Robust DNN-based Decoder Model with an Embedded State-Space Model Layer.

Characterizing biobehavioral time series data recorded under different task conditions is a critical step in neuroscience data analysis. The inherent complexity and stochasticity present in these data pose significant modeling challenges. Deep Neural Network (DNN) models have been widely adopted for analyzing such data. Despite their success, DNNs have significant limitations: they are sensitive to noise in training data and require large training datasets. These issues limit the applicability of DNNs to neuroscience data - where sample size is often smaller and data are volatile and feature noise artifacts. The present manuscript introduces a novel framework that embeds a state-space model (SSM) in the classic DNN structure. We demonstrate that this framework, which we call SSM-DNN, can overcome the sample size and noise sensitivity issues that plague classic DNNs. For this purpose, we illustrate training and inference steps when SSM-DNN is applied to a dataset recorded during a Death Implicit Association Test (D-IAT) task. This task was designed to produce biobehavioral data that facilitate decoding of participant phenotypes (e.g., person with depression vs. psychologically healthy person). We show SSM-DNN performance reaches an accuracy of 78%, which is 20% higher than state-of-art DNN decoder models. Its area under curve (AUC) is 0.8, which reflects its high specificity and sensitivity. The SSM-DNN modeling framework is scalable to high-dimensional time-series data and it can be applied broadly to neuroscience data, providing a robust and accurate decoding performance.