“By using a rich stimulus like a movie, we can drive many regions of the cortex very efficiently. For example, sensory regions will be active to process different features of the movie, and high-level areas will be active to extract semantic and contextual information,” says Reza Rajimehr, a research scientist in the McGovern Institute and the lead author of a paper on the work. “By activating the brain in this way, now we can distinguish different areas or different networks based on their activation patterns.”
Using high-resolution fMRI data collected by an NIH-funded consortium, the researchers analyzed brain activity from 176 people as they watched a variety of movie clips. Then they used a machine-learning algorithm to analyze the activity patterns of each brain region. What they found was 24 networks with different activity patterns and functions. Some are located in sensory areas such as the visual or auditory cortex, while others respond to features such as actions, language, or social interactions. The researchers also identified networks that hadn’t been seen before, including one in the prefrontal cortex that appears highly responsive to visual scenes. This network was most active in response to pictures of scenes within the movie frames.
Three of the networks they found are involved in “executive control” and were most active during transitions between clips. The researchers also observed that when networks specific to a particular feature were very active, the executive control networks were mostly quiet, and vice versa.
“Whenever the activations in domain-specific areas are high, it looks like there is no need for the engagement of these high-level networks,” Rajimehr says. “But in situations where perhaps there is some ambiguity and complexity in the stimulus, and there is a need for the involvement of the executive control networks, then we see that these networks become highly active.”
The researchers hope that their new map will serve as a starting point for more precise study of what each of these networks is doing. For example, within the social processing network, they have found regions that are specific to processing social information about faces and bodies.
“This is a new approach that reveals something different from conventional approaches in neuroimaging,” says Desimone. “It’s not going to give us all the answers, but it generates a lot of interesting ideas.”
