Curvature analysis of perisylvian epilepsy.

PURPOSE: We assess whether alterations in the convolutional anatomy of the deep perisylvian area (DPSA) might indicate focal epileptogenicity. MATERIALS AND METHODS: The DPSA of each hemisphere was segmented on MRI and a 3D gray-white matter interface (GWMI) geometrical model was constructed. Comparative visual and quantitative assessment of the convolutional anatomy of both the left and right DPSA models was performed. Both the density of thorn-like contours (peak percentage) and coarse interface curvatures was computed using Gaussian curvature and shape index, respectively. The proposed method was applied to a total of 14 subjects; 7 patients with an epileptogenic DPSA and 7 non-epileptic subjects. RESULTS: A high peak percentage correlated well with the epileptogenic DPSA. It distinguished between patients and non-epileptic subjects (P = 0.029) and identified laterality of the epileptic focus in all but one case. A diminished regional curvature also identified epileptogenicity (P = 0.016) and, moreover, its laterality (P = 0.001). CONCLUSION: An increased peak percentage from a global view of the GWMI of the DPSA provides some indication of a propensity toward a focal or regional DPSA epileptogenicity. A diminished convolutional anatomy (i.e., smoothing effect) appears also to coincide with the epileptogenic site in the DPSA and to distinguish laterality.

The ventral precuneal-posterior cingulate region as a site of epileptogenicity

The ventral precuneal and posterior cingulate area (VP-PC) represents a distinct but topographically variable mesial parietal site of epileptogenicity that may manifest as a common temporal lobe-mediated ictal expression. In a review of records of 62 presumptive epilepsy surgery cases, two cases of primary epileptogenicity expressed within the VP-PC were identified and are detailed to bring attention to this electroencephalographically-hidden area of ictal expression. Details of their investigation and surgical treatment illustrate distinctly different approaches addressing the problem and bringing about a seizure-free outcome.

A Preliminary Study of the Efficacy of Transcranial Direct Current Stimulation in Trigeminal Neuralgia.

The purpose of this study is to assess the efficacy of transcranial direct current stimulation (tDCS) in patients with treatment-refractory trigeminal neuralgia (TN) and examine the utility of neuroimaging methods in identifying markers of such efficacy. Six patients with classical TN refractory to maximal medical treatment, underwent tDCS (three cases inhibitory/cathodic and three cases excitatory/anodic stimulation). All patients underwent pre- and posttreatment functional magnetic resonance imaging (fMRI) during block-design tasks (i.e., Pain, Pain + tDCS, tDCS) as well as single-shell diffusion MRI (dMRI) acquisition. The precise locations of tDCS electrodes were identified by neuronavigation. Five therapeutic tDCS sessions were carried out for each patient with either anodic or cathodic applications. The Numeric Rating Scale of pain (NRS) and the Headache Disability Index (HDI) were used to score the subjective efficacy of treatment. Altered activity of regional sites was identified by fMRI and associated changes in the spinothalamocortical sensory tract (STCT) were measured by the dMRI indices of fractional anisotropy (FA) and mean diffusivity (MD). Fiber counts of the bilateral trigeminal root entry zone (REZ) were performed as an added measure of fiber loss or recovery. All patients experienced a significant reduction in pain scores with a substantial decline in HDI (P value < 0.01). Following a course of anodic tDCS, the ipsilateral caudate, globus pallidus, somatosensory cortex, and the contralateral globus pallidus showed a significantly attenuated activation whereas cathodic tDCS treatment resulted in attenuation of the thalamus and globus pallidus bilaterally, and the somatosensory cortex and anterior cingulate gyrus contralaterally. dMRI analysis identified a substantial increase (>50%) in the number of contralateral sensory fibers in the STCT with either anodic or cathodic tDCS treatment in four of the six patients. A significant reduction in FA (>40%) was observed in the ipsilateral REZ in the posttreatment phase in five of the six patients. Preliminary evidence suggests that navigated tDCS presents a promising method for alleviating the pain of TN. Different patterns of activation manifested by anodic and cathodic stimulation require further elaboration to understand their implication. Activation and attenuation of responses at various sites may provide further avenues for condition treatment.

