Computational modeling of Neurovascular coupling and FMRI bold responses in Cerebellar circuits

Abstract

Functional roles of brain regions responsible for different sensory, motor and cognitive tasks have become a critical challenge that needs to be addressed in neuroscience. While using methods that allow to span across scales of subcellular, cellular and network activity, interpreting functions across nonlinear scales have newlinebecome a challenge (Elam et al., 2021). Through mathematical modeling, recent advancements in computational neuroscience also allow reconstructing neural activity and its associated circuit behaviour which act as potential hypothesis testing newlinetools(Blundell et al., 2018). The challenge of understanding correlations of neural newlineactivity and blood flow to interpret computations has led to the deconstruction of neural activity, astrocyte-neuron metabolic cooperation and to the generation of newlinehigher level neural circuit signals such as those recorded using functional Magnetic Resonance Imaging (fMRI) and functional Near-Infrared Spectroscopy (fNIRS). In such experimental methods, it is often perceived that neural activity controls newlinevasculature and blood flow related to the supply of glucose and oxygen via direct and indirect pathways in the activated region (Dormanns et al., 2016). Advances in fMRI in brain circuits allows to precisely map the active brain region and to study the newlineneurovascular coupling properties during normal and pathological conditions (Nikos K Logothetis, 2008). The fMRI related blood oxygenation level-dependent (BOLD) signal is widely newlinereferred in neurology and neuroscience to study brain function, and provides an indirect measure of neural activity by correlating hemodynamic changes associated with neuronal activity (S. Ogawa et al., 1990). Measured in small volume of the brain newlinetissue, the region called a voxel, attribute to generating the BOLD responses which represent the changes in blood flow, blood volume, and the concentration of oxy and deoxyhemoglobin (Richard B. Buxton, 2012). This becomes a new tool for modelers to explore top-down and bottom-up behaviors..