Predicting Surgery Targets in Temporal Lobe Epilepsy through Structural Connectome Based Simulations

Temporal lobe epilepsy (TLE) is a prevalent neurological disorder resulting in disruptive seizures. In the case of drug resistant epilepsy resective surgery is often considered. This is a procedure hampered by unpredictable success rates, with many patients continuing to have seizures even after surgery. In this study we apply a computational model of epilepsy to patient specific structural connectivity derived from diffusion tensor imaging (DTI) of 22 individuals with left TLE and 39 healthy controls. We validate the model by examining patient-control differences in simulated seizure onset time and network location. We then investigate the potential of the model for surgery prediction by performing in silico surgical resections, removing nodes from patient networks and comparing seizure likelihood post-surgery to pre-surgery simulations. We find that, first, patients tend to transit from non-epileptic to epileptic states more often than controls in the model. Second, regions in the left hemisphere (particularly within temporal and subcortical regions) that are known to be involved in TLE are the most frequent starting points for seizures in patients in the model. In addition, our analysis also implicates regions in the contralateral and frontal locations which may play a role in seizure spreading or surgery resistance. Finally, the model predicts that patient-specific surgery (resection areas chosen on an individual, model-prompted, basis and not following a predefined procedure) may lead to better outcomes than the currently used routine clinical procedure. Taken together this work provides a first step towards patient specific computational modelling of epilepsy surgery in order to inform treatment strategies in individuals.     

Functional Connectivity Estimated from Intracranial EEG Predicts Surgical Outcome in Intractable Temporal Lobe Epilepsy

This project aimed to determine if a correlation-based measure of functional connectivity can identify epileptogenic zones from intracranial EEG signals, as well as to investigate the prognostic significance of such a measure on seizure outcome following temporal lobe lobectomy. To this end, we retrospectively analyzed 23 adult patients with intractable temporal lobe epilepsy (TLE) who underwent an invasive stereo-EEG (SEEG) evaluation between January 2009 year and January 2012. A follow-up of at least one year was required. The primary outcome measure was complete seizure-freedom at last follow-up. Functional connectivity between two areas in the temporal lobe that were sampled by two SEEG electrode contacts was defined as Pearson’s correlation coefficient of interictal activity between those areas. SEEG signals were filtered between 5 and 50 Hz prior to computing this correlation. The mean and standard deviation of the off diagonal elements in the connectivity matrix were also calculated. Analysis of the mean and standard deviation of the functional connections for each patient reveals that 90% of the patients who had weak and homogenous connections were seizure free one year after temporal lobectomy, whereas 85% of the patients who had stronger and more heterogeneous connections within the temporal lobe had recurrence of seizures. This suggests that temporal lobectomy is ineffective in preventing seizure recurrence for patients in whom the temporal lobe is characterized by weakly connected, homogenous networks. This pilot study shows promising potential of a simple measure of functional brain connectivity to identify epileptogenicity and predict the outcome of epilepsy surgery.     

