Neurovascular physiology and neurocritical care

Singh, Vineeta; Cheng, Roger

doi:10.1016/b978-0-444-64034-5.00014-6

Cited by 10 publications

(12 citation statements)

References 38 publications

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“…Let us note that the tissue Table 1. Relations between ICP values and corresponding pathological conditions [59][60][61][62][63][64]. While there are rare cases [65,66] with survival reported in patients with ICP >50 (mmHg), increased ICP is consistently associated with high mortality [62,67,68].…”

Section: Tumour Dynamicsmentioning

confidence: 99%

Modelling glioma progression, mass effect and intracranial pressure in patient anatomy

Lipková

Menze

Wiestler

et al. 2022

J. R. Soc. Interface.

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Increased intracranial pressure is the source of most critical symptoms in patients with glioma, and often the main cause of death. Clinical interventions could benefit from non-invasive estimates of the pressure distribution in the patient's parenchyma provided by computational models. However, existing glioma models do not simulate the pressure distribution and they rely on a large number of model parameters, which complicates their calibration from available patient data. Here we present a novel model for glioma growth, pressure distribution and corresponding brain deformation. The distinct feature of our approach is that the pressure is directly derived from tumour dynamics and patient-specific anatomy, providing non-invasive insights into the patient's state. The model predictions allow estimation of critical conditions such as intracranial hypertension, brain midline shift or neurological and cognitive impairments. A diffuse-domain formalism is employed to allow for efficient numerical implementation of the model in the patient-specific brain anatomy. The model is tested on synthetic and clinical cases. To facilitate clinical deployment, a high-performance computing implementation of the model has been publicly released.

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Section: Tumour Dynamicsmentioning

confidence: 99%

Modelling glioma progression, mass effect and intracranial pressure in patient anatomy

Lipková

Menze

Wiestler

et al. 2022

J. R. Soc. Interface.

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“…Calculated pressures for cases A and B are visualized in Figure 7 for one cardiac cycle together with the average physiological range of 7–15 mmHg ( Eide and Kerty, 2011 ; Dai et al, 2020 ; Singh and Cheng, 2021 ). Also, the absolute threshold of 22 mmHg for intracranial pressure (recommended for management of traumatic brain injury ( Carney et al, 2017 ; Hawryluk et al, 2020 )) is added.…”

Section: Resultsmentioning

confidence: 99%

“…R tot is calculated as the ratio of average intracranial pressure (ICP avg ) and the CSF production rate (Q production ). The average intracranial pressure is assumed 10 mmHg within the normal physiological range of 7–15 mmHg ( Eide and Kerty, 2011 ; Dai et al, 2020 ; Singh and Cheng, 2021 ).

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Section: Methodsmentioning

confidence: 99%

“…(B) Flow through spinal SAS space cross-section with literature values reported in and reproduced from (Tangen et al, 2015;Benninghaus et al, 2019;Fillingham et al, 2022;Khani et al, 2022). Calculated pressures for cases A and B are visualized in Figure 7 for one cardiac cycle together with the average physiological range of 7-15 mmHg (Eide and Kerty, 2011;Dai et al, 2020;Singh and Cheng, 2021). Also, the absolute threshold of 22 mmHg for intracranial pressure (recommended for management of traumatic brain injury (Carney et al, 2017;Hawryluk et al, 2020)) is added.…”

Section: Csf Pressurementioning

confidence: 99%

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Computational fluid dynamics model to predict the dynamical behavior of the cerebrospinal fluid through implementation of physiological boundary conditions

Vandenbulcke

Pauw

Dewaele

et al. 2022

Front. Bioeng. Biotechnol.

