title: Brain Network Architecture and Global Intelligence in Children with Focal Epilepsy.
abstract: The biologic basis for intelligence rests to a large degree on the capacity for efficient integration of information across the cerebral network. We aimed to measure the relationship between network architecture and intelligence in the pediatric, epileptic brain.
  
  
  
  Patients were retrospectively identified with the following: 1) focal epilepsy; 2) brain MR imaging at 3T, including resting-state functional MR imaging; and 3) full-scale intelligence quotient measured by a pediatric neuropsychologist. The cerebral cortex was parcellated into approximately 700 gray matter network "nodes." The strength of a connection between 2 nodes was defined by the correlation between their blood oxygen level-dependent time-series. We calculated the following topologic properties: clustering coefficient, transitivity, modularity, path length, and global efficiency. A machine learning algorithm was used to measure the independent contribution of each metric to the intelligence quotient after adjusting for all other metrics.
  
  
  
  Thirty patients met the criteria (4-18 years of age); 20 patients required anesthesia during MR imaging. After we accounted for age and sex, clustering coefficient and path length were independently associated with full-scale intelligence quotient. Neither motion parameters nor general anesthesia was an important variable with regard to accurate intelligence quotient prediction by the machine learning algorithm. A longer history of epilepsy was associated with shorter path lengths (P= .008), consistent with reorganization of the network on the basis of seizures. Considering only patients receiving anesthesia during machine learning did not alter the patterns of network architecture contributing to global intelligence.
  
  
  
  These findings support the physiologic relevance of imaging-based metrics of network architecture in the pathologic, developing brain.
authors.affiliation: From the Departments of Radiology (M.J.P., F.G., Z.D.C.) Michael.paldino@gmail.com.
authors.forename: M J
authors.identifier: 0000-0002-3769-8508
authors.initials: MJ
authors.lastname: Paldino
authors.affiliation: From the Departments of Radiology (M.J.P., F.G., Z.D.C.).
authors.forename: F
authors.identifier: 0000-0003-4086-7058
authors.initials: F
authors.lastname: Golriz
authors.affiliation: Psychology (M.L.C.).
authors.forename: M L
authors.identifier: 0000-0002-6720-2147
authors.initials: ML
authors.lastname: Chapieski
authors.affiliation: Outcomes and Impact Service (W.Z.), Texas Children's Hospital, Houston, Texas.
authors.forename: W
authors.identifier: 0000-0001-8369-480X
authors.initials: W
authors.lastname: Zhang
authors.affiliation: From the Departments of Radiology (M.J.P., F.G., Z.D.C.).
authors.forename: Z D
authors.identifier: 0000-0003-1201-5552
authors.initials: ZD
authors.lastname: Chu
mesh_terms: D000293:Adolescent; D000375:Aging; D000465:Algorithms; D001921:Brain / Q000000981:diagnostic imaging* / Q000503:physiopathology; D002540:Cerebral Cortex / Q000000981:diagnostic imaging; D002648:Child; D002675:Child, Preschool; D016000:Cluster Analysis; D004828:Epilepsies, Partial / Q000000981:diagnostic imaging* / Q000503:physiopathology / Q000523:psychology*; D005260:Female; D006801:Humans; D007360:Intelligence; D007361:Intelligence Tests; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging; D008297:Male; D009415:Nerve Net / Q000000981:diagnostic imaging* / Q000503:physiopathology; D010100:Oxygen / Q000097:blood; D012189:Retrospective Studies
chemical_list: D010100:Oxygen