Leptomeningeal collaterals regulate reperfusion in ischemic stroke and rescue the brain from futile recanalization.

Recanalization is the mainstay of ischemic stroke treatment. However, even with timely clot removal, many stroke patients recover poorly. Leptomeningeal collaterals (LMCs) are pial anastomotic vessels with yet-unknown functions. We applied laser speckle imaging, ultrafast ultrasound, and two-photon microscopy in a thrombin-based mouse model of stroke and fibrinolytic treatment to show that LMCs maintain cerebral autoregulation and allow for gradual reperfusion, resulting in small infarcts. In mice with poor LMCs, distal arterial segments collapse, and deleterious hyperemia causes hemorrhage and mortality after recanalization. In silico analyses confirm the relevance of LMCs for preserving perfusion in the ischemic region. Accordingly, in stroke patients with poor collaterals undergoing thrombectomy, rapid reperfusion resulted in hemorrhagic transformation and unfavorable recovery. Thus, we identify LMCs as key components regulating reperfusion and preventing futile recanalization after stroke. Future therapeutic interventions should aim to enhance collateral function, allowing for beneficial reperfusion after stroke.

Convection-Enhanced Delivery In Silico Study for Brain Cancer Treatment.

Brain cancer therapy remains a formidable challenge in oncology. Convection-enhanced delivery (CED) is an innovative and promising local drug delivery method for the treatment of brain cancer, overcoming the challenges of the systemic delivery of drugs to the brain. To improve our understanding about CED efficacy and drug transport, we present an in silico methodology for brain cancer CED treatment simulation. To achieve this, a three-dimensional finite element formulation is utilized which employs a brain model representation from clinical imaging data and is used to predict the drug deposition in CED regimes. The model encompasses biofluid dynamics and the transport of drugs in the brain parenchyma. Drug distribution is studied under various patho-physiological conditions of the tumor, in terms of tumor vessel wall pore size and tumor tissue hydraulic conductivity as well as for drugs of various sizes, spanning from small molecules to nanoparticles. Through a parametric study, our contribution reports the impact of the size of the vascular wall pores and that of the therapeutic agent on drug distribution during and after CED. The in silico findings provide useful insights of the spatio-temporal distribution and average drug concentration in the tumor towards an effective treatment of brain cancer.

Decompressive craniectomy of post-traumatic brain injury: an in silico modelling approach for intracranial hypertension management.

Traumatic brain injury (TBI) causes brain edema that induces increased intracranial pressure and decreased cerebral perfusion. Decompressive craniectomy has been recommended as a surgical procedure for the management of swollen brain and intracranial hypertension. Proper location and size of a decompressive craniectomy, however, remain controversial and no clinical guidelines are available. Mathematical and computational (in silico) models can predict the optimum geometric conditions and provide insights for the brain mechanical response following a decompressive craniectomy. In this work, we present a finite element model of post-traumatic brain injury and decompressive craniectomy that incorporates a biphasic, nonlinear biomechanical model of the brain. A homogenous pressure is applied in the brain to represent the intracranial pressure loading caused by the tissue swelling and the models calculate the deformations and stresses in the brain as well as the herniated volume of the brain tissue that exits the skull following craniectomy. Simulations for different craniectomy geometries (unilateral, bifrontal and bifrontal with midline bar) and sizes are employed to identify optimal clinical conditions of decompressive craniectomy. The reported results for the herniated volume of the brain tissue as a function of the intracranial pressure loading under a specific geometry and size of craniectomy are exceptionally relevant for decompressive craniectomy planning.