Fouladivanda Mahshid, Iraji Armin, Wu Lei, van Erp Theo G M, Belger Aysenil, Hawamdeh Faris, Pearlson Godfrey D, Calhoun Vince D
Tri-institute Translational Research in Neuroimaging and Data Science (TReNDS Center), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA.
Georgia State University, Atlanta, GA, USA.
Netw Neurosci. 2024 Dec 10;8(4):1212-1242. doi: 10.1162/netn_a_00398. eCollection 2024.
There are a growing number of neuroimaging studies motivating joint structural and functional brain connectivity. The brain connectivity of different modalities provides an insight into brain functional organization by leveraging complementary information, especially for brain disorders such as schizophrenia. In this paper, we propose a multimodal independent component analysis (ICA) model that utilizes information from both structural and functional brain connectivity guided by spatial maps to estimate intrinsic connectivity networks (ICNs). Structural connectivity is estimated through whole-brain tractography on diffusion-weighted MRI (dMRI), while functional connectivity is derived from resting-state functional MRI (rs-fMRI). The proposed structural-functional connectivity and spatially constrained ICA (sfCICA) model estimates ICNs at the subject level using a multiobjective optimization framework. We evaluated our model using synthetic and real datasets (including dMRI and rs-fMRI from 149 schizophrenia patients and 162 controls). Multimodal ICNs revealed enhanced functional coupling between ICNs with higher structural connectivity, improved modularity, and network distinction, particularly in schizophrenia. Statistical analysis of group differences showed more significant differences in the proposed model compared with the unimodal model. In summary, the sfCICA model showed benefits from being jointly informed by structural and functional connectivity. These findings suggest advantages in simultaneously learning effectively and enhancing connectivity estimates using structural connectivity.
越来越多的神经影像学研究推动了大脑结构和功能连接的联合研究。不同模态的大脑连接通过利用互补信息，为洞察大脑功能组织提供了线索，特别是对于精神分裂症等脑部疾病。在本文中，我们提出了一种多模态独立成分分析（ICA）模型，该模型利用基于空间图谱引导的大脑结构和功能连接信息来估计内在连接网络（ICN）。结构连接通过对扩散加权磁共振成像（dMRI）进行全脑纤维束成像来估计，而功能连接则来自静息态功能磁共振成像（rs-fMRI）。所提出的结构-功能连接和空间约束ICA（sfCICA）模型使用多目标优化框架在个体水平上估计ICN。我们使用合成数据集和真实数据集（包括来自149名精神分裂症患者和162名对照的dMRI和rs-fMRI）对我们的模型进行了评估。多模态ICN显示，具有较高结构连接性的ICN之间功能耦合增强、模块性提高以及网络区分度增强，尤其是在精神分裂症中。组间差异的统计分析表明，与单模态模型相比，所提出的模型差异更显著。总之，sfCICA模型显示出从结构和功能连接的联合信息中受益。这些发现表明，在同时有效学习并利用结构连接增强连接性估计方面具有优势。
