Events Calendar

Understanding Brain Abnormalities In Neuropsychiatric Disorders

Speaker: Ravi Bansal, Ph.D., University of Southern California

Talk Title: Understanding Brain Abnormalities In Neuropsychiatric Disorders

Series: Medical Imaging Seminar Series

Abstract: We have developed sophisticated mathematical and statistical techniques of identifying and quantifying the abnormalities in the morphology of brain regions that are associated with various neuropsychiatric disorders. Applying these techniques to large dataset of patients with various neuropsychiatric disorders we showed that the spatial patterns of these abnormalities are unique across mental illnesses. We quantified the spatial patterns of these abnormalities and applied machine learning algorithms for diagnosing individual patients as having a neuropsychiatric disorder or not. Rigorous, split-half cross validation showed that individuals can be diagnosed with high sensitivity and specificity. However, understanding the biological bases of these abnormalities is important not only for the reproducibility and but also for assessing validity of the MRI derived brain measures: Only reproducible MRI measures would be valid representation brain abnormalities and can increase our understanding of the causal mechanics in disease and subsequent development of early and effective treatments for mental illnesses. I therefore present findings from several studies that show how these together enhance our understanding of the various neuroplastic brain mechanisms in individuals with ADHD, thereby providing strong support for the validity of the MRI-derived findings.

Biography: My primary research interest is in the design and development of algorithms for the automated analysis of medical images. In particular, I am interested in the automated shape analysis of brain regions delineated on high-resolution anatomical MR images, and its application to studying the neurodevelopment of psychiatric disorders. I have developed and validated numerous important methods for the detailed analysis of anatomical surfaces in the brain, including strategies for controlling false positive (Type I) errors that can plague the multiple statistical tests involved in such analyses. Additionally, I am conducting research on mathematical and statistical models for the analyses of diffusion tensor images, white matter fiber tracking and registration, detection of signal in functional magnetic resonance images, nonrigid warping and coregistraiton of magnetic resonance images, and correction of intensity non-uniformities.


Host: Professor Richard Leahy

Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132

Audiences: Everyone Is Invited

Contact: Talyia Veal