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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