A Bayesian supervised dual‐dimensionality reduction model for simultaneous decoding of LFP and spike train signals. Issue 1 (7th February 2017)

• Record Type: Journal Article
• Authors: Holbrook, Andrew; Vandenberg‐Rodes, Alexander; Fortin, Norbert; Shahbaba, Babak
• Is Part Of: Stat. Volume 6:Issue 1(2017)
• Page Start: 53
• Page End: 67
• Publication Date: 2017-02-07
• Subjects: Bayesian methods -- discrimination -- high‐dimensional data -- statistical learning; Statistics -- Periodicals; 519.2
• DOI: 10.1002/sta4.137
• Languages: English
• ISSNs: 2049-1573

Abstract

Abstract : Neuroscientists are increasingly collecting multimodal data during experiments and observational studies. Different data modalities—such as electroencephalogram, functional magnetic resonance imaging, local field potential (LFP) and spike trains—offer different views of the complex systems contributing to neural phenomena. Here, we focus on joint modelling of LFP and spike train data and present a novel Bayesian method for neural decoding to infer behavioural and experimental conditions. This model performs supervised dual‐dimensionality reduction: it learns low‐dimensional representations of two different sources of information that not only explain variation in the input data itself but also predict extraneuronal outcomes. Despite being one probabilistic unit, the model consists of multiple modules: exponential principal components analysis (PCA) and wavelet PCA are used for dimensionality reduction in the spike train and LFP modules, respectively; these modules simultaneously interface with a Bayesian binary regression module. We demonstrate how this model may be used for prediction, parametric inference and identification of influential predictors. In prediction, the hierarchical model outperforms other models trained on LFP alone, spike train alone and combined LFP and spike train data. We compare two methods for modelling the loading matrix and find them to perform similarly. Finally, model parameters and their posterior distributions yield scientific insights. Copyright © 2017 John Wiley & Sons, Ltd.