Thursday, November 8, 2018 - 10:40am - 11:10am
David S. Matteson (Cornell University)
Independent component analysis (ICA) is an unsupervised learning method popular in functional magnetic resonance imaging (fMRI). Group ICA has been used to identify biomarkers in neurological disorders including Autism spectrum disorder [1] and dementia [2]. However, current group ICA methods use a PCA step that may remove important information associated with low-variance non-Gaussian features. Linear non-Gaussian component analysis (LNGCA) enables dimension reduction and component estimation simultaneously in single-subject fMRI [3].
Wednesday, February 21, 2018 - 8:30am - 9:10am
Mark Fiecas (University of Minnesota, Twin Cities)
In this talk, we will give an overview of statistical methodologies for spectral analysis of time series data. We will briefly discuss the common approaches for spectral analysis, and discuss their limitations for analyzing data whenever the study has a longitudinal experimental design. To address the limitations, we propose a Bayesian model for spectral analysis that accounts for the covariation within a subject.
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