CHRONOTYPES AND SUBJECTIVE SLEEP PARAMETERS IN EPILEPSY PATIENTS: A LARGE QUESTIONNAIRE STUDY

Accumulating evidence suggests epilepsy and seizures may influence circadian rhythms and that circadian rhythms may influence epilepsy. It is also conceivable that seizure timing influences the timing of daily activities, sleeping, and wakefulness (i.e., chronotype). Only one group has studied the distribution of chronotypes of epileptics, showing significant differences between the diurnal activity patterns in two groups of patients with different epilepsy syndromes. The authors performed a questionnaire-based study of 200 epilepsy patients to compare the distribution of chronotypes and subjective sleep parameters of sleep duration and time of mid-sleep on free days to the distribution in the general population (n?=?4042). Within this large group of epilepsy patients, we also compared the chronotypes of subsamples with well-defined epilepsy syndromes, i.e., temporal lobe epilepsy [TLE; n?=?46], frontal lobe epilepsy [FLE; n?=?30], and juvenile myoclonic epilepsy [JME; n?=?38]. In addition, 27 patients who had had surgery for TLE were compared with those with TLE who had not had surgery. Both the Morningness-Eveningness Questionnaire and Munich Chronotype Questionnaire were used to determine chronotypes and subjective sleep parameters. Significant differences in morningness/eveningness distribution, timing of mid-sleep (corrected for sleep duration), and total sleep time on free days were found between epileptics and healthy controls. Those with epilepsy were more morning oriented, had earlier mid-sleep on free days, and longer sleep duration on free days (p?<?.001). However, distributions of chronotypes and sleep parameters between the groups of people with TLE, FLE, and JME did not differ. Persons who had surgery for TLE had similar morningness-eveningness parameters and similar sleep durations compared to those without surgery, but mid-sleep on free days was earlier in operated patients (p?=?.039). In conclusion, this is the first large study focusing on chronotypes in people with epilepsy. We show that the distribution of chronotypes and subjective sleep parameters of epileptics, in general, is different from that of healthy controls. Nevertheless, no differences are observed between patients with specified epilepsy syndromes, although they exhibit seizures with different diurnal patterns. Our results suggest that epilepsy, itself, rather than seizure timing, has a significant influence on chronotype behavior and subjective sleep parameters. (Author correspondence: w.hofstra@mst.nl)     

Long-Term Effects of Temporal Lobe Epilepsy on Local Neural Networks: A Graph Theoretical Analysis of Corticography Recordings

Purpose

Pharmaco-resistant temporal lobe epilepsy (TLE) is often treated with surgical intervention at some point. As epilepsy surgery is considered a last resort by most physicians, a long history of epileptic seizures prior to surgery is not uncommon. Little is known about the effects of ongoing TLE on neural functioning. A better understanding of these effects might influence the moment of surgical intervention. Functional connectivity (interaction between spatially distributed brain areas) and network structure (integration and segregation of information processing) are thought to be essential for optimal brain functioning. We report on the impact of TLE duration on temporal lobe functional connectivity and network characteristics.

Methods

Functional connectivity of the temporal lobe at the time of surgery was assessed by means of interictal electrocorticography (ECoG) recordings of 27 TLE patients by using the phase lag index (PLI). Graphs (abstract network representations) were reconstructed from the PLI matrix and characterized by the clustering coefficient C (local clustering), the path length L (overall network interconnectedness), and the “small world index” S (network configuration).

Results

Functional connectivity (average PLI), clustering coefficients, and the small world index were negatively correlated with TLE duration in the broad frequency band (0.5–48 Hz).

Discussion

Temporal lobe functional connectivity is lower in patients with longer TLE history, and longer TLE duration is correlated with more random network configuration. Our findings suggest that the neural networks of TLE patients become more pathological over time, possibly due to temporal lobe changes associated with long-standing lesional epilepsy.     

Increased Metallothionein I/II Expression in Patients with Temporal Lobe Epilepsy

In the central nervous system, zinc is released along with glutamate during neurotransmission and, in excess, can promote neuronal death. Experimental studies have shown that metallothioneins I/II (MT-I/II), which chelate free zinc, can affect seizures and reduce neuronal death after status epilepticus. Our aim was to evaluate the expression of MT-I/II in the hippocampus of patients with temporal lobe epilepsy (TLE). Hippocampi from patients with pharmacoresistant mesial temporal lobe epilepsy (MTLE) and patients with TLE associated with tumor or dysplasia (TLE-TD) were evaluated for expression of MT-I/II, for the vesicular zinc levels, and for neuronal, astroglial, and microglial populations. Compared to control cases, MTLE group displayed widespread increase in MT-I/II expression, astrogliosis, microgliosis and reduced neuronal population. In TLE-TD, the same changes were observed, except that were mainly confined to fascia dentata. Increased vesicular zinc was observed only in the inner molecular layer of MTLE patients, when compared to control cases. Correlation and linear regression analyses indicated an association between increased MT-I/II and increased astrogliosis in TLE. MT-I/II levels did not correlate with any clinical variables, but MTLE patients with secondary generalized seizures (SGS) had less MT-I/II than MTLE patients without SGS. In conclusion, MT-I/II expression was increased in hippocampi from TLE patients and our data suggest that it is associated with astrogliosis and may be associated with different seizure spread patterns.     

