Firman-Sadler Lyndon, Singh Mervyn, Domínguez D Juan F, Simpson-Kent Ivan L, Caeyenberghs Karen
Cognitive Neuroscience Unit, School of Psychology, Deakin University, Burwood VIC Australia.
University of Calgary, Calgary, Alberta, Canada.
bioRxiv. 2025 Jul 15:2024.10.30.621202. doi: 10.1101/2024.10.30.621202.
Multilayer network analyses allow for the exploration of complex relationships across different modalities. Specifically, this study employed a novel method that integrates psychometric networks with structural covariance networks to explore the relationships between cognition, emotion and the brain. Psychological (NIH Toolbox Cognition Battery and NIH Toolbox Emotion Battery) and anatomical MRI (cortical volume) data were extracted from the Human Connectome Project Young Adult dataset ( = 1109). Partial correlation networks with graphical lasso regularisation and extended Bayesian information criterion tuning were used to model a psychometric bi-layer network consisting of seven cognitive nodes and four emotion nodes, as well as a neuro-psychometric tri-layer network consisting of these same nodes in addition to 24 brain nodes from the central executive and salience networks. Bridge strength centrality was used to identify nodes that bridged between layers. For the bi-layer network, it was found that stress was the only bridge node. For the tri-layer network, six bridge nodes were identified, with the left insula emerging as the most central. These findings demonstrate the utility of multilayer networks in integrating psychological and neurobiological data for the potential identification of targets to improve psychological wellbeing.
多层网络分析有助于探索不同模态之间的复杂关系。具体而言，本研究采用了一种将心理测量网络与结构协方差网络相结合的新方法，以探究认知、情感与大脑之间的关系。心理数据（美国国立卫生研究院工具箱认知电池和美国国立卫生研究院工具箱情感电池）和解剖学磁共振成像（皮质体积）数据取自人类连接组计划青年成人数据集（ = 1109）。使用具有图形拉索正则化和扩展贝叶斯信息准则调整的偏相关网络，对由七个认知节点和四个情感节点组成的心理测量双层网络，以及除了来自中央执行网络和突显网络的24个脑节点之外还包含这些相同节点的神经心理测量三层网络进行建模。桥接强度中心性用于识别跨层的节点。对于双层网络，发现压力是唯一的桥接节点。对于三层网络，识别出六个桥接节点，其中左脑岛最为核心。这些发现证明了多层网络在整合心理和神经生物学数据以潜在识别改善心理健康目标方面的效用。
