Nonepileptic Subjects Research Articles

Curvature analysis of perisylvian epilepsy.

We assess whether alterations in the convolutional anatomy of the deep perisylvian area (DPSA) might indicate focal epileptogenicity. 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. 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). 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.

Electrocardiogram Abnormalities and Associated Factors Among People with and without Epilepsy Attending the Outpatient Department at Referral Hospitals in Amhara Regional State, Northern Ethiopia, 2022: Institutional-Based Comparative Cross-Sectional Study.

Epilepsyisabraindiseasethat is associated with electrocardiographic (ECG) abnormalities.However, this evidence is limited in Ethiopia. Studying ECG abnormality in epileptic patients could help minimize the cardiac problems and death of epileptic patients by early screening. This study was aimed to assess electrocardiogram abnormalities and associated factors among people with and without epilepsy at referral hospitals in Amhara Regional State (ARS), northern Ethiopia, 2022. Institutional-based comparative cross-sectional study was conducted from April 20 to June 20, 2022. Three referral hospitals in ARS were selected by simple random sampling.Epileptic patients were selected by systematic random sampling, and 403 age- and sex-matched participants were selected from attendantsof patientsin the outpatient department.Interviewer-administered questionnaireanddigital electrocardiograph were used for data collection. Data were entered to EpiData version 4.6 and exported to stata version14 for analysis. Variables with P value <0.25 in bivariable analysis were entered to multivariable binary logistic regression. Strength of association was determined by adjusted odds ratio with a 95%confidence interval (CI). The prevalence of ECG abnormality was 40.0% (95% CI = 34.7-46.8) and 29.0% (95% CI =23.2-34.3) in epileptic and non-epileptic subjects, respectively. The odds of ECG abnormality was 58% higher among epileptic patients. Divorce, duration of epilepsy, uncontrolled epilepsy,former/current smoker, adequate physical activity, hypertension, and overweight/obesity were associated with ECG abnormality among epileptic patients. The proportion of ECG abnormality was higher among the epileptic than the non-epileptic group.This should be a wake-up call for stakeholders to establish appropriate measurements to reduce cardiac issues of epileptic patients.

Inter- and intra-operator variations in manual segmentation of hippocampus from MRI

PurposeWe evaluate inter- and intra-operator variations in manual segmentation of hippocampus and present their potential sources. Hippocampal atrophy is common in mesial temporal lobe epilepsy (mTLE). Effective diagnosis and treatment of mTLE depends on accurate and efficient segmentation of hippocampus from magnetic resonance imaging (MRI) data. Manual segmentation by expert radiologists remains the gold standard, although automated segmentation methods exist. MethodsHippocampus was segmented in MRI of 118 unilateral mTLE patients and 25 non-epileptic subjects (65 males, 78 females; mean age 39 years) by three operators (M1, M2, M3) manually and by three software tools (FreeSurfer, LocalInfo, ABSS) automatically. Segmentation results were evaluated using 7 volume-, voxel-, and distance-based performance measures. Inter-operator variation was evaluated by comparing the segmentation results of the three operators. Intra-operator variation was evaluated by comparing manual and automatic segmentation results. Segmentation results were used to lateralize epileptogenicity in mTLE patients. ResultsRanking of performance measures differed when using M3 segmentations as ground truth rather than M1 or M2 segmentations. The standard deviation tended to be higher when using M3 as ground truth and M1 or M2 as test segmentation. Variation in performance measures and increased standard deviation when using M3 as ground truth are indicative of inter-operator variability. Standard deviations were larger when using M2 segmentations as ground truth and automated segmentations as test segmentation, rather than M1 or M3. Large standard deviations between M2 segmentations and automated segmentations are indicative of intra-operator variation. Among automated segmentation methods, ABSS produced segmentations most similar to manual segmentations, but FreeSurfer and LocalInfo lateralized epileptogenicity more accurately. ConclusionsPart of inter-operator variation might be due to temporal separation of M3 segmentations from M1 and M2 segmentations (M3 performed segmentations a few years before operators M1 and M2). Inter-operator variation will likely reduce if all operators segment within the same time frame, reducing discrepancies in training of operators. Intra-operator variation can likely be mitigated with additional oversight by a neuroradiologist. Inter- and intra-operator variability may generate inconsistencies in outcomes. Future automated segmentation techniques may integrate neural-network-based (ABSS) and atlas-based (FreeSurfer and LocalInfo) segmentation techniques for optimal performance.

