Department of Psychology I, University of Würzburg, Würzburg 97070, Germany
Department of Biology, University of Würzburg, Würzburg 97074, Germany.
eNeuro. 2023 Feb 8;10(2). doi: 10.1523/ENEURO.0345-22.2022. Print 2023 Feb.
Spontaneous brain activity builds the foundation for human cognitive processing during external demands. Neuroimaging studies based on functional magnetic resonance imaging (fMRI) identified specific characteristics of spontaneous (intrinsic) brain dynamics to be associated with individual differences in general cognitive ability, i.e., intelligence. However, fMRI research is inherently limited by low temporal resolution, thus, preventing conclusions about neural fluctuations within the range of milliseconds. Here, we used resting-state electroencephalographical (EEG) recordings from 144 healthy adults to test whether individual differences in intelligence (Raven's Advanced Progressive Matrices scores) can be predicted from the complexity of temporally highly resolved intrinsic brain signals. We compared different operationalizations of brain signal complexity (multiscale entropy, Shannon entropy, Fuzzy entropy, and specific characteristics of microstates) regarding their relation to intelligence. The results indicate that associations between brain signal complexity measures and intelligence are of small effect sizes ( ∼ 0.20) and vary across different spatial and temporal scales. Specifically, higher intelligence scores were associated with lower complexity in local aspects of neural processing, and less activity in task-negative brain regions belonging to the default-mode network. Finally, we combined multiple measures of brain signal complexity to show that individual intelligence scores can be significantly predicted with a multimodal model within the sample (10-fold cross-validation) as well as in an independent sample (external replication, =57). In sum, our results highlight the temporal and spatial dependency of associations between intelligence and intrinsic brain dynamics, proposing multimodal approaches as promising means for future neuroscientific research on complex human traits.
自发脑活动为人类在外部需求下的认知处理奠定基础。基于功能磁共振成像（fMRI）的神经影像学研究确定了自发（内在）脑动力学的特定特征与个体间一般认知能力（即智力）差异相关。然而，fMRI 研究受到时间分辨率低的固有限制，因此无法得出关于毫秒范围内神经波动的结论。在这里，我们使用来自 144 名健康成年人的静息态脑电图（EEG）记录来测试个体智力差异（瑞文高级渐进矩阵分数）是否可以从时间分辨率高的内在脑信号的复杂性中预测。我们比较了脑信号复杂性的不同操作化（多尺度熵、香农熵、模糊熵和微状态的特定特征），以研究它们与智力的关系。结果表明，脑信号复杂性测量值与智力之间的关联具有较小的效应大小（约 0.20），并且在不同的空间和时间尺度上有所变化。具体而言，较高的智力分数与神经处理局部方面的较低复杂性以及默认模式网络中属于任务负相关脑区的较低活动相关。最后，我们结合了脑信号复杂性的多个测量值，以显示个体智力分数可以在样本内（10 倍交叉验证）以及在独立样本（外部复制，n=57）中通过多模态模型显著预测。总之，我们的结果强调了智力与内在脑动力学之间关联的时间和空间依赖性，提出了多模态方法作为未来关于复杂人类特征的神经科学研究的有前途的手段。
