Decoding dynamic brain patterns from evoked responses: A tutorial on multivariate pattern analysis applied to time-series neuroimaging data

TL;DR

This tutorial reviews multivariate pattern analysis (MVPA) for time-series neuroimaging data like MEG and EEG, highlighting analysis choices' impact on decoding perceptual and cognitive states over time.

Contribution

It provides a comprehensive guide on applying MVPA to time-series data, including analysis options, extensions, and interpretation considerations from a Cognitive Neuroscience perspective.

Findings

Analysis decisions significantly influence decoding results.

Extensions like representational similarity analysis and temporal generalisation are valuable.

Proper interpretation of classifier weights is crucial for understanding neural representations.

Abstract

Multivariate pattern analysis (MVPA) or brain decoding methods have become standard practice in analysing fMRI data. Although decoding methods have been extensively applied in Brain Computing Interfaces (BCI), these methods have only recently been applied to time-series neuroimaging data such as MEG and EEG to address experimental questions in Cognitive Neuroscience. In a tutorial-style review, we describe a broad set of options to inform future time-series decoding studies from a Cognitive Neuroscience perspective. Using example MEG data, we illustrate the effects that different options in the decoding analysis pipeline can have on experimental results where the aim is to 'decode' different perceptual stimuli or cognitive states over time from dynamic brain activation patterns. We show that decisions made at both preprocessing (e.g., dimensionality reduction, subsampling, trial averaging) and decoding (e.g., classifier selection, cross-validation design) stages of the analysis can significantly affect the results. In addition to standard decoding, we describe extensions to MVPA for time-varying neuroimaging data including representational similarity analysis, temporal generalisation, and the interpretation of classifier weight maps. Finally, we outline important caveats in the design and interpretation of time-series decoding experiments.