title: Brain functional networks in syndromic and non-syndromic autism: a graph theoretical study of EEG connectivity.
abstract: BACKGROUND
  Graph theory has been recently introduced to characterize complex brain networks, making it highly suitable to investigate altered connectivity in neurologic disorders. A current model proposes autism spectrum disorder (ASD) as a developmental disconnection syndrome, supported by converging evidence in both non-syndromic and syndromic ASD. However, the effects of abnormal connectivity on network properties have not been well studied, particularly in syndromic ASD. To close this gap, brain functional networks of electroencephalographic (EEG) connectivity were studied through graph measures in patients with Tuberous Sclerosis Complex (TSC), a disorder with a high prevalence of ASD, as well as in patients with non-syndromic ASD.
  
  
  METHODS
  EEG data were collected from TSC patients with ASD (n = 14) and without ASD (n = 29), from patients with non-syndromic ASD (n = 16), and from controls (n = 46). First, EEG connectivity was characterized by the mean coherence, the ratio of inter- over intra-hemispheric coherence and the ratio of long- over short-range coherence. Next, graph measures of the functional networks were computed and a resilience analysis was conducted. To distinguish effects related to ASD from those related to TSC, a two-way analysis of covariance (ANCOVA) was applied, using age as a covariate.
  
  
  RESULTS
  Analysis of network properties revealed differences specific to TSC and ASD, and these differences were very consistent across subgroups. In TSC, both with and without a concurrent diagnosis of ASD, mean coherence, global efficiency, and clustering coefficient were decreased and the average path length was increased. These findings indicate an altered network topology. In ASD, both with and without a concurrent diagnosis of TSC, decreased long- over short-range coherence and markedly increased network resilience were found.
  
  
  CONCLUSIONS
  The altered network topology in TSC represents a functional correlate of structural abnormalities and may play a role in the pathogenesis of neurological deficits. The increased resilience in ASD may reflect an excessively degenerate network with local overconnection and decreased functional specialization. This joint study of TSC and ASD networks provides a unique window to common neurobiological mechanisms in autism.
authors: Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Boston, MA 02115, USA. jurriaan.peters@childrens.harvard.edu
authors: Jurriaan M
authors: JM
authors: Peters
authors: Maxime
authors: M
authors: Taquet
authors: Clemente
authors: C
authors: Vega
authors: Shafali S
authors: SS
authors: Jeste
authors: Iván Sánchez
authors: IS
authors: Fernández
authors: Jacqueline
authors: J
authors: Tan
authors: Charles A
authors: CA
authors: Nelson
authors: Mustafa
authors: M
authors: Sahin
authors: Simon K
authors: SK
authors: Warfield
mesh_terms: D000293:Adolescent; D000328:Adult; D001921:Brain / Q000503:physiopathology*; D002648:Child; D002659:Child Development Disorders, Pervasive / Q000503:physiopathology*; D002675:Child, Preschool; D063132:Connectome; D004569:Electroencephalography / Q000379:methods*; D005260:Female; D006801:Humans; D007223:Infant; D008297:Male; D014402:Tuberous Sclerosis / Q000503:physiopathology; D055815:Young Adult