  For many psychiatric disorders, neuroimaging offers a potential for
revolutionizing diagnosis, and potentially treatment, by providing access to
preverbal mental processes. In their study "Machine learning of neural
representations of suicide and emotion concepts identifies suicidal youth."1,
Just and colleagues report that a Naive Bayes classifier, trained on voxelwise
fMRI responses in human participants during the presentation of words and
concepts related to mortality, can predict whether an individual had reported
having suicidal ideations with a classification accuracy of 91%. Here we report
a reappraisal of the methods employed by the authors, including re-analysis of
the same data set, that calls into question the accuracy of the authors
findings. The analysis is a case study in the dangers of overfitting in machine
learning.