Prospective Quantitative Neuroimaging Analysis of Putative Temporal Lobe Epilepsy.

Purpose: A prospective study of individual and combined quantitative imaging applications for lateralizing epileptogenicity was performed in a cohort of consecutive patients with a putative diagnosis of mesial temporal lobe epilepsy (mTLE). Methods: Quantitative metrics were applied to MRI and nuclear medicine imaging studies as part of a comprehensive presurgical investigation. The neuroimaging analytics were conducted remotely to remove bias. All quantitative lateralizing tools were trained using a separate dataset. Outcomes were determined after 2 years. Of those treated, some underwent resection, and others were implanted with a responsive neurostimulation (RNS) device. Results: Forty-eight consecutive cases underwent evaluation using nine attributes of individual or combinations of neuroimaging modalities: 1) hippocampal volume, 2) FLAIR signal, 3) PET profile, 4) multistructural analysis (MSA), 5) multimodal model analysis (MMM), 6) DTI uncertainty analysis, 7) DTI connectivity, and 9) fMRI connectivity. Of the 24 patients undergoing resection, MSA, MMM, and PET proved most effective in predicting an Engel class 1 outcome (>80% accuracy). Both hippocampal volume and FLAIR signal analysis showed 76% and 69% concordance with an Engel class 1 outcome, respectively. Conclusion: Quantitative multimodal neuroimaging in the context of a putative mTLE aids in declaring laterality. The degree to which there is disagreement among the various quantitative neuroimaging metrics will judge whether epileptogenicity can be confined sufficiently to a particular temporal lobe to warrant further study and choice of therapy. Prediction models will improve with continued exploration of combined optimal neuroimaging metrics.

Distinct patterns of hippocampal subfield volume loss in left and right mesial temporal lobe epilepsy.

OBJECTIVE: To investigate the pattern and severity of hippocampal subfield volume loss in patients with left and right mesial temporal lobe epilepsy (mTLE) using quantitative MRI volumetric analysis. METHODS: A total of 21 left and 14 right mTLE subjects, as well as 15 healthy controls, were enrolled in this cross-sectional study. A publically available magnetic resonance imaging (MRI) brain volumetry system (volBrain) was used for volumetric analysis of hippocampal subfields. The T1-weighted images were processed with a HIPS pipeline. RESULTS: A distinct pattern of hippocampal subfield atrophy was found between left and right mTLE patients when compared with controls. Patients with left mTLE exhibited ipsilateral hippocampal atrophy and segmental volume depletion of the Cornu Ammonis (CA) 2/CA3, CA4/dentate gyrus (DG), and strata radiatum-lacunosum-moleculare (SR-SL-SM). Those with right mTLE exhibited similar ipsilateral hippocampal atrophy but with additional segmental CA1 volume depletion. More extensive bilateral subfield volume loss was apparent with right mTLE patients. CONCLUSION: We demonstrate that left and right mTLE patients show a dissimilar pattern of hippocampal subfield atrophy, suggesting the pathophysiology of epileptogenesis in left and right mTLE to be different.

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

OBJECTIVE: To investigate the application of graph theory with functional connectivity to distinguish left from right temporal lobe epilepsy (TLE). METHODS: Alterations 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. RESULTS: Left 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. CONCLUSION: Our 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.

Brain-responsive corticothalamic stimulation in the centromedian nucleus for the treatment of regional neocortical epilepsy.

