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Activity Number:
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517
- Multivariate Analysis of Brain Imaging Data in Mental Disorders
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Type:
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Topic Contributed
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Date/Time:
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Wednesday, August 1, 2018 : 10:30 AM to 12:20 PM
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Sponsor:
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Mental Health Statistics Section
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Abstract #329720
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Presentation
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Title:
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New Statistical Methods for Analyzing Whole Brain Metabolites Using High-Resolution MRS Data
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Author(s):
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Shuo Chen*
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Companies:
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University of Maryland, School of Medicine
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Keywords:
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MRS;
brain metabolite;
peripheral chemicals;
covariance;
spatial dependence
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Abstract:
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The recent development of magnetic resonance spectroscopy (MRS) technology provides an opportunity to study the association between high-resolution metabolite concentration within the whole brain and peripheral chemicals. We develop a new statistical network shrinkage approach to estimate the particular spatial dependence structure of high-resolution MRS data. The new method integrates spatial closeness and network topology properties to yield flexible and accurate estimate of the large covariance matrix and thus more efficient statistical inference. We also develop novel statistical tests to identify clusters of brain metabolites that are associated with peripheral cholesterol. We apply our methods to a high-resolution MRS data of 78 study subjects and simulation data sets.
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Authors who are presenting talks have a * after their name.