Intraoperative microseizure detection using a high-density micro-electrocorticography electrode array.

One-third of epilepsy patients suffer from medication-resistant seizures. While surgery to remove epileptogenic tissue helps some patients, 30–70% of patients continue to experience seizures following resection. Surgical outcomes may be improved with more accurate localization of epileptogenic tissue. We have previously developed novel thin-film, subdural electrode arrays with hundreds of microelectrodes over a 100–1000 mm2 area to enable high-resolution mapping of neural activity. Here, we used these high-density arrays to study microscale properties of human epileptiform activity. We performed intraoperative micro-electrocorticographic recordings in nine patients with epilepsy. In addition, we recorded from four patients with movement disorders undergoing deep brain stimulator implantation as non-epileptic controls. A board-certified epileptologist identified microseizures, which resembled electrographic seizures normally observed with clinical macroelectrodes. Recordings in epileptic patients had a significantly higher microseizure rate (2.01 events/min) than recordings in non-epileptic subjects (0.01 events/min; permutation test, P = 0.0068). Using spatial averaging to simulate recordings from larger electrode contacts, we found that the number of detected microseizures decreased rapidly with increasing contact diameter and decreasing contact density. In cases in which microseizures were spatially distributed across multiple channels, the approximate onset region was identified. Our results suggest that micro-electrocorticographic electrode arrays with a high density of contacts and large coverage are essential for capturing microseizures in epilepsy patients and may be beneficial for localizing epileptogenic tissue to plan surgery or target brain stimulation.

Alexithymia and emotion dysregulation in adult patients with epilepsy

ObjectiveThe concept of alexithymia refers to difficulty perceiving, identifying, and describing emotions. We aimed at evaluating the prevalence of alexithymia in a sample of adult people with epilepsy (PWE) with and without psychogenic nonepileptic seizures (PNES) and healthy control subjects (HC) and identifying major factors able to affect it. Materials and methodsWe enrolled consecutively 91 PWE (12 of which with PNES in addition to seizures) and 146 HC age- and gender-matched. Both groups’ subjects completed the following questionnaires: TAS-20, Beck Depression Inventory-II (BDI-II), Difficulties in Emotion Regulation Scale (DERS) and the Italian translation of Stigma Scale of Epilepsy (SSE), able to evaluate stigma related to epilepsy both in epileptic and nonepileptic subjects. Moreover, PWE completed the well-known Jacoby's Stigma Scale (JSS), dedicated to the evaluation of stigma only by patients with epilepsy and QOLIE-31 (Q31) for evaluating the quality of life. We analyzed correlations between alexithymia and several epilepsy-related (seizure frequency, antiseizure medications-ASMs) and psychosocial factors. Finally, a stepwise multiple regression analysis was performed to identify major factor affecting alexithymia in both groups. ResultsAlexithymia was prevalent in PWE compared to controls (17.6% of alexithymic subjects in PWE vs 11% in HC), without discriminating epileptic subjects with and without PNES. This predominance disappeared when depressive symptoms (DS) were controlled for. The difficulties of identifying feelings and emotions resulted to be clearly higher in PWE, even when DS are controlled for, and significantly correlated with stigma perception. Alexithymia in PWE was also strongly associated with lower quality of life and education and greater number of ASMs and difficulties in emotion regulation (ER), that turned out to be the main factor affecting alexithymia in both groups (PWE and HC). ConclusionsAlexithymia is prevalent in PWE, mostly influenced by DS and significantly associated with worse quality of life and higher emotion dysregulation and stigma perception. The latter finding could be explained by difficulty identifying emotions (DIE) that selectively characterizes PWE.

Identification of epilepsy-associated neuronal subtypes and gene expression underlying epileptogenesis

Epilepsy is one of the most common neurological disorders, yet its pathophysiology is poorly understood due to the high complexity of affected neuronal circuits. To identify dysfunctional neuronal subtypes underlying seizure activity in the human brain, we have performed single-nucleus transcriptomics analysis of >110,000 neuronal transcriptomes derived from temporal cortex samples of multiple temporal lobe epilepsy and non-epileptic subjects. We found that the largest transcriptomic changes occur in distinct neuronal subtypes from several families of principal neurons (L5-6_Fezf2 and L2-3_Cux2) and GABAergic interneurons (Sst and Pvalb), whereas other subtypes in the same families were less affected. Furthermore, the subtypes with the largest epilepsy-related transcriptomic changes may belong to the same circuit, since we observed coordinated transcriptomic shifts across these subtypes. Glutamate signaling exhibited one of the strongest dysregulations in epilepsy, highlighted by layer-wise transcriptional changes in multiple glutamate receptor genes and strong upregulation of genes coding for AMPA receptor auxiliary subunits. Overall, our data reveal a neuronal subtype-specific molecular phenotype of epilepsy.

Automated Detection of Interictal Epileptiform Discharges from Scalp Electroencephalograms by Convolutional Neural Networks

Visual evaluation of electroencephalogram (EEG) for Interictal Epileptiform Discharges (IEDs) as distinctive biomarkers of epilepsy has various limitations, including time-consuming reviews, steep learning curves, interobserver variability, and the need for specialized experts. The development of an automated IED detector is necessary to provide a faster and reliable diagnosis of epilepsy. In this paper, we propose an automated IED detector based on Convolutional Neural Networks (CNNs). We have evaluated the proposed IED detector on a sizable database of 554 scalp EEG recordings (84 epileptic patients and 461 nonepileptic subjects) recorded at Massachusetts General Hospital (MGH), Boston. The proposed CNN IED detector has achieved superior performance in comparison with conventional methods with a mean cross-validation area under the precision-recall curve (AUPRC) of 0.838[Formula: see text]±[Formula: see text]0.040 and false detection rate of 0.2[Formula: see text]±[Formula: see text]0.11 per minute for a sensitivity of 80%. We demonstrated the proposed system to be noninferior to 30 neurologists on a dataset from the Medical University of South Carolina (MUSC). Further, we clinically validated the system at National University Hospital (NUH), Singapore, with an agreement accuracy of 81.41% with a clinical expert. Moreover, the proposed system can be applied to EEG recordings with any arbitrary number of channels.

Acute Effects and the Dreamy State Evoked by Deep Brain Electrical Stimulation of the Amygdala: Associations of the Amygdala in Human Dreaming, Consciousness, Emotions, and Creativity.

Accurate localization of complex human experiences such as emotions, dreaming, creativity, and consciousness to specific cerebral structures or neural networks has remained elusive despite technological advances. We report the use of acute deep brain stimulation (DBS) to evoke behavioral and emotional effects by applying electrical stimulation (ES) at various voltage strengths to the basolateral and central subnuclei of the amygdala in addition to the head of hippocampus (HC) for two subjects with medically refractory post-traumatic stress disorder (PTSD). Our results suggest that the amygdala could be a node in a neural network responsible for the generation of complex vivid mental imagery and integrated sensory experiences similar to John Hughlings Jackson’s “dreamy state” and “double consciousness,” which have been classically associated with temporal lobe epilepsy during uncinate seizures. That we were able to elicit similar vivid, dynamic, complex, bizarre, and original mental imagery with ES in non-epileptic subjects suggests that Jackson’s seizure related “dreamy state” and “double consciousness” may arise from heightened innate brain mechanisms with the amygdala acting as a node in the neural network responsible for physiologic dreaming and creative functions. Furthermore, our subjects experienced different emotions with different stimulation strengths at various electrode contacts. Our results suggest that higher voltage stimulation of the amygdala and HC at 4–5 V leads to predominantly negative responses and 2–4 V stimulation showed inversely coupled positive and negative responses of the amygdala in either hemisphere which may imply hemispheric dominance of emotional valences without relation to handedness. Due to the unique and complex responses dependent on location and strength of stimulation, we advise that all patients receiving DBS of the amygdala undergo acute stimulation mapping in a monitored setting before selecting therapeutic parameters for chronic stimulation.

Clinical evaluation of psychiatric and behavioral disorders in adolescents with epilepsy: a cross-sectional study

Background: Epilepsy is a neurological disease that is often associated with psychiatric comorbidities.Subjects and methods: The aim of the study was to examine the frequency of psychic symptoms and their association with different types of epilepsy in the adolescent population. The study involved 100 adolescents with epilepsy and 100 healthy adolescents of both sexes at the age of 13–19. The examinees completed a standardized Symptom Checklist-90-R (SCL-90-R) questionnaire, concerning self-assessment of psychological disorders in the previous week. The value system of ​​nine dimensions and three global indexes of SCL-90-R scales were analyzed.Results: Our results suggest that adolescents with epilepsy have more than one psychic disorder compared to a healthy group of respondents. Somatic symptoms are more common in non-epileptic subjects, while obsessive-compulsive, depressive and phobic symptoms, as well as anxiety, aggressiveness, interpersonal vulnerability and paranoia, are more common in adolescents with epilepsy. Due to the type of epilepsy, obsessive compulsive symptoms are more common in adolescents with focal epilepsy, while the symptoms of phobia are more present in those with generalized epilepsy. The depth and intensity of psychological stress is higher in the group of adolescents with epilepsy compared to the healthy group of respondents.Conclusions: Psychiatric comorbidity is very common in epileptic patients and depending on the type of diagnosed epilepsy, various symptoms are expressed. Furthermore, psychological stress is more observable in adolescents with epilepsy. For patients with epilepsy, mental health care and seizure control is extremely important in the prevention of serious mental disabilities.

Epilepsy and migraine in adolescent and adult subjects in West Africa

Objective The aim of this work was to study migraine exposure in epileptics and non-epileptics in Parakou in 2018. Framework and methods This study was conducted in Parakou in Northen Benin.It was a case - control study of adolescents and adults of both sexes in Parakou. Each cases that were epileptic subjects were matched to three non-epileptic controls by age, sex and place of residence. Thirty epileptics ans ninegthy controls were included. The EPIDATA analysis EPI INFO 7.1 and SPSS2.1 software. Were used for the analysis. The p Results The mean age of the two groups was 32.33 ± 15.28 years [15–64 years]; with a sex ratio at 1.45. 43.33% of epileptics had at least one seizure per month; 56.67% suffered from generalized epilepsy. Frequency of migraine in epileptics was 63.33% and 17.78% in non-epileptics. There was a statistically significant association between migraine and epilepsy (p = .000). The relationship between migraine and epilepsy adjusted to religion, socioeconomic level, stress, temperature change, excessive light, menstrual period shows that epileptics are 6.18 times more likely to have migraine compared to non-epileptic subjects. Conclusion Epileptics are more exposed to migraine and migraine is more common in these subjects compared to non-epileptics.

