Neural Footprints of Cardiovascular Risk: Personalized Neuroimaging and Potential for Precision Medicine

Neural Footprints of Cardiovascular Risk: Personalized Neuroimaging and Potential for Precision Medicine

Abstract: Cardiovascular and metabolic risk factors (CVMs) pose a significant threat to brain health, contributing to a substantial portion of dementia cases. What if we could detect early signs of brain alterations caused by common health conditions like hypertension and diabetes in midlife, well before cognitive symptoms appear? In this talk, I will describe how leveraged large, harmonized MRI datasets and machine learning to create in-silico severity markers to quantify the impact of CVMs on individual brain MRIs. With these personalized markers, we can potentially identify individuals who are vulnerable to the cognitive effects of CVMs much earlier than current methods allow, opening a critical window for early intervention. These data-driven tools offer critical insights into the link between heart health and brain health, enabling us to identify at-risk individuals and pave the way for precision medicine approaches to dementia prevention.

Bio

Dr. Sindhuja Tirumalai Govindarajan is an Alzheimer’s Association Research Fellow and postdoctoral researcher at the University of Pennsylvania, where she develops machine learning methods for analyzing brain images to detect early signs of neurodegeneration, particularly those related to modifiable lifetime risk factors. Dr. Govindarajan received her doctoral degree in Biomedical Engineering from Stony Brook University and was awarded the President’s Award to Distinguished Doctoral Students. Prior to her doctoral work, she trained at the Martinos Center for Biomedical Imaging (Massachusetts General Hospital and Harvard Medical School), where she investigated cortical degeneration in Multiple Sclerosis using ultra-high field (7 Tesla) MRI and quantitative neuroimaging techniques.

Beyond her research, Dr. Govindarajan is dedicated to mentorship and advocacy for underrepresented early career researchers. She currently serves as the Chair of the Professional Interest Area for Elevating Early career Researchers (PEERs PIA) within the International Society to Advance Alzheimer's Research and Treatment (ISTAART). She is also actively involved in the scientific community, serving as Program Committee Chair for the Machine Learning in Clinical Neuroimaging (MLCN) workshop at MICCAI and editor for its proceedings.