Wednesday, December 27, 2006

Synchronies to bind our brains... check out the movie

A commentary by Sporns and Honey and an article by Bassett et al in PNAS delve into (quoting Spors and Honey) "explaining how functional brain states emerge from the interactions of dozens, perhaps hundreds, of brain regions, each containing millions of neurons. Much evidence supports the view that highly evolved nervous systems are capable of rapid, real-time integration of information across segregated sensory channels and brain regions. This integration happens without the need for a central controller or executive: It is the functional outcome of dynamic interactions within and between the complex structural networks of the brain... the study by Bassett et al. reveals the existence of large-scale functional networks in magnetoencephalographic (MEG) recordings with attributes that are preserved across multiple frequency bands and that flexibly adapt to task demands. These networks exhibit "small-world" structure, i.e., high levels of clustering and short path lengths. The authors' analysis reveals that the small-world topology of brain functional networks is largely preserved across multiple frequency bands and behavioral tasks."

From Bassett et al: "Coherent or correlated oscillation of large-scale, distributed neural networks is widely regarded as an important physiological substrate for motor, perceptual and cognitive representations in the brain...The topology of networks can range from entirely random to fully ordered (a lattice). In this spectrum, small-world topology is characteristic of complex networks that demonstrate both clustered or cliquish interconnectivity within groups of nodes sharing many nearest neighbors in common (like regular lattices), and a short path length between any two nodes in the network (like random graphs). This is an attractive configuration, in principle, for the anatomical and functional architecture of the brain, because small-world networks are known to optimize information transfer, increase the rate of learning, and support both segregated and distributed information processing."

"Magnetoencephalographic data were acquired from 22 subjects, half of whom performed a finger-tapping task, whereas the other half were studied at rest. Signals were recorded from a set of 275 points overlying the scalp surface, to provide a time-frequency decomposition of human brain activity... brain functional networks were characterized by small-world properties at all six wavelet scales considered, corresponding approximately to classical {delta} (low and high), {theta}, {alpha}, beta, and {gamma} frequency bands. Global topological parameters (path length, clustering) were conserved across scales, most consistently in the frequency range 2–37 Hz, implying a scale-invariant or fractal small-world organization. Dynamical analysis showed that networks were located close to the threshold of order/disorder transition in all frequency bands. The highest-frequency {gamma} network had greater synchronizability, greater clustering of connections, and shorter path length than networks in the scaling regime of (lower) frequencies. Behavioral state did not strongly influence global topology or synchronizability; however, motor task performance was associated with emergence of long-range connections in both beta and {gamma} networks. Long-range connectivity, e.g., between frontal and parietal cortex, at high frequencies during a motor task may facilitate sensorimotor binding. Human brain functional networks demonstrate a fractal small-world architecture that supports critical dynamics and task-related spatial reconfiguration while preserving global topological parameters."

The above figure is a demonstration model by Sporns and Honey of the relationship of structural to functional connectivity networks consisting of a set of 1,600 modeled neural mean field units arranged on a sphere and engaging in noise-driven spontaneous activity. (A) The anatomical connection pattern, shown only for a few randomly selected neural units, consists of a mix of mostly local (clustered) connections and a few connections made over longer distances. (B) A snapshot and an EEG-like recording trace of the dynamical neuronal activity pattern. Neuronal dynamics is characterized by complex spatial and temporal structure across multiple scales [Click here to see a supporting movie]. (C) A functional connectivity network obtained from a thresholded correlation matrix calculated from the dynamics shown in B. In this example, both structural and functional connectivity patterns exhibit small-world attributes.

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