López-Vicente Mónica, Agcaoglu Oktay, Pérez-Crespo Laura, Estévez-López Fernando, Heredia-Genestar José María, Mulder Rosa H, Flournoy John C, van Duijvenvoorde Anna C K, Güroğlu Berna, White Tonya, Calhoun Vince, Tiemeier Henning, Muetzel Ryan L
Department of Child and Adolescent Psychiatry and Psychology, Erasmus MC University Medical Center, Rotterdam, Netherlands.
The Generation R Study Group, Erasmus MC University Medical Center, Rotterdam, Netherlands.
Front Syst Neurosci. 2021 Nov 22;15:724805. doi: 10.3389/fnsys.2021.724805. eCollection 2021.
The longitudinal study of typical neurodevelopment is key for understanding deviations due to specific factors, such as psychopathology. However, research utilizing repeated measurements remains scarce. Resting-state functional magnetic resonance imaging (MRI) studies have traditionally examined connectivity as 'static' during the measurement period. In contrast, dynamic approaches offer a more comprehensive representation of functional connectivity by allowing for different connectivity configurations (time varying connectivity) throughout the scanning session. Our objective was to characterize the longitudinal developmental changes in dynamic functional connectivity in a population-based pediatric sample. Resting-state MRI data were acquired at the ages of 10 (range 8-to-12, = 3,327) and 14 (range 13-to-15, = 2,404) years old using a single, study-dedicated 3 Tesla scanner. A fully-automated spatially constrained group-independent component analysis (ICA) was applied to decompose multi-subject resting-state data into functionally homogeneous regions. Dynamic functional network connectivity (FNC) between all ICA time courses were computed using a tapered sliding window approach. We used a -means algorithm to cluster the resulting dynamic FNC windows from each scan session into five dynamic states. We examined age and sex associations using linear mixed-effects models. First, independent from the dynamic states, we found a general increase in the temporal variability of the connections between intrinsic connectivity networks with increasing age. Second, when examining the clusters of dynamic FNC windows, we observed that the time spent in less modularized states, with low intra- and inter-network connectivity, decreased with age. Third, the number of transitions between states also decreased with age. Finally, compared to boys, girls showed a more mature pattern of dynamic brain connectivity, indicated by more time spent in a highly modularized state, less time spent in specific states that are frequently observed at a younger age, and a lower number of transitions between states. This longitudinal population-based study demonstrates age-related maturation in dynamic intrinsic neural activity from childhood into adolescence and offers a meaningful baseline for comparison with deviations from typical development. Given that several behavioral and cognitive processes also show marked changes through childhood and adolescence, dynamic functional connectivity should also be explored as a potential neurobiological determinant of such changes.
典型神经发育的纵向研究对于理解特定因素（如精神病理学）导致的偏差至关重要。然而，利用重复测量的研究仍然很少。静息态功能磁共振成像（MRI）研究传统上在测量期间将连接性视为“静态”。相比之下，动态方法通过允许在整个扫描过程中存在不同的连接配置（时变连接性），提供了功能连接性的更全面表示。我们的目标是在一个基于人群的儿科样本中表征动态功能连接性的纵向发育变化。使用一台专门用于该研究的3特斯拉扫描仪，在10岁（范围8至12岁，n = 3327）和14岁（范围13至15岁，n = 2404）时采集静息态MRI数据。应用一种完全自动化的空间受限组独立成分分析（ICA），将多受试者静息态数据分解为功能上均匀的区域。使用锥形滑动窗口方法计算所有ICA时间序列之间的动态功能网络连接性（FNC）。我们使用k均值算法将每次扫描会话产生的动态FNC窗口聚类为五个动态状态。我们使用线性混合效应模型检查年龄和性别关联。首先，独立于动态状态，我们发现随着年龄增长，内在连接网络之间连接的时间变异性普遍增加。其次，在检查动态FNC窗口的聚类时，我们观察到处于模块化程度较低状态（网络内和网络间连接性较低）的时间随着年龄增长而减少。第三，状态之间的转换次数也随着年龄增长而减少。最后，与男孩相比，女孩表现出更成熟的动态脑连接模式，表现为在高度模块化状态下花费的时间更多，在较年轻时经常观察到的特定状态下花费的时间更少，以及状态之间的转换次数更少。这项基于人群的纵向研究证明了从童年到青春期动态内在神经活动中与年龄相关的成熟，并为与典型发育偏差的比较提供了有意义的基线。鉴于一些行为和认知过程在童年和青春期也显示出显著变化，动态功能连接性也应作为此类变化的潜在神经生物学决定因素进行探索。
