The developmental nature of many neuropsychiatric disorders such as schizophrenia necessitates the detection of functional biomarkers during the prodromal phase of disease that can predict symptomatic conversion and outcomes. Structural chromosomal aberrations, such as copy number variants, confer high risk and penetrance of neuropsychiatric disorders. We used functional ultrasound imaging (fUS) to characterize the functional connectivity profile of the 15q13.3+/−copy number variant mouse model during major developmental milestones on post-natal day (p)35, 42, 60, and 90 in comparison to wild type littermates. We identified divergent trajectories for 15q13.3+/−mice and WT littermate controls where functional connectivity was reduced for both genotypes with age, but to a lesser extent for 15q13.3+/−mice. We were then able to isolate the distinct differences between genotypes to identify a large-scale network where 15q13.3+/−mice displayed global cortical hyperconnectivity and elevated intra-connectivity within the hippocampus and amygdala, in particular. In order to determine the stage of development where the connectivity trajectories bifurcated, we used machine learning to predict genotype. We found that the connectivity profile from p42, but not p35, predicted the genotype of individual mice at p90 with 82% accuracy. All together, these results suggest a crucial period of network maturation from early to late pubescence that is pivotal in the transition of healthy network connectivity into adulthood. This novel application of fUS longitudinally through development shows promise in improving the understanding of the disease biology of mouse models of psychiatric diseases.