Dr. Goubran’s research interests combine translational and basic science research. He aims to develop novel computational, machine learning and imaging tools for interrogating brain circuits across resolution scales and modelling brain pathology. He is particularly interested in studying the underlying mechanisms and pathways behind disease progression of neurological and cerebrovascular disorders that involve disruption of neural circuits, including Alzheimer’s disease (AD) and stroke.
As part of his postdoctoral fellowship at Stanford University, Dr. Goubran developed a computational pipeline for investigating connectome dynamics in 3D cleared tissue and MRI in animal models, working with the pioneers of tissue clearing and optogenetics. His tools are being used by many collaborating labs internationally. At Sunnybrook, he has been developing and using artificial intelligence (AI) techniques for improved structural and lesion segmentation in populations with extensive brain atrophy. He is also working on creating diagnostic models of neurogenerative diseases from rich imaging and clinical data to aid in the great challenge of early and accurate diagnosis.
Dr. Goubran’s research focus includes multi-parametric and morphological analysis of the hippocampus and its subregions in AD. He is interested in studying human hippocampal circuit remodelling and developing imaging approaches to help predict hippocampal subregion pathology from in vivo imaging. Understanding how cellular and structural changes occur in the connected regions to the ischemic core in stroke is important for developing targeted therapies. Dr. Goubran is applying advanced diffusion and functional imaging, in combination with computational methods in preclinical models, to investigate the spatiotemporal effects of stroke on remote connected areas. Another line of his research focuses on modelling stroke outcomes with AI, to predict who will have cognitive and motor impairments.