Dataset Information

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

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.

SUBMITTER: Lee D

PROVIDER: S-EPMC5544208 | biostudies-other | 2017

REPOSITORIES: biostudies-other

ACCESS DATA

Similar Datasets

Project description:FMRI studies of autobiographical memory (AM) retrieval typically ask subjects to retrieve memories silently to avoid speech-related motion artifacts. Recently, some fMRI studies have started to use overt (spoken) retrieval to probe moment-to-moment retrieved content. However, the extent to which the overt retrieval method alters fMRI activations during retrieval is unknown. Here we examined this question by eliciting unrehearsed AMs during fMRI scanning either overtly or silently, in the same subjects, in different runs. Differences between retrieval modality (silent vs. narrated) included greater activation for silent retrieval in the anterior hippocampus, left angular gyrus, PCC, and superior PFC, and greater activation for narrated retrieval in speech production regions, posterior hippocampus, and the DLPFC. To probe temporal dynamics, we divided each retrieval period into an initial search phase and a later elaboration phase. The activations during the search and elaboration phases were broadly similar regardless of modality, and these activations were in line with previous fMRI studies of AM temporal dynamics employing silent retrieval. For both retrieval modalities, search activated the hippocampus, mPFC, ACC, and PCC, and elaboration activated the left DLPFC and middle temporal gyri. To examine content-specific reactivation during retrieval, the timecourse of narrated memory content was transcribed and modeled. We observed dynamic activation associated with object content in the lateral occipital complex, and activation associated with scene content in the retrosplenial cortex. The current findings show that both silent and narrated AMs activate a broadly similar memory network, with some key differences, and add to current knowledge regarding the content-specific dynamics of AM retrieval. However, these observed differences between retrieval modality suggest that studies using overt retrieval should carefully consider this method's possible effects on cognitive and neural processing.

Dataset Information

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets