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hypothesis that only specific classes of connectivity patterns (structurally similar
to cortical networks) simultaneously support short wiring, small-world attributes,
clustered architectures (all structural features), and high complexity (combining
functional segregation and integration).
The discovery of small-world connectivity patterns in functional connectivity
(e.g. Stam, 2004; Bassett et al ., 2006) and the demonstration of small-world
attributes in structural connection patterns (e.g. He et al . 2007; Hagmann et al .,
2007, 2008) raises the question of how closely functional connections map onto
structural connections. The state- and task-dependence of functional connectivity
suggests that a one-to-one mapping of structural to functional connections does
not exist. However, it is likely that at least some structural characteristics of
individual nodes are reflected in their functional interactions - for example,
structural hub regions should maintain larger numbers of functional relations.
To examine the relation of structural and functional connectivity directly, Honey
et al . (2007) implemented and analyzed a large-model of cortical connectivity
derived from anatomical tract tracing studies of the macaque monkey visual and
somatomotor systems. The neuronal model was a neural mass model, adapted
from a classical conductance-based model of neuronal dynamics (Morris and
Lecar, 1981) for local population activity. Units of the model describe local
populations of densely interconnected inhibitory and excitatory neurons whose
behaviours are determined by voltage and ligand-gated membrane channels.
Activity in the system arose purely from nonlinear instabilities, generating
spontaneous oscillations whose spatiotemporal patterns are shaped through re-
entrant excitatory-excitatory internode coupling, provided by the macaque
neocortical connectivity matrix. Functional and effective connectivity in the
modeled neuronal activity was examined using a range of methods across
multiple time scales. Patterns of directed interactions were derived using the
information-theoretic measure of transfer entropy (Schreiber, 2000), phase
synchrony was measured using the phase locking value (Lachaux et al ., 1999)
and simulated fMRI responses were derived using a nonlinear Balloon-
Windkessel haemodynamic model (Buxton et al ., 1998). An analysis of
structural and functional connectivity in the model revealed that over long time
scales (several minutes of data), functional connectivity patterns closely
resembled structural connectivity patterns, and that structural hubs (i.e. nodes
with high centrality) also became functional hubs (Fig. 9.4). Closer examination
of modeled time series data showed that the model's fast neuronal dynamics
exhibited intermittent synchronization and desynchronization at a time scale of
hundreds of milliseconds enabling the system to continually explore a repertoire
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