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Activity Number: 225 - The Interface of Functional Data Analysis and Biomedical Applications
Type: Topic Contributed
Date/Time: Monday, July 30, 2018 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract #328762
Title: Gradient Synchronization to Quantify Brain Functional Connectivity
Author(s): Jane-Ling Wang* and Yang Zhou and Hans Mueller and Owen Carmichael
Companies: Univ of California-Davis and UC Davis and UC Davis and Pennington Biomedical Research Center
Keywords: functional data analysis; dynamic functional connectivity; Pearson correlation; functional correlations; runs test ; fMRI

When quantifying functional connectivity based on functional magnetic resonance imaging (fMRI) signals, the challenge is to assess the similarity of fMRI time courses that are observed for anatomically separated brain regions. The most prevalent measure is the temporal Pearson correlation (PC) but this static measure is less useful if connectivity fluctuates during a period of data collection. Dynamic functional connectivity has been proposed as an alternative to capture the dynamic features of functional connectivity. In this talk, we present a different approach that has a built-in dynamic feature but can be summarized by a static measure. The proposed measure is based on quantifying gradient synchronization by tracking concordance and discordance of the gradients between paired random curves. This gradient synchronization measure can be obtained by a simple procedure and consistency and asymptotic normality of the estimates towards a suitable target measure are derived under mild conditions. Our method is illustrated via simulations and with resting state blood oxygen level dependent (BOLD) fMRI signals from 20 specified hubs for both normal and Alzheimer's patients.

Authors who are presenting talks have a * after their name.

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