OBJECTIVE: The aim of the study was to determine if corticothalamic responsive stimulation targeting the centromedian nucleus of the thalamus (CMT) is a potential treatment for neocortical epilepsies with regional onsets. METHODS: We assessed efficacy and safety of CMT and neocortical responsive stimulation, detection, and stimulation programming, methods for implantation, and location and patterns of electrographic seizure onset and spread in 7 patients with medically intractable focal seizures with a regional neocortical onset. RESULTS: The median follow-up duration was 17 months (average: 17 months, range: 8-28 months). The median % reduction in disabling seizures (excludes auras) in the 7 patients was 88% (mean: 80%, range: 55-100%). The median % reduction in all seizure types (disabling + auras) was 73% (mean: 67%, range: 15-94%). There were no adverse events related to implantation of the responsive neurostimulator and leads or related to the delivery of responsive stimulation. Stimulation-related contralateral paresthesias were addressed by adjusting stimulation parameters in the clinic during stimulation testing. Electrographic seizures were detected in the CMT and neocortex in all seven patients. Four patients had simultaneous or near simultaneous seizure onsets in the neocortex and CMT and three had onsets in the neocortex with spread to the CMT. CONCLUSION: In this small series of patients with medically intractable focal seizures and regional neocortical onset, responsive neurostimulation to the neocortex and CMT improved seizure control and was well tolerated. SIGNIFICANCE: Responsive corticothalamic neurostimulation of the CMT and neocortex is a potential treatment for patients with regional neocortical epilepsies.

Multimodal data analysis of epileptic EEG and rs-fMRI via deep learning and edge computing.

BACKGROUND AND OBJECTIVE: Multimodal data analysis and large-scale computational capability is entering medicine in an accelerative fashion and has begun to influence investigational work in a variety of disciplines. It is also informing us of therapeutic interventions that will come about with such development. Epilepsy is a chronic brain disorder in which functional changes may precede structural ones and which may be detectable using existing modalities. METHODS: Functional connectivity analysis using electroencephalography (EEG) and resting state-functional magnetic resonance imaging (rs-fMRI) has provided such meaningful input in cases of epilepsy. By leveraging the potential of autonomic edge computing in epilepsy, we develop and deploy both noninvasive and invasive methods for monitoring, evaluation, and regulation of the epileptic brain. First, an autonomic edge computing framework is proposed for the processing of big data as part of a decision support system for surgical candidacy. Second, a multimodal data analysis using independently acquired EEG and rs-fMRI is presented for estimation and prediction of the epileptogenic network. Third, an unsupervised feature extraction model is developed for EEG analysis and seizure prediction based on a Convolutional deep learning (CNN) structure for distinguishing preictal (pre-seizure) state from non-preictal periods by support vector machine (SVM) classifier. RESULTS: Experimental and simulation results from actual patient data validate the effectiveness of the proposed methods. CONCLUSIONS: The combination of rs-fMRI and EEG/iEEG can reveal more information about dynamic functional connectivity. However, simultaneous fMRI and EEG data acquisition present challenges. We have proposed system models for leveraging and processing independently acquired fMRI and EEG data.

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.

Leukoaraiosis Predicts Short-term Cognitive But not Motor Recovery in Ischemic Stroke Patients During Rehabilitation.

BACKGROUND: Leukoaraiosis has been shown to impact functional outcomes after acute ischemic stroke. However, its association with domain specific recovery after ischemic stroke is uncertain. We sought to determine whether pre-existing leukoaraiosis is associated with short-term motor and cognitive recovery after stroke. METHODS: We retrospectively studied ischemic stroke patients admitted to acute inpatient rehabilitation (AIR) between January 2013 and September 2015. Patient baseline characteristics, infarct volume, prestroke modified Rankin Scale, stroke cause, rehabilitation length of stay, and Functional Independence Measure (FIM) scores were recorded. Leukoaraiosis severity was graded on brain magnetic resonance imaging using the Fazekas scale. Multiple linear regression was used to determine factors independently associated with the total, cognitive, and motor FIM scores at AIR discharge, respectively. RESULTS: Of 1600 ischemic stroke patients screened, 109 patients were included in the final analysis. After adjustment, the initial National Institute of Health Stroke Scale (ß -0.541, confidence interval [CI] -0.993 to -0.888; P = 0.020) and pre-existing leukoaraiosis severity (ß -1.448, CI -2.861 to -0.034; P = 0.045) independently predicted the total FIM score. Domain specific analysis showed that infarct volume (ß -0.012, CI -0.019 to -0.005; P = 0.002) and leukoaraiosis severity (ß -0.822, CI -1.223 to -0.410; P = 0.0001) independently predicted FIM cognitive scores at discharge from AIR. Leukoaraiosis did not predict FIM motor score (P = 0.17). CONCLUSIONS: Leukoaraiosis severity is an independent predictor of total and cognitive, but not motor FIM scores after AIR for acute ischemic stroke. This highlights that leukoaraiosis affects poststroke recovery in a domain specific fashion, information that may aid counseling of patients and families as well as tailor rehabilitative efforts.

