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Abstract #0776

Common pitfalls in machine learning applications to multi-center data: tests on the ABIDE I and ABIDE II collections

Elisa Ferrari1,2, Paolo Bosco2, Giovanna Spera2, Maria Evelina Fantacci1,2, and Alessandra Retico2

1Physics, University of Pisa, Pisa, Italy, 2National Institute for Nuclear Physics, Pisa, Italy

Applying Machine Learning (ML) techniques on neuroanatomical MRI data, is becoming widespread for studying psychiatric disorders. However, such instruments require some precautions that, if not applied, may lead to inconsistent results that depend on the procedural choices made in the analysis. In this work, taking neuroimaging studies on Autism Spectrum Disorders as a reference, it is demonstrated that the strong dependency of the cerebral quantities extracted with the segmentation software FreeSurfer 6.0 on the MRI acquisition parameters can, in a multivariate analysis based on ML, obscure the differences due to medical conditions and give inconsistent and meaningless results.

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