Machine learning classification of mesial temporal sclerosis in epilepsy patients

Rudie, Jeffrey D.; Colby, John B.; Salamon, Noriko

doi:10.1016/j.eplepsyres.2015.09.005

Cited by 46 publications

(33 citation statements)

References 32 publications

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“…An SVM study using T1‐weighted images performed by Rudie et al. achieved a prediction accuracy of up to 81% for patients with TLE compared to those with other structural abnormalities, and it additionally found correlations between predictive value and clinical disease progression (Rudie, Colby, & Salamon, 2015). …”

Section: Discussionmentioning

confidence: 96%

Using machine learning to classify temporal lobe epilepsy based on diffusion MRI

et al. 2017

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BackgroundIt is common for patients diagnosed with medial temporal lobe epilepsy (TLE) to have extrahippocampal damage. However, it is unclear whether microstructural extrahippocampal abnormalities are consistent enough to enable classification using diffusion MRI imaging. Therefore, we implemented a support vector machine (SVM)‐based method to predict TLE from three different imaging modalities: mean kurtosis (MK), mean diffusivity (MD), and fractional anisotropy (FA). While MD and FA can be calculated from traditional diffusion tensor imaging (DTI), MK requires diffusion kurtosis imaging (DKI).MethodsThirty‐two TLE patients and 36 healthy controls underwent DKI imaging. To measure predictive capability, a fivefold cross‐validation (CV) was repeated for 1000 iterations. An ensemble of SVM models, each with a different regularization value, was trained with the subject images in the training set, and had performance assessed on the test set. The different regularization values were determined using a Bayesian‐based method.ResultsMean kurtosis achieved higher accuracy than both FA and MD on every iteration, and had far superior average accuracy: 0.82 (MK), 0.68 (FA), and 0.51 (MD). Finally, the MK voxels with the highest coefficients in the predictive models were distributed within the inferior medial aspect of the temporal lobes.ConclusionThese results corroborate our earlier publications which indicated that DKI shows more promise in identifying TLE‐associated pathological features than DTI. Also, the locations of the contributory MK voxels were in areas with high fiber crossing and complex fiber anatomy. These traits result in non‐Gaussian water diffusion, and hence render DTI less likely to detect abnormalities. If the location of consistent microstructural abnormalities can be better understood, then it may be possible in the future to identify the various phenotypes of TLE. This is important since treatment outcome varies dependent on type of TLE.

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Section: Discussionmentioning

confidence: 96%

Using machine learning to classify temporal lobe epilepsy based on diffusion MRI

et al. 2017

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“…[5][6][7][8][9][10] Focke et al generated maps of gray matter voxel relevance across the brain by visualizing the weights assigned to each feature by a support vector machine (SVM) classifier trained to lateralize temporal lobe epilepsy (TLE) in 38 MRI-positive patients. [5][6][7][8][9][10] Focke et al generated maps of gray matter voxel relevance across the brain by visualizing the weights assigned to each feature by a support vector machine (SVM) classifier trained to lateralize temporal lobe epilepsy (TLE) in 38 MRI-positive patients.…”

Section: Introductionmentioning

confidence: 99%

“…Detection of brain abnormalities on magnetic resonance (MR) images using contemporary methods such as machine learning is an emerging and promising area of research. [5][6][7][8][9][10] Focke et al generated maps of gray matter voxel relevance across the brain by visualizing the weights assigned to each feature by a support vector machine (SVM) classifier trained to lateralize temporal lobe epilepsy (TLE) in 38 MRI-positive patients. 6 These relevance maps provided an interesting demonstration of the distribution of morphological gray matter changes found in MRI-positive TLE patients.…”

Section: Introductionmentioning

confidence: 99%

Learning to see the invisible: A data‐driven approach to finding the underlying patterns of abnormality in visually normal brain magnetic resonance images in patients with temporal lobe epilepsy

et al. 2019

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Objective: To find the covert patterns of abnormality in patients with unilateral temporal lobe epilepsy (TLE) and visually normal brain magnetic resonance images (MRI-negative), comparing them to those with visible abnormalities (MRI-positive). Methods: We used multimodal brain MRI from patients with unilateral TLE and employed contemporary machine learning methods to predict the known laterality of seizure onset in 104 subjects (82 MRI-positive, 22 MRI-negative). A visualization approach entitled "Importance Maps" was developed to highlight image features predictive of seizure laterality in both the MRI-positive and MRI-negative cases. Results: Seizure laterality could be predicted with an area under the receiver operating characteristic curve of 0.981 (95% confidence interval [CI] =0.974-0.989) in MRI-positive and 0.842 (95% CI = 0.736-0.949) in MRI-negative cases. The known image features arising from the hippocampus were the leading predictors of seizure laterality in the MRI-positive cases, whereas widespread temporal lobe abnormalitieswere revealed in the MRI-negative cases. Significance: Covert abnormalities not discerned on visual reading were detected in MRI-negative TLE, with a spatial pattern involving the whole temporal lobe, rather than just the hippocampus. This suggests that MRI-negative TLE may be associated with subtle but widespread temporal lobe abnormalities. These abnormalities merit close inspection and postacquisition processing if there is no overt lesion. K E Y W O R D Sabnormality, data-driven, epilepsy, machine learning, MRI-negative

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“…Additionally, graph-theory metrics are able to summarize the network properties with less computational cost than the voxel-based and skeletonbased methods [8,10,59]. Additionally, for classification of TLE lateralization, MEG has higher temporal resolution than MRI [9,45], fMRI [33], or DTI [8,10,59]. Thus, method that can analyze the effects of different frequency bands, especially the frequency band in which epileptiform discharges, was generated.…”

Section: Complexitymentioning

confidence: 99%

“…As a classification tool, the SVM technique is flexible, automated, and sufficiently fast to operate in a clinical setting [8]. SVM algorithms have been applied for measuring brain morphology [9], including 2 Complexity cortical thickness, volume, curvature, and identification of MTS in TLE patients. SVM approaches have been utilized to determine lateralization of the TLE epileptogenic focus with diffusion tensor imaging (DTI) structural connectomes [10].…”

Section: Introductionmentioning

confidence: 99%

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

Chen

et al. 2018

Complexity

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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.

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Machine learning classification of mesial temporal sclerosis in epilepsy patients

Cited by 46 publications

References 32 publications

Using machine learning to classify temporal lobe epilepsy based on diffusion MRI

Using machine learning to classify temporal lobe epilepsy based on diffusion MRI

Learning to see the invisible: A data‐driven approach to finding the underlying patterns of abnormality in visually normal brain magnetic resonance images in patients with temporal lobe epilepsy

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

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