Paper Info
Title
Association Between Fmri Brain Entropy Features And Behavioral Measures
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 rs-fMRI 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 SampEn-based 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.
Year
DOI
Venue
2020
10.1117/12.2549342
MEDICAL IMAGING 2020: IMAGE PROCESSING
Keywords
DocType
Volume
rs-fMRI, behavioral measures, SampEn-based brain complexity measure, HCP, multiple linear regression
Conference
11313
ISSN
Citations 
PageRank 
0277-786X
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Shengchao Zhang100.34
Baxter P. Rogers2447.26
Victoria L. Morgan3346.21
Catie Chang411.71