Metrics of brain network architecture capture the impact of disease in children with epilepsy

• Published in: NeuroImage clinical 2017-01, Vol.13 (C), p.201-208
• Main Authors: Paldino, Michael J, Zhang, Wei, Chu, Zili D, Golriz, Farahnaz
• Format: Article
• Language: English
• Subjects: Adolescent; Adult; Brain; Cerebral Cortex - diagnostic imaging; Child; Child, Preschool; Connectome - methods; Epilepsies, Partial - diagnostic imaging; Epilepsy; Female; Graph theory; Humans; Intelligence; Intelligence - physiology; Machine Learning; Magnetic Resonance Imaging; Male; Nerve Net - diagnostic imaging; Network; Regular; Retrospective Studies; Time Factors; Young Adult
• ISSN: 2213-1582; 2213-1582
• DOI: 10.1016/j.nicl.2016.12.005

Epilepsy is associated with alterations in the structural framework of the cerebral network. The aim of this study was to measure the potential of global metrics of network architecture derived from resting state functional MRI to capture the impact of epilepsy on the developing brain. Pediatric patients were retrospectively identified with: 1. Focal epilepsy; 2. Brain MRI at 3 Tesla, including resting state functional MRI; 3. Full scale IQ measured by a pediatric neuropsychologist. The cerebral cortex was parcellated into approximately 700 gray matter network nodes. The strength of a connection between two nodes was defined as the correlation between their resting BOLD signal time series. The following global network metrics were then calculated: clustering coefficient, transitivity, modularity, path length, and global efficiency. Epilepsy duration was used as an index for the cumulative impact of epilepsy on the brain. 45 patients met criteria (age: 4-19 years). After accounting for age of epilepsy onset, epilepsy duration was inversely related to IQ ( : 0.01). Epilepsy duration predicted by a machine learning algorithm on the basis of the five global network metrics was highly correlated with actual epilepsy duration (r: 0.95; : 0.0001). Specifically, modularity and to a lesser extent path length and global efficiency were independently associated with epilepsy duration. We observed that a machine learning algorithm accurately predicted epilepsy duration based on global metrics of network architecture derived from resting state fMRI. These findings suggest that network metrics have the potential to form the basis for statistical models that translate quantitative imaging data into patient-level markers of cognitive deterioration.