Identifying an increased risk of epileptic seizures using a multi-feature EEG–ECG classification

Epilepsy, a neurological disorder in which patients suffer from recurring seizures, affects approximately 1% of the world population. In spite of available drug and surgical treatment options, more than 25% of individuals with epilepsy have seizures that are uncontrollable. For these patients with intractable epilepsy, the unpredictability of seizure occurrence underlies an enhanced risk of sudden unexpected death or morbidity. A system that could warn the patient of the impending event or trigger an antiepileptic device would dramatically increase the quality of life for those patients. Here, we proposed a patient-specific algorithm for possible seizure warning using machine learning classification of 34 algorithmic features derived from EEG–ECG recordings. We evaluated our algorithm on unselected and continuous recordings of 12 patients (total of 108 seizures and 3178-h). Good out-of-sample performances were observed around 25% of the patients with an average preictal period around 30 min and independently of the EEG type (scalp or intracranial). Inspection of the most discriminative EEG–ECG features revealed that good classification rates reflected specific physiological precursors, particularly related to certain stages of sleep. From these observations, we conclude that our algorithmic strategy enables a quantitative way to identify “pro-ictal” states with a high risk of seizure generation.     

Mapping the spatio-temporal pattern of the mammalian target of rapamycin (mTOR) activation in temporal lobe epilepsy

Growing evidence from rodent models of temporal lobe epilepsy (TLE) indicates that dysregulation of the mammalian target of rapamycin (mTOR) pathway is involved in seizures and epileptogenesis. However, the role of the mTOR pathway in the epileptogenic process remains poorly understood. Here, we used an animal model of TLE and sclerotic hippocampus from patients with refractory TLE to determine whether cell-type specific activation of mTOR signaling occurs during each stage of epileptogenesis. In the TLE mouse model, we found that hyperactivation of the mTOR pathway is present in distinct hippocampal subfields at three different stages after kainate-induced seizures, and occurs in neurons of the granular and pyramidal cell layers, in reactive astrocytes, and in dispersed granule cells, respectively. In agreement with the findings in TLE mice, upregulated mTOR was observed in the sclerotic hippocampus of TLE patients. All sclerotic hippocampus (n = 13) exhibited widespread reactive astrocytes with overactivated mTOR, some of which invaded the dispersed granular layer. Moreover, two sclerotic hippocampus exhibited mTOR activation in some of the granule cells, which was accompanied by cell body hypertrophy. Taken together, our results indicate that mTOR activation is most prominent in reactive astrocytes in both an animal model of TLE and the sclerotic hippocampus from patients with drug resistant TLE.     

癫痫与海马结构的改变

癫痫作为一种严重危害人类健康的常见病、多发病，其致病机理至今尚未阐明。30—60％的患者药物治疗无效，称“难治性癫痫”。随着现代医学的发展，外科手术的开展对于癫痫患者治疗也没有满意的效果。这就对于我们探求癫痫患者病灶的起源有了更深层次的要求。大量动物实验表明，海马作为中枢神经系统的重要结构不仅同学习、记忆、情绪等密切相关，还同癫痫的发生发展有着重要的联系。本文就大脑可塑性与癫痫的关系进行综述。     

Important role of matrix metalloproteinase 9 in epileptogenesis

Temporal lobe epilepsy (TLE) is a devastating disease in which aberrant synaptic plasticity plays a major role. We identify matrix metalloproteinase (MMP) 9 as a novel synaptic enzyme and a key pathogenic factor in two animal models of TLE: kainate-evoked epilepsy and pentylenetetrazole (PTZ) kindling-induced epilepsy. Notably, we show that the sensitivity to PTZ epileptogenesis is decreased in MMP-9 knockout mice but is increased in a novel line of transgenic rats overexpressing MMP-9. Immunoelectron microscopy reveals that MMP-9 associates with hippocampal dendritic spines bearing asymmetrical (excitatory) synapses, where both the MMP-9 protein levels and enzymatic activity become strongly increased upon seizures. Further, we find that MMP-9 deficiency diminishes seizure-evoked pruning of dendritic spines and decreases aberrant synaptogenesis after mossy fiber sprouting. The latter observation provides a possible mechanistic basis for the effect of MMP-9 on epileptogenesis. Our work suggests that a synaptic pool of MMP-9 is critical for the sequence of events that underlie the development of seizures in animal models of TLE.     

