Li Kanghui, Zhang Yong, Wang Zhaohui
Department of Children's Health Center, Xi'an People's Hospital (Xi'an Fourth Hospital), Xi'an, 710000, China.
Sci Rep. 2025 Aug 12;15(1):29461. doi: 10.1038/s41598-025-14774-7.
The infant brain is primitive and immature. Investigating changes in the brain during infants' rapid developmental stages has been a research hotspot. However, due to the unique characteristics of the infant population, maintaining an absolute resting state is limited. Consequently, studies comparing differences between absolute resting-state and task-state brain networks are extremely difficult. Therefore, studying infant brain networks during sleep has become a promising direction. Based on adult studies, task-state brain networks demonstrate stronger behavioral correlations, but research on differences between task-state and resting-state brain networks reveals individual variability. This study provides evidence for individual differences in infant brain networks transitioning from the resting-state to the task-state during sleep. Furthermore, it analyzes differences in graph-theoretical brain network properties among groups with different response types. This work establishes a scientific basis for future investigations into the differences between task-state and resting-state brain networks in sleeping infants exposed to various stimuli. The goal is to find intuitive methods that can reveal individual sensitivity to stimuli and group individuals based on different response types, in order to study the connectivity of brain networks corresponding to different response patterns. A total of 21 normally developing infants were included in the study. All infants underwent 5 min of resting-state data collection after naturally falling asleep, followed by 15 s of white noise stimulation and 20 s of rest, repeated for 5 cycles in the task-state. The task frequency was kept constant at 0.0286 Hz. We developed an intuitive approach to assess individual responses to stimuli, characterized by the sparsity of Pearson correlation coefficients across distinct narrow frequency bands. This method revealed three distinct response patterns: Sensitive-Positive, Sensitive-Negative, and Insensitive. Upon analyzing the sparsity associated with these response patterns, we observed that the reduction in functional connectivity during task engagement may be linked to interference between the task-related frequency and the individual's baseline cognitive frequency. Furthermore, when examining brain network properties through graph-theoretical analysis, we found that individuals exhibiting stronger small-worldness in the resting-state tended to show heightened sensitivity to stimuli. Different individual infants show different changes in brain functional connectivity after receiving the same stimulus. Different response types should be analyzed separately.
婴儿大脑原始且未成熟。研究婴儿快速发育阶段大脑的变化一直是研究热点。然而，由于婴儿群体的独特特征，维持绝对静止状态受到限制。因此，比较绝对静止状态和任务状态脑网络差异的研究极其困难。所以，研究睡眠期间的婴儿脑网络已成为一个有前景的方向。基于成人研究，任务状态脑网络表现出更强的行为相关性，但关于任务状态和静止状态脑网络差异的研究揭示了个体变异性。本研究为婴儿脑网络在睡眠期间从静止状态过渡到任务状态的个体差异提供了证据。此外，它分析了不同反应类型组之间的图论脑网络属性差异。这项工作为未来研究睡眠中暴露于各种刺激的婴儿任务状态和静止状态脑网络之间的差异奠定了科学基础。目标是找到能揭示个体对刺激敏感性并基于不同反应类型对个体进行分组的直观方法，以便研究对应不同反应模式的脑网络连通性。该研究共纳入21名正常发育的婴儿。所有婴儿自然入睡后进行5分钟的静止状态数据采集，随后进行15秒的白噪声刺激和20秒的休息，在任务状态下重复5个周期。任务频率保持恒定在0.0286赫兹。我们开发了一种直观方法来评估个体对刺激的反应，其特征是不同窄频带内皮尔逊相关系数的稀疏性。该方法揭示了三种不同的反应模式：敏感 - 阳性、敏感 - 阴性和不敏感。在分析与这些反应模式相关的稀疏性时，我们观察到任务参与期间功能连通性的降低可能与任务相关频率和个体基线认知频率之间的干扰有关。此外，通过图论分析检查脑网络属性时，我们发现静止状态下表现出更强小世界特性的个体往往对刺激表现出更高的敏感性。不同的个体婴儿在接受相同刺激后大脑功能连通性会有不同变化。应分别分析不同的反应类型。
