Evaluation of Brain Network Properties in Patients with MRI-Negative Temporal Lobe Epilepsy: An MEG Study

2021 ◽  
Author(s):  
Yuejun Li ◽  
Haitao Zhu ◽  
Qiqi Chen ◽  
Lu Yang ◽  
Xincai Bao ◽  
...  
Keyword(s):  
Temporal Lobe Epilepsy ◽  
Temporal Lobe ◽  
Brain Network ◽  
Network Properties

scholarly journals A study of brain functional network and alertness changes in temporal lobe epilepsy with and without focal to bilateral tonic–clonic seizures

BMC Neurology ◽  
2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Liluo Nie ◽  
Yanchun Jiang ◽  
Zongxia Lv ◽  
Xiaomin Pang ◽  
Xiulin Liang ◽  
...  
Keyword(s):  
Temporal Lobe Epilepsy ◽  
Temporal Lobe ◽  
Information Integration ◽  
Refractory Epilepsy ◽  
Brain Network ◽  
Clustering Coefficient ◽  
Global Network ◽  
Seizure Type ◽  
Topological Properties ◽  
Clonic Seizures

Abstract Background Temporal lobe epilepsy (TLE) is commonly refractory. Epilepsy surgery is an effective treatment strategy for refractory epilepsy, but patients with a history of focal to bilateral tonic-clonic seizures (FBTCS) have poor outcomes. Previous network studies on epilepsy have found that TLE and idiopathic generalized epilepsy with generalized tonic-clonic seizures (IGE-GTCS) showed altered global and nodal topological properties. Alertness deficits also were found in TLE. However, FBTCS is a common type of seizure in TLE, and the implications for alertness as well as the topological rearrangements associated with this seizure type are not well understood. Methods We obtained rs-fMRI data and collected the neuropsychological assessment data from 21 TLE patients with FBTCS (TLE- FBTCS), 18 TLE patients without FBTCS (TLE-non- FBTCS) and 22 controls, and constructed their respective functional brain networks. The topological properties were analyzed using the graph theoretical approach and correlations between altered topological properties and alertness were analyzed. Results We found that TLE-FBTCS patients showed more serious impairment in alertness effect, intrinsic alertness and phasic alertness than the patients with TLE-non-FBTCS. They also showed significantly higher small-worldness, normalized clustering coefficient (γ) and a trend of higher global network efficiency (gE) compared to TLE-non-FBTCS patients. The gE showed a significant negative correlation with intrinsic alertness for TLE-non-FBTCS patients. Conclusion Our findings show different impairments in brain network information integration, segregation and alertness between the patients with TLE-FBTCS and TLE-non-FBTCS, demonstrating that impairments of the brain network may underlie the disruptions in alertness functions.

scholarly journals Automatic Lateralization of Temporal Lobe Epilepsy Based on MEG Network Features Using Support Vector Machines

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Ting Wu ◽  
Duo Chen ◽  
Qiqi Chen ◽  
Rui Zhang ◽  
Wenyu Zhang ◽  
...  
Keyword(s):  
Temporal Lobe Epilepsy ◽  
Temporal Lobe ◽  
Betweenness Centrality ◽  
Binary Classification ◽  
Brain Network ◽  
Support Vector ◽  
Classification Problems ◽  
Healthy Control ◽  
Control Right ◽  
Network Metrics

Correct lateralization of temporal lobe epilepsy (TLE) is critical for improving surgical outcomes. As a relatively new noninvasive clinical recording system, magnetoencephalography (MEG) has rarely been applied for determining lateralization of unilateral TLE. Here we propose a framework for using resting-state brain-network features and support vector machine (SVM) for TLE lateralization based on MEG. We recruited 15 patients with left TLE, 15 patients with right TLE, and 15 age- and sex-matched healthy controls. The lateralization problem was then transferred into a series of binary classification problems, including left TLE versus healthy control, right TLE versus healthy control, and left TLE versus right TLE. Brain-network features were extracted for each participant using three network metrics (nodal degree, betweenness centrality, and nodal efficiency). A radial basis function kernel SVM (RBF-SVM) was employed as the classifier. The leave-one-subject-out cross-validation strategy was used to test the ability of this approach to overcome individual differences. The results revealed that the nodal degree performed best for left TLE versus healthy control and right TLE versus healthy control, with accuracy of 80.76% and 75.00%, respectively. Betweenness centrality performed best for left TLE versus right TLE with an accuracy of 88.10%. The proposed approach demonstrated that MEG is a good candidate for solving the lateralization problem in unilateral TLE using various brain-network features.

scholarly journals Disruption, emergence and lateralization of brain network hubs in mesial temporal lobe epilepsy

2018 ◽  
Vol 20 ◽  
pp. 71-84 ◽  
Author(s):  
Kangjoo Lee ◽  
Hui Ming Khoo ◽  
Jean-Marc Lina ◽  
François Dubeau ◽  
Jean Gotman ◽  
...  
Keyword(s):  
Temporal Lobe Epilepsy ◽  
Temporal Lobe ◽  
Mesial Temporal Lobe Epilepsy ◽  
Brain Network ◽  
Mesial Temporal Lobe

scholarly journals Individual Brain Metabolic Connectome Indicator Based on Jensen-Shannon Divergence Similarity Estimation Predicts Seizure Outcomes of Temporal Lobe Epilepsy

2022 ◽  
Vol 9 ◽  
Author(s):  
Zehua Zhu ◽  
Zhimin Zhang ◽  
Xin Gao ◽  
Li Feng ◽  
Dengming Chen ◽  
...  
Keyword(s):  
Temporal Lobe Epilepsy ◽  
Temporal Lobe ◽  
Age At Onset ◽  
Brain Network ◽  
Classification Performance ◽  
Imaging Biomarker ◽  
Support Vector ◽  
Similarity Estimation ◽  
Jensen Shannon Divergence

Objective: We aimed to use an individual metabolic connectome method, the Jensen-Shannon Divergence Similarity Estimation (JSSE), to characterize the aberrant connectivity patterns and topological alterations of the individual-level brain metabolic connectome and predict the long-term surgical outcomes in temporal lobe epilepsy (TLE).Methods: A total of 128 patients with TLE (63 females, 65 males; 25.07 ± 12.01 years) who underwent Positron emission tomography (PET) with 18F-fluorodeoxyglucose (FDG) imaging were enrolled. Patients were classified either as experiencing seizure recurrence (SZR) or seizure free (SZF) at least 1 year after surgery. Each individual’s metabolic brain network was ascertained using the proposed JSSE method. We compared the similarity and difference in the JSSE network and its topological measurements between the two groups. The two groups were then classified by combining the information from connection and topological metrics, which was conducted by the multiple kernel support vector machine. The validation was performed using the nested leave-one-out cross-validation strategy to confirm the performance of the methods.Results: With a median follow-up of 33 months, 50% of patients achieved SZF. No relevant differences in clinical features were found between the two groups except age at onset. The proposed JSSE method showed marked degree reductions in IFGoperc.R, ROL. R, IPL. R, and SMG. R; and betweenness reductions in ORBsup.R and IOG. R; meanwhile, it found increases in the degree analysis of CAL. L and PCL. L, and in the betweenness analysis of PreCG.R, IOG. R, PoCG.R, PCL. L and PCL.R. Exploring consensus significant metabolic connections, we observed that the most involved metabolic motor networks were the INS-TPOmid.L, MTG. R-SMG. R, and MTG. R-IPL.R pathways between the two groups, and yielded another detailed individual pathological connectivity in the PHG. R-CAU.L, PHG. R-HIP.L, TPOmid.L-LING.R, TPOmid.L-DCG.R, MOG. R-MTG.R, MOG. R-ANG.R, and IPL. R-IFGoperc.L pathways. These aberrant functional network measures exhibited ideal classification performance in predicting SZF individuals from SZR ones at a sensitivity of 75.00%, a specificity of 92.79%, and an accuracy of 83.59%.Conclusion: The JSSE method indicator can identify abnormal brain networks in predicting an individual’s long-term surgical outcome of TLE, thus potentially constituting a clinically applicable imaging biomarker. The results highlight the biological meaning of the estimated individual brain metabolic connectome.

scholarly journals Multi-Head Self-Attention Model for Classification of Temporal Lobe Epilepsy Subtypes

2020 ◽  
Vol 11 ◽  
Author(s):  
Peipei Gu ◽  
Ting Wu ◽  
Mingyang Zou ◽  
Yijie Pan ◽  
Jiayang Guo ◽  
...  
Keyword(s):  
Temporal Lobe Epilepsy ◽  
Temporal Lobe ◽  
Clinical Symptoms ◽  
Brain Network ◽  
Optimal Number ◽  
The Self ◽  
Support Vector ◽  
Great Effort ◽  
Attention Model

