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Bachelor of Arts
Mathematics and Statistics
Brain networks, Clustering, Brain parcellation, Random walk
A functional brain network is a simpliﬁed representation of the interactions (temporal corre lations) between regions of interest in the brain. To deﬁne a region of interest of a reasonable size, brain parcellation is required to group smaller regions that share certain similarity. We analyzed one piece of resting-state fMRI brain data with graph theoretic-measures and re alized the importance of spatial scale in determining the brain network structure. Temporal scale can also heavily inﬂuence the network representation; we propose a modiﬁed change point detection method to extract a stationary time series of brain activity. To explore the possibility of applying a popular graph clustering algorithm to our brain data, we study a variety of synthetic networks to further understand the average commute time (ACT). This ACT notion of distance deﬁnes the clustering algorithm; we oﬀer tentative explanations for some special properties of ACT distances as well as the performance of the algorithm on resting-state brain data.
©2019 Tingshan Liu. Access limited to the Smith College community and other researchers while on campus. Smith College community members also may access from off-campus using a Smith College log-in. Other off-campus researchers may request a copy through Interlibrary Loan for personal use.
Liu, Tingshan, "Investigating the application of graph clustering algorithms in network neuroscience" (2019). Honors Project, Smith College, Northampton, MA.
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