Lateralization Of Temporal Lobe Epilepsy Research Articles

Language lateralization in temporal lobe epilepsy: A behavioral screening tool for surgical planning.

Temporal lobe epilepsy can disturb eloquent areas, affecting language. We applied a visually-mediated task to measure lateralization of language recognition in drug-resistant temporal lobe epilepsy. Patients with left (n = 26), right (n = 28) temporal lobe epilepsy and controls (n = 30) were administered the translingual lexical decision task. We performed repeated measures analyses of variance, with the visual half-field as an intrasubject factor and the group as an intersubject factor. A main effect of visual half-field was found, showing the right visual field (left hemisphere) advantage for both accuracy and response time. A main effect of the group was found in accuracy, showing that both epilepsy groups performed less accurately than controls, and left temporal lobe epilepsy performed less accurately than right temporal lobe epilepsy. Also, the group-by-visual half-field interaction was significant. Post hoc t tests indicated the controls and right temporal lobe epilepsy performed better in the right visual field than in the left visual field, whereas no visual half-field effect was found in left temporal lobe epilepsy. For response times, the interaction was also significant. Post hoc t tests showed a significant right visual-field advantage for controls (two-tailed) and for the right temporal lobe epilepsy (one-tailed). Right visual-field advantage was absent in left temporal lobe epilepsy. The translingual lexical decision task can efficiently distinguish between left and right temporal lobe epilepsy. Compared to right temporal lobe epilepsy and controls, language lateralization is diminished in left temporal lobe epilepsy. The potential use of the translingual lexical decision task as an effective noninvasive presurgical language lateralization screening tool is highlighted. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Supplemental Material for Language Lateralization in Temporal Lobe Epilepsy: A Behavioral Screening Tool for Surgical Planning

Supplemental Material for Language Lateralization in Temporal Lobe Epilepsy: A Behavioral Screening Tool for Surgical Planning

Detection of interictal epileptiform discharge in focal seizures

Introduction: Many factors underlying basic epileptic conditions determine the characteristics of epileptic seizures and the therapeutic outcome. Visual obvious abnormalities in resting baseline EEG are cardinal but incompletely understood like feature of seizure onset zone in focal epilepsy and interictal epileptiform discharge. Present study is an attempt to diagnose that evidence of epileptic discharge in temporal lobe epilepsy (TLE), would persist during interictal period in absence of abnormalities in baseline EEG, which could increase the impact of automatic analysis of EEG waves for clinical relevance.
 Material and Methods: By using 10-20 system, functional connectivity was estimated in the 20 channels of delta, theta, alpha, beta and gamma frequency bands of EEG, from 16 diagnosed focal epileptic seizure patients and 16 age and sex matched controls. To observe the dynamics of the healthy brain, differ from the brain of dynamically focal epileptic patients during interictal period treated with anti-epileptic drugs in the context of resting state during eye close session of EEG. Such differences can be observed by using absolute spectral power from BESS ((Brain Electro Scan Software) of the Axxonet System and statistically measure by applied unpaired student t -test.
 Result: The high significance results in slow frequency EEG waves (delta and theta) in power spectral analysis were observed that demonstrates the potential epileptic discharge occurring during interictal period without visible pathological activity for helping in the diagnosis and lateralization of TLE.
 Conclusion: The detailed spectral analysis of EEG waves offers novel insight into focal epileptic patients when visually EEG findings were normal. This linear analysis helpful in extracting information from EEG signals in diagnosing specific neuronal correlates for TLE. Present findings concluded that epileptic discharges occur at different topographical regions of brain during interictal period and power spectral analysis plays a new insight in diagnosis of focal discharge.

Convolutional Neural Network Algorithm to Determine Lateralization of Seizure Onset in Patients With Epilepsy: A Proof-of-Principle Study.

