Impaired consciousness in epilepsy

Spectral and network characterization of focal seizure types and phases

BACKGROUND AND OBJECTIVE

Currently, epileptic seizure characterization relies on several clinical features that allow their classification into different types. The present work aims to characterize both seizure types and phases based exclusively on electrophysiological characteristics.

METHODS

Based on the analysis of intracranial EEG recordings of 129 seizures from 22 patients obtained from the European Epilepsy Database, network and spectral measures were calculated in five-second temporal windows. Statistically significant differences between each window of the seizure phases (preictal, ictal, and postictal) and the interictal phase were used to identify/classify seizure types and their phases. A support vector machine (SVM) working on a multidimensional feature space of network and spectral measures was implemented for the classification of each seizure type; a traditional statistical approach was also conducted to highlight the underlying patterns to each seizure type or phase.

RESULTS

The percentage of correct classification of seizure types, corrected by chance, provided by the SVM exceeded 70%, considering all measures and the entire seizure (preictal + ictal + postictal). This percentage increased to more than 80% when all the measures during the ictal period for the depth electrodes or during the postictal for subdural electrodes were considered. Regarding the statistical approach, several measures presented a monotonic ascending and descending behavior with respect to seizure severity; these changes were observed during the ictal and postictal periods. Some measures were specific of each seizure type.

CONCLUSIONS

Our results provide a new framework to seizure characterization and reveal the possibility of an exclusively intracranial EEG-based classification. This could be used to build an automatic seizure classification system and provides new evidence of the network-related physiopathology of epilepsies. Thus, the novelty of this work is the possibility of differentiating seizure types based exclusively on the EEG recordings, providing evidence of the underlying patterns or characteristics to each seizure type and/or phase that would allow their optimal classification.

Alterations in Cortical-Subcortical Metabolism in Temporal Lobe Epilepsy With Impaired Awareness Seizures

Objective

The features of cerebral metabolism associated with loss of consciousness in patients with temporal lobe epilepsy (TLE) have not been fully elucidated. We aim to investigate the alterations in cortical-subcortical metabolism in temporal lobe epilepsy with impaired awareness seizures (IAS).

Methods

Regional cerebral metabolism was measured using fluorine-18-fluorodeoxyglucose positron emission tomography (18F-FDG PET) in patients with TLE-IAS and healthy controls. All patients had a comprehensive evaluation to confirm their seizure origin and lateralization. Videos of all seizures were viewed and rated by at least two epileptologists to identify the state of consciousness when a seizure occurred. By synthesizing the seizure history, semeiology, and video EEG of all patients, as long as the patients had one seizure with impaired awareness, she/he will be included. 76 patients with TLE-IAS and 60 age-matched healthy controls were enrolled in this study. Regional cerebral metabolic patterns were analyzed for TLE-IAS and healthy control groups using statistical parametric mapping. Besides, we compared the MRI-negative patients and MRI-positive patients with healthy controls, respectively.

Results

There were no significant differences in the age and sex of TLE-IAS patients and healthy control. TLE-IAS patients showed extensive bilateral hypermetabolism in the frontoparietal regions, cingulate gyrus, corpus callosum, occipital lobes, basal ganglia, thalamus, brainstem, and cerebellum. The region of metabolic change was more extensive in right TLE-IAS than that of the left, including extensive hypometabolism in the ipsilateral temporal, frontal, parietal, and insular lobes. And contralateral temporal lobe, bilateral frontoparietal regions, occipital lobes, the anterior and posterior regions of the cingulate gyrus, bilateral thalamus, bilateral basal ganglia, brainstem, and bilateral cerebellum showed hypermetabolism. The TLE patients with impaired awareness seizure showed hypermetabolism in the cortical-subcortical network including the arousal system. Additionally, 48 MRI-positive and 28 MRI-negative TLE-IAS patients were included in our study. TLE-IAS patients with MRI-negative and MRI-positive were both showed hypermetabolism in the cingulate gyrus. Hypometabolism in the bilateral temporal lobe was showed in the TLE-IAS with MRI-positive.

Conclusion

These findings suggested that the repetitive consciousness impairing ictal events may have an accumulative effect on brain metabolism, resulting in abnormal interictal cortical-subcortical metabolic disturbance in TLE patients with impaired awareness seizure. Understanding these metabolic mechanisms may guide future clinical treatments to prevent seizure-related awareness deficits and improve quality of life in people with TLE.

