Fractal complexity of spontaneous brain activity and the effect of scanning parameters

The relationship between the multifractal of the resting-state functional magnetic resonance imaging (rs-fMRI) and its parameters still needs to be discovered. So, the primary purpose of this study is to evaluate the effects of repetition time (TR) on rs-fMRI signals, and the relationship between multifractal and session. Our findings revealed that the larger the TR, the more scattered the analysis results of multifractal, and the multifractal increase with the TR between Youths and Adults except for the degree of multifractal. However, it is worth noting that when TR = 1.4 s, the average degree of multifractal is generally greater than TR = 0.645 s and TR = 2.5 s. Furthermore, the reliability decreases with the increase in session numbers, increases with rs-fMRI data length increase, and is higher for the short intervals than long ones. Besides, with the rise in the number of sessions, when the data length is ≥400, the intraclass correlation coefficient (ICC) > 0.5 of the Hurst exponent (HE); when the data length is equal to 808, the ICC > 0.4 of the maximum gradient, and the spectrum width of ICC < 0.3. With the increase of the session interval, when the data length is ≥200, the ICC > 0.4 of the HE; when the rs-fMRI data length is equal to 808, the ICC ≈ 0.4 of the maximum gradient, and the spectrum width and degree of multifractal of ICC < 0.3. The results are of great significance for understanding the relationship between nonlinear dynamic characteristics and parameters of rs-fMRI.

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Publication Details

Journal
Advances in Continuous and Discrete Models
Published
2025-02-10
DOI
https://doi.org/10.1186/s13662-025-03910-2
Citations
1
Primary Topic
Complex Systems and Time Series Analysis
Type
article
Field-Weighted Citation Impact
3.13

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article

Fractal complexity of spontaneous brain activity and the effect of scanning parameters

Sihai Guan, Chun Meng, B. Biswal Bharat
1 citations
Advances in Continuous and Discrete Models
Complex Systems and Time Series Analysis
3.13
article

Fractal complexity of spontaneous brain activity and the effect of scanning parameters

Sihai Guan, Chun Meng, B. Biswal Bharat
article en
1 citations

Abstract

The relationship between the multifractal of the resting-state functional magnetic resonance imaging (rs-fMRI) and its parameters still needs to be discovered. So, the primary purpose of this study is to evaluate the effects of repetition time (TR) on rs-fMRI signals, and the relationship between multifractal and session. Our findings revealed that the larger the TR, the more scattered the analysis results of multifractal, and the multifractal increase with the TR between Youths and Adults except for the degree of multifractal. However, it is worth noting that when TR = 1.4 s, the average degree of multifractal is generally greater than TR = 0.645 s and TR = 2.5 s. Furthermore, the reliability decreases with the increase in session numbers, increases with rs-fMRI data length increase, and is higher for the short intervals than long ones. Besides, with the rise in the number of sessions, when the data length is ≥400, the intraclass correlation coefficient (ICC) > 0.5 of the Hurst exponent (HE); when the data length is equal to 808, the ICC > 0.4 of the maximum gradient, and the spectrum width of ICC < 0.3. With the increase of the session interval, when the data length is ≥200, the ICC > 0.4 of the HE; when the rs-fMRI data length is equal to 808, the ICC ≈ 0.4 of the maximum gradient, and the spectrum width and degree of multifractal of ICC < 0.3. The results are of great significance for understanding the relationship between nonlinear dynamic characteristics and parameters of rs-fMRI.

Advances in Continuous and Discrete ModelsVol. 2025(1)
New Jersey Institute of Technology (US), University of Electronic Science and Technology of China (CN), State Ethnic Affairs Commission (CN), Southwest Minzu University (CN)
National Natural Science Foundation of China, Southwest Minzu University, Fundamental Research Funds for the Central Universities
Openalex Percentile: Top 10%
Complex Systems and Time Series Analysis
3.13
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