As a long-standing chronic disease, Temporal Lobe Epilepsy (TLE), resulting from abnormal discharges of neurons and characterized by recurrent episodic central nervous system dysfunctions, has affected more than 70% of drug-resistant epilepsy patients across the world. As the etiology and clinical symptoms are complicated, differential diagnosis of TLE mainly relies on experienced clinicians, and specific diagnostic biomarkers remain unclear. Though great effort has been made regarding the genetics, pathology, and neuroimaging of TLE, an accurate and effective diagnosis of TLE, especially the TLE subtypes, remains an open problem. It is of a great importance to explore the brain network of TLE, since it can provide the basis for diagnoses and treatments of TLE. To this end, in this paper, we proposed a multi-head self-attention model (MSAM). By integrating the self-attention mechanism and multilayer perceptron method, the MSAM offers a promising tool to enhance the classification of TLE subtypes. In comparison with other approaches, including convolutional neural network (CNN), support vector machine (SVM), and random forest (RF), experimental results on our collected MEG dataset show that the MSAM achieves a supreme performance of 83.6% on accuracy, 90.9% on recall, 90.7% on precision, and 83.4% on F1-score, which outperforms its counterparts. Furthermore, effectiveness of varying head numbers of multi-head self-attention is assessed, which helps select the optimal number of multi-head. The self-attention aspect learns the weights of different signal locations which can effectively improve classification accuracy. In addition, the robustness of MSAM is extensively assessed with various ablation tests, which demonstrates the effectiveness and generalizability of the proposed approach.

Brain network alteration in patients with temporal lobe epilepsy with cognitive impairment

2018 ◽  
Vol 81 ◽  
pp. 41-48 ◽  
Author(s):  
Hongyu Yang ◽  
Chao Zhang ◽  
Chang Liu ◽  
Tao Yu ◽  
Guojun Zhang ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Temporal Lobe Epilepsy ◽  
Temporal Lobe ◽  
Brain Network

scholarly journals Divergent network properties that predict early surgical failure versus late recurrence in temporal lobe epilepsy

2020 ◽  
Vol 132 (5) ◽  
pp. 1324-1333 ◽  
Author(s):  
Victoria L. Morgan ◽  
Baxter P. Rogers ◽  
Adam W. Anderson ◽  
Bennett A. Landman ◽  
Dario J. Englot
Keyword(s):  
Temporal Lobe Epilepsy ◽  
Temporal Lobe ◽  
Outcome Prediction ◽  
Late Recurrence ◽  
Seizure Outcome ◽  
Long Term Outcome ◽  
Network Properties ◽  
Surgical Failure ◽  
Left And Right

OBJECTIVEThe objectives of this study were to identify functional and structural network properties that are associated with early versus long-term seizure outcomes after mesial temporal lobe epilepsy (mTLE) surgery and to determine how these compare to current clinically used methods for seizure outcome prediction.METHODSIn this case-control study, 26 presurgical mTLE patients and 44 healthy controls were enrolled to undergo 3-T MRI for functional and structural connectivity mapping across an 8-region network of mTLE seizure propagation, including the hippocampus (left and right), insula (left and right), thalamus (left and right), one midline precuneus, and one midline mid-cingulate. Seizure outcome was assessed annually for up to 3 years. Network properties and current outcome prediction methods related to early and long-term seizure outcome were investigated.RESULTSA network model was previously identified across 8 patients with seizure-free mTLE. Results confirmed that whole-network propagation connectivity patterns inconsistent with the mTLE model predict early surgical failure. In those patients with networks consistent with the mTLE network, specific bilateral within-network hippocampal to precuneus impairment (rather than unilateral impairment ipsilateral to the seizure focus) was associated with mild seizure recurrence. No currently used clinical variables offered the same ability to predict long-term outcome.CONCLUSIONSIt is known that there are important clinical differences between early surgical failure that lead to frequent disabling seizures and late recurrence of less frequent mild seizures. This study demonstrated that divergent network connectivity variability, whole-network versus within-network properties, were uniquely associated with these disparate outcomes.

scholarly journals Functional brain network mechanism of executive control dysfunction in temporal lobe epilepsy

BMC Neurology ◽  
2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Yanping Ren ◽  
Liping Pan ◽  
Xueyun Du ◽  
Yuying Hou ◽  
Xun Li ◽  
...  
Keyword(s):  
Temporal Lobe Epilepsy ◽  
Temporal Lobe ◽  
Executive Control ◽  
Brain Network ◽  
Functional Brain ◽  
Functional Brain Network ◽  
Network Mechanism
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