A probable dual mode of action for both L- and D-lactate neuroprotection in cerebral ischemia.

Lactate has been shown to offer neuroprotection in several pathologic conditions. This beneficial effect has been attributed to its use as an alternative energy substrate. However, recent description of the expression of the HCA1 receptor for lactate in the central nervous system calls for reassessment of the mechanism by which lactate exerts its neuroprotective effects. Here, we show that HCA1 receptor expression is enhanced 24 hours after reperfusion in an middle cerebral artery occlusion stroke model, in the ischemic cortex. Interestingly, intravenous injection of L-lactate at reperfusion led to further enhancement of HCA1 receptor expression in the cortex and striatum. Using an in vitro oxygen-glucose deprivation model, we show that the HCA1 receptor agonist 3,5-dihydroxybenzoic acid reduces cell death. We also observed that D-lactate, a reputedly non-metabolizable substrate but partial HCA1 receptor agonist, also provided neuroprotection in both in vitro and in vivo ischemia models. Quite unexpectedly, we show D-lactate to be partly extracted and oxidized by the rodent brain. Finally, pyruvate offered neuroprotection in vitro whereas acetate was ineffective. Our data suggest that L- and D-lactate offer neuroprotection in ischemia most likely by acting as both an HCA1 receptor agonist for non-astrocytic (most likely neuronal) cells as well as an energy substrate.

Accelerated Microstructure Imaging via Convex Optimization (AMICO) in crossing fibers

Mapping the microstructure properties of the local tissues in the brain is crucial to understand any pathological condition from a biological perspective. Most of the existing techniques to estimate the microstructure of the white matter assume a single axon orientation whereas numerous regions of the brain actually present a fiber-crossing configuration. The purpose of the present study is to extend a recent convex optimization framework to recover microstructure parameters in regions with multiple fibers.

On evaluating the accuracy and biological plausibility of diffusion MRI tractograms

In diffusion MRI, traditional tractography algorithms do not recover truly quantitative tractograms and the structural connectivity has to be estimated indirectly by counting the number of fiber tracts or averaging scalar maps along them. Recently, global and efficient methods have emerged to estimate more quantitative tractograms by combining tractography with local models for the diffusion signal, like the Convex Optimization Modeling for Microstructure Informed Tractography (COMMIT) framework. In this abstract, we show the importance of using both (i) proper multi-compartment diffusion models and (ii) adequate multi-shell acquisitions, in order to evaluate the accuracy and the biological plausibility of the tractograms.