Correction: Data mining MR image features of select structures for lateralization of mesial temporal lobe epilepsy.

[This corrects the article DOI: 10.1371/journal.pone.0199137.].

Data mining MR image features of select structures for lateralization of mesial temporal lobe epilepsy.

PURPOSE: This study systematically investigates the predictive power of volumetric imaging feature sets extracted from select neuroanatomical sites in lateralizing the epileptogenic focus in mesial temporal lobe epilepsy (mTLE) patients. METHODS: A cohort of 68 unilateral mTLE patients who had achieved an Engel class I outcome postsurgically was studied retrospectively. The volumes of multiple brain structures were extracted from preoperative magnetic resonance (MR) images in each. The MR image data set consisted of 54 patients with imaging evidence for hippocampal sclerosis (HS-P) and 14 patients without (HS-N). Data mining techniques (i.e., feature extraction, feature selection, machine learning classifiers) were applied to provide measures of the relative contributions of structures and their correlations with one another. After removing redundant correlated structures, a minimum set of structures was determined as a marker for mTLE lateralization. RESULTS: Using a logistic regression classifier, the volumes of both hippocampus and amygdala showed correct lateralization rates of 94.1%. This reflected about 11.7% improvement in accuracy relative to using hippocampal volume alone. The addition of thalamic volume increased the lateralization rate to 98.5%. This ternary-structural marker provided a 100% and 92.9% mTLE lateralization accuracy, respectively, for the HS-P and HS-N groups. CONCLUSIONS: The proposed tristructural MR imaging biomarker provides greater lateralization accuracy relative to single- and double-structural biomarkers and thus, may play a more effective role in the surgical decision-making process. Also, lateralization of the patients with insignificant atrophy of hippocampus by the proposed method supports the notion of associated structural changes involving the amygdala and thalamus.

Contribution of Quantitative Amygdalar MR FLAIR Signal Analysis for Lateralization of Mesial Temporal Lobe Epilepsy.

BACKGROUND AND PURPOSE: This study evaluates the contribution of an automated amygdalar fluid-attenuated inversion recovery (FLAIR) signal analysis for the lateralization of mesial temporal lobe epilepsy (mTLE). METHODS: Sixty-nine patients (27 M, 42 F) who had undergone surgery and achieved an Engel class Ia postoperative outcome were identified as a pure cohort of mTLE cases. Forty-six nonepileptic subjects comprised the control group. The amygdala was segmented in T1-weighted images using an atlas-based segmentation. The right/left ratios of amygdalar FLAIR mean and standard deviation were calculated for each subject. A linear classifier (ie, discriminator line) was designed for lateralization using the FLAIR features and a boundary domain, within which lateralization was assumed to be less definitive, was established using the same features from control subjects. Hippocampal FLAIR and volume analysis was performed for comparison. RESULTS: With the boundary domain in place, lateralization accuracy was found to be 70% with hippocampal FLAIR and 67% with hippocampal volume. Taking amygdalar analysis into account, 22% of cases that were found to have uncertain lateralization by hippocampal FLAIR analysis were confidently lateralized by amygdalar FLAIR. No misclassified case was found outside the amygdalar FLAIR boundary domain. CONCLUSIONS: Amygdalar FLAIR analysis provides an additional metric by which to establish mTLE in those cases where hippocampal FLAIR and volume analysis have failed to provide lateralizing information.

A Comparison of Vagal Nerve Stimulation and Responsive Neurostimulation for the Treatment of Medically Refractory Complex Partial Epilepsy.

