Functional MRI Signal Complexity Analysis Using Sample Entropy

Abstract

Resting-state functional magnetic resonance imaging (rs-fMRI) is an immensely powerful method in neuroscience that uses the blood oxygenation level-dependent (BOLD) signal to record and analyze neural activity in the brain. We examined the complexity of brain activity acquired by rs-fMRI to determine whether it exhibits variation across brain regions. In this study the complexity of regional brain activity was analyzed by calculating the sample entropy of 200 whole-brain BOLD volumes as well as of distinct brain networks, cortical regions, and subcortical regions of these brain volumes. It can be seen that different brain regions and networks exhibit distinctly different levels of entropy/complexity, and that entropy in the brain significantly differs between brains at rest and during task performance.

Assessment of temporal complexity in functional MRI between rest and task conditions

“…This study reinforces the existence of complex dynamics in brain function [29] and provides further evidence for the hypothesis of distinct complexity features in human behaviour and cognition [31]. Our results suggest that: (i ) task-based and rsfMRI signals exhibit temporal complexity, inferred by Hurst exponent and multiscale entropy, (ii ) rest and task periods of brain function can be distinguished from each other with high accuracy based on their temporal complexity profiles, (iii ) cognitive load can suppress the complex dynamics of fMRI in contrast to the resting state, (iv ) spatial distribution of Hurst exponent and entropy-based complexity index in fMRI are highly correlated, and (v ) the frontoparietal network and default mode network represent maximal complex behaviour compared to the rest of the brain regardless of the mental state.…”

“…Region-specific properties of fMRI were also studied in [32] where higher complexity was reported in sub-cortical regions such as the caudate, the olfactory gyrus, the amygdala, and the hippocampus, whilst primary sensorimotor and visual areas were associated with lower complexity. Nezafati et al [29] confirmed this finding and also, showed that networks exhibit distinct complex properties which may change between resting state and during task performance. Omidvarnia et al [1] reproduced the findings of RSN-specific temporal complexity in rsfMRI and lower complexity of sub-cortical regions in contrast to cortical networks across 1000 healthy subjects.…”

“…Different measures have been used for temporal complexity analysis of fMRI including time-resolved graph theory measures [34], entropy measures [1,17,20,29,34], and self-similarity measures [30,31,38,39]. Among these, Hurst exponent [4] and multiscale entropy [3] have found a wide acceptance due to their straightforward interpretation and fairly simple implementation.…”

“…In this study, we the rest and task fMRI and diffusion-weighted scans of 100 unrelated subjects (ages [22][23][24][25][26][27][28][29][30][31][32][33][34][35] from the Human Connectome Project (HCP) 1200-release [44]. Each subject underwent a number of fMRI recording sessions including four rsfMRI runs, seven task-based fMRI runs, and a diffusion MRI run.…”

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

“…Time-varying analysis (rather than measures averaged over the whole scan, like typical correlation-based functional connectivity) attempts to characterize the underlying low-dimensional structure of the brain’s dynamics ( Hutchison et al, 2013 ; Keilholz et al, 2017 ; Preti et al, 2017 ). At the voxel/parcel level, concepts from non-linear dynamics like entropy, Hurst exponent and Lyapunov exponent have been used to characterize the complexity of the signal from each area, based on the hypothesis that the complexity of the BOLD signal carries information about the complexity of the underlying neural activity ( Wang et al, 2011 ; Yang et al, 2013 ; Ciuciu et al, 2014 ; Jia et al, 2017 ; Wang Y. et al, 2018 ; Liu et al, 2018 , 2019 ; Song et al, 2019 ; Nezafati et al, 2020 ). However, these metrics are difficult to interpret in the context of rs-fMRI.…”