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Activity Number:
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620
- Axles for Voxels: Recent Statistical Advances in Neuroimaging Data Analysis
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Type:
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Topic Contributed
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Date/Time:
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Thursday, August 2, 2018 : 8:30 AM to 10:20 AM
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Sponsor:
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ENAR
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Abstract #329855
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Presentation
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Title:
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Non-Stationary High-Dimensional Time Series Networks for Brain Imaging Data
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Author(s):
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Ivor Cribben*
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Companies:
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University of Alberta
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Keywords:
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High dimension;
Time Series;
Networks;
Change point;
fMRI;
Graphs
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Abstract:
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We develop original statistical methodology on the evolving interdependencies between high-dimensional multivariate time series. Specifically, we introduce a data-driven method which detects change points in the network summary statistics of a (very high dimensional) multivariate time series, with each component of the time series represented by a node in the network. The novel method allows for estimation of both the time of change in the network summary statistics without prior knowledge of the number or location of the change points. We also propose a new multiple change point algorithm that begins by segmenting the data into partitions and then looks for changes locally. We show the improvement of our method over classical binary segmentation methods. We apply these methods to various simulated high dimensional data sets as well as to a resting state functional magnetic resonance imaging (fMRI) data set from the from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The method allows us to characterize the large scale resting state dynamic brain networks that are related to Alzheimer's disease.
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Authors who are presenting talks have a * after their name.
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