Decoding Task States by Spotting Salient Patterns at Time Points and Brain Regions

Abstract

During task performance, brain states change dynamically and can appear recurrently. Recently, recurrent neural networks (RNN) have been used for identifying functional signatures underlying such brain states from task functional Magnetic Resonance Imaging (fMRI) data. While RNNs only model temporal dependence between time points, brain task decoding needs to model temporal dependencies of the underlying brain states. Furthermore, as only a subset of brain regions are involved in task performance, it is important to consider subsets of brain regions for brain decoding. To address these issues, we present a customised neural network architecture, Salient Patterns Over Time and Space (SPOTS), which not only captures dependencies of brain states at different time points but also pays attention to key brain regions associated with the task. On language and motor task data gathered in the Human Connectome Project, SPOTS improves brain state prediction by 17% to 40% as compared to the baseline RNN model. By spotting salient spatio-temporal patterns, SPOTS is able to infer brain states even on small time windows of fMRI data, which the present state-of-the-art methods struggle with. This allows for quick identification of abnormal task-fMRI scans, leading to possible future applications in task-fMRI data quality assurance and disease detection. Code is available at https://github.com/SCSE-Biomedical-Computing-Group/SPOTS.