Determining Clinically-Viable Biomarkers for Ischaemic Stroke Through a Mechanistic and Machine Learning Approach

Abstract

Assessment of distal cerebral perfusion after ischaemic stroke is currently only possible through expensive and time-consuming imaging procedures which require the injection of a contrast medium. Alternative approaches that could indicate earlier the impact of blood flow occlusion on distal cerebral perfusion are currently lacking. The aim of this study was to identify novel biomarkers suitable for clinical implementation using less invasive diagnostic techniques such as Transcranial Doppler (TCD). We used 1D modelling to simulate pre- and post-stroke velocity and flow wave propagation in a typical arterial network, and Sobol’s sensitivity analysis, supported by the use of Gaussian process emulators, to identify biomarkers linked to cerebral perfusion. We showed that values of pulsatility index of the right anterior cerebral artery > 1.6 are associated with poor perfusion and may require immediate intervention. Three additional biomarkers with similar behaviour, all related to pulsatility indices, were identified. These results suggest that flow pulsatility measured at specific locations could be used to effectively estimate distal cerebral perfusion rates, and ultimately improve clinical diagnosis and management of ischaemic stroke.

A multiscale computational framework to evaluate flow alterations during mechanical thrombectomy for treatment of ischaemic stroke

“…Examples of such mechanisms are the collateral flow due to the leptomeningeal anastomoses (LMAs), an extensive network of small arterioles which connect different parts of the brain and guarantee continuity of perfusion following occlusion of large vessels such as the MCA ( 52 ), and autoregulation mechanisms which modify peripheral resistance and compliance. LMAs were not included in this model as previous studies ( 27 – 30 , 52 , 53 ) have shown that they act in restoring perfusion in distal brain districts and have little effect on blood flow in the CoW and the retrieval path. Similarly, autoregulation intervenes to support perfusion in infarcted tissues and there is no conclusive evidence that it induces modification in large proximal vessels ( 54 ).…”

“…Researchers have investigated the effect of vessel occlusion on the haemodynamics of the CoW using 1D models: in Refs. ( 27 ) and ( 28 ) Padmos and colleagues coupled an extensive, steady-state 1D cerebral network to a diffusion model to study how the collateral circulation supports brain perfusion in case of IS; a similar study was performed in Phan et al ( 29 ), where the authors assessed the conditions necessary for distributed collateral circulation to provide perfusion above a 30% threshold; in 2022, Benemerito and co-authors combined the 1D description with statistical emulators to identify a number of easily measurable biomarkers that correlate strongly with distal MCA perfusion following an ischaemic event ( 30 ). To the best of our knowledge, only one study has been published that simulates the retrieval of a clot and the consequent impact on the haemodynamic of the CoW ( 31 ), where they modeled the movement of the clot as arterial stenosis of variable magnitude.…”

Reducing the Complexity of Musculoskeletal Models using Gaussian Process Emulators

“…where 𝑦 𝑠 𝑛 and 𝑦 𝑒 𝑛 are the 𝑛-th run of the simulator and emulator respectively, 𝑦 𝑠 ̅ is the time average of the simulator output, and 𝑁 𝑣 is the number of points in the validation dataset [35,36]. Following a convergence study, the size of the training dataset which provided a low emulation error (MAPE<3%) while identified the computational cost was chosen to be 50 points.…”

“…A reduced MSKM model was defined as a model having a reduced number of personalised muscle Fmax, selected according to a ranking strategy based on the muscles contribution to the determination of total JCF. Similarly to previous SA studies [35,36], a threshold of VRSI = 0.1 was set to identify the muscles that required personalisation. For each of the 100 frames of the gait cycle, the muscles that showed VRSI ≥ 0.1 for at least one of the JCFs were identified and ranked based on the number of time frames where they were deemed as influential.…”

High resolution simulation of basilar artery infarct and flow within the circle of Willis

“…Such studies may occur in simulations at all scales of physical realism. For example Benemerito et al [2] uses a 1D model to develop a set of training data for a machine learning model to identify biomarkers for ischemic stroke. Similarly, Padmos et al [3,4] make use of a 1D model connected to perfusion territories to examine how an acute ischemic stroke impacts flow into the brain tissue beyond the circle of Willis.…”