The explosion of data analysis and machine learning has prompted an exploration of quantifying biological networks. Using functional MRI data from mouse and rat brains, we propose to utilize connectomics to better understand network characteristics. Network analysis will be performed via Brain Net Viewer and further MATLAB code developments. Through comparing diseased and healthy networks, we hope to gain insight into the differences in network connectivity and nodal strength. Utilizing the strength of connections and varying network quantifiers can lead to a greater understanding of the overall performance of the network. Furthermore, targeted network attacks can lead to developments in identifying the vulnerability of the network and possibly neurological disease progression. We then plan to redirect this understanding into immediate fMRI analysis to identify varying characteristics of individual specimens. Through augmenting our comprehension of fMRI data and connectomics, we hope to enhance the identification of biomarkers that can better predict and early diagnose the onset of neurodegenerative diseases like Alzheimer’s and Parkinson’s.