Detecting depression from fMRI using relational association rules and artificial neural networks

Functional Resonance Imaging (fMRI) methods are used to identify brain abnormalities associated to psychiatric disorders. One of the most prevalent psychiatric disorders is depression that is marked by blunted reward sensitivity, the brain function becoming different compared to healthy individuals in dopamine irrigated fronto-striatal regions. In the current paper we approach, from a machine learning perspective, depression detection using fMRI data of 19 depressed and 20 healthy participants during a music listening sequence, a stimulus that has been shown to elicit reward-like responses in the brain. To this effect and in the context in which the machine learning approaches to the problem are very limited, we propose novel models based on Relational Association Rules and Artificial Neural Networks. Using the present data set, we performed leave-one-out cross-validation for the methods we propose. The experimental results are promising, the comparison with the related work favoring our solutions.