Multivariate methods for disease classification

Traditionally, scientists have used magnetic resonance imaging techniques to better quantify brain difference and neuroanatomical trends in healthy controls and disease groups. However, advances in machine learning algorithms that can handle vast quantities of data can allow us to use these data to automatically classify individuals into specific groups based solely on the neuroanatomical features that we derive. Our goal is build diagnosis algorithms that can perform the classification of individuals into disease groups based only on the information (using both native and derived measures) contained in magnetic resonance imaging and computationally derived indices of brain anatomy. We are currently developing projects that perform this type of classification in both schizophrenia and Alzheimer’s disease.