Abstract Functional ultrasound (fUS) is a promising imaging method for evaluating brain function in animals and human neonates. fUS images local cerebral blood volume changes to map brain activity. One application of fUS imaging is the quantification of functional connectivity (FC), which characterizes the strength of the connections between functionally connected brain areas. fUS-FC enables characterization of important cerebral alterations in pathological animal models, with potential for translation into identification of biomarkers of neurodevelopmental disorders. However, the sensitivity of fUS to signal sources other than cerebral activity, such as motion artifacts, cardiac pulsatility, anesthesia (if present), and respiration, limits its capacity to distinguish milder cerebral alterations. Here, we show that using canonical correlation analysis (CCA) preprocessing and dynamic functional connectivity analysis, we can efficiently decouple noise signals from the fUS-FC signal. We use this method to characterize the effects of a mild perinatal inflammation on FC in mice. The inflammation mouse model showed lower occurrence of states of high FC between the cortex, hippocampus, thalamus, and cerebellum as compared with controls, while connectivity states limited either to intracortical connections or to ventral pathways were more often observed in the inflammation model. These important differences could not be distinguished using other preprocessing techniques that we compared, such as global signal regression, highlighting the advantage of canonical correlation analysis for preprocessing fUS data. CCA preprocessing is applicable to a wide variety of fUS imaging experimental situations, from anesthetized to awake animal studies, or for neonatal, perinatal, or neurodevelopmental imaging. Beyond fUS imaging, this method can also be applied to FC data from any neuroimaging modality when the sources of noise can be spatially identified.