B1308
Title: Time varying models for brain imaging data
Authors: Ivor Cribben - Alberta School of Business (Canada) [presenting]
Abstract: In functional magnetic resonance imaging (fMRI) studies, the networks between brain regions are assumed to be stationary over time. However, there is now more evidence that the network is changing over time even when the subjects are at rest. Firstly, we formulate the problem in a high-dimensional time series framework and introduce a data-driven method which detects change points in the network structure of a multivariate time series, with each component of the time series represented by a node in the network. Secondly, we introduce a new time varying approach that is model-free, data-adaptive, and is applicable in situations where the (global) stationarity of the time series from the brain regions fails, such as the cases of local stationarity and/or change points. We apply both new methods to simulated data and to a resting-state fMRI data set.
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