The Barnes Maze Task Reveals Specific Impairment of Spatial Learning Strategy in the Intrahippocampal Kainic Acid Model for Temporal Lobe Epilepsy

Neurochemical Research - Temporal lobe epilepsy (TLE) is an acquired form of focal epilepsy, in which patients not only suffer from unprovoked, devastating seizures, but also from severe...     

Feature Selection and the Class Imbalance Problem in Predicting Protein Function from Sequence

When the standard approach to predict protein function by sequence homology fails, other alternative methods can be used that require only the amino acid sequence for predicting function. One such approach uses machine learning to predict protein function directly from amino acid sequence features. However, there are two issues to consider before successful functional prediction can take place: identifying discriminatory features, and overcoming the challenge of a large imbalance in the training data. We show that by applying feature subset selection followed by undersampling of the majority class, significantly better support vector machine (SVM) classifiers are generated compared with standard machine learning approaches. As well as revealing that the features selected could have the potential to advance our understanding of the relationship between sequence and function, we also show that undersampling to produce fully balanced data significantly improves performance. The best discriminating ability is achieved using SVMs together with feature selection and full undersampling; this approach strongly outperforms other competitive learning algorithms. We conclude that this combined approach can generate powerful machine learning classifiers for predicting protein function directly from sequence.     

Distinct Types of White Matter Changes Are Observed after Anterior Temporal Lobectomy in Epilepsy

Anterior temporal lobectomy (ATL) is commonly adopted to control medically intractable temporal lobe epilepsy (TLE). Depending on the side of resection, the degree to which Wallerian degeneration and adaptive plasticity occur after ATL has important implications for understanding cognitive and clinical outcome. We obtained diffusion tensor imaging from 24 TLE patients (12 left) before and after surgery, and 12 matched controls at comparable time intervals. Voxel-based analyses were performed on fractional anisotropy (FA) before and after surgery. Areas with postoperative FA increase were further investigated to distinguish between genuine plasticity and processes related to the degeneration of crossing fibers. Before surgery, both patient groups showed bilateral reduced FA in numerous tracts, but left TLE patients showed more extensive effects, including language tracts in the contralateral hemisphere (superior longitudinal fasciculus and uncinate). After surgery, FA decreased ipsilaterally in both ATL groups, affecting the fornix, uncinate, stria terminalis, and corpus callosum. FA increased ipsilaterally along the superior corona radiata in both left and right ATL groups, exceeding normal FA values. In these clusters, the mode of anisotropy increased as well, confirming fiber degeneration in an area with crossing fibers. In left ATL patients, pre-existing low FA values in right superior longitudinal and uncinate fasciculi normalized after surgery, while MO values did not change. Preoperative verbal fluency correlated with FA values in all areas that later increased FA in left TLE patients, but postoperative verbal fluency correlated only with FA of the right superior longitudinal fasciculus. Our results demonstrate that genuine reorganization occurs in non-dominant language tracts after dominant hemisphere resection, a process that may help implement the inter-hemispheric shift of language activation found in fMRI studies. The results indicate that left TLE patients, despite showing more initial white matter damage, have the potential for greater adaptive changes postoperatively than right TLE patients.     

基于二阶C0复杂度的癫痫发作预测

癫痫发作的预测是近年来在临床医学和神经系统科学研究领域中备受关注的问题。如果癫痫发作能够被可靠地预测,则可以提前采取有效的临床预防措施,从而能较大程度地改善癫痫患者的生活质量。文章提出了一种基于二阶C0复杂度的预测算法用于预测癫痫发作。该算法通过分析癫痫患者颅内脑电信号的二阶C0复杂度,利用发作前期复杂度曲线的变化特征预测癫痫发作。作者运用该算法对21组癫痫病人87次发作的临床颅内脑电数据和4组大鼠4次发作的颅内脑电数据进行分析计算,预测准确率分别为94.3%和100%。实验结果表明该算法可以有效地预测癫痫发作,具有潜在的重要临床应用价值。     

