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This is an empirical study to investigate the impact of scanner effects whenusing machine learning on multi-site neuroimaging data. We utilize structuralT1-weighted brain MRI obtained from two different studies, Cam-CAN and UKBiobank. For the purpose of our investigation, we construct a datasetconsisting of brain scans from 592 age- and sex-matched individuals, 296subjects from each original study. Our results demonstrate that even aftercareful pre-processing with state-of-the-art neuroimaging pipelines aclassifier can easily distinguish between the origin of the data with very highaccuracy. Our analysis on the example application of sex classificationsuggests that current approaches to harmonize data are unable to removescanner-specific bias leading to overly optimistic performance estimates andpoor generalization. We conclude that multi-site data harmonization remains anopen challenge and particular care needs to be taken when using such data withadvanced machine learning methods for predictive modelling.

Machine Learning with Multi-Site Imaging Data: An Empirical Study on the Impact of Scanner Effects