今天，我们很高兴在播客中邀请到Chen博士。Chen博士是NIH功能磁共振成像社区的统计学权威，也是全球备受尊敬的科学家。他是开发AFNI软件包的团队中的一名员工科学家。作为一名应用数学家，Chen博士在过去七年中发表了一系列富有洞见的论文，这些论文颠覆了功能磁共振成像数据处理的现状——本质上是说，目前的研究者们的同行研究方法：卡阈值、依赖过于简单和僵硬的血液动力学反应模型、不展示效应量统计图以及只使用极值来描述团块，浪费了太多宝贵的信息。Chen博士用所有优秀统计学家特有的严谨方法尝试改善这些。他是在捕获有用数据的同时避免假阳性结果和噪声方面的大师，也总能找到总结结果的方法中的最佳点以平衡实用性与敏感性。
在这一集中，我们将听到陈博士通过这些论文分享的观点，这些论文非常重要但在该领域内并未被广泛认知或接受。希望您喜欢！
节目制作人：
OmerFaruk Gulban
Xu-QianMichelle Li
Neurosalience#S4E12 with Gang Chen - Statistician on mission to reduce fMRI informationwaste
Today,we are excited to have Dr. Gang Chen on the podcast. Dr. Chen is the go-tostatistics guru for the fMRI community at the NIH and a well-respectedscientist worldwide. He is a staff scientist in the group that developed theAFNI software package. As an applied mathematician, Dr. Chen has written aseries of insightful papers in the past seven years, bucking the status quo infMRI processing - essentially saying that we are throwing away too muchvaluable information by thresholding our data, relying on overly simple andrigid models of the hemodynamic response, not mapping effect sizes, and usingcenter of mass measures to describe clusters of activation. He backs it all upwith a rigorous approach characterized by all good statisticians. He is a masterin the art of casting a wide net to capture useful data without taking inartifact and noise, finding that sweet spot in data reduction to balanceutility with sensitivity.
In thisepisode, we hear all about Dr. Chen’s perspectives through these papers, whichare so important yet not widely known or embraced by the field. We hope youenjoy it!
Episodeproducers:
OmerFaruk Gulban
XuqianMichelle Li
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