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Brain pulsatility offers a compelling application in the study of cerebral biomechanics, particularly for mild traumatic brain injury (mTBI) and elevated intracranial pressure (ICP). In this study, we used amplified MRI to quantify brain tissue pulsations. Dynamic mode decomposition (DMD) processing was then applied to provide a spatio-temporal analysis of motion. Four distinct use cases were examined: (i) resting versus exertion-induced heart rate changes, (ii) pre- and post-lumbar puncture (LP), (iii) baseline versus post-brain injury, and (iv) a test-retest case. Results demonstrate that brain tissue motion varies significantly across conditions, with DMD revealing distinct modes and frequencies corresponding to physiological changes. Notably, mTBI showed an increase in pulsatile motion post-injury, while elevated ICP exhibited altered pulsatility patterns post-LP, indicating a potential biomarker for injury and pressure-related changes. This approach offers new insights into physiological and pathological brain pulsatility; however, the study's limited sample size, reliance on retrospective gating and assumptions regarding pulsatile motion highlight the need for larger and more diverse cohorts to confirm these findings. Despite these limitations, our results suggest our dynamical analysis approach could become a valuable tool for assessing intracranial dynamics, with applications in clinical diagnostics and research on neurovascular and neurological conditions.

The hippocampus is a highly scrutinized brain structure due to its entanglement in multiple neuropathologies and vulnerability to metabolic insults. This study aims to non-invasively assess the perfusion-mechanics relationship of the hippocampus in the healthy brain across magnetic resonance imaging sequences and magnetic field strengths. In total, 17 subjects (aged 22-35, 7 males/10 females) were scanned with magnetic resonance elastography and arterial spin labelling acquisitions at 3T and 7T in a baseline physiological state. No significant differences in perfusion or stiffness were observed across magnetic field strengths or acquisitions. The hippocampus had the highest vascularity within the deep grey matter, followed closely by the caudate nucleus and putamen. We discovered a positive perfusion-mechanics correlation in the hippocampus across both 3T and 7T groups, with a highly significant correlation overall (R = 0.71, p = 0.0019), which was not observed in the caudate nucleus, a similarly vascular region. Furthermore, we supported our hypothesis that increased perfusion in the hippocampus would lead to greater pulsatile displacement in a small cohort (n = 10). Given that the hippocampus is an exceptionally vulnerable structure, with perfusion deficits often seen in diseases related to learning and memory, our results suggest a unique mechanistic link between metabolic health and stiffness biomarkers in this key region for the first time.

Cardiac arrest (CA) is associated with high incidence and mortality rates. Among patients who survive the acute phase, brain injury stands out as a primary cause of death or disability. Effective intensive care management, including targeted temperature management, seizure treatment and maintenance of normal physiological parameters, plays a crucial role in improving survival and neurological outcomes. Current guidelines advocate for neuroprotective strategies to mitigate secondary brain injury following CA, although certain treatments remain subjects of debate. Clinical examination and neuroimaging studies, both invasive and non-invasive neuromonitoring methods and serum biomarkers are valuable tools for predicting outcomes in comatose resuscitated patients. Neuromonitoring, in particular, provides vital insights for identifying complications, personalizing treatment approaches and forecasting prognosis in patients with brain injury post-CA. In this review, we offer an overview of advanced strategies and best practices aimed at optimizing brain protection after CA.

This article documents the early development of the first transcranial Doppler (TCD)-based ultrasound system for continuous monitoring of brain tissue pulsations (BTPs). Transcranial tissue Doppler (TCTD) uses a lightweight, wearable single-element ultrasound probe to track tissue motion perpendicular to the skin's surface, providing tissue displacement estimates along a single beam line. Feasibility tests using an adapted TCD system confirmed that brain tissue motion data can be obtained from existing TCD hardware. Brain Tissue Velocimetry (Brain TV), a TCTD data acquisition system, was then developed to provide a lightweight and portable means of continuously recording TCTD data in real-time. Brain TV measurements are synchronized to a 3-lead electrocardiogram and can be recorded alongside other physiological measurements, such as blood pressure, heart rate and end-tidal carbon dioxide. We have shown that Brain TV is able to record BTPs from sample depths ranging from 22 to 80 mm below the probe's surface and from multiple positions on the head. Studies in healthy volunteers, stroke patients and ultrasound phantom brain models demonstrate how TCTD might provide insights into the relationships between physiological measurements and brain tissue motion and show promise for rapid clinical assessment and continuous monitoring of BTPs.

Intracerebral blood volume changes along the cardiac cycle cause volumetric strain in brain tissue, measurable with displacement encoding with stimulated echoes (DENSE) magnetic resonance imaging. Individual volumetric strain maps show compressing and expanding voxels, raising the question whether systolic compressions reflect a physiological phenomenon. In DENSE data from nine healthy volunteers, voxels were grouped into three clusters according to volumetric strain in a tissue mask excluding extracerebral blood vessels and cerebrospinal fluid using a two-stage clustering approach. To confirm the physiological source of the compressions, data from a patient with a cranial opening was analysed. Spatial patterns of compressing and expanding clusters were matched to high-resolution anatomical scans, acquired in one additional individual. All healthy subjects consistently showed a cluster with compressive volumetric strain during systole, covering 10.2% [7.3-13.1%] (mean [95% confidence interval]) of the tissue mask, besides two expansion clusters. In the patient, no compression was observed. Although the compression cluster did not consistently co-localize with intracerebral veins or perivascular spaces on the anatomical scans, the first-stage clustering results suggested that the distinction between the clusters has a (peri)vascular source. In conclusion, brain tissue shows heartbeat-induced volumetric compressions, possibly indicating compression of porous structures such as intracerebral veins or perivascular spaces.