A new frontier in diagnostic radiology is the inclusion of machine-assisted support tools that facilitate the identification of subtle lesions often not visible to the human eye. Structural neuroimaging plays an essential role in the identification of lesions in patients with epilepsy, which often coincide with the seizure focus. In this study, we explored the potential for a convolutional neural network (CNN) to determine lateralization of seizure onset in patients with epilepsy using T1-weighted structural MRI scans as input. Using a dataset of 359 patients with temporal lobe epilepsy (TLE) from 7 surgical centers, we tested whether a CNN based on T1-weighted images could classify seizure laterality concordant with clinical team consensus. This CNN was compared with a randomized model (comparison with chance) and a hippocampal volume logistic regression (comparison with current clinically available measures). Furthermore, we leveraged a CNN feature visualization technique to identify regions used to classify patients. Across 100 runs, the CNN model was concordant with clinician lateralization on average 78% (SD = 5.1%) of runs with the best-performing model achieving 89% concordance. The CNN outperformed the randomized model (average concordance of 51.7%) on 100% of runs with an average improvement of 26.2% and outperformed the hippocampal volume model (average concordance of 71.7%) on 85% of runs with an average improvement of 6.25%. Feature visualization maps revealed that in addition to the medial temporal lobe, regions in the lateral temporal lobe, cingulate, and precentral gyrus aided in classification. These extratemporal lobe features underscore the importance of whole-brain models to highlight areas worthy of clinician scrutiny during temporal lobe epilepsy lateralization. This proof-of-concept study illustrates that a CNN applied to structural MRI data can visually aid clinician-led localization of epileptogenic zone and identify extrahippocampal regions that may require additional radiologic attention. This study provides Class II evidence that in patients with drug-resistant unilateral temporal lobe epilepsy, a convolutional neural network algorithm derived from T1-weighted MRI can correctly classify seizure laterality.

Revisiting emotion recognition in different types of temporal lobe epilepsy: The influence of facial expression intensity

Temporal lobe epilepsy (TLE) can induce various difficulties in recognizing emotional facial expressions (EFE), particularly for negative valence emotions. However, these difficulties have not been systematically examined according to the localization of the epileptic focus. For this purpose, we used a forced-choice recognition task in which faces expressing fear, sadness, anger, disgust, surprise, or happiness were presented in different intensity levels from moderate to high intensity. The first objective of our study was to evaluate the impact of emotional intensity on the recognition of different categories of EFE in TLE patients compared to control participants. The second objective was to assess the effect of localizationof epileptic focus on the recognition of EFE in patients with medial temporal lobe epilepsy (MTLE) associated or not with hippocampal sclerosis (HS), or lateral temporal lobe epilepsy (LTLE). The results showed that the 272 TLE patients and the 68 control participants were not differently affected by the intensity of EFE. However, we obtained group differences within the clinical population when we took into account the localization of the temporal lobe epileptic focus. As predicted, TLE patients were impaired in recognizing fear and disgust relative to controls. Moreover, the scores of these patients varied according to the localization of the epileptic focus, but not according to the cerebral lateralization of TLE. The facial expression of fear was less well recognized by MTLE patients, with or without HS, and the expression of disgust was less well recognized by LTLE as well as MTLE without HS patients. Moreover, emotional intensity modulated differently the recognition of disgust and surprise of the three patient groups underlying the relevance of using moderate emotional intensity to distinguish the effect of epileptic focus localization. These findings should be taken into account for interpreting the emotional behaviors and deserve to befurther investigated before considering TLE surgical treatment or social cognition interventions in TLE patients.

Testing an Adapted Auditory Verbal Learning Test Paradigm for fMRI to Lateralize Verbal Memory in Patients with Epilepsy.

