title: Brain Network Connectivity and Topological Analysis During Voluntary Arm Movements.
abstract: Functional connectivity estimates the temporal synchrony among functionally homogeneous brain regions based on the assessment of the dynamics of topologically localized neurophysiological responses. The aim of this study was to investigate task-related changes in brain activity and functional connectivity by applying different methods namely event-related desynchronization (ERD), coherence, and graph-theoretical analysis to electroencephalographic (EEG) recordings, for comparing their respective descriptive power and complementarity. As it is well known, ERD provides an estimate of differences in power spectral densities between active (or task) and rest conditions, functional connectivity allows assessing the level of synchronization between the signals recorded at different scalp locations and graph analysis enables the estimation of the functional network features and topology. EEG activity was recorded on 10 subjects during left/right arm movements. The theta, alpha, and beta bands were considered. Conventional analysis showed a significant ERD in both alpha and beta bands over the sensorimotor cortex during the left arm movement and in beta band during the right arm movement, besides identifying the regions involved in the task, as it was expected. On the other hand, connectivity assessment highlighted that stronger connections are those that involved the motor regions for which graph analysis revealed reduced accessibility and an increased centrality during the movement. Jointly, the last two methods allow identifying the cortical areas that are functionally related in the active condition as well as the topological organization of the functional network. Results support the hypothesis that network analysis brings complementary knowledge with respect to established approaches for modeling motor-induced functional connectivity and could be profitably exploited in clinical contexts.
authors.affiliation: Department of Computer Science, University of Verona, Verona, Italy.
authors.forename: Silvia Francesca
authors.initials: SF
authors.lastname: Storti
authors.affiliation: Department of Neurophysiology, Foundation IRCCS San Camillo Hospital, Venice, Italy.
authors.forename: Emanuela
authors.initials: E
authors.lastname: Formaggio
authors.affiliation: Department of Medical, Surgical and Health Sciences, Clinical Neurology Unit, Cattinara University Hospital, Trieste, Italy.
authors.forename: Paolo
authors.initials: P
authors.lastname: Manganotti
authors.affiliation: Department of Computer Science, University of Verona, Verona, Italy gloria.menegaz@univr.it.
authors.forename: Gloria
authors.initials: G
authors.lastname: Menegaz
mesh_terms: D000328:Adult; D000465:Algorithms; D001132:Arm / Q000502:physiology; D001921:Brain / Q000033:anatomy & histology / Q000502:physiology*; D063132:Connectome / Q000379:methods*; D003344:Cortical Synchronization / Q000502:physiology; D004569:Electroencephalography / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D009068:Movement / Q000502:physiology*; D009415:Nerve Net / Q000033:anatomy & histology / Q000502:physiology*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014836:Volition / Q000502:physiology*
keywords: EEG power; ERD; coherence; functional connectivity; graph analysis