title: MEG network differences between low- and high-grade glioma related to epilepsy and cognition.
abstract: OBJECTIVE
  To reveal possible differences in whole brain topology of epileptic glioma patients, being low-grade glioma (LGG) and high-grade glioma (HGG) patients. We studied functional networks in these patients and compared them to those in epilepsy patients with non-glial lesions (NGL) and healthy controls. Finally, we related network characteristics to seizure frequency and cognitive performance within patient groups.
  
  
  METHODS
  We constructed functional networks from pre-surgical resting-state magnetoencephalography (MEG) recordings of 13 LGG patients, 12 HGG patients, 10 NGL patients, and 36 healthy controls. Normalized clustering coefficient and average shortest path length as well as modular structure and network synchronizability were computed for each group. Cognitive performance was assessed in a subset of 11 LGG and 10 HGG patients.
  
  
  RESULTS
  LGG patients showed decreased network synchronizability and decreased global integration compared to healthy controls in the theta frequency range (4-8 Hz), similar to NGL patients. HGG patients' networks did not significantly differ from those in controls. Network characteristics correlated with clinical presentation regarding seizure frequency in LGG patients, and with poorer cognitive performance in both LGG and HGG glioma patients.
  
  
  CONCLUSIONS
  Lesion histology partly determines differences in functional networks in glioma patients suffering from epilepsy. We suggest that differences between LGG and HGG patients' networks are explained by differences in plasticity, guided by the particular lesional growth pattern. Interestingly, decreased synchronizability and decreased global integration in the theta band seem to make LGG and NGL patients more prone to the occurrence of seizures and cognitive decline.
authors.affiliation: Department of Neurology, VU University Medical Center, Amsterdam, The Netherlands.
authors.forename: Edwin
authors.initials: E
authors.lastname: van Dellen
authors.forename: Linda
authors.initials: L
authors.lastname: Douw
authors.forename: Arjan
authors.initials: A
authors.lastname: Hillebrand
authors.forename: Irene H M
authors.initials: IH
authors.lastname: Ris-Hilgersom
authors.forename: Menno M
authors.initials: MM
authors.lastname: Schoonheim
authors.forename: Johannes C
authors.initials: JC
authors.lastname: Baayen
authors.forename: Philip C
authors.initials: PC
authors.lastname: De Witt Hamer
authors.forename: Demetrios N
authors.initials: DN
authors.lastname: Velis
authors.forename: Martin
authors.initials: M
authors.lastname: Klein
authors.forename: Jan J
authors.initials: JJ
authors.lastname: Heimans
authors.forename: Cornelis J
authors.initials: CJ
authors.lastname: Stam
authors.forename: Jaap C
authors.initials: JC
authors.lastname: Reijneveld
mesh_terms: D000328:Adult; D000704:Analysis of Variance; D001932:Brain Neoplasms / Q000473:pathology*; D016000:Cluster Analysis; D003071:Cognition / Q000502:physiology*; D003198:Computer Simulation; D004827:Epilepsy / Q000473:pathology*; D005260:Female; D005910:Glioma / Q000473:pathology*; D006801:Humans; D015225:Magnetoencephalography; D008297:Male; D008875:Middle Aged; D008959:Models, Neurological; D060787:Neoplasm Grading; D009415:Nerve Net / Q000502:physiology*; D018709:Statistics, Nonparametric; D013826:Theta Rhythm / Q000502:physiology