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22 Whole-brain functional connectivity based on the graph theory analysiisn Psychogenic Non-Epileptic Seizures (PNES)
  1. M Arbabi1,
  2. S Amiri2,
  3. F Badragheh3,
  4. MM Mirbagheri4,
  5. AA Asadi-Pooya5
  1. 1Associate Professor of Psychiatry, Tehran University of Medical Sciences, Tehran, Iran
  2. 2Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran
  3. 3Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran
  4. 4Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences and Neural engineering Research Center, Noorafshar Hospital, Tehran, Medico Rehabilitation Research Center, Iran and Department of Physical Medicine and Rehabilitation, Northwestern University, USA
  5. 5Professor of Epileptology, Director, Shiraz Epilepsy Center and Epilepsy Surgery Program, Department of Neurology, Shiraz University of Medical Sciences, Shiraz, Iran and an Adjunct Research Associate Professor, Department of Neurology, Thomas Jefferson University, Philadelphia, PA

Abstract

Objective Despite being the subject of many studies over the past two decades, mechanisms underlying psychogenic non-epileptic seizures (PNES) are still poorly understood. We tried to address this issue by utilizing brain functional connectivity analysis to identify brain regions with abnormal activities in patients with PNES. In a case-control study, we performed graph based network analysis, a robust technique that determines the organization of brain connectivity and characterizes topological properties of the brain networks.

Methods Twelve individuals with PNES and twenty-one healthy control subjects were examined. Resting state functional magnetic resonance imaging (rsfMRI) was acquired. All subjects were asked to keep their eyes open during the scanning process. The rsfMRI analysis consisted of pre-processing, extracting the functional connectivity matrix (FCM) based on the AAL atlas, threshold for binary FCM, constructing a graph network from FCM and extracting graph features, and finally statistical analysis. For all cortical and subcortical regions of the AAL atlas, we calculated measures of ‘degree,’ which is one of the features of the graph theory. Results: Our results revealed that, as compared to the healthy control subjects, patients with PNES had a significantly lower degree in some brain regions including their left and right insula (INS), right Putamen (PUT), left and right Supramarginal gyrus (SMG), right Middle occipital gyrus (MOG), and left and right Rolandic operculum (ROL). In contrast, degree was significantly greater in two regions [i.e., right Caudate (CAU) and left Inferior frontal gyrus orbital part (ORBinf)] in patients with PNES compared to that in controls.

Conclusion Our findings suggest that functional connectivity of several major brain regions are different in patients with PNES compared with that in healthy individuals. While there is hypoactivity in regions important in perception, motor control, self- awareness, and cognitive functioning (e.g., insula) and also movement regulation (e.g., putamen), there is hyperactivity in areas involved in feedback processing (i.e., using information from past experiences to influence future actions and decisions) (e.g., caudate) in patients with PNES. The observation that individuals with PNES suffer from a wide range of abnormal activities in functional connectivity of their brain networks is consistent with the fact that PNES occur in a heterogeneous patient population; no single mechanism or contributing factor could explain PNES in all patients.

  • PNES
  • fMRI
  • connectivity
  • graph theory
  • seizure

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