The AI Catalyst project is focused on finding Alzheimer’s Disease (AD) biomarkers. Early identification of AD is vital to slowing disease progression in the brain. In our lab, we use data from neuroimaging techniques for identification. This project utilizes the field of connectomics by using imaging modalities to create mathematical brain networks composed of nodes and edges. Network analysis allows us to obtain quantifiers that can reveal local and global brain connectivity. We compare differences between brains with statistics, machine learning, and quantifiers. The project also involves percolation theory, in which we delete nodes and edges in patterns that result in different attack schemes. These attack schemes model possible brain degradation and allow us to track brain quantifiers through a simulated AD progression. These attack scheme patterns can be based on specific qualities of the brain or at random. MATLAB and Brain Net Viewer are common tools used in our lab.