Background: It is a daunting task to identify all the metabolic pathways of brain energy metabolism and develop a dynamic simulation environment that will cover a time scale ranging from seconds to hours. To simplify this task and make it more practicable, we undertook stoichiometric modeling of brain energy metabolism with the major aim of including the main interacting pathways in and between astrocytes and neurons.Model: The constructed model includes central metabolism (glycolysis, pentose phosphate pathway, TCA cycle), lipid metabolism, reactive oxygen species (ROS) detoxification, amino acid metabolism (synthesis and catabolism), the well-known glutamate-glutamine cycle, other coupling reactions between astrocytes and neurons, and neurotransmitter metabolism. This is, to our knowledge, the most comprehensive attempt at stoichiometric modeling of brain metabolism to date in terms of its coverage of a wide range of metabolic pathways. We then attempted to model the basal physiological behaviour and hypoxic behaviour of the brain cells where astrocytes and neurons are tightly coupled.Results: The reconstructed stoichiometric reaction model included 217 reactions (184 internal, 33 exchange) and 216 metabolites (183 internal, 33 external) distributed in and between astrocytes and neurons. Flux balance analysis (FBA) techniques were applied to the reconstructed model to elucidate the underlying cellular principles of neuron-astrocyte coupling. Simulation of resting conditions under the constraints of maximization of glutamate/glutamine/GABA cycle fluxes between the two cell types with subsequent minimization of Euclidean norm of fluxes resulted in a flux distribution in accordance with literature-based findings. As a further validation of our model, the effect of oxygen deprivation (hypoxia) on fluxes was simulated using an FBA-derivative approach, known as minimization of metabolic adjustment (MOMA). The results show the power of the constructed model to simulate disease behaviour on the flux level, and its potential to analyze cellular metabolic behaviourin silico.Conclusion: The predictive power of the constructed model for the key flux distributions, especially central carbon metabolism and glutamate-glutamine cycle fluxes, and its application to hypoxia is promising. The resultant acceptable predictions strengthen the power of such stoichiometric models in the analysis of mammalian cell metabolism.

Functional near-infrared spectroscopy (fNIRS) has been proposed as an affordable, fast, and robust alternative to many neuroimaging modalities yet it still has long way to go to be adapted in the clinic. One request from the clinicians has been the delivery of a simple and straightforward metric (a so-called biomarker) from the vast amount of data a multichannel fNIRS system provides. We propose a simple-straightforward signal processing algorithm derived from fNIRS-HbO2 data collected during a modified version of the color-word matching Stroop task that consists of three different conditions. The algorithm starts with a wavelet-transform-based preprocessing, then uses partial correlation analysis to compute the functional connectivity matrices at each condition and then computes the global efficiency values. To this end, a continuous wave 16 channels fNIRS device (ARGES Cerebro, Hemosoft Inc., Turkey) was used to measure the changes in HbO2 concentrations from 12 healthy volunteers. We have considered 10% of strongest connections in each network. A strong Stroop interference effect was found between the incongruent against neutral condition (p=0.01) while a similar significance was observed for the global efficiency values decreased from neutral to congruent to incongruent conditions [F(2,33)=3.46, p=0.043]. The findings bring us closer to delivering a biomarker derived from fNIRS data that can be reliably and easily adopted by the clinicians.

Functional near-infrared spectroscopy (fNIRS) is an emerging technique for monitoring the concentration changes of oxy- and deoxy-hemoglobin (oxy-Hb and deoxy-Hb) in the brain. An important consideration in fNIRS-based neuroimaging modality is to conduct group-level analysis from a set of time series measured from a group of subjects. We investigate the feasibility of multilevel statistical inference for fNIRS. As a case study, we search for hemodynamic activations in the prefrontal cortex during Stroop interference. Hierarchical general linear model (GLM) is used for making this multilevel analysis. Activation patterns both at the subject and group level are investigated on a comparative basis using various classical and Bayesian inference methods. All methods showed consistent left lateral prefrontal cortex activation for oxy-Hb during interference condition, while the effects were much less pronounced for deoxy-Hb. Our analysis showed that mixed effects or Bayesian models are more convenient for faithful analysis of fNIRS data. We arrived at two important conclusions. First, fNIRS has the capability to identify activations at the group level, and second, the mixed effects or Bayesian model is the appropriate mechanism to pass from subject to group-level inference.