The brain is arguably the most complex human organ and modelling its mechanical behaviour has challenged researchers for decades. There is still a lack of understanding on how this multiphase tissue responds to mechanical loading and how material parameters can be reliably calibrated. While previous viscoelastic models with two relaxation times have successfully captured the response of brain tissue, the Theory of Porous Media provides a continuum mechanical framework to explore the underlying physical mechanisms, including interactions between solid matrix and free-flowing interstitial fluid. Following our previously published experimental testing protocol, here we perform finite element simulations of cyclic compression-tension loading and compression-relaxation experiments on human brain white and gray matter specimens. The solid volumetric stress proves to be a crucial factor for the overall biphasic tissue behaviour as it strongly interferes with porous effects controlled by the permeability. An inverse parameter identification reveals that poroelasticity alone is insufficient to capture the time-dependent material behaviour, but a poro-viscoelastic formulation captures the response of brain tissue well. We provide valuable insights into the individual contributions of viscous and porous effects. However, due to the strong coupling between porous, viscous, and volumetric effects, additional experiments are required to reliably determine all material parameters.

Intracranial pressure (ICP) monitoring is crucial in the management of traumatic brain injury (TBI) and other neurological conditions. Elevated ICP or too low intracranial compliance (ICC) can compromise brain perfusion. Simultaneous monitoring of ICP and ICC is needed to optimize patient-specific brain perfusion in pathological conditions. Surrogate ICC changes can be extracted by analysis of ICP pulse wave morphology. Non-invasive, fully passive sensor and ICC changes monitoring are needed. This study introduces Archimedes, a novel, fully passive, non-invasive ICP wave monitor that utilizes mechanical pulsatile movement of the eyeball to assess ICP pulse waveforms. Preliminary findings indicate a high correlation r = [0.919; 0.96] between non-invasive and invasive ICP pulse wave morphologies, demonstrating the device's potential for accurate ICP pulse waveform monitoring. Additionally, the monitor can discern ICC changes, providing valuable insights for TBI and normal tension glaucoma patients according to the shape of non-invasive measured ICP pulse wave. The k-nearest neighbours algorithm used in preliminary glaucoma studies yielded promising diagnostic performance, with an accuracy of 0.89, sensitivity of 0.82, specificity of 1.0 and area under curve 0.91. Ethical approvals for ongoing studies have been secured. Initial results indicate that Archimedes real-time ICC non-invasive monitor is safe, cost-effective alternative to conventional monitoring techniques.

Cerebrospinal fluid (CSF) flow is crucial for clearing metabolic waste from the brain, a process whose dysregulation is linked to neurodegenerative diseases like Alzheimer's. Traditional approaches like particle tracking velocimetry (PTV) are limited by their reliance on single-plane two-dimensional measurements, which fail to capture the complex dynamics of CSF flow fully. To overcome these limitations, we employ artificial intelligence velocimetry (AIV) to reconstruct three-dimensional velocities, infer pressure and wall shear stress and quantify flow rates. Given the experimental nature of the data and inherent variability in biological systems, robust uncertainty quantification (UQ) is essential. Towards this end, we have modified the baseline AIV architecture to address aleatoric uncertainty caused by noisy experimental data, enhancing our measurement refinement capabilities. We also implement UQ for the model and epistemic uncertainties arising from the governing equations and network representation. Towards this end, we test multiple governing laws, representation models and initializations. Our approach not only advances the accuracy of CSF flow quantification but also can be adapted to other applications that use physics-informed machine learning to reconstruct fields from experimental data, providing a versatile tool for inverse problems.

Functional magnetic resonance imaging (fMRI) captures rich physiological and neuronal information, offering insight into neurofluid dynamics, vascular health and waste clearance. Accurate cerebral vessel segmentation could greatly facilitate fluid dynamics research in fMRI. However, existing vessel identification methods, such as magnetic resonance angiography or deep-learning-based segmentation on structural MRI, cannot reliably locate cerebral vessels in fMRI space due to misregistration from inherent fMRI distortions. To address this challenge, we developed a data-driven, automatic segmentation of cerebral vessels directly within fMRI space. This approach identified large cerebral arteries and the superior sagittal sinus (SSS) by leveraging these vessels' distinct pulsatile signal patterns during the cardiac cycle. The method was validated in a local dataset by comparing it to ground truth cerebral artery and SSS segmentations. Using the Human Connectome Project (HCP) ageing dataset, the method's reproducibility was tested on 422 participants aged 36-90, each with four repeated fMRI scans. The method demonstrated high reproducibility, with an intraclass correlation coefficient > 0.7 in both cerebral artery and SSS segmentation volumes. This study demonstrates that large cerebral arteries and SSS can be reproducibly and automatically segmented in fMRI datasets, facilitating reliable fluid dynamics investigation in these regions.