Resting state fMRI (rs-fMRI) is a widely used technique for identifying resting state networks (RSNs) and investigating brain disorders. However, the characterization of RSNs can be seriously hindered by the presence of random and structural noise in the measured fMRI signal. Most tools that correct for these effects are tailored for human brain and are not readily transposable to rat data. Here we propose a data processing pipeline for rat rs-fMRI data which can robustly remove artefacts and clean the rs-fMRI data. We report the performance of the pipeline for analyzing rat RSNs and discriminating between control and disease groups.
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