fMRI is a noninvasive tool for predicting postsurgical deficits in candidates with pharmacoresistant temporal lobe epilepsy. We aimed to test an adapted paradigm of the Rey Auditory Verbal Learning Test to evaluate differences in memory laterality indexes between patients and healthy controls and its association with neuropsychological scores. We performed a prospective study of 50 patients with temporal lobe epilepsy and 22 healthy controls. Participants underwent a block design language and memory fMRI. Laterality indexes and the hippocampal anterior-posterior index were calculated. Language and memory lateralization was organized into typical and atypical on the basis of laterality indexes. A neuropsychological assessment was performed with a median time from fMRI of 8 months and was compared with fMRI performance. We studied 40 patients with left temporal lobe epilepsy and 10 with right temporal lobe epilepsy. Typical language occurred in 65.3% of patients and 90.9% of healthy controls (P = .04). The memory fMRI laterality index was obtained in all healthy controls and 92% of patients. The verbal memory laterality index was bilateral (24.3%) more frequently than the language laterality index (7.69%) in patients with left temporal lobe epilepsy. Atypical verbal memory was greater in patients with left temporal lobe epilepsy (56.8%) than in healthy controls (36.4%), and the proportion of bilateral laterality indexes (53.3%) was larger than right laterality indexes (46.7%). Atypical verbal memory might be associated with higher cognitive scores in patients. No relevant differences were seen in the hippocampal anterior-posterior index according to memory impairment. The adapted Rey Auditory Verbal Learning Test paradigm fMRI might support verbal memory lateralization. Temporal lobe epilepsy laterality influences hippocampal memory laterality indexes. Left temporal lobe epilepsy has shown a higher proportion of atypical verbal memory compared with language, potentially to memory functional reorganization.

Global and local shape features of the hippocampus based on Laplace-Beltrami eigenvalues and eigenfunctions: a potential application in the lateralization of temporal lobe epilepsy.

Using magnetic resonance (MR) images to evaluate changes in the shape of the hippocampus has been an active research topic. This paper presents a new shape analysis approach to quantify and visualize deformations of the hippocampus in epilepsy. The proposed method is based on Laplace-Beltrami (LB) eigenvalues and eigenfunctions as isometric invariant shape features, and thus, the procedure does not require any image registration. In addition to the LB-based shape features, total hippocampal volume and surface area are calculated using manually segmented images. Theses shape and volumetric descriptors are used to distinguish the patients with temporal lobe epilepsy (TLE) (N = 55) from healthy control subjects (N = 12, age = 32.2 ± 9.1, sex (M/F) = 6/6) and patients with right TLE (N = 26, age = 45.1 ± 11.0, sex (M/F) = 9/17) from left TLE (N = 29, age = 45.4 ± 11.9, sex (M/F) = 10/19). Experimental results illustrate the usefulness of the proposed approach for the diagnosis and lateralization of TLE with 93.0% and 86.4% of the cases, respectively. Moreover, the proposed method outperforms the volumetric analysis in terms of both sensitivity (94.9% vs. 88.1%) and specificity (83.3% vs. 50.0%) of the lateralization. The analysis of local hippocampal thickness variations suggests significant deformation in both ipsilateral and contralateral hippocampi of epileptic patients, while there were no differences between right and left hippocampi in controls. It is anticipated that the proposed method could be advantageous in the presurgical evaluation of patients with drug-resistant epilepsy; however, further validation of the method using a larger dataset is required.

Deep learning resting state functional magnetic resonance imaging lateralization of temporal lobe epilepsy.

Localization of focal epilepsy is critical for surgical treatment of refractory seizures. There remains a great need for noninvasive techniques to localize seizures for surgical decision-making. We investigate the use of deep learning using resting state functional magnetic resonance imaging (RS-fMRI) to identify the hemisphere of seizure onset in temporal lobe epilepsy (TLE) patients. A total of 2132 healthy controls and 32 preoperative TLE patients were studied. All participants underwent structural MRI and RS-fMRI. Healthy control data were used to generate training samples for a three-dimensional convolutional neural network (3DCNN). RS-fMRI was synthetically altered in randomly lateralized regions in the healthy control participants. The model was then trained to classify the hemisphere containing synthetic noise. Finally, the model was tested on TLE patients to assess its performance for detecting biological seizure onset zones, and gradient-weighted class activation mapping (Grad-CAM) identified the strongest predictive regions. The 3DCNN classified healthy control hemispheres known to contain synthetic noise with 96% accuracy, and TLE hemispheres clinically identified to be seizure onset zones with 90.6% accuracy. Grad-CAM identified a range of temporal, frontal, parietal, and subcortical regions that were strong anatomical predictors of the seizure onset zone, and the resting state networks that colocalized with Grad-CAM results included default mode, medial temporal, and dorsal attention networks. Lastly, in an analysis of a subset of patients with postsurgical outcomes, the 3DCNN leveraged a more focal set of regions to achieve classification in patients with Engel Class >I compared to Engel Class I. Noninvasive techniques capable of localizing the seizure onset zone could improve presurgical planning in patients with intractable epilepsy. We have demonstrated the ability of deep learning to identify the correct hemisphere of the seizure onset zone in TLE patients using RS-fMRI with high accuracy. This approach represents a novel technique of seizure lateralization that could improve preoperative surgical planning.