BACKGROUND: Neuromodulatory applications such as vagus nerve stimulation (VNS) and responsive neurostimulation (RNS) are safe and effective strategies for medically intractable epilepsy secondary to complex partial seizures, but researchers have yet to compare their efficacies. OBJECTIVE: The goal of this study is to compare VNS and RNS efficacy at reducing seizure frequency and complication rates in subjects with medically intractable epilepsy secondary to complex partial seizures. METHODS: This is a retrospective chart review of 30 patients with medically intractable complex partial epilepsy, who underwent either VNS or RNS placement at a single institution between June 2012 and January 2016. There was a mean follow-up of 19 months. Seizure frequency reduction and complications were identified. RESULTS: The median seizure frequency reduction was similar for VNS (66%) and RNS (58%). There was no major morbidity or mortality, and the frequency of minor complications was similar between VNS (15%) and RNS (18%). CONCLUSION: We found that VNS and RNS reduced the median seizure frequency similarly with no difference in morbidity or mortality. Further prospective studies are warranted as VNS and RNS therapy improves over time.

Random ensemble learning for EEG classification.

Real-time detection of seizure activity in epilepsy patients is critical in averting seizure activity and improving patients' quality of life. Accurate evaluation, presurgical assessment, seizure prevention, and emergency alerts all depend on the rapid detection of seizure onset. A new method of feature selection and classification for rapid and precise seizure detection is discussed wherein informative components of electroencephalogram (EEG)-derived data are extracted and an automatic method is presented using infinite independent component analysis (I-ICA) to select independent features. The feature space is divided into subspaces via random selection and multichannel support vector machines (SVMs) are used to classify these subspaces. The result of each classifier is then combined by majority voting to establish the final output. In addition, a random subspace ensemble using a combination of SVM, multilayer perceptron (MLP) neural network and an extended k-nearest neighbors (k-NN), called extended nearest neighbor (ENN), is developed for the EEG and electrocorticography (ECoG) big data problem. To evaluate the solution, a benchmark ECoG of eight patients with temporal and extratemporal epilepsy was implemented in a distributed computing framework as a multitier cloud-computing architecture. Using leave-one-out cross-validation, the accuracy, sensitivity, specificity, and both false positive and false negative ratios of the proposed method were found to be 0.97, 0.98, 0.96, 0.04, and 0.02, respectively. Application of the solution to cases under investigation with ECoG has also been effected to demonstrate its utility.

Brain region and epilepsy-associated differences in inflammatory mediator levels in medically refractory mesial temporal lobe epilepsy.

BACKGROUND: Epilepsy patients have distinct immune/inflammatory cell profiles and inflammatory mediator levels in the blood. Although the neural origin of inflammatory cells and mediators has been implied, few studies have measured these inflammatory components in the human brain itself. This study examines the brain levels of chemokines (8), cytokines (14), and vascular injury mediators (3) suspected of being altered in epilepsy. METHODS: Soluble protein extracts of fresh frozen resected hippocampus, entorhinal cortex, and temporal cortex from 58 medically refractory mesial temporal lobe epilepsy subjects and 4 nonepileptic neurosurgical subjects were assayed for 25 inflammation-related mediators using ultrasensitive low-density arrays. RESULTS: Brain mediator levels were compared between regions and between epileptic and nonepileptic cases, showing a number of regional and possible epilepsy-associated differences. Eotaxin, interferon-γ, interleukin (IL)-2, IL-4, IL-12 p70, IL-17A, tumor necrosis factor-α, and intercellular adhesion molecule (ICAM)-1 levels were highest in the hippocampus, the presumptive site of epileptogenesis. Surprisingly, IL-1ß and IL-1α were lowest in the hippocampus, compared to cortical regions. In the temporal cortex, IL-1ß, IL-8, and MIP-1α levels were highest, compared to the entorhinal cortex and the hippocampus. The most pronounced epilepsy-associated differences were decreased levels of eotaxin, IL-1ß, C-reactive protein, and vascular cell adhesion molecule (VCAM)-1 and increased IL-12 p70 levels. Caution must be used in interpreting these results, however, because nonepileptic subjects were emergent neurosurgical cases, not a control group. Correlation analyses of each mediator in each brain region yielded valuable insights into the regulation of these mediator levels in the brain. Over 70 % of the associations identified were between different mediators in a single brain region, providing support for local control of mediator levels. Correlations of different mediators in different brain regions suggested more distributed control mechanisms, particularly in the hippocampus. Interestingly, only four mediators showed robust correlations between the brain regions, yet levels in three of these were significantly different between regions, indicating both global and local controls for these mediators. CONCLUSIONS: Both brain region-specific and epilepsy-associated changes in inflammation-related mediators were detected. Correlations in mediator levels within and between brain regions indicated local and global regulation, respectively. The hippocampus showed the majority of interregional associations, suggesting a focus of inflammatory control between these regions.