Integrating Genetic,Neuropsychological and Neuroimaging Data to Model Early-Onset Obsessive Compulsive Disorder Severity

We propose an integrative approach that combines structural magnetic resonance imaging data (MRI), diffusion tensor imaging data (DTI), neuropsychological data, and genetic data to predict early-onset obsessive compulsive disorder (OCD) severity. From a cohort of 87 patients, 56 with complete information were used in the present analysis. First, we performed a multivariate genetic association analysis of OCD severity with 266 genetic polymorphisms. This association analysis was used to select and prioritize the SNPs that would be included in the model. Second, we split the sample into a training set (N = 38) and a validation set (N = 18). Third, entropy-based measures of information gain were used for feature selection with the training subset. Fourth, the selected features were fed into two supervised methods of class prediction based on machine learning, using the leave-one-out procedure with the training set. Finally, the resulting model was validated with the validation set. Nine variables were used for the creation of the OCD severity predictor, including six genetic polymorphisms and three variables from the neuropsychological data. The developed model classified child and adolescent patients with OCD by disease severity with an accuracy of 0.90 in the testing set and 0.70 in the validation sample. Above its clinical applicability, the combination of particular neuropsychological, neuroimaging, and genetic characteristics could enhance our understanding of the neurobiological basis of the disorder.     

Exposure to Mozart music reduces cognitive impairment in pilocarpine-induced status epilepticus rats

Patients with temporal lobe epilepsy (TLE) often display cognitive deficits. However, current epilepsy therapeutic interventions mainly aim at how to reduce the frequency and degree of epileptic seizures. Recovery of cognitive impairment is not attended enough, resulting in the lack of effective approaches in this respect. In the pilocarpine-induced temporal lobe epilepsy rat model, memory impairment has been classically reported. Here we evaluated spatial cognition changes at different epileptogenesis stages in rats of this model and explored the effects of long-term Mozart music exposure on the recovery of cognitive ability. Our results showed that pilocarpine rats suffered persisting cognitive impairment during epileptogenesis. Interestingly, we found that Mozart music exposure can significantly enhance cognitive ability in epileptic rats, and music intervention may be more effective for improving cognitive function during the early stages after Status epilepticus. These findings strongly suggest that Mozart music may help to promote the recovery of cognitive damage due to seizure activities, which provides a novel intervention strategy to diminish cognitive deficits in TLE patients.     

Epileptic seizures may begin hours in advance of clinical onset: a report of five patients

Mechanisms underlying seizure generation are traditionally thought to act over seconds to minutes before clinical seizure onset. We analyzed continuous 3- to 14-day intracranial EEG recordings from five patients with mesial temporal lobe epilepsy obtained during evaluation for epilepsy surgery. We found localized quantitative EEG changes identifying prolonged bursts of complex epileptiform discharges that became more prevalent 7 hr before seizures and highly localized subclinical seizure-like activity that became more frequent 2 hr prior to seizure onset. Accumulated energy increased in the 50 min before seizure onset, compared to baseline. These observations, from a small number of patients, suggest that epileptic seizures may begin as a cascade of electrophysiological events that evolve over hours and that quantitative measures of preseizure electrical activity could possibly be used to predict seizures far in advance of clinical onset.     

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Functional connectivity MRI (fcMRI) is an fMRI method that examines the connectivity of different brain areas based on the correlation of BOLD signal fluctuations over time. Temporal Lobe Epilepsy (TLE) is the most common type of adult epilepsy and involves multiple brain networks. The default mode network (DMN) is involved in conscious, resting state cognition and is thought to be affected in TLE where seizures cause impairment of consciousness. The DMN in epilepsy was examined using seed based fcMRI. The anterior and posterior hubs of the DMN were used as seeds in this analysis. The results show a disconnection between the anterior and posterior hubs of the DMN in TLE during the basal state. In addition, increased DMN connectivity to other brain regions in left TLE along with decreased connectivity in right TLE is revealed. The analysis demonstrates how seed-based fcMRI can be used to probe cerebral networks in brain disorders such as TLE.