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Characterizing functional connectivity of interictal MEG activity in epileptic patients using graph theoretical analysis
Áreas de investigación
  • Ingenierías
The aim of this study is to analyze the complex network parameters obtained from the connectivity patters detected in magnetoencephalography (MEG) recordings during resting state of two different types of epileptic patients and healthy subjects. Forty five subjects were studied: 15 patients suffering from frontal focal epilepsy (FE), 15 patients suffering from generalized epilepsy (GE) and 15 healthy subjects (HS). For the characterization of epileptic syndromes the seizure classification and other criteria, e.g. case history, age of first manifestation, neurological findings, EEG and MRI were used. MEG recordings were completed with the 306-channel Elekta Neuromag® system (102 magnetometers and 204 planar). To correct the head position and the associated movement-related artifacts, a spatio-temporal signal space separation method (tSSS) with movement compensation was realized. MEG data was acquired at a sampling rate of 1 kHz. A period of 10 min of resting state with closed eyes was selected for the study. All the analysis was performed over 1200 non-overlapping segments (the 40 most stationary segments per subject, using the KPSS test for stationarity) of 5000 ms, far from recent epileptic discharges. A downsampling to 500Hz was applied. The Phase Synchronization (PS) analysis was performed using the Phase Locking Value and Phase Lag Index. Testing with surrogate data the significance of the synchronization index value of each pair of sensors. Thus, obtaining the connectivity matrix for each single subject. To characterize the network structure of brain activity, we evaluated a list of measures for weighted graphs. In this approach, MEG sensors were considered as vertices and the PS values between sensors as edge weights. The edge weight represents the strength of the connection between the vertices. We focused on two global parameters: the average degree, strength and two measures of segregation: the clustering coefficient and modularity. To statistically compare network parameters from the three groups (FE, GE, HS), we performed a Kruskal Wallis test, and in those cases where this test was significant, we further analyzed pairwise difference between any two groups by means of a two-sided rank sum test, obtaining significant differences among the three groups.
Nombre congreso
International Society for the Advancement of Clinical Magnetoencephalography
Tipo de participación
Lugar del congreso
Sapporo, Japan
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Título de las actas
ISACM meeting 2013
Esta actividad pertenece a memorias de investigación
  • Autor: Julia Guiomar Niso Galan (UPM)
  • Autor: Ernesto Pereda (Universidad de La Laguna)
  • Autor: Sira Carrasco (Universidad de Castilla La Mancha)
  • Autor: María Gudín (Universidad de Castilla La Mancha)
  • Autor: Leonides Canuet Delis (UPM)
  • Autor: Francisco del Pozo Guerrero (UPM)
  • Autor: Fernando Maestu Unturbe (UPM)
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Grupo de Investigación: Grupo de Bioingeniería y Telemedicina
  • Centro o Instituto I+D+i: Centro de tecnología Biomédica CTB
  • Departamento: Tecnología Fotónica y Bioingeniería
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