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儿童静息态脑电图的分形维数随年龄增长而增加。

The fractal dimension of resting state EEG increases over age in children.

作者信息

Tou Si Long Jenny, Chau Tom

机构信息

Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada.

Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, 150 Kilgour Rd, Toronto, ON M4G 1R8, Canada.

出版信息

Cereb Cortex. 2025 Jun 4;35(6). doi: 10.1093/cercor/bhaf138.

DOI:10.1093/cercor/bhaf138
PMID:40501056
Abstract

Resting-state electroencephalography (rs-EEG) represents spontaneous neural activity and is increasingly analyzed using nonlinear measures to assess brain complexity. The Higuchi Fractal Dimension (HFD) is a widely used metric for quantifying the fractal properties of EEG signals, yet its developmental trajectory remains largely unexplored. In this study, we examined age-related changes in HFD across childhood, adolescence, and early adulthood. We analyzed eyes-closed rs-EEG from 128 channels in 83 neurotypical participants (8 to 30 yr) from the MIPDB database. To assess developmental patterns, we applied a Gaussian Linear Mixed Model with age, electrode location, and their interaction as predictors, alongside non-parametric cluster-based permutation analysis to evaluate topographical differences. We observed a significant increase in HFD with age (P = 0.001), most pronounced between childhood and adolescence, followed by stabilization in early adulthood. HFD also varied across electrode locations, with higher values in frontal, central, and temporal regions and lower values in parietal and occipital areas. These findings provide new insights into the maturation of neural complexity in rs-EEG, aligning with known structural and functional changes in brain development. This study contributes to the growing body of research on nonlinear EEG dynamics and their relevance to neurodevelopment.

摘要

静息态脑电图(rs - EEG)代表自发神经活动,目前越来越多地使用非线性测量方法来分析,以评估大脑复杂性。Higuchi分形维数(HFD)是一种广泛用于量化脑电信号分形特性的指标,但其发育轨迹在很大程度上仍未得到探索。在本研究中,我们研究了儿童期、青少年期和成年早期HFD随年龄的变化。我们分析了来自MIPDB数据库的83名神经典型参与者(8至30岁)128个通道的闭眼rs - EEG。为了评估发育模式,我们应用了一个以年龄、电极位置及其交互作用作为预测因子的高斯线性混合模型,同时采用基于非参数聚类的置换分析来评估地形差异。我们观察到HFD随年龄显著增加(P = 0.001),在儿童期和青少年期之间最为明显,随后在成年早期趋于稳定。HFD在电极位置之间也存在差异,额叶、中央和颞叶区域的值较高,顶叶和枕叶区域的值较低。这些发现为rs - EEG中神经复杂性的成熟提供了新的见解,与大脑发育中已知的结构和功能变化相一致。本研究为关于非线性脑电动力学及其与神经发育相关性的不断增长的研究做出了贡献。

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Age-related differences in resting-state functional connectivity from childhood to adolescence.从儿童期到青春期静息态功能连接的年龄相关性差异。
Cereb Cortex. 2023 May 24;33(11):6928-6942. doi: 10.1093/cercor/bhad011.
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Brain entropy, fractal dimensions and predictability: A review of complexity measures for EEG in healthy and neuropsychiatric populations.脑熵、分形维数和可预测性:健康和神经精神人群脑电图复杂度测量的综述。
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Fractal Dimension Feature as a Signature of Severity in Disorders of Consciousness: An EEG Study.
分形维数特征作为意识障碍严重程度的特征:一项 EEG 研究。
Int J Neural Syst. 2022 Jul;32(7):2250031. doi: 10.1142/S0129065722500319.
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Efficient calculation of fractal properties via the Higuchi method.通过 Higuchi 方法高效计算分形特性。
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Stationary EEG pattern relates to large-scale resting state networks - An EEG-fMRI study connecting brain networks across time-scales.静息态脑电图模式与大规模静息态网络相关——一项跨时间尺度连接脑网络的脑电图-功能磁共振成像研究。
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Cortical maturation from childhood to adolescence is reflected in resting state EEG signal complexity.皮质从儿童期到青春期的成熟反映在静息状态 EEG 信号的复杂性上。
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Development of the default-mode network during childhood and adolescence: A longitudinal resting-state fMRI study.儿童和青少年时期默认模式网络的发展:一项纵向静息态 fMRI 研究。
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