1 hr 13 min
Neurosalience #S4E12 with Gang Chen - Statistician on mission to reduce fMRI information waste OHBM Neurosalience
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- Life Sciences
Today, we are excited to have Dr. Gang Chen on the podcast. Dr. Chen is the go-to statistics guru for the fMRI community at the NIH and a well-respected scientist worldwide. He is a staff scientist in the group that developed the AFNI software package. As an applied mathematician, Dr. Chen has written a series of insightful papers in the past seven years, bucking the status quo in fMRI processing - essentially saying that we are throwing away too much valuable information by thresholding our data, relying on overly simple and rigid models of the hemodynamic response, not mapping effect sizes, and using center of mass measures to describe clusters of activation. He backs it all up with a rigorous approach characterized by all good statisticians. He is a master in the art of casting a wide net to capture useful data without taking in artifact and noise, finding that sweet spot in data reduction to balance utility with sensitivity.
In this episode, we hear all about Dr. Chen’s perspectives through these papers, which are so important yet not widely known or embraced by the field. We hope you enjoy it!
Episode producers:
Omer Faruk Gulban
Xuqian Michelle Li
Today, we are excited to have Dr. Gang Chen on the podcast. Dr. Chen is the go-to statistics guru for the fMRI community at the NIH and a well-respected scientist worldwide. He is a staff scientist in the group that developed the AFNI software package. As an applied mathematician, Dr. Chen has written a series of insightful papers in the past seven years, bucking the status quo in fMRI processing - essentially saying that we are throwing away too much valuable information by thresholding our data, relying on overly simple and rigid models of the hemodynamic response, not mapping effect sizes, and using center of mass measures to describe clusters of activation. He backs it all up with a rigorous approach characterized by all good statisticians. He is a master in the art of casting a wide net to capture useful data without taking in artifact and noise, finding that sweet spot in data reduction to balance utility with sensitivity.
In this episode, we hear all about Dr. Chen’s perspectives through these papers, which are so important yet not widely known or embraced by the field. We hope you enjoy it!
Episode producers:
Omer Faruk Gulban
Xuqian Michelle Li
1 hr 13 min