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bioRxiv2022

Establishment of a hypoxia ischemia reperfusion brain damagemodel in neonatal rats

ObjectiveRice-Vannucci model has been widely used as HIE (Hypoxic ischemic encephalopathy) animal model in the past forty years, but it does not mimic reperfusion injury that occurs during HIE. The aim of the present study was to establish a new neonatal rat model by simulating hypoxia ischemia reperfusion brain damage (HIRBD) through "common carotid artery (CCA) muscle bridge". MethodsSixty 7-day-old male Sprague-Dawley rats were randomly assigned to group A (HIRBD groups, n=36), group B (Rice-Vannucci group, n=12), and group C (sham-operated group, n=12). Rats in group A were assigned to 3 subgroups (A1-A3, 12 animals/subgroup). Dynamic changes in cerebral blood flow (CBF) were evaluated by the laser speckle imaging system. The status of the CCA was observed under a stereomicroscope. Changes in body weight, gross morphology as well as pathological sections of brain tissue were examined to evaluate the feasibility of the model. ResultsThe results indicated that CCA muscle bridge successfully blocked the CBF. CBF was restored after removal of the CCA muscle bridge in HIRBD groups. The CCA was in good condition after removing the muscle bridge, and blood supply was not affected. Changes in body weight, gross morphology and pathological sections of brain tissue indicated that ischemia reperfusion induced by the CCA muscle bridge method caused varying degrees of brain damage. ConclusionCCA muscle bridge method is effective for establishing a reliable, stable, and reproducible neonatal rat model for study of HIRBD.

neuroscience↗

bioRxiv2020

Differential covariance: A new method to estimate functional connectivity in fMRI

Measuring functional connectivity from fMRI recordings is important in understanding processing in cortical networks. However, because the brains connection pattern is complex, currently used methods are prone to producing false functional connections. We introduce differential covariance analysis, a new method that uses derivatives of the signal for estimating functional connectivity. We generated neural activities from Dynamical Causal Modeling and a neural network of Hodgkin-Huxley neurons and then converted them to hemodynamic signals using the forward Balloon model. The simulated fMRI signals together with the ground truth connectivity pattern were used to benchmark our method with other commonly used methods. Differential covariance achieved better results in complex network simulations. This new method opens an alternative way to estimate functional connectivity.

neuroscience↗