Automated lateralization of temporal lobe epilepsy with cross frequency coupling using magnetoencephalography

ObjectiveLateralization of seizure focus in temporal lobe epilepsy (TLE) is a prime step in pre-surgical evaluation requiring prolonged seizure monitoring using video EEG and manual inspection of recordings. This study uses phase amplitude coupling (PAC) in resting state magnetoencephalography to automatically lateralize TLE focus. MethodsFifty-four patients with drug resistant TLE and 21 healthy controls who underwent MEG were considered for the study. Classification was carried out for PAC calculated for source transformed resting state of controls vs left TLE (LTLE)/right TLE (RTLE) and LTLE vs RTLE between beta, low-gamma and high-gamma as high frequency (HF) bands and low frequency (LF) 1–13 Hz, with decision tree (DT), support vector machines (SVM) and naïve Bayes with feature selection by chi-square test. Further, lateralization classification was also calculated with LF sub-bands (delta, theta, alpha). ResultsPAC was higher in the TLE compared to controls. LTLE and RTLE showed differences in low gamma-alpha and high gamma-delta coupling (p < 0.05). Accuracy was highest with SVM between controls and LTLE in the low gamma-LF (92.92%, AUC-1), between controls and RTLE in DT and SVM (93.54%, AUC-0.97, 1) in the low gamma-LF band and in low gamma-delta band in SVM (92.04%, AUC-1) between LTLE and RTLE. ConclusionsPAC shows distinct patterns of coupling in each subject group. Feature selection showed involvement of major network hubs and resting state networks. SVM showed best classification potential in the low gamma band. SignificancePAC in resting state MEG can supplement pre-surgical evaluation in drug resistant TLE.

MRI-Based Machine Learning Prediction Framework to Lateralize Hippocampal Sclerosis in Patients With Temporal Lobe Epilepsy.

MRI fails to reveal hippocampal pathology in 30% to 50% of temporal lobe epilepsy (TLE) surgical candidates. To address this clinical challenge, we developed an automated MRI-based classifier that lateralizes the side of covert hippocampal pathology in TLE. We trained a surface-based linear discriminant classifier that uses T1-weighted (morphology) and T2-weighted and fluid-attenuated inversion recovery (FLAIR)/T1 (intensity) features. The classifier was trained on 60 patients with TLE (mean age 35.6 years, 58% female) with histologically verified hippocampal sclerosis (HS). Images were deemed to be MRI negative in 42% of cases on the basis of neuroradiologic reading (40% based on hippocampal volumetry). The predictive model automatically labeled patients as having left or right TLE. Lateralization accuracy was compared to electroclinical data, including side of surgery. Accuracy of the classifier was further assessed in 2 independent TLE cohorts with similar demographics and electroclinical characteristics (n = 57, 58% MRI negative). The overall lateralization accuracy was 93% (95% confidence interval 92%-94%), regardless of HS visibility. In MRI-negative TLE, the combination of T2 and FLAIR/T1 intensities provided the highest accuracy in both the training (84%, area under the curve [AUC] 0.95 ± 0.02) and validation (cohort 1 90%, AUC 0.99; cohort 2 76%, AUC 0.94) cohorts. This prediction model for TLE lateralization operates on readily available conventional MRI contrasts and offers gain in accuracy over visual radiologic assessment. The combined contribution of decreased T1- and increased T2-weighted intensities makes the synthetic FLAIR/T1 contrast particularly effective in MRI-negative HS, setting the basis for broad clinical translation. This study provides Class II evidence that in people with TLE and MRI-negative HS, an automated MRI-based classifier accurately determines the side of pathology.