Epilepsy-associated stigma from the perspective of people with epilepsy and the community in Italy

ObjectiveThe objective of this study was to assess the stigma related to epilepsy from the perspective of people with epilepsy (PWE) and from the Italian community (Rome and central Italy); moreover, the impact of the perceived stigma on the mood and quality of life of patients was also evaluated. Materials and methodsWe consecutively enrolled 100 PWE and 202 nonepileptic subjects (NES). Both PWE and NES completed an Italian version of the Stigma Scale of Epilepsy (SSE), a 24-items questionnaire that has been demonstrated to allow the quantification of the stigma perception by patients and people from the community. Moreover, the PWE fulfilled a 3-item Jacoby's Stigma Scale, the Quality of Life in Epilepsy-31 (QOLIE-31 [Q3])), and Beck Depression Inventory II (BDI-II) questionnaires for the evaluation of the quality of life and depressive symptoms. The results of the SSE were correlated with clinical and demographic details of PWE and NES, as well as the Q31 and BDI-II scores in PWE. ResultsThe SSE scores were significantly higher in NES with respect to PWE (respectively 47.1 vs 39.5, p < .001). Forty-two percent of PWE reported feeling stigmatized, with 5% reporting feeling highly stigmatized. In PWE, the perceived stigma was not correlated with seizure frequency but was significantly associated with worse quality of life, more severe depressive symptoms, and higher number of AEDs. The multiple regression analysis showed that the quality-of-life overall score and Q31 subscale exploring “social function” are the most significant predictors of stigma. ConclusionsBy using an Italian translation of the SSE questionnaire, even if we cannot consider our sample representative of the whole Italian community our study evidenced higher rates of stigma related to epilepsy in NES than in PWE. The PWE still experience feelings of stigmatization strongly correlated with higher depressive symptoms and worse quality of life that has proven to be the most significant predictor of stigma. Finally, seizure frequency does not affect the perceived stigma, which is instead significantly influenced by antiepileptic therapy.

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

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). 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. 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. 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.

Performance analysis of power and power variance for classification, detection and localization of epileptic multi-channel EEG

There are a large number of data sets of EEG signal for which, it is difficult to judge and monitor brain activity through observations. Epilepsy is a disorder in which a recurrent and sudden malfunction of the brain is characterized. It is proposed to classify, detect and localize Epileptic multi-channel EEG through various power and novel power variance features non-invasively. This work presents power spectral estimation (PSE) using time–frequency analysis of EEG signals in both parametric (FFT) and non-parametric methods (i.e. Welch, Burg, Covariance, MUSIC and Yule–Walker). To examine the robustness of power features for different methods, the analysis of p value is performed. The detection of epileptic seizure is classified using different kernels through SVM. It is observed from the PSE that the power features have higher values in epileptic subjects as compared to non-epileptic subjects. Amongst all the parametric and non-parametric methods, the MUSIC method gives the highest average power. Sensitivity, specificity, and classification accuracy are 100% for Welch, Burg, Covariance, and Yule–Walker methods while MUSIC and FFT methods deliver 98.73 and 99.52% respectively. The novelty is introduced through the quantification of power and power variance robust feature region/lobe-wise. This quantification is used for the localization of 25 epileptic subjects. Analysis of the parametric and non-parametric PSD methods for extraction of power and power variance features is not used by any study. These are effectively utilized for detection and localization of epilepsy non-invasively.

Sleep spindles in rats with absence epilepsy

Absence epilepsy is an idiopathic generalized non-convulsive type of epilepsy associated with aberrant activity in the thalamocortical network. The common neuronal network mechanism of spike–wave discharges (a manifestation of absence epilepsy) and sleep spindles suggests a close relationship between them. This paper overviews electroencephalographic (EEG) properties of anterior sleep spindles in a genetic Wistar Albino Glaxo from Rijswijk (WAG/Rij) rat model of absence epilepsy. Epileptic discharges in WAG/Rij rats appear spontaneously, and their incidence increases with age. In epileptic rats, time–frequency profile of sleep spindles distinguished from that in non-epileptic subjects by shorter duration, lower intraspindle frequency, and contained less slow-wave components, etc. Some pro-epileptic modifications of spindle activity can also be observed in EEG in epileptic WAG/Rij rats.

