Srushti Dhavalkumar Desai, a Master of Science candidate in the Department of Mathematics and Statistics, will present the Masters Research Project titled Graph-Theoretical Analysis of Resting-State Functional Connectivity in the Human Brain: Weighted vs. Binary Representations on Tuesday, November 4, 2025 at 11:30 AM.
The examination committee includes Supervisor Dr. William Marshall and Supervisory Committee Member Dr. Xiaojian Xu.
Students (both graduate and undergraduate) as well as other members of the Brock Community are invited to attend. If you are interested in the presentation, please contact [email protected] for the room location.
Keywords: Functional connectivity; resting-state fMRI; graph theory; small-world propensity; weighted networks; binary networks; resilience; ADHD
Abstract: The brain is always active, even during rest, as different regions continuously interact and exchange information. Understanding these patterns of interaction is essential for exploring how the brain functions as a networked system. When regions exhibit consistent and statistically significant coactivation over time, they are considered functionally connected. Functional Magnetic Resonance Imaging (fMRI) allows researchers to study these connections and construct functional connectivity networks. Graph theory provides a powerful framework for analyzing such networks, where brain regions are represented as nodes and their interactions as edges. Through measures such as small worldness, graph analysis can characterize how efficiently information is processed, reflecting networks that are both highly clustered and globally integrated. However, most graph theoretical metrics were originally designed for binary networks, where connections are treated as either present or absent. Preserving the continuous weights of functional connectivity can provide a more nuanced representation of connection strength and potentially yield deeper insights into brain organization. The present study aimed to determine which representation, binary or weighted, better estimates Small World Propensity (SWP), a measure that quantifies how strongly a network exhibits small world characteristics, and to examine whether SWP is associated with resilience, defined as the capacity to adapt and recover from adversity. Results indicated that weighted graphs performed better for dense networks, while selecting appropriate thresholds improved binary representations for sparse networks. No significant relationship was found between resilience and small worldness within the ADHD group, suggesting that small world organization alone may not account for individual differences in resilience in this dataset.
