Intersubject consistent dynamic connectivity during natural vision revealed by functional MRI

Abstract

The functional communications between brain regions are thought to be dynamic. However, it is usually difficult to elucidate whether the observed dynamic connectivity is functionally meaningful or simply due to noise during unconstrained task conditions such as resting-state. During naturalistic conditions, such as watching a movie, it has been shown that brain activities in the same region, e.g. visual cortex, are consistent across subjects. Following similar logic, we proposed to study intersubject correlations of the time courses of dynamic connectivity during naturalistic conditions to extract functionally meaningful dynamic connectivity patterns. We analyzed a functional MRI (fMRI) dataset when the subjects watched a short animated movie. We calculated dynamic connectivity by using sliding window technique, and further quantified the intersubject correlations of the time courses of dynamic connectivity. Although the time courses of dynamic connectivity are thought to be noisier than the original signals, we found similar level of intersubject correlations of dynamic connectivity. Most importantly, highly consistent dynamic connectivity could occur between regions that did not show intersubject correlations of regional activity, and between regions with little stable functional connectivity. The analysis highlighted higher order brain regions such as the lateral prefrontal cortex and the default mode network that dynamically interact with posterior visual regions during the movie watching, which may be associated with the understanding of the movie.

Highlights

• Intersubject shared time courses may provide a complementary approach to study dynamic connectivity

• Widespread regions showed highly shared dynamic connectivity during movie watching, while these regions themselves did not show shared regional activity

• Shared dynamic connectivity often occurred between regions from different functional systems