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Petra E. Vértes

Researcher at University of Cambridge

Publications -  115
Citations -  7380

Petra E. Vértes is an academic researcher from University of Cambridge. The author has contributed to research in topics: Connectome & Medicine. The author has an hindex of 35, co-authored 97 publications receiving 5400 citations. Previous affiliations of Petra E. Vértes include Cambridge University Hospitals NHS Foundation Trust & National Health Service.

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Cognitive relevance of the community structure of the human brain functional coactivation network

TL;DR: In this paper, the authors used a meta-analysis of the large primary literature that used fMRI or PET to measure task-related activation and estimated the similarity (Jaccard index) of the activation patterns across experimental tasks between each pair of 638 brain regions.
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Adolescence is associated with genomically patterned consolidation of the hubs of the human brain connectome

TL;DR: It is concluded that normative human brain maturation involves a genetically patterned process of consolidating anatomical network hubs and developmental variation of this consolidation process may be relevant both to normal cognitive and behavioral changes and the high incidence of schizophrenia during human brain adolescence.
Journal Article

Correction for Cognitive relevance of the community structure of the human brain functional coactivation network

TL;DR: It is concluded that the community structure of human brain networks is relevant to cognitive function and deactivations may play a role in flexible reconfiguration of the network according to cognitive demand, varying the integration between modules, and between the periphery and a central rich club.
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Simple models of human brain functional networks

TL;DR: This work proposes a model in which the embedded topology of brain networks emerges from two competing factors: a distance penalty based on the cost of maintaining long-range connections; and a topological term that favors links between regions sharing similar input.
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A wavelet method for modeling and despiking motion artifacts from resting-state fMRI time series

TL;DR: It is concluded that there is a real risk of motion-related bias in connectivity analysis of fMRI data, but that this risk is generally manageable, by effective time series denoising strategies designed to attenuate synchronized signal transients induced by abrupt head movements.