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Deep Learning for Prognosis Using Task-fMRI: A Novel Architecture and Training Scheme

Most existing brain imaging work focuses on resting-state fMRI (rs-fMRI) data where the subject is at rest in the scanner typically for disease diagnosis problems. Here we analyze task fMRI (t-fMRI) data where the subject performs a multi-event task over multiple trials. t-fMRI data allows exploring more challenging applications such as prognosis of treatment but at the cost of being more complex to analyze. Not only do multiple types of trials exist but the trials of each type are repeated a varying number of times for each subject. This leads to a multi-view (multiple types of trials) and multi-instance (multiple trials of each type of each subject) setting. We propose a deep multi-model architecture to encode multi-view brain activities from t-fMRI data and a multi-layer perceptron ensemble model to combine these view models and make subject-wise predictions. We explore domain adaptation transfer learning between models to address unbalanced views and a novel way to make predictions out of multi-instance embeddings. We evaluate our model's performance on subject-wise cross-validations to accurately determine performance. The experimental results show the proposed method outperforms published methods on the AX-CPT fMRI data for the prognosis problem of predicting treatment improvement in recent-onset childhood schizophrenia. To our knowledge, this is the first data-driven study of the aforementioned task on voxel-wise t-fMRI data of the whole brain.