Diversified social cognition in temporal lobe epilepsy.

In an integrated model of social cognition (SC), the theory of mind (ToM), the recognition of behavior in social situations (RBSS), empathy, and sensitivity to moral and conventional rules (SMCR) cooperate in generating mental representations of the interpersonal relationships. The aim of this study was to extend our knowledge of the SC of temporal lobe epilepsy (TLE) patients by characterizing its various aspects and predictors. Fifty adult patients with TLE and 50 healthy controls were assessed using ToM, RBSS and SMCR neuropsychological tests, the Empathy Questionnaire, and the psychopathology Symptoms Check List 90R (SCL90-R). Patients and controls were similar in terms of occupation, income level, age, sex, marital status and the number of family members. Multivariate analysis of variance with demographic variables as the covariates showed that they were similar in SMCR and empathy. The patients, conversely, had lower ToM and RBSS scores, and higher scores on the SCL90-R psychoticism, depression, paranoid ideation, obsessive-compulsive, somatization and anxiety scales. Impaired RBSS was predicted by psychopathological symptoms, income level, schooling and the duration of epilepsy; ToM related to TLE laterality, seizure frequency and epilepsy duration. In adult patients with TLE, SC is simultaneously partially impaired and partially preserved, and the fact this is associated with clinical, demographic and psychological variables suggests that SC depends on the integrity of the temporal lobe and the interconnected brain regions, as well as psychosocial stimuli. This approach may contribute to clarify the neurobehavioural phenotype of TLE.

Temporal and extratemporal atrophic manifestation of temporal lobe epilepsy using voxel-based morphometry and corticometry: clinical application in lateralization of epileptogenic zone.

Advances in MRI acquisition and data processing have become important for revealing brain structural changes. Previous studies have reported widespread structural brain abnormalities and cortical thinning in patients with temporal lobe epilepsy (TLE), as the most common form of focal epilepsy. In this research, healthy control cases (n = 20) and patients with left TLE (n = 19) and right TLE (n = 14) were recruited, all underwent 3.0T MRI with magnetization-prepared rapid gradient echo sequence to acquire T1-weighted images. Morphometric alterations in gray matter were identified using voxel-based morphometry (VBM). Volumetric alterations in subcortical structures and cortical thinning were also determined. Patients with left TLE demonstrated more prevailing and widespread changes in subcortical volumes and cortical thickness than right TLE, mainly in the left hemisphere, compared to the healthy group. Both VBM analysis and subcortical volumetry detected significant hippocampal atrophy in ipsilateral compared to contralateral side in TLE group. In addition to hippocampus, subcortical volumetry found the thalamus and pallidum bilaterally vulnerable to the TLE. Furthermore, the TLE patients underwent cortical thinning beyond the temporal lobe, affecting gray matter cortices in frontal, parietal, and occipital lobes in the majority of patients, more prevalently for left TLE cases. Exploiting volume changes in individual patients in the hippocampus alone led to 63.6% sensitivity and 100% specificity for lateralization of TLE. Alteration of gray matter volumes in subcortical regions and neocortical temporal structures and also cortical gray matter thickness were evidenced as common effects of epileptogenicity, as manifested by the majority of cases in this study.

Is formal scoring better than just looking? A comparison of subjective and objective scoring methods of the Rey Complex Figure Test for lateralizing temporal lobe epilepsy