DTI-based response-driven modeling of mTLE laterality.

PURPOSE: To develop lateralization models for distinguishing between unilateral and bilateral mesial temporal lobe epilepsy (mTLE) and determining laterality in cases of unilateral mTLE. BACKGROUND: mTLE is the most common form of medically refractory focal epilepsy. Many mTLE patients fail to demonstrate an unambiguous unilateral ictal onset. Intracranial EEG (icEEG) monitoring can be performed to establish whether the ictal origin is unilateral or truly bilateral with independent bitemporal ictal origin. However, because of the expense and risk of intracranial electrode placement, much research has been done to determine if the need for icEEG can be obviated with noninvasive neuroimaging methods, such as diffusion tensor imaging (DTI). METHODS: Fractional anisotropy (FA) was used to quantify microstructural changes reflected in the diffusivity properties of the corpus callosum, cingulum, and fornix, in a retrospective cohort of 31 patients confirmed to have unilateral (n = 24) or bilateral (n = 7) mTLE. All unilateral mTLE patients underwent resection with an Engel class I outcome. Eleven were reported to have hippocampal sclerosis on pathological analysis; nine had undergone prior icEEG. The bilateral mTLE patients had undergone icEEG demonstrating independent epileptiform activity in both right and left hemispheres. Twenty-three nonepileptic subjects were included as controls. RESULTS: In cases of right mTLE, FA showed significant differences from control in all callosal subregions, in both left and right superior cingulate subregions, and in forniceal crura. Comparison of right and left mTLE cases showed significant differences in FA of callosal genu, rostral body, and splenium and the right posteroinferior and superior cingulate subregions. In cases of left mTLE, FA showed significant differences from control only in the callosal isthmus. Significant differences in FA were identified when cases of right mTLE were compared with bilateral mTLE cases in the rostral and midbody callosal subregions and isthmus. Based on 11 FA measurements in the cingulate, callosal and forniceal subregions, a response-driven lateralization model successfully differentiated all cases (n = 54) into groups of unilateral right (n = 12), unilateral left (n = 12), and bilateral mTLE (n = 7), and nonepileptic control (23). CONCLUSION: The proposed response-driven DTI biomarker is intended to lessen diagnostic ambiguity of laterality in cases of mTLE and help optimize selection of surgical candidates. Application of this model shows promise in reducing the need for invasive icEEG in prospective cases.

MEG Coherence and DTI Connectivity in mTLE.

