Altered functional and structural connectivity networks in psychogenic non-epileptic seizures

• Published in: PloS one 2013-05, Vol.8 (5), p.e63850-e63850
• Main Authors: Ding, Ju-Rong, An, Dongmei, Liao, Wei, Li, Jinmei, Wu, Guo-Rong, Xu, Qiang, Long, Zhiliang, Gong, Qiyong, Zhou, Dong, Sporns, Olaf, Chen, Huafu
• Format: Article
• Language: English
• Subjects: Aberration; Adult; Age; Attention; Biology; Biomarkers; Biomarkers - metabolism; Brain; Brain - metabolism; Brain - physiopathology; Brain architecture; Brain Mapping - methods; Cognition & reasoning; Decoupling; Education; Electric properties; Epilepsy; Epilepsy - metabolism; Epilepsy - physiopathology; Female; Functional magnetic resonance imaging; Functional morphology; Hospitals; Humans; Laboratories; Life sciences; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Medical research; Medicine; Nerve Net - metabolism; Nerve Net - physiopathology; Networks; Neural networks; Neural Pathways - metabolism; Neural Pathways - physiopathology; Neuroimaging; Neurology; NMR; Nuclear magnetic resonance; Patients; Seizing; Seizures; Seizures (Medicine); Sensorimotor system; Structure-function relationships; Studies; Theoretical analysis; Young Adult
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0063850

Psychogenic non-epileptic seizures (PNES) are paroxysmal behaviors that resemble epileptic seizures but lack abnormal electrical activity. Recent studies suggest aberrant functional connectivity involving specific brain regions in PNES. Little is known, however, about alterations of topological organization of whole-brain functional and structural connectivity networks in PNES. We constructed functional connectivity networks from resting-state functional MRI signal correlations and structural connectivity networks from diffusion tensor imaging tractography in 17 PNES patients and 20 healthy controls. Graph theoretical analysis was employed to compute network properties. Moreover, we investigated the relationship between functional and structural connectivity networks. We found that PNES patients exhibited altered small-worldness in both functional and structural networks and shifted towards a more regular (lattice-like) organization, which could serve as a potential imaging biomarker for PNES. In addition, many regional characteristics were altered in structural connectivity network, involving attention, sensorimotor, subcortical and default-mode networks. These regions with altered nodal characteristics likely reflect disease-specific pathophysiology in PNES. Importantly, the coupling strength of functional-structural connectivity was decreased and exhibited high sensitivity and specificity to differentiate PNES patients from healthy controls, suggesting that the decoupling strength of functional-structural connectivity might be an important characteristic reflecting the mechanisms of PNES. This is the first study to explore the altered topological organization in PNES combining functional and structural connectivity networks, providing a new way to understand the pathophysiological mechanisms of PNES.