Learning Personal Representations from fMRIby Predicting Neurofeedback Performance
This work addresses the challenge of personalized modeling in neuroscience for individuals undergoing neuromodulation, though it appears incremental as it builds on existing deep learning approaches for fMRI analysis.
The authors tackled the problem of learning personal representations from fMRI data during a neurofeedback task, showing that their method improves next-frame amygdala activity prediction by a considerable margin and outperforms clinical data in predicting psychiatric traits.
We present a deep neural network method for learning a personal representation for individuals that are performing a self neuromodulation task, guided by functional MRI (fMRI). This neurofeedback task (watch vs. regulate) provides the subjects with a continuous feedback contingent on down regulation of their Amygdala signal and the learning algorithm focuses on this region's time-course of activity. The representation is learned by a self-supervised recurrent neural network, that predicts the Amygdala activity in the next fMRI frame given recent fMRI frames and is conditioned on the learned individual representation. It is shown that the individuals' representation improves the next-frame prediction considerably. Moreover, this personal representation, learned solely from fMRI images, yields good performance in linear prediction of psychiatric traits, which is better than performing such a prediction based on clinical data and personality tests. Our code is attached as supplementary and the data would be shared subject to ethical approvals.