Brain multiphysics最新文献

Endovascular techniques, such as endoluminal or endosaccular reconstruction, have emerged as the preferred method for treating both ruptured and unruptured intracranial aneurysms, replacing open surgery in most cases. The minimally invasive approach has been shown to result in better surgical outcomes and lower mortality rates. Before the procedure, neuroradiologists rely only on their experience and visual aids from medical imaging techniques to select the appropriate endovascular option, device model and size for each patient. Despite the benefits of endovascular techniques, significant complications can arise during and after the procedures, including intraprocedural aneurysm perforation, delayed rupture, aneurysm regrowth, in-stent restenosis and thromboembolic events. Therefore, predictive virtual replicas of these interventions can serve as a valuable tool to assist neuroradiologists in the decision-making process and optimise treatment success, especially in cases involving complex geometries. Computational modelling can enable the simulation of different treatment strategies considering the most clinically relevant short- and long-term outcomes of the deployment and the postoperative complications that may arise over time.

Statement of significance: This review explores the state of the art in modelling the mechanics of the main neurovascular devices, their deployment within patient-specific geometries, their interaction with the vessel wall and their influence on the local hemodynamics. As it strongly affects their applicability in clinical practice, particular attention is paid to the computational accuracy and efficiency of the different modelling strategies. The aim is to evaluate how these scientific tools and discoveries can support practitioners in making informed decisions and highlight the challenges that require further study.

Rhenium-186 (¹⁸⁶Re) labeled nanoliposome (RNL) therapy for recurrent glioblastoma patients has shown promise to improve outcomes by locally delivering radiation to affected areas. To optimize the delivery of RNL, we have developed a framework to predict patient-specific response to RNL using image-guided mathematical models.

Methods

We calibrated a family of reaction-diffusion type models with multi-modality imaging data from ten patients (NCR01906385) to predict the spatio-temporal dynamics of each patient's tumor. The data consisted of longitudinal magnetic resonance imaging (MRI) and single photon emission computed tomography (SPECT) to estimate tumor burden and local RNL activity, respectively. The optimal model from the family was selected and used to predict future growth. A simplified version of the model was used in a leave-one-out analysis to predict the development of an individual patient's tumor, based on cohort parameters.

Results

Across the cohort, predictions using patient-specific parameters with the selected model were able to achieve Spearman correlation coefficients (SCC) of 0.98 and 0.93 for tumor volume and total cell number, respectively, when compared to the measured data. Predictions utilizing the leave-one-out method achieved SCCs of 0.89 and 0.88 for volume and total cell number across the population, respectively.

Conclusion

We have shown that patient-specific calibrations of a biology-based mathematical model can be used to make early predictions of response to RNL therapy. Furthermore, the leave-one-out framework indicates that radiation doses determined by SPECT can be used to assign model parameters to make predictions directly following the conclusion of RNL treatment.

The cerebral vasculature plays a critical role in the transport of oxygen and other nutrients to brain tissue. However, the size, complexity, and paucity of detailed anatomical information of this system makes understanding cerebral behaviour in normal and pathological conditions, as well as its response to stimuli, highly challenging. Whole-brain mathematical models have a valuable role to play in the understanding and measurement of cerebral parameters. However, for the same reasons, whole-brain models are highly complex to construct. In this study, we propose a novel multi-compartment approach to blood flow and oxygen transport. Building on prior models, we propose a new formulation based on a multiple compartment porous medium approach. Using non-dimensional analysis, we derive the most compact form of the equations and constrain the parameter space using clinically measurable quantities, such as baseline perfusion and blood volume. We illustrate the spatially and temporally varying response of the brain by simulating the response to changes in both arterial blood pressure and arterial oxygen saturation, showing that the oxygen response is strongly dependent upon depth, with large but slow responses being found deep in the brain and small but fast responses nearer the surface, whereas the flow response is very rapid in comparison. Blood flow and oxygenation are thus shown to exhibit very different characteristic time scales. This has significant implications for how we consider the response of the brain to external stimuli, such the autoregulation and reactivity responses, and how we model the brain at different time scales.

Statement of Significance

In this study we present a new mathematical model for simulations of blood flow and oxygen transport in the human brain. A compact representation is obtained from analysis of the governing equations and different time scales are identified. We show that the behaviour is strongly depth dependent and that 3D models exhibit very different behaviour from simplified 1D models. This will be important in developing further models of the brain, particularly in simulating its active response.

Periventricular white matter hyperintensities (WMH) are a common finding in medical images of the aging brain and are associated with white matter damage resulting from cerebral small vessel disease, white matter inflammation, and a degeneration of the lateral ventricular wall. Despite extensive work, the etiology of periventricular WMHs remains unclear. We pose that there is a strong coupling between age-related ventricular expansion and the degeneration of the ventricular wall which leads to a dysregulated fluid exchange across this brain–fluid barrier. Here, we present a multiphysics model that couples cerebral atrophy-driven ventricular wall loading with periventricular WMH formation and progression. We use patient data to create eight 2D finite element models and demonstrate the predictive capabilities of our damage model. Our simulations show that we accurately capture the spatiotemporal features of periventricular WMH growth. For one, we observe that damage appears first in both the anterior and posterior horns and then spreads into deeper white matter tissue. For the other, we note that it takes up to 12 years before periventricular WMHs first appear and derive an average annualized periventricular WMH damage growth rate of 15.2 ± 12.7 mm${}^2$/year across our models. A sensitivity analysis demonstrated that our model parameters provide sufficient sensitivity to rationalize subject-specific differences with respect to onset time and damage growth. Moreover, we show that the septum pellucidum, a membrane that separates the left and right lateral ventricles, delays the onset of periventricular WMHs at first, but leads to a higher WMH load in the long-term.

Statement of Significance: Brain aging is accompanied by many structural and functional changes. In nearly all aged brains, white matter lesions appear in periventricular and diffuse subcortical regions which are associated with progressive functional decline. In our work, we present a multiphysics model that not only predicts the onset location of periventricular white matter lesions but also their subsequent growth as a result of age-related cerebral atrophy and ventricular enlargement. Our model provides a mechanics-based rationale for their characteristic spatiotemporal progression patterns and will allow to identify at-risk subjects for early lesion formation.

Cortical folds, known as gyri and sulci, are prominent features of the human brain that play a crucial role in its function. These folds exhibit both consistency and variation within and across individuals and species, presenting a scientific challenge to our understanding of the underlying mechanisms. In this perspective paper, we summarize current knowledge about fold development and placement. We discuss the temporal and anatomical differences between primary, secondary, and tertiary folds, highlighting the consistency of primary folds and the increasing variation in later-developing folds. We explore the biological and mechanical factors that influence fold placement, including gene expression, tissue growth, axonal tension, curvature, thickness, and stiffness, which likely work together in a complex, coupled manner. We also highlight the need for advanced computational modeling approaches to unravel the mechanisms of precise placement of primary folds and further our understanding of brain complexity.

Statement of significance: Understanding the factors driving both the consistency and variation in fold patterns is essential for unraveling the functional implications and potential links to neurological and psychiatric disorders. Ultimately, gaining deeper insights into fold development and placement could have significant implications for our fundamental understanding of the brain, as well as mental health research and clinical applications.