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Leila Cammoun

Researcher at École Polytechnique Fédérale de Lausanne

Publications -  48
Citations -  12115

Leila Cammoun is an academic researcher from École Polytechnique Fédérale de Lausanne. The author has contributed to research in topics: Diffusion MRI & Population. The author has an hindex of 21, co-authored 47 publications receiving 10889 citations. Previous affiliations of Leila Cammoun include University Hospital of Lausanne & University of Lausanne.

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Mapping the Structural Core of Human Cerebral Cortex

TL;DR: The spatial and topological centrality of the core within cortex suggests an important role in functional integration and a substantial correspondence between structural connectivity and resting-state functional connectivity measured in the same participants.
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Predicting human resting-state functional connectivity from structural connectivity

TL;DR: Although resting state functional connectivity is variable and is frequently present between regions without direct structural linkage, its strength, persistence, and spatial statistics are nevertheless constrained by the large-scale anatomical structure of the human cerebral cortex.

Predicting human resting-state functional connectivity from structural connectivity

TL;DR: In this article, the authors investigate whether systems-level properties of functional networks can be explained by structural properties of the underlying anatomical network, using functional MRI and diffusionspectrum imaging tractography.
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White matter maturation reshapes structural connectivity in the late developing human brain

TL;DR: The strengthening of the correlation between structural and functional connectivity with age suggests that white matter connectivity in combination with other factors, such as differential modulation of axonal diameter and myelin thickness, play an increasingly important role in creating brain-wide coherence and synchrony.
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Modeling the impact of lesions in the human brain.

TL;DR: The model suggests that dynamic lesion effects can be predicted on the basis of specific network measures of structural brain networks and that these effects may be related to known behavioral and cognitive consequences of brain lesions.