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Over the last twenty five years, advances in the collection and analysis offMRI data have enabled new insights into the brain basis of human health anddisease. Individual behavioral variation can now be visualized at a neurallevel as patterns of connectivity among brain regions. Functional brain imagingis enhancing our understanding of clinical psychiatric disorders by revealingties between regional and network abnormalities and psychiatric symptoms.Initial success in this arena has recently motivated collection of largerdatasets which are needed to leverage fMRI to generate brain-based biomarkersto support development of precision medicines. Despite methodological advancesand enhanced computational power, evaluating the quality of fMRI scans remainsa critical step in the analytical framework. Before analysis can be performed,expert reviewers visually inspect raw scans and preprocessed derivatives todetermine viability of the data. This Quality Control (QC) process is laborintensive, and the inability to automate at large scale has proven to be alimiting factor in clinical neuroscience fMRI research. We present a novelmethod for automating the QC of fMRI scans. We train machine learningclassifiers using features derived from brain MR images to predict the"quality" of those images, based on the ground truth of an expert's opinion. Weemphasize the importance of these classifiers' ability to generalize theirpredictions across data from different studies. To address this, we propose anovel approach entitled "FMRI preprocessing Log mining for Automated,Generalizable Quality Control" (FLAG-QC), in which features derived from miningruntime logs are used to train the classifier. We show that classifiers trainedon FLAG-QC features perform much better (AUC=0.79) than previously proposedfeature sets (AUC=0.56) when testing their ability to generalize acrossstudies.

A Generalizable Method for Automated Quality Control of Functional Neuroimaging Datasets