Relationship Between Basic Properties of BOLD Fluctuations and Calculated Metrics of Complexity in the Human Connectome Project

Frontiers in Neuroscience

Wen-Ju Pan¹  Nan Xu¹  Behnaz Yousefi¹  Maysam Nezafati¹  Theodore J. LaGrow¹  Shella Keilholz¹  Eric Maltbie¹  Xiaodi Zhang¹  Ying Guo² 

[1] Department of Biomedical Engineering, Georgia Institute of Technology, Emory University, Atlanta, GA, United States;Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, United States;

关键词: resting state – fMRI;    complexity systems;    BOLD signal;    frequency;    entropy;   

DOI  :  10.3389/fnins.2020.550923

来源: DOAJ

【 摘 要 】

Resting state functional MRI (rs-fMRI) creates a rich four-dimensional data set that can be analyzed in a variety of ways. As more researchers come to view the brain as a complex dynamical system, tools are increasingly being drawn from other fields to characterize the complexity of the brain’s activity. However, given that the signal measured with rs-fMRI arises from the hemodynamic response to neural activity, the extent to which complexity metrics reflect neural complexity as compared to signal properties related to image quality remains unknown. To provide some insight into this question, correlation dimension, approximate entropy and Lyapunov exponent were calculated for different rs-fMRI scans from the same subject to examine their reliability. The metrics of complexity were then compared to several properties of the rs-fMRI signal from each brain area to determine if basic signal features could explain differences in the complexity metrics. Differences in complexity across brain areas were highly reliable and were closely linked to differences in the frequency profiles of the rs-fMRI signal. The spatial distributions of the complexity and frequency metrics suggest that they are both influenced by location-dependent signal properties that can obscure changes related to neural activity.

【 授权许可】

Unknown