Effective Diffusion and Tortuosity in Brain White Matter

Photobiomodulation in 3D tissue engineering

SIGNIFICANCE

The method of photobiomodulation (PBM) has been used in medicine for a long time to promote anti-inflammation and pain-resolving processes in different organs and tissues. PBM triggers numerous cellular pathways including stimulation of the mitochondrial respiratory chain, alteration of the cytoskeleton, cell death prevention, increasing proliferative activity, and directing cell differentiation. The most effective wavelengths for PBM are found within the optical window (750 to 1100 nm), in which light can permeate tissues and other water-containing structures to depths of up to a few cm. PBM already finds its applications in the developing fields of tissue engineering and regenerative medicine. However, the diversity of three-dimensional (3D) systems, irradiation sources, and protocols intricate the PBM applications.

AIM

We aim to discuss the PBM and 3D tissue engineered constructs to define the fields of interest for PBM applications in tissue engineering.

APPROACH

First, we provide a brief overview of PBM and the timeline of its development. Then, we discuss the optical properties of 3D cultivation systems and important points of light dosimetry. Finally, we analyze the cellular pathways induced by PBM and outcomes observed in various 3D tissue-engineered constructs: hydrogels, scaffolds, spheroids, cell sheets, bioprinted structures, and organoids.

RESULTS

Our summarized results demonstrate the great potential of PBM in the stimulation of the cell survival and viability in 3D conditions. The strategies to achieve different cell physiology states with particular PBM parameters are outlined.

CONCLUSIONS

PBM has already proved itself as a convenient and effective tool to prevent drastic cellular events in the stress conditions. Because of the poor viability of cells in scaffolds and the convenience of PBM devices, 3D tissue engineering is a perspective field for PBM applications.

Effect of Particle Size and Surface Charge on Nanoparticles Diffusion in the Brain White Matter

Purpose

Brain disorders have become a serious problem for healthcare worldwide. Nanoparticle-based drugs are one of the emerging therapies and have shown great promise to treat brain diseases. Modifications on particle size and surface charge are two efficient ways to increase the transport efficiency of nanoparticles through brain-blood barrier; however, partly due to the high complexity of brain microstructure and limited visibility of Nanoparticles (NPs), our understanding of how these two modifications can affect the transport of NPs in the brain is insufficient.

Methods

In this study, a framework, which contains a stochastic geometric model of brain white matter (WM) and a mathematical particle tracing model, was developed to investigate the relationship between particle size/surface charge of the NPs and their effective diffusion coefficients (D) in WM.

Results

The predictive capabilities of this method have been validated using published experimental tests. For negatively charged NPs, both particle size and surface charge are positively correlated with D before reaching a size threshold. When Zeta potential (Zp) is less negative than -10 mV, the difference between NPs’ D in WM and pure interstitial fluid (IF) is limited.

Conclusion

A deeper understanding on the relationships between particle size/surface charge of NPs and their D in WM has been obtained. The results from this study and the developed modelling framework provide important tools for the development of nano-drugs and nano-carriers to cure brain diseases.

On the microstructural origin of brain white matter hydraulic permeability

Brain microstructure plays a key role in driving the transport of drug molecules directly administered to the brain tissue, as in Convection-Enhanced Delivery procedures. The proposed research analyzes the hydraulic permeability of two white matter (WM) areas (corpus callosum and fornix) whose three-dimensional microstructure was reconstructed starting from the acquisition of electron microscopy images. We cut the two volumes with 20 equally spaced planes distributed along two perpendicular directions, and, on each plane, we computed the corresponding permeability vector. Then, we considered that the WM structure is mainly composed of elongated and parallel axons, and, using a principal component analysis, we defined two principal directions, parallel and perpendicular, with respect to the axons' main direction. The latter were used to define a reference frame onto which the permeability vectors were projected to finally obtain the permeability along the parallel and perpendicular directions. The results show a statistically significant difference between parallel and perpendicular permeability, with a ratio of about two in both the WM structures analyzed, thus demonstrating their anisotropic behavior. Moreover, we find a significant difference between permeability in corpus callosum and fornix, which suggests that the WM heterogeneity should also be considered when modeling drug transport in the brain. Our findings, which demonstrate and quantify the anisotropic and heterogeneous character of the WM, represent a fundamental contribution not only for drug-delivery modeling, but also for shedding light on the interstitial transport mechanisms in the extracellular space.

Integrating Diffusion Tensor Imaging and Neurite Orientation Dispersion and Density Imaging to Improve the Predictive Capabilities of CED Models

This paper aims to develop a comprehensive and subject-specific model to predict the drug reach in Convection-Enhanced Delivery (CED) interventions. To this end, we make use of an advance diffusion imaging technique, namely the Neurite Orientation Dispersion and Density Imaging (NODDI), to incorporate a more precise description of the brain microstructure into predictive computational models. The NODDI dataset is used to obtain a voxel-based quantification of the extracellular space volume fraction that we relate to the white matter (WM) permeability. Since the WM can be considered as a transversally isotropic porous medium, two equations, respectively for permeability parallel and perpendicular to the axons, are derived from a numerical analysis on a simplified geometrical model that reproduces flow through fibre bundles. This is followed by the simulation of the injection of a drug in a WM area of the brain and direct comparison of the outcomes of our results with a state-of-the-art model, which uses conventional diffusion tensor imaging. We demonstrate the relevance of the work by showing the impact of our newly derived permeability tensor on the predicted drug distribution, which differs significantly from the alternative model in terms of distribution shape, concentration profile and infusion linear penetration length.

FCNN-based axon segmentation for convection-enhanced delivery optimization

PURPOSE

Glioblastoma multiforme treatment is a challenging task in clinical oncology. Convection- enhanced delivery (CED) is showing encouraging but still suboptimal results due to drug leakages. Numerical models can predict drug distribution within the brain, but require retrieving brain physical properties, such as the axon diameter distribution (ADD), through axon architecture analysis. The goal of this work was to provide an automatic, accurate and fast method for axon segmentation in electronic microscopy images based on fully convolutional neural network (FCNN) as to allow automatic ADD computation.

METHODS

The segmentation was performed using a residual FCNN inspired by U-Net and Resnet. The FCNN training was performed exploiting mini-batch gradient descent and the Adam optimizer. The Dice coefficient was chosen as loss function.

RESULTS

The proposed segmentation method achieved results comparable with already existing methods for axon segmentation in terms of Information Theoretic Scoring ([Formula: see text]) with a faster training (5 h on the deployed GPU) and without requiring heavy post-processing (testing time was 0.2 s with a non-optimized code). The ADDs computed from the segmented and ground-truth images were statistically equivalent.

CONCLUSIONS

The algorithm proposed in this work allowed fast and accurate axon segmentation and ADD computation, showing promising performance for brain microstructure analysis for CED delivery optimization.