Department of Neuroscience, Medical University of South Carolina, Charleston, SC, USA.
Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Sci Adv. 2023 Jan 18;9(3):eabq8566. doi: 10.1126/sciadv.abq8566.
A confluence of evidence indicates that brain functional connectivity is not static but rather dynamic. Capturing transient network interactions in the individual brain requires a technology that offers sufficient within-subject reliability. Here, we introduce an individualized network-based dynamic analysis technique and demonstrate that it is reliable in detecting subject-specific brain states during both resting state and a cognitively challenging language task. We evaluate the extent to which brain states show hemispheric asymmetries and how various phenotypic factors such as handedness and gender might influence network dynamics, discovering a right-lateralized brain state that occurred more frequently in men than in women and more frequently in right-handed versus left-handed individuals. Longitudinal brain state changes were also shown in 42 patients with subcortical stroke over 6 months. Our approach could quantify subject-specific dynamic brain states and has potential for use in both basic and clinical neuroscience research.
大量证据表明，大脑功能连接并非静态的，而是动态的。在个体大脑中捕捉瞬时网络相互作用需要一种能够提供足够的个体内可靠性的技术。在这里，我们引入了一种基于个体化网络的动态分析技术，并证明它在检测静息状态和认知挑战性语言任务期间的特定于个体的大脑状态时具有可靠性。我们评估了大脑状态显示半球不对称的程度以及各种表型因素（如利手性和性别）如何影响网络动态，发现了一种更频繁出现在男性而非女性以及右利手个体中的右侧大脑状态。还在 42 名皮质下卒中患者中显示了 6 个月的纵向大脑状态变化。我们的方法可以量化特定于个体的动态大脑状态，并且具有在基础和临床神经科学研究中使用的潜力。
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