Dynamic fusion of structural and functional connectivity via joint connectivity matrix ICA

The integration of multimodal MRI images, including functional MRI (fMRI) and diffusion MRI (dMRI), depicts a key advancement in neuroimaging, since it offers a more comprehensive and better understanding of brain function and connections. FMRI captures brain functional activity while dMRI reveals structural connectivity via white matter bundles, each of which provide unique yet complementary insights; however combing these two modalities, particularly at a dynamic level, is challenging due to their drastically different data characteristics. This study introduces a novel framework named "dynamic fusion," which extends joint component analysis (cmICA) to integrate static structural connectivity (SC) with dynamic functional connectivity (FC). Our approach gauges the relationship between these joint components across various temporal states, aiming to discover both static and dynamic features of brain connectivity. We applied this approach to fMRI and dMRI data from the same set of control subjects, which we had previously studied to estimate joint parcellation and their structural and functional connections using a static model only, and also included a comparable number of individuals with schizophrenia from the same study. Our results reveal that dynamic fusion not only highlights diverse temporal dynamics in FC but, more importantly, also shows how SC patterns differ across dynamic functional states at different time frames, providing new insights into brain organization. Furthermore, it successfully detects joint structural-functional connectivity differences between individuals with schizophrenia and controls, demonstrating its potential for detecting hidden group differences. Overall, this study establishes our dynamic fusion as a powerful tool for integrating structural and dynamic functional connectivity data, enhancing our understanding of brain connectivity and offering new perspectives for studying neurological disorders.