Iraji A, Chen J, Lewis N, Faghiri A, Fu Z, Agcaoglu O, Kochunov P, Adhikari B M, Mathalon D H, Pearlson G D, Macciardi F, Preda A, van Erp T G M, Bustillo J R, Díaz-Caneja C M, Andrés-Camazón P, Dhamala M, Adali T, Calhoun V D
Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA, USA.
Department of Computer Science, Georgia State University, Atlanta, GA, USA.
bioRxiv. 2023 Jul 19:2023.07.18.548880. doi: 10.1101/2023.07.18.548880.
Recent advances in resting-state fMRI allow us to study spatial dynamics, the phenomenon of brain networks spatially evolving over time. However, most dynamic studies still use subject-specific, spatially-static nodes. As recent studies have demonstrated, incorporating time-resolved spatial properties is crucial for precise functional connectivity estimation and gaining unique insights into brain function. Nevertheless, estimating time-resolved networks poses challenges due to the low signal-to-noise ratio, limited information in short time segments, and uncertain identification of corresponding networks within and between subjects.
We adapt a reference-informed network estimation technique to capture time-resolved spatial networks and their dynamic spatial integration and segregation. We focus on time-resolved spatial functional network connectivity (spFNC), an estimate of network spatial coupling, to study sex-specific alterations in schizophrenia and their links to multi-factorial genomic data.
Our findings are consistent with the dysconnectivity and neurodevelopment hypotheses and align with the cerebello-thalamo-cortical, triple-network, and frontoparietal dysconnectivity models, helping to unify them. The potential unification offers a new understanding of the underlying mechanisms. Notably, the posterior default mode/salience spFNC exhibits sex-specific schizophrenia alteration during the state with the highest global network integration and correlates with genetic risk for schizophrenia. This dysfunction is also reflected in high-dimensional (voxel-level) space in regions with weak functional connectivity to corresponding networks.
Our method can effectively capture spatially dynamic networks, detect nuanced SZ effects, and reveal the intricate relationship of dynamic information to genomic data. The results also underscore the potential of dynamic spatial dependence and weak connectivity in the clinical landscape.
静息态功能磁共振成像（fMRI）的最新进展使我们能够研究空间动力学，即脑网络随时间在空间上演变的现象。然而，大多数动态研究仍使用特定于个体的、空间上静态的节点。正如最近的研究所表明的，纳入时间分辨的空间特性对于精确的功能连接估计以及深入了解脑功能至关重要。尽管如此，由于信噪比低、短时间段内信息有限以及个体内部和个体之间相应网络的识别不确定，估计时间分辨网络仍面临挑战。
我们采用一种参考信息网络估计技术来捕获时间分辨的空间网络及其动态空间整合和分离。我们专注于时间分辨的空间功能网络连接性（spFNC），即网络空间耦合的一种估计，以研究精神分裂症中的性别特异性改变及其与多因素基因组数据的联系。
我们的发现与失连接和神经发育假说一致，并与小脑 - 丘脑 - 皮质、三网络和额顶叶失连接模型相符，有助于将它们统一起来。这种潜在的统一为潜在机制提供了新的理解。值得注意的是，后默认模式/突显spFNC在全局网络整合最高的状态下表现出精神分裂症的性别特异性改变，并与精神分裂症的遗传风险相关。这种功能障碍在与相应网络功能连接较弱的区域的高维（体素水平）空间中也有所体现。
我们的方法可以有效地捕获空间动态网络，检测细微的精神分裂症效应，并揭示动态信息与基因组数据之间的复杂关系。结果还强调了动态空间依赖性和弱连接性在临床领域的潜力。
