Uncovering Invariant Representations in Functional Neuroimaging with Deep Metric Learning

With the increasing ability to record neuroimaging with higher spatial and temporal resolution, there is a growing need for methods that reduce these high-dimensional representations into latent low-dimensional structures that are discriminative and/or predictive of behavior, disease, or in general experimental context. We propose a metric learning framework to extract meaningful latent structures from high-dimensional fMRI data. This method learns the latent embeddings that reduce the intra-group variability while maximizing the inter-group variability. In addition, our method leverages advances in few-shot learning approaches to adapt to small sample-size fMRI datasets, allowing one to learn the latent structure from just a few samples per context. We evaluate our work on two publicly available fMRI datasets and report superior results compared to popular alternative approaches such as Principal Component Analysis (84.7% vs. 60%; 21.8% vs. 8.3%). We provide the Python code as open-source at Github.