Automated detection of fMRI artefacts from Shannon entropy distributions

As the number of subjects in modern fMRI experiments increases, the use of automated analysis pipelines is becoming more popular, leading to less manual inspection of the data. Here we promote the use of Shannon entropy distributions to discover those datasets in large studies suffering from various artefacts. Entropy distributions of 1444 resting state fMRI datasets from the 1000 Functional Connectomes Project are examined and mean distributions found after each of several different preprocessing steps. Empirically derived envelopes are generated so that significantly outlying datasets may be identified. This process of outlier detection may be automated such that those datasets with characteristic shifts in entropy caused by specific artefacts may be flagged for further manual examination or removed from further analysis. We conclude this technique will be a useful quality control method when dealing with data from large studies.