Abstract 121: Visualization and Quantification of Post-Stroke Neural Connectivity in Mice Using Serial Two-Photon Tomography

Abstract

Background: It is a challenge to characterize post-stroke changes in neural connectivity at microscopic scale across the entire rodent brain. Serial two-photon tomography (STPT) is an advanced laser-scanning microscopy technique which collects serial fluorescence images across the brain and reconstructs 3D datasets. We examined changes in motor connectivity after cortical infarcts in mice, using retrograde viral tract tracing, STPT imaging, automated registration workflow, and machine learning algorithms. Methods: Young male C57/B6 mice received a photothrombotic motor cortex (M1) stroke (n=3) or sham surgery (n=3). 15 days later, a retrograde pseudorabies trans-synaptic virus encoding fluorescent protein was injected into the left forelimb flexor muscles to label motor system projections. Mice were sacrificed 3 weeks post-stroke. STPT images were analyzed using supervised machine learning (pixel-wise random forest via the “ilastik” software package) and datasets were mapped to the Allen Mouse Brain Atlas for region-specific visualization and quantification of fluorescent signals. Results: Machine learning algorithms successfully identified neuronal cell bodies, neuronal processes, and ischemic tissue throughout the brain. The fluorescent signal of cells and neuronal processes was higher in the right M1 and SS of uninjured mice than the left M1 and SS. After stroke, this signal was diminished in the right M1 and SS. Labeled neurons were also reduced in the left M1 suggesting the presence of secondary transcortical connections. Conclusions: STPT generates whole brain datasets that when analyzed with ML algorithms show early alterations in post-stroke neural connectivity in the corticospinal tract. Further studies utilizing monosynaptic and conditional viral tracers will better assess the full spectrum of connectivity changes during post-stroke functional recovery.