Reconstructing blood flow in data-poor regimes: a vasculature network kernel for Gaussian process regression.

Blood flow reconstruction in the vasculature is important for many clinical applications. However, in clinical settings, the available data are often quite limited. For instance, transcranial Doppler ultrasound is a non-invasive clinical tool that is commonly used in clinical settings to measure blood velocity waveforms at several locations. This amount of data is grossly insufficient for training machine learning surrogate models, such as deep neural networks or Gaussian process regression. In this work, we propose a Gaussian process regression approach based on empirical kernels constructed by data generated from physics-based simulations-enabling near-real-time reconstruction of blood flow in data-poor regimes. We introduce a novel methodology to reconstruct the kernel within the vascular network. The proposed kernel encodes both spatiotemporal and vessel-to-vessel correlations, thus enabling blood flow reconstruction in vessels that lack direct measurements. We demonstrate that any prediction made with the proposed kernel satisfies the conservation of mass principle. The kernel is constructed by running stochastic one-dimensional blood flow simulations, where the stochasticity captures the epistemic uncertainties, such as lack of knowledge about boundary conditions and uncertainties in vasculature geometries. We demonstrate the performance of the model on three test cases, namely, a simple Y-shaped bifurcation, abdominal aorta and the circle of Willis in the brain.

Cellular and molecular mechanisms of the blood-brain barrier dysfunction in neurodegenerative diseases.

BACKGROUND: Maintaining the structural and functional integrity of the blood-brain barrier (BBB) is vital for neuronal equilibrium and optimal brain function. Disruptions to BBB performance are implicated in the pathology of neurodegenerative diseases. MAIN BODY: Early indicators of multiple neurodegenerative disorders in humans and animal models include impaired BBB stability, regional cerebral blood flow shortfalls, and vascular inflammation associated with BBB dysfunction. Understanding the cellular and molecular mechanisms of BBB dysfunction in brain disorders is crucial for elucidating the sustenance of neural computations under pathological conditions and for developing treatments for these diseases. This paper initially explores the cellular and molecular definition of the BBB, along with the signaling pathways regulating BBB stability, cerebral blood flow, and vascular inflammation. Subsequently, we review current insights into BBB dynamics in Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, and multiple sclerosis. The paper concludes by proposing a unified mechanism whereby BBB dysfunction contributes to neurodegenerative disorders, highlights potential BBB-focused therapeutic strategies and targets, and outlines lessons learned and future research directions. CONCLUSIONS: BBB breakdown significantly impacts the development and progression of neurodegenerative diseases, and unraveling the cellular and molecular mechanisms underlying BBB dysfunction is vital to elucidate how neural computations are sustained under pathological conditions and to devise therapeutic approaches.

What does computational fluid dynamics tell us about intracranial aneurysms? A meta-analysis and critical review.

Despite the plethora of published studies on intracranial aneurysms (IAs) hemodynamic using computational fluid dynamics (CFD), limited progress has been made towards understanding the complex physics and biology underlying IA pathophysiology. Guided by 1733 published papers, we review and discuss the contemporary IA hemodynamics paradigm established through two decades of IA CFD simulations. We have traced the historical origins of simplified CFD models which impede the progress of comprehending IA pathology. We also delve into the debate concerning the Newtonian fluid assumption used to represent blood flow computationally. We evidently demonstrate that the Newtonian assumption, used in almost 90% of studies, might be insufficient to describe IA hemodynamics. In addition, some fundamental properties of the Navier-Stokes equation are revisited in supplementary material to highlight some widely spread misconceptions regarding wall shear stress (WSS) and its derivatives. Conclusively, our study draws a roadmap for next-generation IA CFD models to help researchers investigate the pathophysiology of IAs.

Evidence for hysteresis in the cerebral pressure-flow relationship in healthy men.

The cerebrovasculature is more efficient at compensating for pharmacologically induced transient hypertension versus transient hypotension. Whether this phenomenon exists during nonpharmacologically induced hypertension and hypotension is currently unknown. We compared the percent change in mean velocity in the middle cerebral artery (MCAvmean) per percent change in mean arterial pressure (MAP) (%ΔMCAVmean/%ΔMAP) during transient hypertension and hypotension induced during squat-stand maneuvers performed at 0.05 Hz (20-s cycles) and 0.10 Hz (10-s cycles) in 58 male volunteers. %ΔMCAvmean/%ΔMAP was attenuated by 25% (P = 0.03, 0.05 Hz) and 47% (P < 0.0001, 0.10 Hz) during transient hypertension versus hypotension. Thus, these findings indicate that the brain in healthy men is better adapted to compensate for physiologically relevant transient hypertension than hypotension.NEW & NOTEWORTHY The novel finding of this study is that the change in middle cerebral artery mean flow velocity is attenuated during hypertension compared with hypotension physiologically induced by oscillations in blood pressure in men. These results support that the human brain is more effective at compensating for transient hypertension than hypotension.