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

• Autor(a) principal: Lee, Dongha
• Data de Publicação: 2017
• Outros Autores: Yun, Sungjae, Jang, Changwon, Park, Hae-Jeong
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10316/108324; https://doi.org/10.1371/journal.pone.0182657
• topic: Activities of Daily Living; Algorithms; Brain; Brain Mapping; Humans; Image Processing, Computer-Assisted; Magnetic Resonance Imaging; Mental Recall; Reaction Time; Signal-To-Noise Ratio; Bayes Theorem; Models, Statistical

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.