the fine anatomical substructures of the brain should not be ignored in traumatic brain injury modeling. However, incorporation of blood vessels in a 3-D FE head model is not practical at this stage due to the lack of computing power.
Zhang
Liying
L
Bioengineering Center, Wayne State University, Detroit, Michigan.
Bae
Jinho
J
Hardy
Warren N
WN
Monson
Kenneth L
KL
Manley
Geoffrey T
GT
Goldsmith
Werner
W
Yang
King H
KH
King
Albert I
AI