The Effects of Pediatric Epilepsy on a Language Connectome

• Type of Material: Article
• Authors: Anas Salah Eddin, Florida Polytechnical University; Jin Wang, Florida International University; Wensong Wu, Florida International University; Saman Sargolzaei, Florida International University; Bruce Bjornson, British Columbia Childrens Hospital; Richard Jones, Emory University; William D. Gaillard, George Washington University; Malek Adjouadi, Florida International University
• Language: English
• Date: 2014-12-01
• Publisher: Wiley
• Publication Version: Final Published Version
• Copyright Statement: © 2014 Wiley Periodicals, Inc.
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1002/hbm.22600
• Title of Journal or Parent Work: Human Brain Mapping
• Volume: 35
• Issue: 12
• Start Page: 5996
• End Page: 6010
• Grant/Funding Information: None declared
• Keywords: Functional connectivity; Life Sciences & Biomedicine; functional network; Lateralization; independent component analysis; functional magnetic resonance imaging; Atypical language; Brain networks; Neurosciences; Temporal lobe epilepsy; graph theory; Resting state; Independent component analysis; Neuroimaging; Radiology, Nuclear Medicine & Medical Imaging; Small world organization; Default mode network (DMN); Science & Technology; Impaired attention; Neurosciences & Neurology; language network
• Research Categories: Computer Science; Health Sciences, Radiology; Biology, Neuroscience; Engineering, Biomedical

Abstract

This study introduces a new approach for assessing the effects of pediatric epilepsy on a language connectome. Two novel data-driven network construction approaches are presented. These methods rely on connecting different brain regions using either extent or intensity of language related activations as identified by independent component analysis of fMRI. An auditory word definition decision task paradigm was used to activate the language network for 29 patients and 30 controls. Evaluations illustrated that pediatric epilepsy is associated with a network efficiency reduction. Patients showed a propensity to inefficiently use the whole brain network to perform the language task; whereas, controls seemed to efficiently use smaller segregated network components to achieve the same task. To explain the causes of the decreased efficiency, graph theoretical analysis was performed. The analysis revealed substantial global network feature differences between the patients and controls for the extent of activation network. It also showed that for both subject groups the language network exhibited small-world characteristics; however, the patient's extent of activation network showed a tendency toward randomness. It was also shown that the intensity of activation network displayed ipsilateral hub reorganization on the local level. We finally showed that a clustering scheme was able to fairly separate the subjects into their respective patient or control groups. The clustering was initiated using local and global nodal measurements. Compared to the intensity of activation network, the extent of activation network clustering demonstrated better precision. This ascertained that the network differences presented by the networks were associated with pediatric epilepsy.