Institute of Cognitive Sciences and Technologies (ISTC) - National Research Council (CNR) Rome, Italy.
Centre for Human Brain Health and School of Psychology, University of Birmingham, Birmingham, UK.
Int J Neural Syst. 2020 Dec;30(12):2050061. doi: 10.1142/S0129065720500616. Epub 2020 Oct 9.
Intrinsic brain activity is organized into large-scale networks displaying specific structural-functional architecture, known as resting-state networks (RSNs). RSNs reflect complex neurophysiological processes and interactions, and have a central role in distinct sensory and cognitive functions, making it crucial to understand and quantify their anatomical and functional properties. Fractal dimension (FD) provides a parsimonious way of summarizing self-similarity over different spatial and temporal scales but despite its suitability for functional magnetic resonance imaging (fMRI) signal analysis its ability to characterize and investigate RSNs is poorly understood. We used FD in a large sample of healthy participants to differentiate fMRI RSNs and examine how the FD property of RSNs is linked with their functional roles. We identified two clusters of RSNs, one mainly consisting of sensory networks (C1, including auditory, sensorimotor and visual networks) and the other more related to higher cognitive (HCN) functions (C2, including dorsal default mode network and fronto-parietal networks). These clusters were defined in a completely data-driven manner using hierarchical clustering, suggesting that quantification of Blood Oxygen Level Dependent (BOLD) signal complexity with FD is able to characterize meaningful physiological and functional variability. Understanding the mechanisms underlying functional RSNs, and developing tools to study their signal properties, is essential for assessing specific brain alterations and FD could potentially be used for the early detection and treatment of neurological disorders.
大脑的固有活动组织成具有特定结构-功能架构的大规模网络，这些网络被称为静息态网络（RSN）。RSN 反映了复杂的神经生理过程和相互作用，在不同的感觉和认知功能中起着核心作用，因此了解和量化它们的解剖学和功能特性至关重要。分形维数（FD）为总结不同空间和时间尺度上的自相似性提供了一种简洁的方法，但尽管它适用于功能磁共振成像（fMRI）信号分析，但它描述和研究 RSN 的能力仍未被充分理解。我们在大量健康参与者中使用 FD 来区分 fMRI RSN，并研究 RSN 的 FD 属性与其功能角色之间的关系。我们确定了两个 RSN 集群，一个主要由感觉网络组成（C1，包括听觉、感觉运动和视觉网络），另一个与更高的认知（HCN）功能更相关（C2，包括背侧默认模式网络和额顶叶网络）。这些集群是使用层次聚类以完全数据驱动的方式定义的，这表明使用 FD 量化血氧水平依赖（BOLD）信号的复杂性能够描述有意义的生理和功能变异性。了解功能 RSN 的潜在机制，并开发研究其信号特性的工具，对于评估特定的大脑改变至关重要，FD 可能可用于早期发现和治疗神经障碍。
