Thursday, November 07, 2013

New Insights Into Brain Neuronal Networks

A bow tie representation of the network of connections between cortical areas in the brain. (Credit: University of Notre Dame)

The image above represents the authors' conception of how the brain organizes itself for the most efficient processing of information. Here are a couple of passages from the research summary, explaining the architecture and how it functions (with comparisons to the internet and other complex information processing systems):
Using brain-wide and consistent tracer data, the researchers describe the cortex as a network of connections with a "bow tie" structure characterized by a high-efficiency, dense core connecting with "wings" of feed-forward and feedback pathways to the rest of the cortex (periphery). The local circuits, reaching to within 2.5 millimeters and taking up more than 70 percent of all the connections in the macaque cortex, are integrated across areas with different functional modalities (somatosensory, motor, cognitive) with medium- to long-range projections.
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This bow tie arrangement is a typical feature of self-organizing information processing systems. The paper notes that the cortex has some analogies with information-processing networks such as the World Wide Web, as well as metabolism, the immune system and cell signaling. The core-periphery bow tie structure, they say, is "an evolutionarily favored structure for a wide variety of complex networks" because "these systems are not in thermodynamic equilibrium and are required to maintain energy and matter flow through the system." The brain, however, also shows important differences from such systems. For example, destination addresses are encoded in information packets sent along the Internet, apparently unlike in the brain, and location and timing of activity are critical factors of information processing in the brain, unlike in the Internet.
This is another piece of info in the growing awareness of how the brain works as a collection of networks.

Full Citation:
N. T. Markov, M. Ercsey-Ravasz, D. C. Van Essen, K. Knoblauch, Z. Toroczkai, H. Kennedy. (2013, Nov 1). Cortical High-Density Counterstream Architectures. Science, 342 (6158): 1238406 DOI: 10.1126/science.1238406

New Insights Into Brain Neuronal Networks


Nov. 4, 2013 — A paper published in a special edition of the journal Science proposes a novel understanding of brain architecture using a network representation of connections within the primate cortex. Zolt├ín Toroczkai, professor of physics at the University of Notre Dame and co-director of the Interdisciplinary Center for Network Science and Applications, is a co-author of the paper "Cortical High-Density Counterstream Architectures."

Using brain-wide and consistent tracer data, the researchers describe the cortex as a network of connections with a "bow tie" structure characterized by a high-efficiency, dense core connecting with "wings" of feed-forward and feedback pathways to the rest of the cortex (periphery). The local circuits, reaching to within 2.5 millimeters and taking up more than 70 percent of all the connections in the macaque cortex, are integrated across areas with different functional modalities (somatosensory, motor, cognitive) with medium- to long-range projections.

The authors also report on a simple network model that incorporates the physical principle of entropic cost to long wiring and the spatial positioning of the functional areas in the cortex. They show that this model reproduces the properties of the connectivity data in the experiments, including the structure of the bow tie. The wings of the bow tie emerge from the counterstream organization of the feed-forward and feedback nature of the pathways. They also demonstrate that, contrary to previous beliefs, such high-density cortical graphs can achieve simultaneously strong connectivity (almost direct between any two areas), communication efficiency, and economy of connections (shown via optimizing total wire cost) via weight-distance correlations that are also consequences of this simple network model.

This bow tie arrangement is a typical feature of self-organizing information processing systems. The paper notes that the cortex has some analogies with information-processing networks such as the World Wide Web, as well as metabolism, the immune system and cell signaling. The core-periphery bow tie structure, they say, is "an evolutionarily favored structure for a wide variety of complex networks" because "these systems are not in thermodynamic equilibrium and are required to maintain energy and matter flow through the system." The brain, however, also shows important differences from such systems. For example, destination addresses are encoded in information packets sent along the Internet, apparently unlike in the brain, and location and timing of activity are critical factors of information processing in the brain, unlike in the Internet.

"Biological data is extremely complex and diverse," Toroczkai said. "However, as a physicist, I am interested in what is common or invariant in the data, because it may reveal a fundamental organizational principle behind a complex system. A minimal theory that incorporates such principle should reproduce the observations, if not in great detail, but in extent. I believe that with additional consistent data, as those obtained by the Kennedy team, the fundamental principles of massive information processing in brain neuronal networks are within reach."

Here is the full abstract from Science (the full article is behind a paywall, but I will have a paper copy in the next few days, thanks to a PT client with a subscription):

Background

The cerebral cortex is divisible into many individual areas, each exhibiting distinct connectivity profiles, architecture, and physiological characteristics. Interactions among cortical areas underlie higher sensory, motor, and cognitive functions. Graph theory provides an important framework for understanding network properties of the interareal weighted and directed connectivity matrix reported in recent studies. 

Graphic

Density and topology of the cortical graph. (Left) The 66% density of the cortical matrix (black triangle) is considerably greater than in previous reports (colored points) and is inconsistent with a small-world network. (Right) A bow-tie representation of the high-density cortical matrix. The high-efficiency cortical core has defined relations with the cortical periphery in the two fans.

Advances

We derive an exponential distance rule that predicts many binary and weighted features of the cortical network, including efficiency of information transfer, the high specificity of long-distance compared to short-distance connections, wire length minimization, and the existence of a highly interconnected cortical core. We propose a bow-tie representation of the cortex, which combines these features with hierarchical processing.

Outlook

The exponential distance rule has important implications for understanding scaling properties of the cortex and developing future large-scale dynamic models of the cortex.
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