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静息态生物电活动的时空复杂性模式解释了流体智力：性别很重要。

Spatiotemporal complexity patterns of resting-state bioelectrical activity explain fluid intelligence: Sex matters.

机构信息

Centre for Modern Interdisciplinary Technologies, Nicolaus Copernicus University, Toruń, Poland.

Faculty of Philosophy and Social Sciences, Institute of Psychology, Nicolaus Copernicus University, Toruń, Poland.

出版信息

Hum Brain Mapp. 2020 Dec;41(17):4846-4865. doi: 10.1002/hbm.25162. Epub 2020 Aug 18.

DOI:10.1002/hbm.25162
PMID:32808732
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7643359/
Abstract

Neural complexity is thought to be associated with efficient information processing but the exact nature of this relation remains unclear. Here, the relationship of fluid intelligence (gf) with the resting-state EEG (rsEEG) complexity over different timescales and different electrodes was investigated. A 6-min rsEEG blocks of eyes open were analyzed. The results of 119 subjects (57 men, mean age = 22.85 ± 2.84 years) were examined using multivariate multiscale sample entropy (mMSE) that quantifies changes in information richness of rsEEG in multiple data channels at fine and coarse timescales. gf factor was extracted from six intelligence tests. Partial least square regression analysis revealed that mainly predictors of the rsEEG complexity at coarse timescales in the frontoparietal network (FPN) and the temporo-parietal complexities at fine timescales were relevant to higher gf. Sex differently affected the relationship between fluid intelligence and EEG complexity at rest. In men, gf was mainly positively related to the complexity at coarse timescales in the FPN. Furthermore, at fine and coarse timescales positive relations in the parietal region were revealed. In women, positive relations with gf were mostly observed for the overall and the coarse complexity in the FPN, whereas negative associations with gf were found for the complexity at fine timescales in the parietal and centro-temporal region. These outcomes indicate that two separate time pathways (corresponding to fine and coarse timescales) used to characterize rsEEG complexity (expressed by mMSE features) are beneficial for effective information processing.

摘要

神经复杂性被认为与有效的信息处理有关，但这种关系的确切性质仍不清楚。在这里，研究了流体智力（gf）与不同时间尺度和不同电极的静息状态 EEG（rsEEG）复杂性之间的关系。分析了 6 分钟的睁眼 rsEEG 块。使用多变量多尺度样本熵（mMSE）检查了 119 名受试者（57 名男性，平均年龄= 22.85±2.84 岁）的结果，该方法量化了在多个数据通道中以精细和粗时间尺度的 rsEEG 信息丰富度的变化。从六个智力测试中提取 gf 因子。偏最小二乘回归分析显示，主要预测因素是额叶顶叶网络（FPN）中的粗时间尺度上的 rsEEG 复杂性和颞顶叶的精细时间尺度上的复杂性，与较高的 gf 相关。性别对静息状态下流体智力和 EEG 复杂性之间的关系有不同的影响。在男性中，gf 主要与 FPN 中的粗时间尺度上的复杂性呈正相关。此外，在精细和粗时间尺度上，在顶叶区域揭示了正相关。在女性中，与 gf 呈正相关的主要是 FPN 中的整体和粗复杂度，而在顶叶和中颞叶区域的精细时间尺度上与 gf 呈负相关。这些结果表明，用于表征 rsEEG 复杂性（由 mMSE 特征表示）的两个独立的时间途径（分别对应于精细和粗时间尺度）有助于有效的信息处理。

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