Spatiotemporal Searchlight Representational Similarity Analysis in EMEG Source Space

摘要

Time resolved imaging techniques, such as MEG and EEG, are unique in their ability to reveal the rich dynamic spatiotemporal patterning of neural activities. Here we propose a technique based on spatiotemporal searchlight Representational Similarity Analysis (RSA) of combined MEG and EEG (EMEG) data to directly analyze the multivariate pattern of information flow across the brain. This novel technique can recognize fine-grained dynamic neural computations both in space and in time. A prime example of such neural computations is our ability to understand spoken words in real time. A computational approach to these processes is suggested by the Cohort Model of spoken-word recognition. Here we show how spatiotemporal searchlight RSA applied to source estimations of EMEG data can provide insights into the neural correlates of the cohort model within bilateral front temporal brain regions.