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学术讲座:层次独立成分分析模型在静息态fMRI图像数据的应用
2019-12-20 17:28:49   

(讲座语言:中文)


题  目:层次独立成分分析模型在静息态fMRI图像数据的应用
A hierarchical independent component analysis model for resting fMRI neuroimaging data studies
内容简介:Independent Component Analysis (ICA) is a powerful tool to analyze brain functional networks (BFNs). Classic ICA method are widely used on single-subject fMRI data, while several extension frameworks of ICA were proposed to make group inferences from fMRI data, such as GIFT (Calhoun et al., 2001), PICA (Beckmann and Smith, 2005) as well as hc-ICA (Ran and Guo, 2016). In this lecture, the hierarchical Independent Component Analysis (hc-ICA) framework will be introduced, and a feasible EM algorithm to calculate maximum likelihood estimator of the model will be discussed together.
主讲人:葛琳(美国埃默里大学博士生)
主持人:谭海珠 副研究员(汕头大学医学院物信教研室)
时  间:2019年12月25日(星期三)下午3:00-4:30
地 点:汕头大学医学院行政楼四楼学术报告厅

欢迎广大师生参加!
主讲人简介见附件

葛琳博士简介.pdf

汕头大学医学院科研处
2019年12月20日