Campbell-Cousins Avalon, Guazzo Federica, Bastin Mark E, Parra Mario A, Escudero Javier
School of Engineering, Institute for Imaging, Data and Communications, University of Edinburgh, Edinburgh, Scotland, United Kingdom.
Human Cognitive Neuroscience, Psychology, University of Edinburgh, Edinburgh, Scotland, United Kingdom.
PLoS One. 2025 Aug 22;20(8):e0328736. doi: 10.1371/journal.pone.0328736. eCollection 2025.
Modularity is a well-established concept for assessing community structures in various single and multi-layer networks, including those in biological and social domains. Brain networks are known to exhibit community structure at a variety of scales-local, meso, and global scale. However, modularity, while useful in describing mesoscale brain organization, is limited as a metric to a global scale describing the overall strength of community structure. This approach, while valuable, overlooks important variations in community structure at node level. To address this limitation, we extended modularity to individual nodes. This novel measure of nodal modularity (nQ) captures both mesoscale and local-scale changes in modularity. We hypothesized that nQ would illuminate granular changes in the brain due to diseases such as Alzheimer's disease (AD), which are known to disrupt the brain's modular structure. We explored nQ in multiplex networks of a visual short-term memory binding task in fMRI and DTI data in the early stages of AD. While limited by sample size, changes in nQ for individual regions of interest (ROIs) in our fMRI networks were predominantly observed in visual, limbic, and paralimbic systems in the brain, aligning with known AD trajectories and linked to amyloid-β and tau deposition. Furthermore, observed changes in white-matter microstructure in our DTI networks in parietal and frontal regions may compliment studies of white-matter integrity in poor memory binders. Additionally, nQ clearly differentiated MCI from MCI converters indicating that nQ may be sensitive to this key turning point of AD. Our findings demonstrate the utility of nQ as a measure of localized group structure, providing novel insights into task and disease-related variability at the node level. Given the widespread application of modularity as a global measure, nQ represents a significant advancement, providing a granular measure of network organization applicable to a wide range of disciplines.
模块化是评估各种单层和多层网络中社区结构的一个成熟概念，包括生物和社会领域的网络。已知脑网络在各种尺度——局部、中观和全局尺度上都呈现出社区结构。然而，模块化虽然在描述中观尺度的脑组织方面很有用，但作为一种度量标准，它仅限于描述社区结构整体强度的全局尺度。这种方法虽然有价值，但忽略了节点层面社区结构的重要变化。为了解决这一局限性，我们将模块化扩展到了单个节点。这种新的节点模块化度量（nQ）捕捉了模块化在中观尺度和局部尺度上的变化。我们假设nQ将揭示由于阿尔茨海默病（AD）等疾病导致的大脑细微变化，已知这些疾病会破坏大脑的模块化结构。我们在AD早期的功能磁共振成像（fMRI）和扩散张量成像（DTI）数据中的视觉短期记忆绑定任务的多重网络中探索了nQ。虽然受样本量限制，但我们fMRI网络中感兴趣的各个区域（ROI）的nQ变化主要在大脑的视觉、边缘和边缘旁系统中观察到，与已知的AD轨迹一致，并与淀粉样蛋白-β和tau沉积相关。此外，我们在顶叶和额叶区域的DTI网络中观察到的白质微观结构变化可能补充了对记忆绑定能力差的个体的白质完整性研究。此外，nQ能清楚地区分轻度认知障碍（MCI）和MCI转化者，这表明nQ可能对AD的这个关键转折点敏感。我们的研究结果证明了nQ作为局部群体结构度量的实用性，为节点层面与任务和疾病相关的变异性提供了新的见解。鉴于模块化作为一种全局度量的广泛应用，nQ代表了一项重大进展，提供了一种适用于广泛学科的网络组织精细度量。
