Sources of multifractality of the brain rs-fMRI signal

Guan, Sihai; Wan, Dongyu; Yang, Yanmiao; Biswal, Bharat B.

doi:10.1016/j.chaos.2022.112222

Cited by 8 publications

(4 citation statements)

References 129 publications

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“…DiffEn (Duan et al, 2013) measures the complexity of a continuous random variable, is the entropy of a continuous random variable, and is also related to minimum description length. Besides, rs-fMRI signals maybe include multi-type noises, complex nonlinear (Guan et al, 2020), and fractal structures (Guan et al, 2022). Therefore, if some entropy methods can be less sensitive to noise or highly sensitive to signal amplitude changes and less affected by variation in the magnitude of signals, then these entropy methods should be able to show good performance.…”

Section: The Entropy Methods Comparisonmentioning

confidence: 99%

“…Although already some journals have discussed the setting of these parameters (Azami & Escudero, 2018;Bandt & Pompe, 2002;Li et al, 2014;Schultz et al, 2008;Tocado et al, 2009), how to get a more suitable parameter was still not sure. So, based on our previous works (Guan et al, 2020(Guan et al, , 2022, the nonlinear complexity of rs-fMRI will further explore the more suitable parameters.…”

Section: Limitationsmentioning

confidence: 99%

“…In the past decades, resting‐state functional connectivity (rsFC) has been increasingly used in mapping the intrinsic functional networks of the human brain, with a focus on the inherently fluctuating signals and their interactions between different brain regions (Biswal et al, 1995 ; Fox & Greicius, 2010 ; Greicius et al, 2003 , 2009 ). There are numerous complexity methods of rs‐fMRI brain signals, such as the Lyapunov exponent, correlation dimension, Lempel‐Ziv complexity, Hurst exponent, and entropy (Ali et al, 2018 ; Guan et al, 2020 , 2022 ; Ju et al, 2019 ; Lin et al, 2022 ; Smith et al, 2013 ; Yakovleva et al, 2020 ). Among these methods, due to its simple algorithm, the small amount of data required, and the strong anti‐noise ability in calculation, entropy is one of the most widely used complexity methods for evaluating the dynamic characteristics of brain signals.…”

Section: Introductionmentioning

confidence: 99%

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The complexity of spontaneous brain activity changes in schizophrenia, bipolar disorder, and ADHD was examined using different variations of entropy

Guan

Wan

Zhao

et al. 2022

Human Brain Mapping

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Adult attention deficit/hyperactivity disorder (ADHD), schizophrenia (SCHZ), and bipolar disorder (BP) have common symptoms and differences, and the underlying neural mechanisms are still unclear. This article will thoroughly discuss the differences between ADHD, BP, and SCHZ (31 healthy control and 31 ADHD; 34 healthy control and 34 BP; 42 healthy control and 42 SCHZ) relative to healthy subjects in combination with three atlases (et al., the Brainnetome atlas, the Dosenbach atlas, the Power atlas) and seven entropies (et al., approximate entropy (ApEn), sample entropy (SaEn), permutation entropy (PeEn), fuzzy entropy (FuEn), differential entropy (DiffEn), range entropy (RaEn), and dispersion entropy (DispEn)), as well as the prominent significant brain regions, in the hope of giving information that is more suitable for analyzing different diseases' entropy. First, the reliability (et al., intraclass correlation coefficient [ICC]) of seven kinds of entropy is calculated and analyzed by using the MSC dataset (10 subjects and 100 sessions in total) and simulation data; then, seven types of entropy and multiscale entropy expanded based on seven kinds of entropy are used to explore the differences and brain regions of ADHD, BP, and SCHZ relative to healthy subjects; and finally, by verifying the classification performance of the seven information entropies on ADHD, BP, and SCHZ, the effectiveness of the seven entropy methods is evaluated through these three methods. The core brain regions that affect the classification are given, and DiffEn performed best on ADHD, SaEn for BP, and RaEn for SCHZ.

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Section: The Entropy Methods Comparisonmentioning

confidence: 99%

Section: Limitationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The complexity of spontaneous brain activity changes in schizophrenia, bipolar disorder, and ADHD was examined using different variations of entropy

Guan

Wan

Zhao

et al. 2022

Human Brain Mapping

Self Cite

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“…Initially, the multifractal analysis found its roots in studying phenomena related to turbulence and chaos theory [1]. Recently, this analytical approach has found widespread applications in several complex dynamical systems, including the meteorological phenomena [2,3], traffic flow [4], human brain [5,6], macroeconophysics phenomena [7], and other [8,9].…”

Section: Introductionmentioning

confidence: 99%

The (In)efficiency of Usa Education Group Stocks: Before, During and After Covid-19

FERNANDES,

SILVA

et al. 2024

Fractals

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This paper represents a pioneering effort to investigate multifractal dynamics that exclusively encompass the return time series of USA Education Group Stocks concerning two non-overlapping periods (before, during, and after COVID-19). Given this, we employ the Multifractal Detrended Fluctuations Analysis (MF-DFA). In this sense, we investigate the generalized Hurst exponent [Formula: see text] and the Rényi exponent [Formula: see text] for each asset and quantify their statistical properties, which allowed us to observe separately the contributing small scale (primarily via the negative moments [Formula: see text]) and the large scale (via the positive moments [Formula: see text]). We perform a fourth-degree polynomial regression fit to estimate the complexity parameters that describe the degree of multifractality of the underlying process. Also, we shall apply the inefficiency multifractal index to assess the COVID-19 shock for both periods. Our findings show that for both periods, the majority of these assets are marked by multifractal dynamics associated with persistent behavior [Formula: see text], a higher degree of multifractality and the dominance of large fluctuations. At the same time, most of these assets show asymmetry parameter [Formula: see text] for both periods, indicating that large fluctuations contributed more to multifractality in the time series of returns.

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A Distributed Adaptive Algorithm Based on the Asymmetric Cost of Error Functions

Guan

Zhao

Wang

et al. 2023

Circuits Syst Signal Process

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Sources of multifractality of the brain rs-fMRI signal

Cited by 8 publications

References 129 publications

The complexity of spontaneous brain activity changes in schizophrenia, bipolar disorder, and ADHD was examined using different variations of entropy

The complexity of spontaneous brain activity changes in schizophrenia, bipolar disorder, and ADHD was examined using different variations of entropy

The (In)efficiency of Usa Education Group Stocks: Before, During and After Covid-19

A Distributed Adaptive Algorithm Based on the Asymmetric Cost of Error Functions

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