Mechanism analysis for excitatory interneurons dominating poly-spike wave and optimization of electrical stimulation

In addition to inhibitory interneurons, there exist excitatory interneurons (EINs) in the cortex, which mainly have excitatory projections to pyramidal neurons. In this study, we improve a thalamocortical model by introducing EIN, investigate the dominant role of EIN in generating spike and slow wave discharges (SWDs), and consider a non-rectangular pulse to control absence seizures. First, we display here that the improved model can reproduce typical SWDs of absence seizures. Moreover, we focus on the function of EIN by means of bifurcation analysis and find that EIN can induce transition behaviors under Hopf-type and fold limit cycle bifurcations. Specifically, the system has three stable solutions composing a tri-stable region. In this region, there are three attraction basins, which hints that external stimulation can drive the system trajectory from one basin to another, thereby eliminating abnormal oscillations. Furthermore, we compare the increasing ramp with rectangular pulse and optimize stimulation waveforms from the perspective of electrical charges input. The controlling role of the single increasing ramp to absence seizures is remarkable and the optimal stimulus parameters have been found theoretically. This work provides a computational model containing EIN and a theoretical basis for future physiological experiments and clinical research studies.

Characterizing Brain Network Dynamics using Persistent Homology in Patients with Refractory Epilepsy

Epilepsy is a common serious neurological disorder that affects more than 65 million persons worldwide and it is characterized by repeated seizures that lead to higher mortality and disabilities with corresponding negative impact on the quality of life of patients. Network science methods that represent brain regions as nodes and the interactions between brain regions as edges have been extensively used in characterizing network changes in neurological disorders. However, the limited ability of graph network models to represent high dimensional brain interactions are being increasingly realized in the computational neuroscience community. In particular, recent advances in algebraic topology research have led to the development of a large number of applications in brain network studies using topological structures. In this paper, we build on a fundamental construct of cliques, which are all-to-all connected nodes with a k-clique in a graph G (V, E), where V is set of nodes and E is set of edges, consisting of k-nodes to characterize the brain network dynamics in epilepsy patients using topological structures. Cliques represent brain regions that are coupled for similar functions or engage in information exchange; therefore, cliques are suitable structures to characterize the dynamics of brain dynamics in neurological disorders. We propose to detect and use clique structures during well-defined clinical events, such as epileptic seizures, to combine non-linear correlation measures in a matrix with identification of geometric structures underlying brain connectivity networks to identify discriminating features that can be used for clinical decision making in epilepsy neurological disorder.

Epilepsy-Connect: An Integrated Knowledgebase for Characterizing Alterations in Consciousness State of Pharmacoresistant Epilepsy Patients

Alterations in consciousness state are a defining characteristic of focal epileptic seizures. Consequently, understanding the complex changes in neurocognitive networks which underpin seizure-induced alterations in consciousness state is important for advancement in seizure classification. Comprehension of these changes are complicated by a lack of data standardization; however, the use of a common terminological system or ontology in a patient registry minimizes this issue. In this paper, we introduce an integrated knowledgebase called Epilepsy-Connect to improve the understanding of changes in consciousness states during focal seizures of pharmacoresistant epilepsy patients. This registry catalogues over 809 seizures from 70 patients at University Hospital's Epilepsy Center who were undergoing stereotactic electroencephalography (SEEG) monitoring as part of an evaluation for surgical intervention. Although Epilepsy-Connect focuses on consciousness states, it aims to enable users to leverage data from an informatics platform to analyze epilepsy data in a streamlined manner. Epilepsy-Connect is available at https://bmhinformatics.case.edu/Epilepsyconnect/login/.

Methodological aspects of studying the mechanisms of consciousness

There are at least two approaches to the definition of consciousness. In the first case, certain aspects of consciousness, called qualia, are considered inaccessible for research from a third person and can only be described through subjective experience. This approach is inextricably linked with the so-called "hard problem of consciousness", that is, the question of why consciousness has qualia or how any physical changes in the environment can generate subjective experience. With this approach, some aspects of consciousness, by definition, cannot be explained on the basis of external observations and, therefore, are outside the scope of scientific research. In the second case, a priori constraints do not constrain the field of scientific investigation, and the best explanation of the experience in the first person is included as a possible subject of empirical research. Historically, in the study of cause-and-effect relationships in biology, it was customary to distinguish between proximate causation and ultimate causation existing in biological systems. Immediate causes are based on the immediate influencing factors [1]. Proximate causation has evolutionary explanations. When studying biological systems themselves, such an approach is undoubtedly justified, but it often seems insufficient when studying the interaction of consciousness and the brain [2,3]. Current scientific communities proceed from the assumption that the physical substrate for the generation of consciousness is a neural network that unites various types of neurons located in various brain structures. Many neuroscientists attach a key role in this process to the cortical and thalamocortical neural networks. This question is directly related to experimental and clinical research in the field of disorder of consciousness. Progress in this area of medicine depends on advances in neuroscience in this area and is also a powerful source of empirical information. In this area of consciousness research, a large amount of experimental data has been accumulated, and in this review an attempt was made to generalize and systematize.