Magnetoencephalography (MEG) is a noninvasive imaging method for localization of focal epileptiform activity in patients with epilepsy. Diffusion tensor imaging (DTI) is a noninvasive imaging method for measuring the diffusion properties of the underlying white matter tracts through which epileptiform activity is propagated. This study investigates the relationship between the cerebral functional abnormalities quantified by MEG coherence and structural abnormalities quantified by DTI in mesial temporal lobe epilepsy (mTLE). Resting state MEG data was analyzed using MEG coherence source imaging (MEG-CSI) method to determine the coherence in 54 anatomical sites in 17 adult mTLE patients with surgical resection and Engel class I outcome, and 17 age- and gender- matched controls. DTI tractography identified the fiber tracts passing through these same anatomical sites of the same subjects. Then, DTI nodal degree and laterality index were calculated and compared with the corresponding MEG coherence and laterality index. MEG coherence laterality, after Bonferroni adjustment, showed significant differences for right versus left mTLE in insular cortex and both lateral orbitofrontal and superior temporal gyri (p < 0.017). Likewise, DTI nodal degree laterality, after Bonferroni adjustment, showed significant differences for right versus left mTLE in gyrus rectus, insular cortex, precuneus and superior temporal gyrus (p < 0.017). In insular cortex, MEG coherence laterality correlated with DTI nodal degree laterality ([Formula: see text] in the cases of mTLE. None of these anatomical sites showed statistically significant differences in coherence laterality between right and left sides of the controls. Coherence laterality was in agreement with the declared side of epileptogenicity in insular cortex (in 82 % of patients) and both lateral orbitofrontal (88 %) and superior temporal gyri (88 %). Nodal degree laterality was also in agreement with the declared side of epileptogenicity in gyrus rectus (in 88 % of patients), insular cortex (71 %), precuneus (82 %) and superior temporal gyrus (94 %). Combining all significant laterality indices improved the lateralization accuracy to 94 % and 100 % for the coherence and nodal degree laterality indices, respectively. The associated variations in diffusion properties of fiber tracts quantified by DTI and coherence measures quantified by MEG with respect to epileptogenicity possibly reflect the chronic microstructural cerebral changes associated with functional interictal activity. The proposed methodology for using MEG and DTI to investigate diffusion abnormalities related to focal epileptogenicity and propagation may provide a further means of noninvasive lateralization.

Comparative performance evaluation of automated segmentation methods of hippocampus from magnetic resonance images of temporal lobe epilepsy patients.

PURPOSE: Segmentation of the hippocampus from magnetic resonance (MR) images is a key task in the evaluation of mesial temporal lobe epilepsy (mTLE) patients. Several automated algorithms have been proposed although manual segmentation remains the benchmark. Choosing a reliable algorithm is problematic since structural definition pertaining to multiple edges, missing and fuzzy boundaries, and shape changes varies among mTLE subjects. Lack of statistical references and guidance for quantifying the reliability and reproducibility of automated techniques has further detracted from automated approaches. The purpose of this study was to develop a systematic and statistical approach using a large dataset for the evaluation of automated methods and establish a method that would achieve results better approximating those attained by manual tracing in the epileptogenic hippocampus. METHODS: A template database of 195 (81 males, 114 females; age range 32-67 yr, mean 49.16 yr) MR images of mTLE patients was used in this study. Hippocampal segmentation was accomplished manually and by two well-known tools (FreeSurfer and hammer) and two previously published methods developed at their institution [Automatic brain structure segmentation (ABSS) and LocalInfo]. To establish which method was better performing for mTLE cases, several voxel-based, distance-based, and volume-based performance metrics were considered. Statistical validations of the results using automated techniques were compared with the results of benchmark manual segmentation. Extracted metrics were analyzed to find the method that provided a more similar result relative to the benchmark. RESULTS: Among the four automated methods, ABSS generated the most accurate results. For this method, the Dice coefficient was 5.13%, 14.10%, and 16.67% higher, Hausdorff was 22.65%, 86.73%, and 69.58% lower, precision was 4.94%, -4.94%, and 12.35% higher, and the root mean square (RMS) was 19.05%, 61.90%, and 65.08% lower than LocalInfo, FreeSurfer, and hammer, respectively. The Bland-Altman similarity analysis revealed a low bias for the ABSS and LocalInfo techniques compared to the others. CONCLUSIONS: The ABSS method for automated hippocampal segmentation outperformed other methods, best approximating what could be achieved by manual tracing. This study also shows that four categories of input data can cause automated segmentation methods to fail. They include incomplete studies, artifact, low signal-to-noise ratio, and inhomogeneity. Different scanner platforms and pulse sequences were considered as means by which to improve reliability of the automated methods. Other modifications were specially devised to enhance a particular method assessed in this study.