Medical imaging based in silico head model for ischaemic stroke simulation

Modelling midline shift and ventricle collapse in cerebral oedema following acute ischaemic stroke

In ischaemic stroke, a large reduction in blood supply can lead to the breakdown of the blood-brain barrier and to cerebral oedema after reperfusion therapy. The resulting fluid accumulation in the brain may contribute to a significant rise in intracranial pressure (ICP) and tissue deformation. Changes in the level of ICP are essential for clinical decision-making and therapeutic strategies. However, the measurement of ICP is constrained by clinical techniques and obtaining the exact values of the ICP has proven challenging. In this study, we propose the first computational model for the simulation of cerebral oedema following acute ischaemic stroke for the investigation of ICP and midline shift (MLS) relationship. The model consists of three components for the simulation of healthy blood flow, occluded blood flow and oedema, respectively. The healthy and occluded blood flow components are utilized to obtain oedema core geometry and then imported into the oedema model for the simulation of oedema growth. The simulation results of the model are compared with clinical data from 97 traumatic brain injury patients for the validation of major model parameters. Midline shift has been widely used for the diagnosis, clinical decision-making, and prognosis of oedema patients. Therefore, we focus on quantifying the relationship between ICP and midline shift (MLS) and identify the factors that can affect the ICP-MLS relationship. Three major factors are investigated, including the brain geometry, blood-brain barrier damage severity and the types of oedema (including rare types of oedema). Meanwhile, the two major types (stress and tension/compression) of mechanical brain damage are also presented and the differences in the stress, tension, and compression between the intraparenchymal and periventricular regions are discussed. This work helps to predict ICP precisely and therefore provides improved clinical guidance for the treatment of brain oedema.

Mechanical and Functional Responses in Astrocytes under Alternating Deformation Modes Using Magneto-Active Substrates

This work introduces NeoMag, a system designed to enhance cell mechanics assays in substrate deformation studies. NeoMag uses multidomain magneto-active materials to mechanically actuate the substrate, transmitting reversible mechanical cues to cells. The system boasts full flexibility in alternating loading substrate deformation modes, seamlessly adapting to both upright and inverted microscopes. The multidomain substrates facilitate mechanobiology assays on 2D and 3D cultures. The integration of the system with nanoindenters allows for precise evaluation of cellular mechanical properties under varying substrate deformation modes. The system is used to study the impact of substrate deformation on astrocytes, simulating mechanical conditions akin to traumatic brain injury and ischemic stroke. The results reveal local heterogeneous changes in astrocyte stiffness, influenced by the orientation of subcellular regions relative to substrate strain. These stiffness variations, exceeding 50% in stiffening and softening, and local deformations significantly alter calcium dynamics. Furthermore, sustained deformations induce actin network reorganization and activate Piezo1 channels, leading to an initial increase followed by a long-term inhibition of calcium events. Conversely, fast and dynamic deformations transiently activate Piezo1 channels and disrupt the actin network, causing long-term cell softening. These findings unveil mechanical and functional alterations in astrocytes during substrate deformation, illustrating the multiple opportunities this technology offers.

A large public dataset of annotated clinical MRIs and metadata of patients with acute stroke

To extract meaningful and reproducible models of brain function from stroke images, for both clinical and research proposes, is a daunting task severely hindered by the great variability of lesion frequency and patterns. Large datasets are therefore imperative, as well as fully automated image post-processing tools to analyze them. The development of such tools, particularly with artificial intelligence, is highly dependent on the availability of large datasets to model training and testing. We present a public dataset of 2,888 multimodal clinical MRIs of patients with acute and early subacute stroke, with manual lesion segmentation, and metadata. The dataset provides high quality, large scale, human-supervised knowledge to feed artificial intelligence models and enable further development of tools to automate several tasks that currently rely on human labor, such as lesion segmentation, labeling, calculation of disease-relevant scores, and lesion-based studies relating function to frequency lesion maps.

Exploring the multiphysics of the brain during development, aging, and in neurological diseases

The human brain remains an endless source of wonder and represents an intruiging scientific frontier. Multiphysics approaches naturally lend themselves to combine our extensive knowledge about the neurobiology of aging and diseases with mechanics to better capture the multiscale behavior of the brain. Our group uses experimental methods, medical image analysis, and constitutive modeling to develop better disease models with the long-term goal to improve diagnosis, treatment, and ultimately enable prevention of many prevalent age- and disease-related brain changes. In the present perspective, we outline on-going work related to neurodevelopment, aging, and neurodegenerative disease.

The ENIGMA Stroke Recovery Working Group: Big data neuroimaging to study brain-behavior relationships after stroke

The goal of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Stroke Recovery working group is to understand brain and behavior relationships using well-powered meta- and mega-analytic approaches. ENIGMA Stroke Recovery has data from over 2,100 stroke patients collected across 39 research studies and 10 countries around the world, comprising the largest multisite retrospective stroke data collaboration to date. This article outlines the efforts taken by the ENIGMA Stroke Recovery working group to develop neuroinformatics protocols and methods to manage multisite stroke brain magnetic resonance imaging, behavioral and demographics data. Specifically, the processes for scalable data intake and preprocessing, multisite data harmonization, and large-scale stroke lesion analysis are described, and challenges unique to this type of big data collaboration in stroke research are discussed. Finally, future directions and limitations, as well as recommendations for improved data harmonization through prospective data collection and data management, are provided.

Brain aging mechanisms with mechanical manifestations

Brain aging is a complex process that affects everything from the subcellular to the organ level, begins early in life, and accelerates with age. Morphologically, brain aging is primarily characterized by brain volume loss, cortical thinning, white matter degradation, loss of gyrification, and ventricular enlargement. Pathophysiologically, brain aging is associated with neuron cell shrinking, dendritic degeneration, demyelination, small vessel disease, metabolic slowing, microglial activation, and the formation of white matter lesions. In recent years, the mechanics community has demonstrated increasing interest in modeling the brain's (bio)mechanical behavior and uses constitutive modeling to predict shape changes of anatomically accurate finite element brain models in health and disease. Here, we pursue two objectives. First, we review existing imaging-based data on white and gray matter atrophy rates and organ-level aging patterns. This data is required to calibrate and validate constitutive brain models. Second, we review the most critical cell- and tissue-level aging mechanisms that drive white and gray matter changes. We focuse on aging mechanisms that ultimately manifest as organ-level shape changes based on the idea that the integration of imaging and mechanical modeling may help identify the tipping point when normal aging ends and pathological neurodegeneration begins.