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Modeling Placebo Response using EEG data through a Hierarchical Reduced Rank Model

Banff International Research Station for Mathematical Innovation and Discovery

There is growing evidence that individual differences among depression patients on Electrophysiology (EEG), fMRI and other brain imaging measurements may be predictive of potential treatment response. In this talk we discuss approaches to identifying potential placebo responders, i.e., a subgroup who benefits sufficiently from inactive drug treatments, using EEG measurements as a matrix (order-2 tensor) predictor. Given the high dimensionality of the problem, we consider a reduced rank regression model with a data-driven regularization. Our approach will be evaluated through simulations and will be applied to data from a large placebo-controlled clinical trial of major depressive disorders.

Mathematics; Statistics; Game theory, economics, social and behavioral sciences

video/mp4

Author affiliation: Columbia University

2016-08-05

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/58682

Unreviewed

http://creativecommons.org/licenses/by-nc-nd/4.0/