The extremely complex connected structure of the brain shapes a functional network whose activation and synchronization mechanisms still represent a major scientific challenge. We report here the results of a study of the brain functional activity from a network theory perspective. An fMRI data-set corresponding to the neuronal activity of 41 mice brains at resting state has been analyzed by applying a range of procedures (e.g. community detection, percolation analysis), in order to gain insight into the collective activity of brain areas. A statistically meaningful signal of collective neuronal activity is detectable even at resting state, permitting to identify functionally related areas.

Detecting cluster structure of resting state fMRI brain networks: percolation and modularity features

Gabrielli A.;Squartini T.;
2015

Abstract

The extremely complex connected structure of the brain shapes a functional network whose activation and synchronization mechanisms still represent a major scientific challenge. We report here the results of a study of the brain functional activity from a network theory perspective. An fMRI data-set corresponding to the neuronal activity of 41 mice brains at resting state has been analyzed by applying a range of procedures (e.g. community detection, percolation analysis), in order to gain insight into the collective activity of brain areas. A statistically meaningful signal of collective neuronal activity is detectable even at resting state, permitting to identify functionally related areas.
2015
Istituto dei Sistemi Complessi - ISC
brain networks
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/292517
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