There are a growing number of neuroimaging studies motivating joint structural and functional brain connectivity. Brain connectivity of different modalities provides insight into brain functional organization by leveraging complementary information, especially for brain disorders such as schizophrenia. In this paper, we propose a multi-modal 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 multi-objective optimization framework. We evaluated our model using synthetic and real datasets (including dMRI and rs-fMRI from 149 schizophrenia patients and 162 controls). Multi-modal 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 to 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.

Brain can be modeled as distinct functional networks, interacting with each other to construct an integrated system. Each network, named intrinsic connectivity network (ICN) is associated with a specific brain function. Neuroimaging studies increasingly explore combined structural and functional brain connectivity networks to identify ICNs, offering valuable insights into brain organization. This paper introduces a multi-modal independent component analysis (ICA) model, sfCICA, which uses both structural (dMRI) and functional (rs-fMRI) connectivity information guided by spatial maps to estimate ICNs. The proposed model reveals improved functional coupling, modularity, and network distinction, especially in schizophrenia. Statistical analysis shows more significant group differences compared to unimodal models. In summary, the sfCICA model, by jointly considering structural and functional connectivity, demonstrates advantages in simultaneous learning and enhanced connectivity estimates.

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Handling Editor: Alex Fornito

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