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.

Infusion Mechanisms in Brain White Matter and Their Dependence on Microstructure: An Experimental Study of Hydraulic Permeability.

OBJECTIVE: Hydraulic permeability is a topic of deep interest in biological materials because of its important role in a range of drug delivery-based therapies. The strong dependence of permeability on the geometry and topology of pore structure and the lack of detailed knowledge of these parameters in the case of brain tissue makes the study more challenging. Although theoretical models have been developed for hydraulic permeability, there is limited consensus on the validity of existing experimental evidence to complement these models. In the present study, we measure the permeability of white matter (WM) of fresh ovine brain tissue considering the localised heterogeneities in the medium using an infusion-based experimental set up, iPerfusion. We measure the flow across different parts of the WM in response to applied pressures for a sample of specific dimensions and calculate the permeability from directly measured parameters. Furthermore, we directly probe the effect of anisotropy of the tissue on permeability by considering the directionality of tissue on the obtained values. Additionally, we investigate whether WM hydraulic permeability changes with post-mortem time. To our knowledge, this is the first report of experimental measurements of the localised WM permeability, also demonstrating the effect of axon directionality on permeability. This work provides a significant contribution to the successful development of intra-tumoural infusion-based technologies, such as convection-enhanced delivery (CED), which are based on the delivery of drugs directly by injection under positive pressure into the brain.

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.

Correction to: A computational fluid dynamics approach to determine white matter permeability.

The article "A computational fluid dynamics approach to determine white matter permeability" written by Marco Vidotto, Daniela Botnariuc, Elena De Momi and Daniele Dini was originally published electronically on the publisher's Internet portal (currently SpringerLink) on 20 February 2019 without open access.

A computational fluid dynamics approach to determine white matter permeability.

Glioblastomas represent a challenging problem with an extremely poor survival rate. Since these tumour cells have a highly invasive character, an effective surgical resection as well as chemotherapy and radiotherapy is very difficult. Convection-enhanced delivery (CED), a technique that consists in the injection of a therapeutic agent directly into the parenchyma, has shown encouraging results. Its efficacy depends on the ability to predict, in the pre-operative phase, the distribution of the drug inside the tumour. This paper proposes a method to compute a fundamental parameter for CED modelling outcomes, the hydraulic permeability, in three brain structures. Therefore, a bidimensional brain-like structure was built out of the main geometrical features of the white matter: axon diameter distribution extrapolated from electron microscopy images, extracellular space (ECS) volume fraction and ECS width. The axons were randomly allocated inside a defined border, and the ECS volume fraction as well as the ECS width maintained in a physiological range. To achieve this result, an outward packing method coupled with a disc shrinking technique was implemented. The fluid flow through the axons was computed by solving Navier-Stokes equations within the computational fluid dynamics solver ANSYS. From the fluid and pressure fields, an homogenisation technique allowed establishing the optimal representative volume element (RVE) size. The hydraulic permeability computed on the RVE was found in good agreement with experimental data from the literature.

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.

Effective Diffusion and Tortuosity in Brain White Matter.

Patients affected by glioblastomas have a very low survival rate. Emerging techniques, such as convection enhanced delivery (CED), need complex numerical models to be effective; furthermore, the estimation of the main parameters to be used to instruct constitutive laws in simulations represents a major challenge. This work proposes a new method to compute tortuosity, a key parameter for drug diffusion in fibrous tissue, starting from a model which incorporates the main white matter geometrical features. It is shown that tortuosity increases from 1.35 to 1.85 as the extracellular space width decreases. The results are in good agreement with experimental data reported in the literature.

Design and Evaluation of a Performance-based Adaptive Curriculum for Robotic Surgical Training: a Pilot Study.

Training with simulation systems has become a primary alternative for learning the fundamental skills of robotic surgery. However, there exists no consensus regarding a standard training curriculum: sessions defined a priori by expert trainers or self-directed by the trainees feature lack of consistency. This study proposes an adaptive approach that structures the curriculum on the basis of an objective assessment of the trainee's performance. The work comprised an experimental session with 12 participants performing training on virtual reality tasks with the da Vinci Research Kit surgical console. Half of the subjects self-managed their training session, while the others underwent the adaptive training. The final performance of the latter trainees was found to be higher compared to the former (p=0.002), showing how outcome-based, dynamic designs could constitute a promising advance in robotic surgical training.

Toward the Virtual Benchmarking of Pneumatic Ventricular Assist Devices: Application of a Novel Fluid-Structure Interaction-Based Strategy to the Penn State 12 cc Device.

The pediatric use of pneumatic ventricular assist devices (VADs) as a bridge to heart transplant still suffers for short-term major complications such as bleeding and thromboembolism. Although numerical techniques are increasingly exploited to support the process of device optimization, an effective virtual benchmark is still lacking. Focusing on the 12 cc Penn State pneumatic VAD, we developed a novel fluid-structure interaction (FSI) model able to capture the device functioning, reproducing the mechanical interplay between the diaphragm, the blood chamber, and the pneumatic actuation. The FSI model included the diaphragm mechanical response from uniaxial tensile tests, realistic VAD pressure operative conditions from a dedicated mock loop system, and the behavior of VAD valves. Our FSI-based benchmark effectively captured the complexity of the diaphragm dynamics. During diastole, the initial slow diaphragm retraction in the air chamber was followed by a more rapid phase; asymmetries were noticed in the diaphragm configuration during its systolic inflation in the blood chamber. The FSI model also captured the major features of the device fluid dynamics. In particular, during diastole, a rotational wall washing pattern is promoted by the penetrating inlet jet with a low-velocity region located in the center of the device. Our numerical analysis of the 12 cc Penn State VAD points out the potential of the proposed FSI approach well resembling previous experimental evidences; if further tested and validated, it could be exploited as a virtual benchmark to deepen VAD-related complications and to support the ongoing optimization of pediatric devices.