Evaluation of cerebrovascular hemodynamics in vascular dementia patients with a new individual computational fluid dynamics algorithm

A mathematical framework for the dynamic interaction of pulsatile blood, brain, and cerebrospinal fluid

BACKGROUND

Shedding light on less-known aspects of intracranial fluid dynamics may be helpful to understand the hydrocephalus mechanism. The present study suggests a mathematical framework based on in vivo inputs to compare the dynamic interaction of pulsatile blood, brain, and cerebrospinal fluid (CSF) between the healthy subject and the hydrocephalus patient.

METHOD

The input data for the mathematical formulations was pulsatile blood velocity, which was measured using cine PC-MRI. Tube law was used to transfer the created deformation by blood pulsation in the vessel circumference to the brain domain. The pulsatile deformation of brain tissue with respect to time was calculated and considered to be inlet velocity in the CSF domain. The governing equations in all three domains were continuity, Navier-Stokes, and concentration. We used Darcy law with defined permeability and diffusivity values to define the material properties in the brain.

RESULTS

We validated the preciseness of the CSF velocity and pressure through the mathematical formulations with cine PC-MRI velocity, experimental ICP, and FSI simulated velocity and pressure. We used the analysis of dimensionless numbers including Reynolds, Womersley, Hartmann, and Peclet to evaluate the characteristics of the intracranial fluid flow. In the mid-systole phase of a cardiac cycle, CSF velocity had the maximum value and CSF pressure had the minimum value. The maximum and amplitude of CSF pressure, as well as CSF stroke volume, were calculated and compared between the healthy subject and the hydrocephalus patient.

CONCLUSION

The present in vivo-based mathematical framework has the potential to gain insight into the less-known points in the physiological function of intracranial fluid dynamics and the hydrocephalus mechanism.

The Quantitative Associations Between Near Infrared Spectroscopic Cerebrovascular Metrics and Cerebral Blood Flow: A Scoping Review of the Human and Animal Literature

Cerebral blood flow (CBF) is an important physiologic parameter that is vital for proper cerebral function and recovery. Current widely accepted methods of measuring CBF are cumbersome, invasive, or have poor spatial or temporal resolution. Near infrared spectroscopy (NIRS) based measures of cerebrovascular physiology may provide a means of non-invasively, topographically, and continuously measuring CBF. We performed a systematically conducted scoping review of the available literature examining the quantitative relationship between NIRS-based cerebrovascular metrics and CBF. We found that continuous-wave NIRS (CW-NIRS) was the most examined modality with dynamic contrast enhanced NIRS (DCE-NIRS) being the next most common. Fewer studies assessed diffuse correlation spectroscopy (DCS) and frequency resolved NIRS (FR-NIRS). We did not find studies examining the relationship between time-resolved NIRS (TR-NIRS) based metrics and CBF. Studies were most frequently conducted in humans and animal studies mostly utilized large animal models. The identified studies almost exclusively used a Pearson correlation analysis. Much of the literature supported a positive linear relationship between changes in CW-NIRS based metrics, particularly regional cerebral oxygen saturation (rSO2), and changes in CBF. Linear relationships were also identified between other NIRS based modalities and CBF, however, further validation is needed.

Machine Learning Model-Based Applications for Food Management in Alzheimer’s Using Regression Analysis Approach

Alzheimer’s disease (AD) has become a public health concern due to its misinterpretation with vascular dementia (VD) and mixed dementia Alzheimer’s disease (MXD). Therefore, an accurate differentiation of these diseases is essential for improving the treatment procedure. It has been seen that nutrition along with several other factors plays a role in the disease progression. Scientists are trying to find a solution using some machine learning (ML) techniques. The ML algorithms used for this purpose are neural networks, support vector machines, regression and many more. The current research is focused on understanding the extent of the application of machine learning tools in enhancing food management for patients with Alzheimer’s since there is no cure known for the same. A total of 100 patient data have been collected where the patients had AD, VD, and MXD. Their demographic data, dietary intake, Fazekas scores, and Hachinski scores were collected (independent variables) and analysed in IBM SPSS by considering the risk of development of AD, VD, and MXD as dependent variables. The findings showed that age is highly related ( $p<0.001$ ) to the development of these three diseases and other demographics are not prioritized. Discussion of other available journal articles showed that nutritional intake, Fazekas scores, Hachinski scores, and gender are also indicators for predicting these diseases ( $p<0.001$ ). Thus, this study concluded that age, gender, diet consumption, and Fazekas and Hachinski scores are important indicators for differentiating AD from other diseases, and ML can be used to create a custom nutrition plan based on the patient’s diet and stage of disease progression. Lastly, future scopes of ML have been explained in this paper.