Recording of Presentation
Speaker: Maged Goubran
Bio: Dr. Goubran is a Scientist at the Sunnybrook Research Institute and Assistant Professor in the Department of Medical Biophysics at the University of Toronto. He earned his PhD in Biomedical Engineering with a specialization in Medical Imaging at the Robarts Research Institute at Western University. He then went on to pursue his postdoctoral training at the Department of Radiology and the Neuroscience Program at Stanford University. His work combines translational and basic science research, with the overarching aim of developing novel computational and machine learning tools to probe and predict circuit alterations in neurological disorders, including Alzheimer’s disease, stroke and traumatic brain injury.
Talk Abstract: 3D tissue clearing, slice-based connectivity atlases, and diffusion MRI are common brain mapping techniques. While there are many resources to analyze and process this data, there is lack of integration across modalities. I will present an automated pipeline that combines histologically cleared volumes with connectivity atlases and MRI, enabling the analysis of histological features across multiple networks, and their correlation with in-vivo imaging. We validated our pipeline in a murine stroke model, demonstrating a strong correspondence between MRI abnormalities and cellular changes, and uncovering acute cellular effects in areas connected to the ischemic core. We also integrated diffusion MRI and CLARITY viral tracing to compare connectivity maps across scales. Finally, I will demonstrate tract-level histological changes of stroke through this multimodal integration. At the end of my talk I will briefly present some of the AI models we are developing to improve this pipeline.