Multimodal Statistical Analysis in Neuroscience

Multimodal analysis in neuroscience has become increasingly important as brain research comes to rely more heavily on integrated analysis of multiple neuroimaging modalities and complementary data sources. In this review, we adopt a statistics-centered perspective, organizing the literature around three core scientific questions: cross-modal association, which focuses on characterizing shared structure and dependence across modalities; outcome association and prediction, which examine how multimodal signals contribute to clinical and behavioral outcomes; and brain causal analysis, which seeks to identify directed effects and mechanistic pathways. We then discuss some emerging trends, especially the integration of unstructured, nontabular data such as clinical and radiology reports, and the growing heterogeneity of data collected across multiple cohorts, sites, and acquisition protocols. Our central message is that multimodal neuroimaging analysis goes beyond simple data fusion and requires a principled statistical foundation, one that enables clear problem formulation, provides valid and efficient inference, and ensures robustness, as modern neuroimaging and complementary data become more complex and heterogeneous.