Chromatic fusion: Generative multimodal neuroimaging data fusion provides multi-informed insights into schizophrenia.

ABSTRACT: This work proposes a novel generative multimodal approach to jointly analyze multimodal data while linking the multimodal information to colors. We apply our proposed framework, which disentangles multimodal data into private and shared sets of features from pairs of structural (sMRI), functional (sFNC and ICA), and diffusion MRI data (FA maps). With our approach, we find that heterogeneity in schizophrenia is potentially a function of modality pairs. Results show (1) schizophrenia is highly multimodal and includes changes in specific networks, (2) non-linear relationships with schizophrenia are observed when interpolating among shared latent dimensions, and (3) we observe a decrease in the modularity of functional connectivity and decreased visual-sensorimotor connectivity for schizophren

SUBMITTER: Geenjaar EPT 

PROVIDER: S-EPMC10619380 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature