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科学研究
RESEARCH
Bayesian change-point detection using weighted stochastic block model
时间  Datetime
2021-01-16 15:00 — 16:15
地点  Venue
教室(901)
报告人  Speaker
Lingbin Bian
单位  Affiliation
Monash University
邀请人  Host
朱圣国
备注  remarks
报告摘要  Abstract

We present a novel Bayesian method for identifying the transition of the brain states in working memory

 task fMRI data via model fitness assessment. Specifically, we detect dynamic community structure

change-point(s) based on overlapped sliding window applied to multivariate time series. We use the

weighted stochastic block model to quantify the likelihood of a network configuration and develop a novel scoring criterion that we call posterior predictive discrepancy by evaluating the goodness of fit between

model and observations within the sliding window. The parameters for this model include the latent label

vector assigning network nodes to interacting communities, and the block model parameters determining

 the weighted connectivity within and between communities. The GLM analyses were conducted in both

subject level and group level and the contrast between 2-back, 0-back and baseline were used to localise the regions of interest in task fMRI data.