The research of the generalized tonic-clonic seizure epilepsy by resting state fMRI

In this study, we investigated the human brain functional network of patients with generalized tonic and clonic seizure (GTCS) epilepsy by using the resting-state functional magnetic resonance imaging (fMRI) and complex network theory. Adjacency matrices of the networks of GTCS group and normal control group were constructed by the method of Pearson correlation coefficient. A intra-group analysis was performed to get a statistical map of each group. And it was contrasted the attributes of brain network from two aspects of the "small world" property and network efficiency. Furthermore, a inter-group analysis was conducted to investigate differences between GTCS group and normal control group. Our results showed the "small world" property and network efficiency of GTCS group were decreased compared to the controls. In addition, the patients exhibited increased correlation coefficient mainly concentrated in the thalamus and the lentiform nucleus, while the decreased correlation coefficient was mainly distributed in the precuneus and frontal lobe, the inferior frontal gyrus, the inferior parietal gyrus, the superior parietal gyrus. The result indicated that information transmission ability of brain function network in patients with GTCS is impaired. And it also suggested that the method of Pearson correlation coefficient can detect synchronization change of blood-oxygen levels in the brain so that achieved the goal of positioning detection of epileptic activity. Introduction Epilepsy is a common neurological disorder characterized by abnormal hyper-synchronization of neural activity. GTCS usually have no obvious abnormity in brain structure. But as the emergence of the fMRI technology and a variety of algorithms, we have a certain understanding of the pathogenesis of the GTCS. For example, the structure, function and metabolism of the thalamus and thalamus cortical circuits are abnormal[1]. The resting-state fMRI need no task design in advance. People just need to be calm when scanned. It is perfect for the study of patients who have the obstacles of executive ability such as epilepsy and schizophrenia. In recent years, the research of the pathogenesis of GTCS by the resting-state fMRI technology has become a hot spot. Some scholars have done the research by using the fMRI techniques such as the independent component analysis (ICA)[2], amplitude of low frequency fluctuation (ALFF)[3]and regional homogeneity (ReHo) etc. This research adopts the method of Pearson correlation coefficient to construct brain function network at resting-state. It is contrasted the attributes of brain network from two aspects of the "small world" property and network efficiency and then explored the pathophysiological mechanism of GTCS and the applications of the algorithm. Materials and Methods Subjects The fMRI data of this study is from the department of imaging diagnosis, Lanzhou general hospital of Lanzhou military command. According to the clinical diagnosis, 15 cases of patients International Conference on Information Sciences, Machinery, Materials and Energy (ICISMME 2015) © 2015. The authors Published by Atlantis Press 1673 participate in the analysis. At the same time, 15 cases of normal volunteers who have no neurological disorder are as the control group. The two groups are all right-handed and the gender, age have no statistical difference. The study is approved by the medical ethics committee of Lanzhou general hospital of Lanzhou military command. All participants know the research purposes before scanned and sign the consent form. Detailed clinical data are shown in table 1. Table 1 The clinical data of patients with GTCS and normal volunteer Mal e Femal e Age (year ) Average age(year) Duratio n (year) Average duration (year) GTCS Volunte er 9 8 6 7 14~49 22~38 24.17±6.4 8 26.12±6.1 2 1~18 5.72±6.68 Data Acquisition All subjects’ resting-state data of the whole brain were collected by the Siemens 3.0 T mri scanners. Subjects were asked to keep head still, close eyes, relax and without specific thinking activity as far as possible when lie in magnetic resonance. At the same time plug their ears and dim the indoor lights. The parameters are as follows: recycling time (TR)=2000ms, echo time (TE)=30ms, filed of view (FOV)=240mm, matrix=64×64, slice thickness=3.8mm, gap=0.38mm. Collect 200 time points in 400 seconds. Data Preprocessing Preprocess fMRI images by DPARSF software: 1Convert DICOM data format into NIFTI.2 Remove the top-ten time points of each participant to achieve stable signal. 3Make the data collected in different time corrected to the same time point. 4Eliminate the participants whose translation of head is greater than 1 mm and rotation is greater than 1.5 degrees. 5Normalize the image to the standard space. 6Smooth by the gaussian kernel function.7Remove the linear trend. 8Low frequency filter. Filter the frequency of 0.01-0.08 Hz to remove the effects of breathing, heart rate and other physiological noise. Functional Connectivity Calculate the function connection between the regions of interesting based on the method of Pearson correlation coefficient. Firstly, remove the covariate. Then divide the whole brain into 90 regions of interesting based on brain atlas Anatomical Automatic Labeling. Thirdly, Extract the time sequence of the region of interesting. Finally calculate the correlation coefficient ( ) s r C , by the time sequence of the region of interesting r and s .Computation formula is as follow: