CCN meeting | Viviana Betti (Sapienza, University of Rome, Italy)

When
14-03-2019 from 15:00 to 16:00
Where
Henri Dunantlaan 2, room 4.5
Language
English
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The spontaneous brain activity as temporal prior for natural vision: insights from the topology of functional connectivity and hub dynamics in the beta band

In the absence of a task, the cerebral cortex exhibits slow fluctuations of activity that are highly
organized in large-scale spatio-temporal structures or resting state networks (RSNs). Resting state
fluctuations are of great interest since they have been linked to task related activity patterns. A
fundamental question today is the functional role of the spontaneous activity, and what information, if any,
is coded in these intrinsic patterns of correlated activity.
In this talk, I will present a novel framework to explain the role of the intrinsic brain activity.
This idea is based on a recent study in which we examined the static and dynamic centrality of hub
regions when measured in the absence of a task (rest) or during the observation of natural or synthetic
visual stimuli. We used Magnetoencephalography (MEG) in humans to measure static and transient
regional and network-level interaction in α- and β-band limited power (BLP) in three conditions: visual
fixation (rest), viewing of movie clips (natural vision), and time-scrambled versions of the same clips
(scrambled vision). Compared with rest, we observed in both movie conditions a robust decrement of α-
BLP connectivity. Moreover, both movie conditions caused a significant reorganization of connections
in the α band, especially between networks. In contrast, β-BLP connectivity was remarkably similar
between rest and natural vision. Not only the topology did not change, but the joint dynamics of hubs in
a core network during natural vision was predicted by similar fluctuations in the resting state.
We interpret these findings by suggesting that slow-varying fluctuations of integration occurring
in higher-order regions in the β-band may be a mechanism to anticipate and predict slow-varying
temporal patterns of natural environment and common behaviours.