Integration of EEG Into Psychiatric Practice: A Step Toward Precision Medicine for Autism Spectrum Disorder.

Data from an EEG is not commonly used by psychiatrists to plan treatment and medication. However, EEG abnormalities such as isolated epileptiform discharges are found to be more prevalent in psychiatric patients, particularly those diagnosed with autism spectrum disorder (ASD). Most medications prescribed for ASD lower seizure threshold and increase side effects. Therefore, it may be prudent to order an EEG for ASD cases, especially those categorized as refractory. The data set was obtained from a multidisciplinary practice that treats a wide variety of neuroatypical children and adolescent refractory patients. This study investigated 140 nonepileptic subjects diagnosed with ASD, aged 4 to 25 years. Visual inspection of the EEG was performed to search for paroxysmal, focal, or lateralizing patterns. Of the 140 subjects, the EEG data identified 36% with isolated epileptiform discharges. The χ analysis found no significant difference between genders among the three age groups. Findings indicated a high prevalence of isolated epileptiform discharges among individuals with ASD. Our results find that compared with the healthy population, a large number of patients with ASD have isolated epileptiform discharges despite never having a seizure. Our findings support the use of EEG in children, adolescents, and young adults with ASD, regardless of gender or age. This is particularly true for those who exhibit aggressive behaviors or those who have failed previous medication attempts with stimulants, antidepressants, and/or antipsychotics.

Imaging of Anterior Nucleus of Thalamus Using 1.5T MRI for Deep Brain Stimulation Targeting in Refractory Epilepsy

Deep brain stimulation (DBS) of the anterior nucleus of thalamus (ANT) is an evolving treatment option in refractory focal epilepsy. Due to poor visualization of ANT in traditional MRI sequences used for movement disorder surgery, targeting of ANT is mainly based on stereotactic atlas information. Sophisticated 3T MRI methods enable visualization of ANT, but 1.5T MRI is still preferred or more readily available in a large number of centers performing DBS. In the present study, we sought to determine whether ANT could be adequately visualized at 1.5T MRI pre- and postoperatively using imaging techniques similar to the ones visualizing ANT in 3T MRI. A total of 15 MRI examinations with short tau inversion recovery (STIR) and T1-weighted magnetization prepared gradient echo (MPRAGE) images were performed to visualize ANT in nonepileptic subjects (n = 2), patients with vagus nerve stimulator (VNS) (n = 3), stereotactic MRI (n = 3), patients with ANT-DBS (n = 7). ANT was distinctly visualized in STIR and T1-weighted MPRAGE images in patients without implanted stimulators, with Leksell stereotactic frame and with fully implanted VNS. Postoperative 1.5T MRI was able to demonstrate some of the anatomical landmarks around ANT enabling assessment of electrode contact locations. The visualization of ANT is possible in preoperative 1.5T MRI enabling direct targeting of ANT all examined situations. The use of indirect targeting and its inherent potential for lead misplacement due to anatomical variation may be avoided using these MRI methods. Furthermore, postoperative MRI with STIR and T1-weighted MPRAGE images enable detailed postoperative assessment of contact locations.

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

BackgroundEpilepsy 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.MethodsSoluble 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.ResultsBrain 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-1β and IL-1α were lowest in the hippocampus, compared to cortical regions. In the temporal cortex, IL-1β, 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-1β, 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.ConclusionsBoth 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12974-016-0727-z) contains supplementary material, which is available to authorized users.

DTI-based response-driven modeling of mTLE laterality.

PurposeTo develop lateralization models for distinguishing between unilateral and bilateral mesial temporal lobe epilepsy (mTLE) and determining laterality in cases of unilateral mTLE.BackgroundmTLE 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).MethodsFractional 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.ResultsIn 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).ConclusionThe 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.

