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Enrique C. A. Hansen

Researcher at Aix-Marseille University

Publications -  8
Citations -  793

Enrique C. A. Hansen is an academic researcher from Aix-Marseille University. The author has contributed to research in topics: Resting state fMRI & Dynamic functional connectivity. The author has an hindex of 7, co-authored 8 publications receiving 589 citations. Previous affiliations of Enrique C. A. Hansen include École Normale Supérieure.

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Functional connectivity dynamics: modeling the switching behavior of the resting state.

TL;DR: It is demonstrated that a slight enhancement of the non-linearity of the network nodes is sufficient to broaden the repertoire of possible network behaviors, leading to modes of fluctuations, reminiscent of some of the most frequently observed Resting State Networks.
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Selective Activation of Resting-State Networks following Focal Stimulation in a Connectome-Based Network Model of the Human Brain.

TL;DR: In this article, the effect of structural connectivity (SC) on the network response to stimulation is investigated. But the extent to which information is processed over short or long-range SC is unclear.
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Selective activation of resting state networks following focal stimulation in a connectome- based network model of the human brain

TL;DR: It is suggested that the stimulus-induced brain activity, which may indicate information and cognitive processing, follows specific routes imposed by structural networks explaining the emergence of functional networks.
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Dynamic Functional Connectivity between order and randomness and its evolution across the human adult lifespan.

TL;DR: This work considers dynamic FC as an ongoing network reconfiguration, including a stochastic exploration of the space of possible steady FC states, and reveals that dynamic FC tends to slow down and becomes less complex as well as more random with increasing age.
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Anatomical connectivity and the resting state activity of large cortical networks.

TL;DR: It is concluded that neural field models with translationally invariant connectivity may be best applied at the mesoscopic scale and that more general models of cortical networks that embed local neural fields, may provide appropriate models of macroscopic cortical dynamics over the whole brain.