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 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.

scholarly journals Hardware-Amenable Structural Learning for Spike-Based Pattern Classification Using a Simple Model of Active Dendrites

2015 ◽  
Vol 27 (4) ◽  
pp. 845-897 ◽  
Author(s):  
Shaista Hussain ◽  
Shih-Chii Liu ◽  
Arindam Basu
Keyword(s):  
Learning Algorithm ◽  
Binary Classification ◽  
Structural Plasticity ◽  
Support Vector ◽  
Classification Problems ◽  
Synaptic Inputs ◽  
Connection Pattern ◽  
Comparable Performance ◽  
Learning Machine ◽  
Event Based

This letter presents a spike-based model that employs neurons with functionally distinct dendritic compartments for classifying high-dimensional binary patterns. The synaptic inputs arriving on each dendritic subunit are nonlinearly processed before being linearly integrated at the soma, giving the neuron the capacity to perform a large number of input-output mappings. The model uses sparse synaptic connectivity, where each synapse takes a binary value. The optimal connection pattern of a neuron is learned by using a simple hardware-friendly, margin-enhancing learning algorithm inspired by the mechanism of structural plasticity in biological neurons. The learning algorithm groups correlated synaptic inputs on the same dendritic branch. Since the learning results in modified connection patterns, it can be incorporated into current event-based neuromorphic systems with little overhead. This work also presents a branch-specific spike-based version of this structural plasticity rule. The proposed model is evaluated on benchmark binary classification problems, and its performance is compared against that achieved using support vector machine and extreme learning machine techniques. Our proposed method attains comparable performance while using 10% to 50% less in computational resource than the other reported techniques.

scholarly journals An Improvement Of Least Square - Twin Support Vector Machine

2021 ◽  
pp. 8-13
Author(s):  
Thế Cường Nguyễn ◽  
Thanh Vi Nguyen
Keyword(s):  
Support Vector Machine ◽  
Binary Classification ◽  
Least Square ◽  
Twin Support Vector Machine ◽  
Support Vector ◽  
Classification Problems ◽  
Training Time ◽  
Data Points

In binary classification problems, two classes of data seem to be different from each other. It is expected to bemore complicated due to the number of data points of clusters in each class also be different. Traditional algorithmsas Support Vector Machine (SVM), Twin Support Vector Machine (TSVM), or Least Square Twin Support VectorMachine (LSTSVM) cannot sufficiently exploit information about the number of data points in each cluster of the data.Which may be effect to the accuracy of classification problems. In this paper, we propose a new Improvement LeastSquare - Support Vector Machine (called ILS-SVM) for binary classification problems with a class-vs-clusters strategy.Experimental results show that the ILS-SVM training time is faster than that of TSVM, and the ILS-SVM accuracy isbetter than LSTSVM and TSVM in most cases.

scholarly journals Role of the Nucleus Basalis as a Key Network Node in Temporal Lobe Epilepsy

Neurology ◽  
2021 ◽  
Vol 96 (9) ◽  
pp. e1334-e1346 ◽  
Author(s):  
Hernán F.J. González ◽  
Saramati Narasimhan ◽  
Graham W. Johnson ◽  
Kristin E. Wills ◽  
Kevin F. Haas ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Temporal Lobe Epilepsy ◽  
Temporal Lobe ◽  
Resting State Fmri ◽  
Clustering Coefficient ◽  
Nucleus Basalis ◽  
Core Network ◽  
Neurocognitive Deficits ◽  
Network Nodes ◽  
Healthy Control

ObjectiveTo determine whether the nucleus basalis of Meynert (NBM) may be a key network structure of altered functional connectivity in temporal lobe epilepsy (TLE), we examined fMRI with network-based analyses.MethodsWe acquired resting-state fMRI in 40 adults with TLE and 40 matched healthy control participants. We calculated functional connectivity of NBM and used multiple complementary network-based analyses to explore the importance of NBM in TLE networks without biasing our results by our approach. We compared patients to controls and examined associations of network properties with disease metrics and neurocognitive testing.ResultsWe observed marked decreases in connectivity between NBM and the rest of the brain in patients with TLE (0.91 ± 0.88, mean ± SD) vs controls (1.96 ± 1.13, p < 0.001, t test). Larger decreases in connectivity between NBM and fronto-parietal-insular regions were associated with higher frequency of consciousness-impairing seizures (r = −0.41, p = 0.008, Pearson). A core network of altered nodes in TLE included NBM ipsilateral to the epileptogenic side and bilateral limbic structures. Furthermore, normal community affiliation of ipsilateral NBM was lost in patients, and this structure displayed the most altered clustering coefficient of any node examined (3.46 ± 1.17 in controls vs 2.23 ± 0.93 in patients). Abnormal connectivity between NBM and subcortical arousal community was associated with modest neurocognitive deficits. Finally, a logistic regression model incorporating connectivity properties of ipsilateral NBM successfully distinguished patients from control datasets with moderately high accuracy (78%).ConclusionsThese results suggest that while NBM is rarely studied in epilepsy, it may be one of the most perturbed network nodes in TLE, contributing to widespread neural effects in this disabling disorder.

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