Functional magnetic resonance imaging helps visualize the human brain as a massive, interacting, complex network

A team headed by scientists at Northwestern University, using functional magnetic resonance imaging (fMRI), has shown how to visualize the human brain as a massive, interacting, complex network governed by a few underlying dynamic principles.

The research opens fascinating possibilities for future basic and applied studies to investigate the dynamics of brain states, particularly in cases of dysfunction -- such as schizophrenia, Alzheimer's disease and chronic pain -- without requiring external markers.

Dante R. Chialvo, research associate professor of physiology at Northwestern University Feinberg School of Medicine, led the study, which appeared in the Dec. 31 online issue of the journal Physical Review Letters. The research group included scientists from the IBM T.J. Watson Research Center, Yorktown Heights, N.Y., and the University of Islas Baleares, Mallorca, Spain.

Chialvo and colleagues described how fMRIs from healthy individuals showed that tens of thousands of discrete brain regions form a network that has the same qualitative features as other complex networks, such as the Internet (technological), friendships (social) and metabolic (biochemical) networks.

The fMRI technology provided, in each recording session, hundreds of consecutive images of brain activity discretized in thousands of tiny cubes (voxels). The image intensity at each cube usually indicates the amount of brain activity at that site.

The investigators then calculated the degree of correlation between the activities among the tens of thousands of brain regions. Through their computations, the group discovered which brain regions were momentarily "linked" in a "network."

When they further analyzed the structure of these networks, they saw a familiar picture: Brain networks share the features of other complex networks, such as the Internet -- very few "jumps" were necessary for connecting any two nodes.

"This so-called 'small world' property allows for the most efficient connectivity," Chialvo said.

The second common characteristic the researchers found was a strong "in-homogeneity" -- many nodes had few connections and a very few nodes connected with many others. These "super-connected" nodes act as hubs, providing the networks with fast transmission of information.

"Overall, our initial results indicate that the brain networks share these two fundamental properties, implying that the underlying properties can be understood using the theoretical framework already advanced in the study of other, disparate, networks," Chialvo said.