Objective Neuropsychologists labor over scoring the Rey Complex Figure Test (RCFT), a measure of visuospatial functioning and nonverbal memory. Compelling arguments suggest that pathognomonic signs of the RCFT are observable to the “naked eye.” Standard scoring systems are insensitive to lateralizing temporal lobe epilepsy (TLE) and alternative “qualitative” scoring systems are ineffective and time-consuming. Method: We examined accuracy of TLE lateralization using subjective classifications and standard scoring. Participants were 84 TLE patients (53 female; mean age=36yrs) and 46 controls (27 female; mean age = 27.5). The former were classified as right (n = 41) or left (n = 43) TLE by neurologists using EEG and MRI studies. RCFT were scored using standard scoring with cut-offs of z ≤ –2 classified as impaired and were rated as “characteristic” of RTLE (Ugly) or LTLE (Not Ugly) performance by neuropsychologists. Accuracy of seizure lateralization for both methods was examined. Results: Neuropsychologists’ ratings accuracy were at or below chance. Standard scoring criteria showed chance or slightly better lateralization prediction. Standard scoring predicted RTLE laterality more accurately than subjective ratings for copy trials; standard scoring was no better at lateralizing RTLE with delays. Subjective ratings were better at distinguishing TLE patients from controls. Conclusion: Findings highlight concerns regarding the usefulness of the RCFT in TLE lateralization, regardless of scoring approach.

Remodeling of brain morphology in temporal lobe epilepsy.

BackgroundMesial temporal lobe epilepsy (TLE) is one of the most widespread neurological network disorders. Computational anatomy MRI studies demonstrate a robust pattern of cortical volume loss. Most statistical analyses provide information about localization of significant focal differences in a segregationist way. Multivariate Bayesian modeling provides a framework allowing inferences about inter‐regional dependencies. We adopt this approach to answer following questions: Which structures within a pattern of dynamic epilepsy‐associated brain anatomy reorganization best predict TLE pathology. Do these structures differ between TLE subtypes?MethodsWe acquire clinical and MRI data from TLE patients with and without hippocampus sclerosis (n = 128) additional to healthy volunteers (n = 120). MRI data were analyzed in the computational anatomy framework of SPM12 using classical mass‐univariate analysis followed by multivariate Bayesian modeling.ResultsAfter obtaining TLE‐associated brain anatomy pattern, we estimate predictive power for disease and TLE subtypes using Bayesian model selection and comparison. We show that ipsilateral para‐/hippocampal regions contribute most to disease‐related differences between TLE and healthy controls independent of TLE laterality and subtype. Prefrontal cortical changes are more discriminative for left‐sided TLE, whereas thalamus and temporal pole for right‐sided TLE. The presence of hippocampus sclerosis was linked to stronger involvement of thalamus and temporal lobe regions; frontoparietal involvement was predominant in absence of sclerosis.ConclusionsOur topology inferences on brain anatomy demonstrate a differential contribution of structures within limbic and extralimbic circuits linked to main effects of TLE and hippocampal sclerosis. We interpret our results as evidence for TLE‐related spatial modulation of anatomical networks.

Graph theory application with functional connectivity to distinguish left from right temporal lobe epilepsy

ObjectiveTo investigate the application of graph theory with functional connectivity to distinguish left from right temporal lobe epilepsy (TLE). MethodsAlterations in functional connectivity within several brain networks - default mode (DMN), attention (AN), limbic (LN), sensorimotor (SMN) and visual (VN) - were examined using resting-state functional MRI (rs-fMRI). The study accrued 21 left and 14 right TLE as well as 17 nonepileptic control subjects. The local nodal degree, a feature of graph theory, was calculated foreach of the brain networks. Multivariate logistic regression analysis was performed to determine the accuracy of identifying seizure laterality based on significant differences in local nodal degree in the selected networks. ResultsLeft and right TLE patients showed dissimilar patterns of alteration in functional connectivity when compared to control subjects. Compared with right TLE, patients with left TLE exhibited greater nodal degree’ (i.e. hyperconnectivity) with right superomedial frontal gyrus (in DMN), inferior frontal gyrus pars triangularis (in AN), right caudate and left superior temporal gyrus (in LN) and left paracentral lobule (in SMN), while showing lesser nodal degree (i.e. hypoconnectivity) with left temporal pole (in DMN), right insula (in LN), left supplementary motor area (in SMN), and left fusiform gyrus (in VN). The LN showed the highest accuracy of 82.9% among all considered networks in determining laterality of the TLE. By combinations of local degree attributes in the DMN, AN, LN, and VN, logistic regression analysis demonstrated an accuracy of 94.3% by comparison. ConclusionOur study demonstrates the utility of graph theory application to brain network analysis as a potential biomarker to assist in the determination of TLE laterality and improve the confidence in presurgical decision-making in cases of TLE.

A Combination of Particle Swarm Optimization and Minkowski Weighted K-Means Clustering: Application in Lateralization of Temporal Lobe Epilepsy

K-Means is one of the most popular clustering algorithms that partitions observations into nonoverlapping subgroups based on a predefined similarity metric. Its drawbacks include a sensitivity to noisy features and a dependency of its resulting clusters upon the initial selection of cluster centroids resulting in the algorithm converging to local optima. Minkowski weighted K-Means (MWK-Means) addresses the issue of sensitivity to noisy features, but is sensitive to the initialization of clusters, and so the algorithm may similarly converge to local optima. Particle Swarm Optimization (PSO) uses a globalized search method to solve this issue. We present a hybrid Particle Swarm Optimization (PSO) + MWK-Means clustering algorithm to address all the above problems in a single framework, while maintaining benefits of PSO and MWK Means methods. This study investigated the utility of this approach in lateralizing the epileptogenic hemisphere for temporal lobe epilepsy (TLE) cases using magnetoencephalography (MEG) coherence source imaging (CSI) and diffusion tensor imaging (DTI). Using MEG-CSI, we analyzed preoperative resting state MEG data from 17 adults TLE patients with Engel class I outcomes to determine coherence at 54 anatomical sites and compared the results with 17 age- and gender-matched controls. Fiber-tracking was performed through the same anatomical sites using DTI data. Indices of both MEG coherence and DTI nodal degree were calculated. A PSO + MWK-Means clustering algorithm was applied to identify the side of temporal lobe epileptogenicity and distinguish between normal and TLE cases. The PSO module was aimed at identifying initial cluster centroids and assigning initial feature weights to cluster centroids and, hence, transferring to the MWK-Means module for the final optimal clustering solution. We demonstrated improvements with the use of the PSO + MWK-Means clustering algorithm compared to that of K-Means and MWK-Means independently. PSO + MWK-Means was able to successfully distinguish between normal and TLE in 97.2% and 82.3% of cases for DTI and MEG data, respectively. It also lateralized left and right TLE in 82.3% and 93.6% of cases for DTI and MEG data, respectively. The proposed optimization and clustering methodology for MEG and DTI features, as they relate to focal epileptogenicity, would enhance the identification of the TLE laterality in cases of unilateral epileptogenicity.

Mean apparent propagator-MRI: A new diffusion model which improves temporal lobe epilepsy lateralization

PurposeTo compare MRI volume measurements, FLAIR image intensity, Diffusion tensor imaging (DTI) and mean apparent propagator (MAP)-MRI measurements in hippocampus ipsilateral and contralateral to the epileptogenic focus for non-invasive lateralization of temporal lobe epilepsy (TLE) and also compare these DTI and MAP-MRI measurements to cognitive function. MethodA cohort of patients with unilateral TLE and aged-and gendered-matched controls were enrolled in this retrospective study. T1-weighted MPRAGE data for the volume, FLAIR image intensity, DTI and MAP-MRI parameters were performed for bilateral hippocampi of all subjects. The sensitivity, specificity, lateralization ratios and Cohen's d effect sizes of all MR measurements were calculated. Pearson correlation analysis was performed to compare DTI and MAP-MRI measurements to cognitive function. ResultsWe evaluated 23 patients and 17 controls. The MAP-MRI parameter ‘return to the plane probability’ (RTPP) had the strongest effect size (d = −1.678, lateralization ratio = 86.36 %) for differentiating hippocampus ipsilateral to the epileptogenic focus from contralateral hippocampus when compared to all other DTI/MAP-MRI parameters, signal intensity on FLAIR and hippocampal volumes. Mean diffusivity (MD), radial diffusivity (RD), mean square displacement (MSD) were each negatively correlated to clinical measures of delayed recall (r = −0.758; r = −0.772; r = −0.684, respectively). While return to the axis probability (RTAP) return to the origin probability (RTOP) and fractional anisotropy (FA) were positively correlated (r = 0.832; r = 0.813; r = 0.717, respectively) (all P < 0.05). ConclusionMAP-MRI measurements are promising radiologic biomarkers for the non-invasive lateralization of epileptogenic foci in TLE.

