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

Author(s):  
Zehua Zhu ◽  
Zhimin Zhang ◽  
Xin Gao ◽  
Li Feng ◽  
Dengming Chen ◽  
...  

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.

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Ting Wu ◽  
Duo Chen ◽  
Qiqi Chen ◽  
Rui Zhang ◽  
Wenyu Zhang ◽  
...  

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.


2020 ◽  
Vol 11 ◽  
Author(s):  
Peipei Gu ◽  
Ting Wu ◽  
Mingyang Zou ◽  
Yijie Pan ◽  
Jiayang Guo ◽  
...  

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.


Epilepsia ◽  
1996 ◽  
Vol 37 (7) ◽  
pp. 651-656 ◽  
Author(s):  
Gregory D. Cascino ◽  
Max R. Trenerry ◽  
Elson L. So ◽  
Frank W. Sharbrough ◽  
Cheolsu Shin ◽  
...  

Cancer ◽  
2009 ◽  
Vol 115 (24) ◽  
pp. 5771-5779 ◽  
Author(s):  
Ji Hoon Phi ◽  
Seung-Ki Kim ◽  
Byung-Kyu Cho ◽  
Seo Young Lee ◽  
Su Yeon Park ◽  
...  

Epilepsia ◽  
2017 ◽  
Vol 58 (8) ◽  
pp. 1473-1485 ◽  
Author(s):  
Bertrand Mathon ◽  
Franck Bielle ◽  
Séverine Samson ◽  
Odile Plaisant ◽  
Sophie Dupont ◽  
...  

Epilepsia ◽  
2010 ◽  
Vol 51 (6) ◽  
pp. 1024-1029 ◽  
Author(s):  
Michael Murphy ◽  
Paul D. Smith ◽  
Martin Wood ◽  
Stephen Bowden ◽  
Terence J. O’Brien ◽  
...  

2021 ◽  
Vol 12 ◽  
pp. 379
Author(s):  
Nobutaka Mukae ◽  
Daisuke Kuga ◽  
Daisuke Murakami ◽  
Noritaka Komune ◽  
Yusuke Miyamoto ◽  
...  

Background: Temporal lobe epilepsy (TLE) associated with temporal lobe encephalocele is rare, and the precise epileptogenic mechanisms and surgical strategies for such cases are still unknown. Although the previous studies have reported good seizure outcomes following chronic subdural electrode recording through invasive craniotomy, only few studies have reported successful epilepsy surgery through endoscopic endonasal lesionectomy. Case Description: An 18-year-old man developed generalized convulsions at the age of 15 years. Despite treatment with optimal doses of antiepileptic drugs, episodes of speech and reading difficulties were observed 2–3 times per week. Long-term video electroencephalogram (EEG) revealed ictal activities starting from the left anterior temporal region. Magnetic resonance imaging revealed a temporal lobe encephalocele in the left lateral fossa of the sphenoidal sinus (sphenoidal encephalocele). Through the endoscopic endonasal approach, the tip of the encephalocele was exposed. A depth electrode was inserted into the encephalocele, which showed frequent spikes superimposed with high-frequency oscillations (HFOs) suggesting intrinsic epileptogenicity. The encephalocele was resected 8 mm from the tip. Twelve months postoperatively, the patient had no recurrence of seizures on tapering of the medication. Conclusion: TLE associated with sphenoidal encephalocele could be controlled with endoscopic endonasal lesionectomy, after confirming the high epileptogenicity with analysis of HFOs of intraoperative EEG recorded using an intralesional depth electrode.


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