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Abstract
Functional ultrasound (fUS) imaging has emerged as an important technology for investigating a multitude of neuro-related research areas in various animal models and patients. Beyond imaging quality and specificity, computational analysis of fUS data sets is critical for accurately and comprehensively characterize brain functions and circuits, under physiological and pathological conditions. To facilitate efficient and reproducible data analysis, we present a python-based open-source software (PyfUS) providing an end-to-end pipeline for registration, signal processing and visualization of fUS datasets. In addition to the conventional analysis – region-based averaging and correlation – we introduce the single-voxel clustering as an alternative analysis method that allows simultaneous spatial and temporal examination of the fUS signals at the finest scale allowed by the fUS. We compare the different strategies for analyzing fUS data and display the results of the analytical pipeline on a dataset comprising awake mice subjected to visual stimulation. In a standard computing environment with 32 GB of memory, a 10-Gb data size of brain-wide fUS images can be loaded in ~1h and fully processed with the 3 analysis methods in few minutes. The flexibility of the software allows for the easy extension to other animal models and user-developed modules. We deliver a tool providing a convenient access to state-of-the-art analysis methods and an open platform for the development of new processing strategies.