In the world of functional Neuroimaging, the focus has historically been on "Gray Matter" (the brain's processors), often ignoring the "White Matter" (the communication cables). This talk introduces WhiFuN, a pipeline designed to extract meaningful signal from these under-explored regions using unsupervised machine learning. We will start by framing Resting-State fMRI as a massive time-series dataset, where each 3D pixel (voxel) in the brain contains a temporal signal of brain activity (movie of the 3D Brain). By calculating Functional Connectivity, essentially the Pearson correlation between these signals, we can treat the brain as a complex graph. The core of the talk will detail how we use K-means clustering on voxel-wise connectivity matrices to identify latent brain networks. We will dive into the engineering challenges of this approach, specifically how to decide the value of K. Finally, we will look at the results: how these data-driven networks allow us to identify group differences and behavioral associations, effectively turning raw, noisy 4D imagery into a structured feature set for clinical analysis. Speaker(s): Pratik Jain, Agenda: Hybrid event, in-person or online: 2:30 Introduction Technical Talks Question and Answer Networking 4:30 Conclusion Room: Meeting Rooms 2,3, 2 Civic Center Drive, East Brunswick, New Jersey, United States, 08816, Virtual: https://events.vtools.ieee.org/m/560974