Optimizing multivariate pattern classification in rapid event-related designs

• Published in: Journal of neuroscience methods Vol. 387; p. 109808
• Main Authors: Stehr, Daniel A., Garcia, Javier O., Pyles, John A., Grossman, Emily D.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.03.2023
• Subjects: Brain; Brain Mapping - methods; functional magnetic resonance imaging; Image Processing, Computer-Assisted - methods; Magnetic Resonance Imaging - methods; Multivariate Analysis; multivariate pattern analysis; pattern classification; pattern decoding; rapid event-related design; Support Vector Machine; pattern classification; rapid event-related design; pattern decoding; multivariate pattern analysis; functional magnetic resonance imaging
• ISSN: 0165-0270; 1872-678X; 1872-678X
• DOI: 10.1016/j.jneumeth.2023.109808

Multivariate pattern analysis (MVPA or pattern decoding) has attracted considerable attention as a sensitive analytic tool for investigations using functional magnetic resonance imaging (fMRI) data. With the introduction of MVPA, however, has come a proliferation of methodological choices confronting the researcher, with few studies to date offering guidance from the vantage point of controlled datasets detached from specific experimental hypotheses. We investigated the impact of four data processing steps on support vector machine (SVM) classification performance aimed at maximizing information capture in the presence of common noise sources. The four techniques included: trial averaging (classifying on separate trial estimates versus condition-based averages), within-run mean centering (centering the data or not), method of cost selection (using a fixed or tuned cost value), and motion-related denoising approach (comparing no denoising versus a variety of nuisance regressions capturing motion-related reference signals). The impact of these approaches was evaluated on real fMRI data from two control ROIs, as well as on simulated pattern data constructed with carefully controlled voxel- and trial-level noise components. We find significant improvements in classification performance across both real and simulated datasets with run-wise trial averaging and mean centering. When averaging trials within conditions of each run, we note a simultaneous increase in the between-subject variability of SVM classification accuracies which we attribute to the reduced size of the test set used to assess the classifier’s prediction error. Therefore, we propose a hybrid technique whereby randomly sampled subsets of trials are averaged per run and demonstrate that it helps mitigate the tradeoff between improving signal-to-noise ratio by averaging and losing exemplars in the test set. Though a handful of empirical studies have employed run-based trial averaging, mean centering, or their combination, such studies have done so without theoretical justification or rigorous testing using control ROIs. Therefore, we intend this study to serve as a practical guide for researchers wishing to optimize pattern decoding without risk of introducing spurious results. •Investigates the impact of four data processing techniques on multivariate decoding.•Within-run trial averaging and mean centering greatly increases decodability.•To maintain a large enough test set, a hybrid method of averaging trials is proposed.