@article {4570, title = {Complexity organization of resting-state functional-MRI networks.}, journal = {Hum Brain Mapp}, volume = {45}, year = {2024}, month = {2024 Aug 15}, pages = {e26809}, abstract = {

Entropy measures are increasingly being used to analyze the structure of neural activity observed by functional magnetic resonance imaging (fMRI), with resting-state networks (RSNs) being of interest for their reproducible descriptions of the brain{\textquoteright}s functional architecture. Temporal correlations have shown a dichotomy among these networks: those that engage with the environment, known as extrinsic, which include the visual and sensorimotor networks; and those associated with executive control and self-referencing, known as intrinsic, which include the default mode network and the frontoparietal control network. While these inter-voxel temporal correlations enable the assessment of synchrony among the components of individual networks, entropic measures introduce an intra-voxel assessment that quantifies signal features encoded within each blood oxygen level-dependent (BOLD) time series. As a result, this framework offers insights into comprehending the representation and processing of information within fMRI signals. Multiscale entropy (MSE) has been proposed as a useful measure for characterizing the entropy of neural activity across different temporal scales. This measure of temporal entropy in BOLD data is dependent on the length of the time series; thus, high-quality data with fine-grained temporal resolution and a sufficient number of time frames is needed to improve entropy precision. We apply MSE to the Midnight Scan Club, a highly sampled and well-characterized publicly available dataset, to analyze the entropy distribution of RSNs and evaluate its ability to distinguish between different functional networks. Entropy profiles are compared across temporal scales and RSNs. Our results have shown that the spatial distribution of entropy at infra-slow frequencies (0.005-0.1 Hz) reproduces known parcellations of RSNs. We found a complexity hierarchy between intrinsic and extrinsic RSNs, with intrinsic networks robustly exhibiting higher entropy than extrinsic networks. Finally, we found new evidence that the topography of entropy in the posterior cerebellum exhibits high levels of entropy comparable to that of intrinsic RSNs.

}, keywords = {Adult, Brain, Connectome, Default Mode Network, Entropy, Humans, Magnetic Resonance Imaging, Nerve Net, Rest}, issn = {1097-0193}, doi = {10.1002/hbm.26809}, author = {Trevino, Gabriel and Lee, John J and Shimony, Joshua S and Luckett, Patrick H and Leuthardt, Eric C} } @article {2152, title = {Power spectrum analysis on the multiparameter electroencephalogram features of physiological mental fatigue.}, journal = {Sheng Wu Yi Xue Gong Cheng Xue Za Zhi}, volume = {26}, year = {2009}, month = {02/2009}, pages = {162-6, 172}, abstract = {

The aim of this experiment is to find a feasible impersonal index for analyzing the physiological mental fatigue level. Three characteristic parameters, relative power in different rhythm, barycenter frequency and power spectral entropy, are extracted from two channels{\textquoteright} electroencephalogram (EEG) under two physiological mental fatigue states. Then relationships between such three parameters and physiological mental fatigue are analyzed to explore whether they can be of use for detecting (or monitoring) the mental fatigue level. The experiment results show that the relative power, barycenter frequency and power spectral entropy of EEG exhibit strong correlation with physiological mental fatigue level. While physiological mental fatigue level increases, the relative power in theta, alpha and beta rhythms, barycenter frequency and power spectral entropy of EEG decrease, but the relative power in delta rhythm of EEG increases. The relative power in four rhythms, barycenter frequency and power spectral entropy of EEG reflect the change of physiological mental fatigue level sensitively, and may hopefully be used as indexes for detecting physiological mental fatigue level.

}, keywords = {Adult, Electroencephalography, Entropy, Humans, Male, Mental Fatigue, Signal Processing, Computer-Assisted, Young Adult}, issn = {1001-5515}, url = {http://www.ncbi.nlm.nih.gov/pubmed/19334577}, author = {Zhang, Ai-hua and Zheng, Shi Dong and Pei, Xiao-Mei and Ouyang, Yi} }