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Cerebrospinal fluid (CSF) dynamics play an important role in maintaining a stable central nervous system environment and are influenced by different physiological processes. Multiple studies have investigated these processes but the impact of each of them on CSF flow is not well understood. A deeper insight into the CSF dynamics and the processes impacting them is crucial to better understand neurological disorders such as hydrocephalus, Chiari malformation, and intracranial hypertension. This study presents a 3D computational fluid dynamics (CFD) model which incorporates physiological processes as boundary conditions. CSF production and pulsatile arterial and venous volume changes are implemented as inlet boundary conditions. At the outlets, 2-element windkessel models are imposed to simulate CSF compliance and absorption. The total compliance is first tuned using a 0D model to obtain physiological pressure pulsations. Then, simulation results are compared with in vivo flow measurements in the spinal subarachnoid space (SAS) and cerebral aqueduct, and intracranial pressure values reported in the literature. Finally, the impact of the distribution of and total compliance on CSF pressures and velocities is evaluated. Without respiration effects, compliance of 0.17 ml/mmHg yielded pressure pulsations with an amplitude of 5 mmHg and an average value within the physiological range of 7–15 mmHg. Also, model flow rates were found to be in good agreement with reported values. However, when adding respiration effects, similar pressure amplitudes required an increase of compliance value to 0.51 ml/mmHg, which is within the range of 0.4–1.2 ml/mmHg measured in vivo. Moreover, altering the distribution of compliance over the four different outlets impacted the local flow, including the flow through the foramen magnum. The contribution of compliance to each outlet was directly proportional to the outflow at that outlet. Meanwhile, the value of total compliance impacted intracranial pressure. In conclusion, a computational model of the CSF has been developed that can simulate CSF pressures and velocities by incorporating boundary conditions based on physiological processes. By tuning these boundary conditions, we were able to obtain CSF pressures and flows within the physiological range.

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“…Although the mean ICP value is often used clinically, it is of interest to also assess the pulsatile ICP variation or morphology. As shown in figure 1(b), the ICP waveform generally has three peaks associated with the cardiac cycle whose amplitudes decrease in a stepwise manner in a healthy individual (Singh & Cheng 2021). The waveform is altered due to the changes in intracranial volume (Unnerbäck, Ottesen & Reinstrup 2018), an important result in the context of disease conditions that produce an increase in mean ICP (TBI or oedema formation), which can result in the waveform becoming more rounded (Ellis, McNames & Aboy 2007), or NPH, which leads to greater fluctuation amplitudes (Eide & Sorteberg 2016).…”

Section: Introductionmentioning

confidence: 99%

A one-dimensional model for the pulsating flow of cerebrospinal fluid in the spinal canal

et al. 2022

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The monitoring of intracranial pressure (ICP) fluctuations, which is needed in the context of a number of neurological diseases, requires the insertion of pressure sensors, an invasive procedure with considerable risk factors. Intracranial pressure fluctuations drive the wave-like pulsatile motion of cerebrospinal fluid (CSF) along the compliant spinal canal. Systematically derived simplified models relating the ICP fluctuations with the resulting CSF flow rate can be useful in enabling indirect evaluations of the former from non-invasive magnetic resonance imaging (MRI) measurements of the latter. As a preliminary step in enabling these predictive efforts, a model is developed here for the pulsating viscous motion of CSF in the spinal canal, assumed to be a linearly elastic compliant tube of slowly varying section, with a Darcy pressure-loss term included to model the fluid resistance introduced by the trabeculae, which are thin collagen-reinforced columns that form a web-like structure stretching across the spinal canal. Use of Fourier-series expansions enables predictions of CSF flow rate for realistic anharmonic ICP fluctuations. The flow rate predicted using a representative ICP waveform together with a realistic canal anatomy is seen to compare favourably with in vivo phase-contrast MRI measurements at multiple sections along the spinal canal. The results indicate that the proposed model, involving a limited number of parameters, can serve as a basis for future quantitative analyses targeting predictions of ICP temporal fluctuations based on MRI measurements of spinal-canal anatomy and CSF flow rate.

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Neurovascular physiology and neurocritical care

Cited by 10 publications

References 38 publications

Modelling glioma progression, mass effect and intracranial pressure in patient anatomy

Modelling glioma progression, mass effect and intracranial pressure in patient anatomy

Computational fluid dynamics model to predict the dynamical behavior of the cerebrospinal fluid through implementation of physiological boundary conditions

A one-dimensional model for the pulsating flow of cerebrospinal fluid in the spinal canal

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