  Recent years have seen a growing interest in methods for predicting an
unknown variable of interest, such as a subject's diagnosis, from medical
images depicting its anatomical-functional effects. Methods based on
discriminative modeling excel at making accurate predictions, but are
challenged in their ability to explain their decisions in anatomically
meaningful terms. In this paper, we propose a simple technique for
single-subject prediction that is inherently interpretable. It augments the
generative models used in classical human brain mapping techniques, in which
the underlying cause-effect relations can be encoded, with a multivariate noise
model that captures dominant spatial correlations. Experiments demonstrate that
the resulting model can be efficiently inverted to make accurate subject-level
predictions, while at the same time offering intuitive visual explanations of
its inner workings. The method is easy to use: training is fast for typical
training set sizes, and only a single hyperparameter needs to be set by the
user. Our code is available at
https://github.com/chiara-mauri/Interpretable-subject-level-prediction.
