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结构不平等与不同样本中的时间大脑动力学。

Structural inequality and temporal brain dynamics across diverse samples.

机构信息

Departamento de Psicología, Universidad de los Andes, Bogota, Colombia.

Global Brain Health Institute (GBHI), University of California, San Francisco, California, USA.

出版信息

Clin Transl Med. 2024 Oct;14(10):e70032. doi: 10.1002/ctm2.70032.

Abstract

BACKGROUND

Structural income inequality - the uneven income distribution across regions or countries - could affect brain structure and function, beyond individual differences. However, the impact of structural income inequality on the brain dynamics and the roles of demographics and cognition in these associations remains unexplored.

METHODS

Here, we assessed the impact of structural income inequality, as measured by the Gini coefficient on multiple EEG metrics, while considering the subject-level effects of demographic (age, sex, education) and cognitive factors. Resting-state EEG signals were collected from a diverse sample (countries = 10; healthy individuals = 1394 from Argentina, Brazil, Colombia, Chile, Cuba, Greece, Ireland, Italy, Turkey and United Kingdom). Complexity (fractal dimension, permutation entropy, Wiener entropy, spectral structure variability), power spectral and aperiodic components (1/f slope, knee, offset), as well as graph-theoretic measures were analysed.

FINDINGS

Despite variability in samples, data collection methods, and EEG acquisition parameters, structural inequality systematically predicted electrophysiological brain dynamics, proving to be a more crucial determinant of brain dynamics than individual-level factors. Complexity and aperiodic activity metrics captured better the effects of structural inequality on brain function. Following inequality, age and cognition emerged as the most influential predictors. The overall results provided convergent multimodal metrics of biologic embedding of structural income inequality characterised by less complex signals, increased random asynchronous neural activity, and reduced alpha and beta power, particularly over temporoposterior regions.

CONCLUSION

These findings might challenge conventional neuroscience approaches that tend to overemphasise the influence of individual-level factors, while neglecting structural factors. Results pave the way for neuroscience-informed public policies aimed at tackling structural inequalities in diverse populations.

摘要

背景

结构性收入不平等——即地区或国家间收入分配不均——可能会影响大脑结构和功能,超出个体差异的影响。然而,结构性收入不平等对大脑动态的影响,以及人口统计学和认知因素在这些关联中的作用仍有待探索。

方法

在这里,我们评估了结构收入不平等(用基尼系数衡量)对多种 EEG 指标的影响,同时考虑了人口统计学(年龄、性别、教育)和认知因素的个体水平效应。从一个多样化的样本中采集了静息态 EEG 信号(国家= 10;阿根廷、巴西、哥伦比亚、智利、古巴、希腊、爱尔兰、意大利、土耳其和英国的健康个体= 1394 人)。分析了复杂性(分形维数、排列熵、维纳熵、谱结构变异性)、功率谱和非周期性成分(1/f 斜率、膝部、偏移)以及图论度量。

结果

尽管样本、数据收集方法和 EEG 采集参数存在差异,但不平等结构系统地预测了电生理大脑动态,证明它是大脑动态的一个更关键的决定因素,而不是个体水平因素。复杂性和非周期性活动指标更好地捕捉了结构性不平等对大脑功能的影响。在不平等之后,年龄和认知成为最具影响力的预测因素。总的来说,这些结果提供了结构收入不平等的生物嵌入的多模态综合指标,其特征是信号复杂性降低、随机异步神经活动增加、以及 alpha 和 beta 波功率降低,尤其是在颞顶区域。

结论

这些发现可能会挑战传统的神经科学方法,这些方法往往过于强调个体水平因素的影响,而忽略了结构性因素。研究结果为旨在解决不同人群中结构性不平等的神经科学为基础的公共政策铺平了道路。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/94b1/11447638/e4eb38d8728c/CTM2-14-e70032-g006.jpg

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