Annals of biomedical engineering
-
This study presents a novel statistical volume element (SVE) for micromechanical modeling of the white matter structures, with histology-informed randomized distribution of axonal tracts within the extracellular matrix. The model was constructed based on the probability distribution functions obtained from the results of diffusion tensor imaging as well as the histological observations of scanning electron micrograph, at two structures of white matter susceptible to traumatic brain injury, i.e. corpus callosum and corona radiata. A simplistic representative volume element (RVE) with symmetrical arrangement of fully alligned axonal fibers was also created as a reference for comparison. ⋯ Moreover, the SVE model displayed a significantly better agreement with the reports of the experiments at high strain rates. The results suggest that the randomized fiber architecture has a great influence on the validity of the micromechanical models of white matter, with a distinguished impact on the model's response to shear deformation and high strain rates. Moreover, it can provide a more detailed presentation of the localized responses of the tissue substructures, including the stress concentrations around the low caliber axonal tracts, which is critical for studying the axonal injury mechanisms.
-
tDCS has been used to treat various brain disorders and its mechanism of action (MoA) was found to be neuronal polarization. Since the blood-brain barrier (BBB) tightly regulates the neuronal microenvironment, we hypothesized that another MoA of tDCS is direct vascular activation by modulating the BBB structures to increase its permeability (P). To test this hypothesis, we used high resolution multiphoton microscopy to determine P of the cerebral microvessels in rat brain. ⋯ A transport model for the BBB was further employed to predict the structural changes by the tDCS. Comparing model predictions with the measured data suggests that tDCS increases P by temporarily disrupting the structural components forming the paracellular pathway of the BBB. That the transient and reversible increase in the BBB permeability also suggests new applications of tDCS such as a non-invasive approach for brain drug delivery through the BBB.
-
Comparative Study
Impact Performance Comparison of Advanced Bicycle Helmets with Dedicated Rotation-Damping Systems.
Bicycle helmets effectively mitigate skull fractures, but there is increasing concern on their effectiveness in mitigating traumatic brain injury (TBI) caused by rotational head acceleration. Bicycle falls typically involve oblique impacts that induce rotational head acceleration. Recently, bicycle helmet with dedicated rotation-damping systems have been introduced to mitigate rotational head acceleration. ⋯ Of the four rotation-damping systems, two systems significantly reduced rotational head acceleration, TBI risk, and brain strain compared to the standard bicycle helmet. One system had no significant effect on impact performance compared to control helmets, and one system significantly increase linear and rotational head acceleration by 62 and 61%, respectively. In conclusion, results revealed significant differences in the effectiveness between rotation-damping systems, whereby some rotation-damping systems significantly reduced rotational head acceleration and associated TBI risk.
-
One major role of an accurate distribution of abdominal adipose tissue is to predict disease risk. This paper proposes a novel effective three-level convolutional neural network (CNN) approach to automate the selection of abdominal computed tomography (CT) images on large-scale CT scans and automatically quantify the visceral and subcutaneous adipose tissue. First, the proposed framework employs support vector machine (SVM) classifier with a configured parameter to cluster abdominal CT images from screening patients. ⋯ The mean accuracy of the configured SVM classifier yields promising performance of 99.83%, while DilaLabPlus achieves a remarkable performance improvement an with average of 98.08 ± 0.84% standard deviation and 0.7 ± 0.8% standard deviation false-positive rate. The performance of DilaLab yields average 97.82 ± 1.34% standard deviation and 1.23 ± 1.33% standard deviation false-positive rate. This study demonstrates considerable improvement in feasibility and reliability for the fully automated recognition of abdominal CT slices and segmentation of selected abdominal CT in subcutaneous and visceral adipose tissue, and it has a high agreement with a manually annotated biomarker.