In this paper we investigate task-related changes in brain functional connectivity (FC) by applying different methods namely event-related desynchronization (ERD), coherence and graph-theoretical analysis to electroencephalographic (EEG) recordings. While ERD provides an estimate of the differences in power spectral densities between task and rest conditions, coherence allows assessing the level of synchronization between the recorded signals and graph analysis enables the estimation of the functional network topology. EEGs were recorded on 10 subjects during left/right arm movements. Conventional analysis showed a significant ERD in both alpha and beta bands over the sensorimotor cortex. Connectivity assessment highlighted that stronger connections are those involving the motor regions for which graph analysis revealed reduced accessibility and an increased cen-trality during the movement. This highlights that network analysis brings complementary knowledge with respect to established approaches for modeling motor-induced FC.
Cortical network modulation during paced arm movements
Formaggio E.;Manganotti P.;Menegaz G.
2015-01-01
Abstract
In this paper we investigate task-related changes in brain functional connectivity (FC) by applying different methods namely event-related desynchronization (ERD), coherence and graph-theoretical analysis to electroencephalographic (EEG) recordings. While ERD provides an estimate of the differences in power spectral densities between task and rest conditions, coherence allows assessing the level of synchronization between the recorded signals and graph analysis enables the estimation of the functional network topology. EEGs were recorded on 10 subjects during left/right arm movements. Conventional analysis showed a significant ERD in both alpha and beta bands over the sensorimotor cortex. Connectivity assessment highlighted that stronger connections are those involving the motor regions for which graph analysis revealed reduced accessibility and an increased cen-trality during the movement. This highlights that network analysis brings complementary knowledge with respect to established approaches for modeling motor-induced FC.File | Dimensione | Formato | |
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