Multivariate Bayesian decoding of single-trial event-related fMRI responses for memory retrieval of voluntary actions

PLoS One. 2017 Aug 4;12(8):e0182657. doi: 10.1371/journal.pone.0182657. eCollection 2017.

Abstract

This study proposes a method for classifying event-related fMRI responses in a specialized setting of many known but few unknown stimuli presented in a rapid event-related design. Compared to block design fMRI signals, classification of the response to a single or a few stimulus trial(s) is not a trivial problem due to contamination by preceding events as well as the low signal-to-noise ratio. To overcome such problems, we proposed a single trial-based classification method of rapid event-related fMRI signals utilizing sparse multivariate Bayesian decoding of spatio-temporal fMRI responses. We applied the proposed method to classification of memory retrieval processes for two different classes of episodic memories: a voluntarily conducted experience and a passive experience induced by watching a video of others' actions. A cross-validation showed higher classification performance of the proposed method compared to that of a support vector machine or of a classifier based on the general linear model. Evaluation of classification performances for one, two, and three stimuli from the same class and a correlation analysis between classification accuracy and target stimulus positions among trials suggest that presenting two target stimuli at longer inter-stimulus intervals is optimal in the design of classification experiments to identify the target stimuli. The proposed method for decoding subject-specific memory retrieval of voluntary behavior using fMRI would be useful in forensic applications in a natural environment, where many known trials can be extracted from a simulation of everyday tasks and few target stimuli from a crime scene.

MeSH terms

  • Activities of Daily Living
  • Algorithms
  • Bayes Theorem*
  • Brain / physiology*
  • Brain Mapping / methods*
  • Humans
  • Image Processing, Computer-Assisted / methods*
  • Magnetic Resonance Imaging / methods*
  • Mental Recall / physiology*
  • Models, Statistical*
  • Reaction Time
  • Signal-To-Noise Ratio

Grant support

This work was supported by a grant from the National Research Foundation of Korea (NRF), funded by the Korean government (MSIP) (No. 2014R1A2A1A10052762;https://www.nrf.re.kr). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.