1 Department of Neuroscience, Medical University of South Carolina, Charleston, South Carolina.
2 Department of Public Health Sciences, and Medical University of South Carolina, Charleston, South Carolina.
Brain Connect. 2019 Mar;9(2):231-239. doi: 10.1089/brain.2018.0607. Epub 2019 Feb 25.
Face processing capacities become more specialized and advanced during development, but neural underpinnings of these processes are not fully understood. The present study applied graph theory-based network analysis to task-negative (resting blocks) and task-positive (viewing faces) functional magnetic resonance imaging data in children (5-17 years) and adults (18-42 years) to test the hypothesis that the development of a specialized network for face processing is driven by task-positive processing (face viewing) more than by task-negative processing (visual fixation) and by both progressive and regressive changes in network properties. Predictive modeling was used to predict age from node-based network properties derived from task-positive and task-negative states in a whole-brain network (WBN) and a canonical face network (FN). The best-fitting model indicated that FN maturation was marked by both progressive and regressive changes in information diffusion (eigenvector centrality) in the task-positive state, with regressive changes outweighing progressive changes. Hence, FN maturation was characterized by reductions in information diffusion potentially reflecting the development of more specialized modules. In contrast, WBN maturation was marked by a balance of progressive and regressive changes in hub-connectivity (betweenness centrality) in the task-negative state. These findings suggest that the development of specialized networks like the FN depends on dynamic developmental changes associated with domain-specific information (e.g., face processing), but maturation of the brain as a whole can be predicted from task-free states.
面部处理能力在发育过程中变得更加专门化和高级，但这些过程的神经基础尚未完全理解。本研究应用基于图论的网络分析方法，对儿童（5-17 岁）和成人（18-42 岁）的任务负（静息块）和任务正（观看面部）功能磁共振成像数据进行分析，以检验以下假设：专门用于面部处理的网络的发展是由任务正处理（观看面部）驱动的，而不是由任务负处理（视觉固定）驱动的，并且由网络特性的渐进和逆行变化驱动。预测建模用于从整个大脑网络（WBN）和典型面部网络（FN）的任务正和任务负状态中基于节点的网络特性预测年龄。最佳拟合模型表明，FN 的成熟度标志着任务正状态中信息扩散（特征向量中心性）的渐进和逆行变化，逆行变化超过了渐进变化。因此，FN 的成熟度的特点是信息扩散的减少，这可能反映了更专门化模块的发展。相比之下，WBN 的成熟度标志着任务负状态中枢纽连接性（介数中心性）的渐进和逆行变化之间的平衡。这些发现表明，像 FN 这样的专门网络的发展取决于与特定领域信息（例如面部处理）相关的动态发展变化，但整个大脑的成熟度可以从无任务状态预测。
