Multimodal data fusion in neuroscience: promises, challenges and future directions

Multimodal fusion provides significant benefits over single modality analysis by leveraging both shared and complementary information across diverse data sources. In this article, we systematically review methods for fusion of heterogonous multimodal biomedical data of varying dimensionality (including neuroimaging, biomics, clinical phenotypes and text), with a focus on neuroscience. We discuss the strengths and limitations of these strategies based on a survey of 302 research articles. Next, we examine the applications of these methods to a variety of scenarios spanning a continuum from scientific research to clinical practice. Finally, an in-depth discussion of common challenges and promising directions for future development of multimodal biomedical data fusion are provided. Overall, multimodal fusion shows substantial benefits and transformative potential in the field of neuroscience. Future research should prioritize improving model generalization, enhancing interpretability, addressing inherent data limitations, and developing unified platforms alongside multimodal foundational models to bridge the gap between fusion techniques, research, and application to various domains.