Functional connectivity of dissociation in patients with psychogenic non-epileptic seizures

2011 ◽  
Vol 83 (3) ◽  
pp. 239-247 ◽  
Author(s):  
Sylvie J M van der Kruijs ◽  
Nynke M G Bodde ◽  
Maarten J Vaessen ◽  
Richard H C Lazeron ◽  
Kristl Vonck ◽  
...  
Epilepsia ◽  
2021 ◽  
Author(s):  
Maxwell G. Farina ◽  
Mani Ratnesh S. Sandhu ◽  
Maxime Parent ◽  
Basavaraju G. Sanganahalli ◽  
Matthew Derbin ◽  
...  

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Yi Liang ◽  
Chunli Chen ◽  
Fali Li ◽  
Dezhong Yao ◽  
Peng Xu ◽  
...  

Epileptic seizures are considered to be a brain network dysfunction, and chronic recurrent seizures can cause severe brain damage. However, the functional brain network underlying recurrent epileptic seizures is still left unveiled. This study is aimed at exploring the differences in a related brain activity before and after chronic repetitive seizures by investigating the power spectral density (PSD), fuzzy entropy, and functional connectivity in epileptic patients. The PSD analysis revealed differences between the two states at local area, showing postseizure energy accumulation. Besides, the fuzzy entropies of preseizure in the frontal, central, and temporal regions are higher than that of postseizure. Additionally, attenuated long-range connectivity and enhanced local connectivity were also found. Moreover, significant correlations were found between network metrics (i.e., characteristic path length and clustering coefficient) and individual seizure number. The PSD, fuzzy entropy, and network analysis may indicate that the brain is gradually impaired along with the occurrence of epilepsy, and the accumulated effect of brain impairment is observed in individuals with consecutive epileptic bursts. The findings of this study may provide helpful insights into understanding the network mechanism underlying chronic recurrent epilepsy.


2014 ◽  
Vol 108 (7) ◽  
pp. 1184-1194 ◽  
Author(s):  
Jurong Ding ◽  
Dongmei An ◽  
Wei Liao ◽  
Guorong Wu ◽  
Qiang Xu ◽  
...  

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Rong Li ◽  
Yibo Li ◽  
Dongmei An ◽  
Qiyong Gong ◽  
Dong Zhou ◽  
...  

2017 ◽  
Vol 247 ◽  
pp. 51-54 ◽  
Author(s):  
Shreekantiah Umesh ◽  
Sai Krishna Tikka ◽  
Nishant Goyal ◽  
Vinod Kumar Sinha ◽  
Shamshul Haque Nizamie

2020 ◽  
Vol 91 (8) ◽  
pp. e17.1-e17
Author(s):  
M Arbabi ◽  
S Amiri ◽  
F Badragheh ◽  
MM Mirbagheri ◽  
AA Asadi-Pooya

ObjectiveDespite 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.MethodsTwelve 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.ConclusionOur 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.


2021 ◽  
Vol 429 ◽  
pp. 119080
Author(s):  
Giovanni Mastroianni ◽  
Sara Gasparini ◽  
Giuseppe Varone ◽  
Edoardo Ferlazzo ◽  
Michele Ascoli ◽  
...  

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 129
Author(s):  
Giuseppe Varone ◽  
Wadii Boulila ◽  
Michele Lo Lo Giudice ◽  
Bilel Benjdira ◽  
Nadia Mammone ◽  
...  

Until now, clinicians are not able to evaluate the Psychogenic Non-Epileptic Seizures (PNES) from the rest-electroencephalography (EEG) readout. No EEG marker can help differentiate PNES cases from healthy subjects. In this paper, we have investigated the power spectrum density (PSD), in resting-state EEGs, to evaluate the abnormalities in PNES affected brains. Additionally, we have used functional connectivity tools, such as phase lag index (PLI), and graph-derived metrics to better observe the integration of distributed information of regular and synchronized multi-scale communication within and across inter-regional brain areas. We proved the utility of our method after enrolling a cohort study of 20 age- and gender-matched PNES and 19 healthy control (HC) subjects. In this work, three classification models, namely support vector machine (SVM), linear discriminant analysis (LDA), and Multilayer perceptron (MLP), have been employed to model the relationship between the functional connectivity features (rest-HC versus rest-PNES). The best performance for the discrimination of participants was obtained using the MLP classifier, reporting a precision of 85.73%, a recall of 86.57%, an F1-score of 78.98%, and, finally, an accuracy of 91.02%. In conclusion, our results hypothesized two main aspects. The first is an intrinsic organization of functional brain networks that reflects a dysfunctional level of integration across brain regions, which can provide new insights into the pathophysiological mechanisms of PNES. The second is that functional connectivity features and MLP could be a promising method to classify rest-EEG data of PNES form healthy controls subjects.


2015 ◽  
Vol 87 (3) ◽  
pp. 332-337 ◽  
Author(s):  
Elham Barzegaran ◽  
Cristian Carmeli ◽  
Andrea O Rossetti ◽  
Richard S Frackowiak ◽  
Maria G Knyazeva

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