The visuospatial pattern of temporal lobe epilepsy

PurposeUnlike temporal lobe lesions, temporal lobe epilepsy (TLE) has no definite effects on visuospatial functions. This retrospective study evaluated these functions in patients with TLE, aiming to clarify their relationships to TLE laterality and magnetic resonance imaging (MRI)-detected brain lesions. MethodsThe Raven Colored Progressive Matrices (RCPM), Attentive Matrices (AM), Trail Making Test A (TMTA), Street Completion Test (SCT), Rey Complex Figure Copying (RCFC) and Delayed Reproduction (RCFDR), and Corsi Blocks Span (CBS) and Supraspan Learning (CBSSL) were used to assess different visuospatial functions in 198 patients with TLE and 90 healthy subjects. ResultsIn 169 patients (83 left), MRI revealed focal temporal lobe lesions [unilateral mesial temporal lobe sclerosis (MTLS) in 88 cases]. The patients with left or right TLE obtained normal scores on the RCPM, AM, TMTA, SCT, and RCFC, but their scores were significantly low on the CBS, CBSSL, and RCFDR. The patients with MTLS obtained lower scores in comparison with the controls and the patients without lesions, whereas those with other lesions obtained low scores only on the CBSSL and those without lesions performed normally. ConclusionsTemporal lobe epilepsy does not affect nonmemory visuospatial functions but significantly impairs visuosopatial memory and learning. This pattern is independent of TLE laterality, in keeping with a modality-specific memory model. On the contrary, the type of temporal lobe lesion is relevant to the severity of impairment.

Evolution of Graph Theory in Dynamic Functional Connectivity for Lateralization of Temporal Lobe Epilepsy.

Resting-state functional magnetic resonance imaging (rsfMRI) has described the functional architecture of the human brain in the absence of any task or stimulus. Since the functional connectivity (FC), has non-stationary nature, it is evidenced to be varying over time. Using dynamic functional connectivity, six graph theoretical characteristics were measured and compared between left and right temporal lobe epilepsy (TLE). We also obtain a trend for each characteristic in the time course of experiments. The results demonstrated that the static connectivity analysis failed to fully separate the left and right TLE patients for some characteristics, whereby the dynamic analysis has been shown capable of identifying the laterality. Furthermore, the results suggest that the temporal trend of some graph theoretical characteristics can be exploited as a novel marker for TLE laterality.

Prediction of Laterality in Temporal Lobe Epilepsy Using White Matter Diffusion Metrics

Diagnostic methods of the epileptogenic area continue to be a challenge in epilepsy surgery research. We hypothesized that temporal lobe epilepsy (TLE) will result in white matter changes that can be detected using diffusion tensor imaging. Measurement of white matter diffusivity will therefore be useful for presurgical assessment. Twelve patients with TLE who had undergone temporal lobectomy and amygdalohippocampectomy were included. In 6 patients, magnetic resonance imaging (MRI) showed evidence of mesial temporal sclerosis (m-TLE), whereas the 6 remaining MRI studies were informed without any abnormality (nl-TLE). All had excellent outcomes from surgery. Patients were compared with 12 age- and sex-matched controls. Five pairs of white matter fiber tracts were traced, and fiber tract fractional anisotropy and mean diffusivity were calculated. There were several alterations in diffusion parameters in white matter tracts, both ipsilateral and on the contralateral side, these alterations were more pronounced in the hemisphere ipsilateral to the epileptogenic focus. m-TLE patients had more alterations on ipsilateral side than nl-TLE patients, but similar alterations on contralateral side and bilateral fornix. The discriminant function analysis successfully lateralized all the patients with left TLE, 83.3% of the patients with right TLE, and all nl-TLE. Our results suggest that there are alterations in diffusion parameters in white matter tracts both in m-TLE and nl-TLE patients. Diffusion tensor imaging could be a useful presurgical tool to help establish the laterality of TLE, including patients with "normal" MRI. Further studies with a larger number of patients would be necessary to confirm these results.