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.
Highlights
In the general linear model (GLM)-based classification, classification of features in the voluntary and passive experiences (VE—PE) contrast map showed significantly higher performance than those in the VE & PE contrast map (one trials: VE−PE = 0.65 ± 0.06 (t(16) = 10.11, p < 0.001), VE & PE = 0.51 ± 0.03, (t(16) = 1.83, p = 0.0864), paired t-test: p = 4.3e-08; two trials: VE−PE = 0.69 ± 0.22 (t(16) = 3.60, p = 0.0024), VE & PE = 0.51 ± 0.21 (t(16) = 0.27, p = 0.7910), p = 0.0446; three trials: VE−PE = 0.76 ± 0.18 (t(16) = 6.22, p < 0.001), VE & PE = 0.52 ± 0.19
This study proposes a framework for trial-by-trial classification in a specialized setting with many known and few unknown stimuli in a single subject
The framework is based on a multivariate Bayesian model comparison to classify noisy hemodynamic responses of the unknown target stimuli presented in a rapid event-related design for each individual
Summary
Decoding brain states using functional magnetic resonance imaging (fMRI) has long been applied in various research areas; for example, fMRI is used to identify explicit responses in vision [1, 2] and motor function [3] and to classify implicit brain states such as mental imagery. MVB decoding for memory retrieval collection and analysis, decision to publish, or preparation of the manuscript
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