Modelling Blood Flow and Metabolism in the Preclinical Neonatal Brain during and Following Hypoxic-Ischaemia

Abstract

Hypoxia-ischaemia (HI) is a major cause of neonatal brain injury, often leading to long-term damage or death. In order to improve understanding and test new treatments, piglets are used as preclinical models for human neonates. We have extended an earlier computational model of piglet cerebral physiology for application to multimodal experimental data recorded during episodes of induced HI. The data include monitoring with near-infrared spectroscopy (NIRS) and magnetic resonance spectroscopy (MRS), and the model simulates the circulatory and metabolic processes that give rise to the measured signals. Model extensions include simulation of the carotid arterial occlusion used to induce HI, inclusion of cytoplasmic pH, and loss of metabolic function due to cell death. Model behaviour is compared to data from two piglets, one of which recovered following HI while the other did not. Behaviourally-important model parameters are identified via sensitivity analysis, and these are optimised to simulate the experimental data. For the non-recovering piglet, we investigate several state changes that might explain why some MRS and NIRS signals do not return to their baseline values following the HI insult. We discover that the model can explain this failure better when we include, among other factors such as mitochondrial uncoupling and poor cerebral blood flow restoration, the death of around 40% of the brain tissue.

Investigating mitochondrial redox state using NADH and NADPH autofluorescence

“…The standard approach of applying pharmacological perturbations to a cell to isolate the contributions of NAD(P)H fluorescence from particular enzymatic subsystems can therefore be troublesome due to unexpected differential effects on neighbouring pathways. Under these circumstances, the understanding of NAD(P)H fluorescence changes may be aided by computational modelling [157], [189], [190]. Such approaches have already been applied successfully to intensity-based monitoring of redox state [191], but their adaptation to lifetime measurements is yet to be performed.…”

Brain cytochrome‐c‐oxidase as a marker of mitochondrial function: A pilot study in major depression using NIRS

“…The majority (6/7) of the patients with MDD were older than 60 years of age and met criteria for LLD. Fluoxetine has so far not been reported to have a clear effect on COX but that may depend on sex (Adzic et al, 2013;Adzic, Mitic, & Radojcic, 2017) and be brain region-specific (Freo, Notably, while MRS measures total, free and protein-bound NAD + and NADH forms because spectroscopy cannot distinguish between subcellular (i.e., cytosolic or mitochondrial) compartments (Du, Cooper, Thida, Sehovic, & Lukas, 2014;Lu, Zhu, Zhang, & Chen, 2014), the current BS model version (Caldwell, Moroz et al, 2015) calculates mitochondrial NAD + and NADH concentrations. Because there is an agedependent decline of mitochondrial functioning N.…”

“…Finally, all the patients with BD were receiving mood stabilizers at the time of scanning. Mood stabilizers, such as lithium or valproic acid, when given as long-term treatment have not been found to affect mitochondrial function (Clay et al, 2011;Kato, 2017), instead, they have 1 Notably, while MRS measures total, free and protein-bound NAD + and NADH forms because spectroscopy cannot distinguish between subcellular (i.e., cytosolic or mitochondrial) compartments (Du, Cooper, Thida, Sehovic, & Lukas, 2014;Lu, Zhu, Zhang, & Chen, 2014), the current BS model version (Caldwell, Moroz et al, 2015) calculates mitochondrial NAD + and NADH concentrations.…”

“…Parameters for each patient and control participant were optimized at once, to reduce the dimensionality of the optimizations (Caldwell, Moroz et al, 2015). Parameters for each patient and control participant were optimized at once, to reduce the dimensionality of the optimizations (Caldwell, Moroz et al, 2015).…”

“…Optimization of all 20 parameters was carried out using a Global Problem solver (solver: de, maximum iterations 1,000) to minimize the difference (Euclidean distance) between the measured and modeled signals on which the corresponding parameters were influential. Parameters for each patient and control participant were optimized at once, to reduce the dimensionality of the optimizations (Caldwell, Moroz et al, 2015). The initial model values (before optimization) and their ranges used for optimization are given in Table 2.…”