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In the last decade, biological psychiatry has witnessed the increased availability of large multi-modal datasets that seek to capture biological variation across the lifespan, in prodromes of major mental illness, and in individuals suffering from major neuropsychiatric disorders. Our group, and several others, have been leveraging these incredible datasets to gain insight into how variation in brain structure and function, detectable via neuroimaging, may relate to other disease-related factors observed in genetics and transcriptomics. However, the outcomes of these studies rely on disparate datasets that were not collected to be analyzed in a single analytic framework. Thus, many of the exciting findings that result from the creative multivariate data fusion techniques employed are somewhat observational. To move beyond these limitations, our group has been working on examining phenotypic variation in model systems exposed to risk factors for neuropsychiatric disorders. Using longitudinal neuroimaging strategies and borrowing many of the magnificent techniques developed in the well-established human literature, we are able to demonstrate meaningful multi-modal integration across behavioural, imaging, cellular, and transcriptomic phenotypes. We believe that well-designed experiments that yield tremendous amounts of data from individual animal models are a critical way to better inform our understanding of the basis of human neuropsychiatric illness.