Deep Recurrent Encoder: A scalable end-to-end network to model brain signals
This work addresses the problem of understanding brain dynamics for neuroscientists, offering a scalable method with interpretability, though it is incremental as it builds on existing deep learning approaches.
The authors tackled the challenge of modeling noisy, high-dimensional brain signals by proposing an end-to-end deep learning architecture that predicts brain responses to words, achieving a three-fold improvement over linear methods on MEG data.
Understanding how the brain responds to sensory inputs is challenging: brain recordings are partial, noisy, and high dimensional; they vary across sessions and subjects and they capture highly nonlinear dynamics. These challenges have led the community to develop a variety of preprocessing and analytical (almost exclusively linear) methods, each designed to tackle one of these issues. Instead, we propose to address these challenges through a specific end-to-end deep learning architecture, trained to predict the brain responses of multiple subjects at once. We successfully test this approach on a large cohort of magnetoencephalography (MEG) recordings acquired during a one-hour reading task. Our Deep Recurrent Encoding (DRE) architecture reliably predicts MEG responses to words with a three-fold improvement over classic linear methods. To overcome the notorious issue of interpretability of deep learning, we describe a simple variable importance analysis. When applied to DRE, this method recovers the expected evoked responses to word length and word frequency. The quantitative improvement of the present deep learning approach paves the way to better understand the nonlinear dynamics of brain activity from large datasets.