Lateralization of temporal lobe epilepsy by multimodal multinomial hippocampal response-driven models

PurposeMultiple modalities are used in determining laterality in mesial temporal lobe epilepsy (mTLE). It is unclear how much different imaging modalities should be weighted in decision-making. The purpose of this study is to develop response-driven multimodal multinomial models for lateralization of epileptogenicity in mTLE patients based upon imaging features in order to maximize the accuracy of noninvasive studies. Methods and materialsThe volumes, means and standard deviations of FLAIR intensity and means of normalized ictal–interictal SPECT intensity of the left and right hippocampi were extracted from preoperative images of a retrospective cohort of 45 mTLE patients with Engel class I surgical outcomes, as well as images of a cohort of 20 control, nonepileptic subjects. Using multinomial logistic function regression, the parameters of various univariate and multivariate models were estimated. Based on the Bayesian model averaging (BMA) theorem, response models were developed as compositions of independent univariate models. ResultsA BMA model composed of posterior probabilities of univariate response models of hippocampal volumes, means and standard deviations of FLAIR intensity, and means of SPECT intensity with the estimated weighting coefficients of 0.28, 0.32, 0.09, and 0.31, respectively, as well as a multivariate response model incorporating all mentioned attributes, demonstrated complete reliability by achieving a probability of detection of one with no false alarms to establish proper laterality in all mTLE patients. ConclusionThe proposed multinomial multivariate response-driven model provides a reliable lateralization of mesial temporal epileptogenicity including those patients who require phase II assessment.

Lateralization of temporal lobe epilepsy using a novel uncertainty analysis of MR diffusion in hippocampus, cingulum, and fornix, and hippocampal volume and FLAIR intensity

PurposeTo analyze the utility of a quantitative uncertainty analysis approach for evaluation and comparison of various MRI findings for the lateralization of epileptogenicity in mesial temporal lobe epilepsy (mTLE), including novel diffusion-based analyses. MethodsWe estimated the hemispheric variation uncertainty (HVU) of hippocampal T1 volumetry and FLAIR (Fluid Attenuated Inversion Recovery) intensity. Using diffusion tensor images of 23 nonepileptic subjects, we estimated the HVU levels of mean diffusivity (MD) in the hippocampus, and fractional anisotropy (FA) in the posteroinferior cingulum and crus of fornix. Imaging from a retrospective cohort of 20 TLE patients who had undergone surgical resection with Engel class I outcomes was analyzed to determine whether asymmetry of preoperative volumetrics, FLAIR intensities, and MD values in hippocampi, as well as FA values in posteroinferior cingula and fornix crura correctly predicted laterality of seizure onset. Ten of the cohort had pathologically proven mesial temporal sclerosis (MTS). Seven of these patients had undergone extraoperative electrocorticography (ECoG) for lateralization or to rule out extra-temporal foci. ResultsHVU was estimated to be 3.1×10−5 for hippocampal MD, 0.027 for FA in posteroinferior cingulum, 0.018 for FA in crus of fornix, 0.069 for hippocampal normalized volume, and 0.099 for hippocampal normalized FLAIR intensity. Using HVU analysis, a higher hippocampal MD value, lower FA within the posteroinferior cingulum and crus of fornix, shrinkage in hippocampal volume, and higher hippocampal FLAIR intensity were observed beyond uncertainty on the side ipsilateral to seizure onset for 10, 10, 9, 9, and 10 out of 10 pathology-proven MTS patients, respectively. Considering all 20 TLE patients, these numbers were 18, 15, 14, 13, and 16, respectively. However, consolidating the lateralization results of HVU analysis on these quantities by majority voting has detected the epileptogenic side for 19 out of 20 cases with no wrong lateralization. ConclusionThe presence of MTS in TLE patients is associated with an elevated MD value in the ipsilateral hippocampus and a reduced FA value in the posteroinferior subregion of the ipsilateral cingulum and crus of ipsilateral fornix. When considering all TLE patients, among the mentioned biomarkers the hippocampal MD had the best performance with true detection rate of 90% without any wrong lateralization. The proposed uncertainty based analyses hold promise for improving decision-making for surgical resection.