Vuksanović Vesna
Health Data Science, Swansea University Medical School, Swansea University, Data Science Building, Swansea SA2 8PP, Wales, United Kingdom.
Cereb Cortex Commun. 2022 Jun 16;3(3):tgac024. doi: 10.1093/texcom/tgac024. eCollection 2022.
The cerebral cortex is represented through multiple multilayer morphometric similarity networks to study their modular structures. The approach introduces a novel way for studying brain networks' metrics across individuals, and can quantify network properties usually not revealed using conventional network analyses.
A total of 8 combinations or types of morphometric similarity networks were constructed - 4 combinations of the inter-regional cortical features on 2 brain atlases. The networks' modular structures were investigated by identifying those modular interactions that stay consistent across the combinations of inter-regional morphometric features and individuals.
The results provide evidence of the community structures as the property of (i) cortical lobar divisions, and also as (ii) the product of different combinations of morphometric features used for the construction of the multilayer representations of the cortex. For the first time, this study has mapped out flexible and inflexible morphometric similarity hubs, and evidence has been provided about variations of the modular network topology across the multilayers with age and IQ.
The results contribute to understanding of intra-regional characteristics in cortical interactions, which potentially can be used to map heterogeneous neurodegeneration patterns in diseased brains.
通过多个多层形态相似性网络来表征大脑皮层，以研究其模块化结构。该方法引入了一种研究个体间脑网络指标的新方法，并能够量化通常使用传统网络分析无法揭示的网络特性。
构建了总共8种组合或类型的形态相似性网络——2个脑图谱上区域间皮质特征的4种组合。通过识别在区域间形态特征和个体组合中保持一致的模块化相互作用来研究网络的模块化结构。
结果提供了证据，表明群落结构是（i）皮质叶划分的属性，也是（ii）用于构建皮质多层表示的形态特征不同组合的产物。本研究首次绘制了灵活和不灵活的形态相似性枢纽，并提供了模块化网络拓扑随年龄和智商在多层之间变化的证据。
这些结果有助于理解皮质相互作用中的区域内特征，这可能可用于描绘患病大脑中的异质性神经退行性变模式。
