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Proceedings Paper

Association between fMRI brain entropy features and behavioral measures
Author(s): Shengchao Zhang; Baxter P. Rogers; Victoria L. Morgan; Catie Chang
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Paper Abstract

An important goal in neuroscience is to understand the relationship between brain activity and cognitive traits. Toward this aim, many studies draw upon resting-state Functional Magnetic Resonance Imaging (rs-fMRI) datasets, which provide a means of probing the spatial and temporal structure of spontaneous brain activity in human subjects. However, as rsfMRI and behavioral data are both noisy, obtaining a robust relationship between them is difficult. Further, given the large number of candidate features in fMRI data, it is challenging to select those which may be most relevant for predicting a specific behavioral trait. In our research, we examined brain fMRI features based upon Sample Entropy (SampEn), which is a nonlinear signal processing measure that captures the complexity of a time series. Using 90 selected regions of interest (ROIs) across 96 unrelated subjects from the Human Connectome Project (HCP), we found that our SampEn-based features contained reproducible patterns over different rs-fMRI scans. Further, we report the relative stability of each ROI’s SampEn over four different scans of these 96 subjects. Finally, we apply multivariate models to relate SampEnbased brain features to cognition and emotion-related behavioral measures, and show that these models are reproducible when applied to different scans from the same individuals. Overall, these results suggest that SampEn of regional fMRI signals may be a reproducible metric of brain activity in healthy subjects.

Paper Details

Date Published: 10 March 2020
PDF: 10 pages
Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 113130V (10 March 2020); doi: 10.1117/12.2549342
Show Author Affiliations
Shengchao Zhang, Vanderbilt Univ. (United States)
Baxter P. Rogers, Vanderbilt Univ. (United States)
Institute of Imaging Science, Vanderbilt Univ. Medical Ctr. (United States)
Victoria L. Morgan, Vanderbilt Univ. (United States)
Institute of Imaging Science, Vanderbilt Univ. Medical Ctr. (United States)
Catie Chang, Vanderbilt Univ. (United States)
Institute of Imaging Science, Vanderbilt Univ. Medical Ctr. (United States)


Published in SPIE Proceedings Vol. 11313:
Medical Imaging 2020: Image Processing
Ivana Išgum; Bennett A. Landman, Editor(s)

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