Characteristics of Epileptiform Spike-wave Discharges and Chronic Histopathology in Controlled Cortical Impact Model of Sprague-Dawley Rats

Post-traumatic epilepsy (PTE) is a serious complication that can occur following traumatic brain injury (TBI). Sustained secondary changes after TBI promote the process of PTE. Here, we aim to evaluate changes in behavior, electrocorticogram, and histomorphology in rats following chronic TBI models. We observed intensive 7-8 Hz spike-wave-discharges (SWDs) at frontal recording sites and quantified them in SD rats with different degrees of TBI and compared them with age-matched sham rats to evaluate the association between SWDs and injury severity. Notably, although SWDs were even presented in the sham group, the number and duration of events were much lower than those in the TBI groups. SWDs have numerous similarities to absence seizures, such as abrupt onset, termination, and lack of postictal suppression, which may be the nonconvulsive characteristics of PTE. Retigabine, a novel antiepileptic drug, is ineffective in reducing SWDs. In addition, we examined chronic histopathological changes in TBI rats. Rats subjected to moderate and severe TBI exhibited significantly impaired neurological function, which was accompanied by marked cortical injury, hippocampus deformation, reactive gliosis, and mossy fiber sprouting. Long-term progressive structural changes in the brain are one of the characteristics of epileptogenesis after TBI. Our study provided the potential value of epileptiform SWDs in reflecting the nonconvulsive characteristic of PTE and highlighted the vital role of chronic pathological changes, such as reactive gliosis, in promoting the epileptogenesis following TBI.

Reliability of patient self-report of cognition, awareness, and consciousness during seizures

Objective

Clinicians rely on patient self‐report of impairment during seizures for decisions including driving eligibility. However, the reliability of patient reports on cognitive and behavioral functions during seizures remains unknown.

Methods

We administered a daily questionnaire to epilepsy patients undergoing continuous video‐EEG monitoring, asking about responsiveness, speech, memory, awareness, and consciousness during seizures in the preceding 24 hours. We also administered a questionnaire upon admission about responsiveness, speech, and awareness during seizures. Subjective questionnaire answers were compared with objective behavioral ratings on video review. Criteria for agreement were Cohen’s kappa >0.60 and proportions of positive and negative agreement both >0.75.

Results

We analyzed 86 epileptic seizures in 39 patients. Memory report on the daily questionnaire met criteria for agreement with video review (κ = 0.674 for early, 0.743 for late recall). Subjective report of awareness also met agreement criteria with video ratings of memory (κ = 0.673 early, 0.774 late). Concordance for speech was relatively good (κ = 0.679) but did not meet agreement criteria, nor did responsiveness or consciousness. On the admission questionnaire, agreement criteria were met for subjective report of awareness versus video ratings of memory (κ = 0.814 early, 0.806 late), but not for other comparisons.

Interpretation

Patient self‐report of memory or awareness showed the best concordance with objective memory impairment during seizures. Self‐report of impairment in other categories was less reliable. These findings suggest that patient reports about impaired memory during seizures may be most reliable, and otherwise determining functional impairments should be based on objective observations.

Correlation Between the Functional Connectivity of Basal Forebrain Subregions and Vigilance Dysfunction in Temporal Lobe Epilepsy With and Without Focal to Bilateral Tonic-Clonic Seizure

PURPOSE

Previous research has shown that subcortical brain regions are related to vigilance in temporal lobe epilepsy (TLE). However, it is unknown whether alterations in the function and structure of basal forebrain (BF) subregions are associated with vigilance impairment in distinct kinds of TLE. We aimed to investigate changes in the structure and function BF subregions in TLE patients with and without focal to bilateral tonic-clonic seizures (FBTCS) and associated clinical features.

METHODS

A total of 50 TLE patients (25 without and 25 with FBTCS) and 25 healthy controls (HCs) were enrolled in this study. The structural and functional alterations of BF subregions in TLE were investigated using voxel-based morphometry (VBM) and resting-state functional connectivity (rsFC) analysis. Correlation analyses were utilized to investigate correlations between substantially altered imaging characteristics and clinical data from patients.

RESULTS

FBTCS patients had a lower rsFC between Ch1-3 and the bilateral striatum as well as the left cerebellum posterior lobe than non-FBTCS patients. In comparison to non-FBTCS patients, the rsFC between Ch4 and the bilateral amygdala was also lower in FBTCS patients. Compared to HCs, the TLE patients had reduced rsFC between the BF subregions and the cerebellum, striatum, default mode network, frontal lobe, and occipital lobes. In the FBTCS group, the rsFC between the left Ch1-3 and striatum was positive correlated with the vigilance measures. In the non-FBTCS group, the rsFC between the left Ch4 and striatum was significantly negative correlated with the alertness measure.

CONCLUSION

These results extend current understanding of the pathophysiology of impaired vigilance in TLE and imply that the BF subregions may serve as critical nodes for developing and categorizing TLE biomarkers.

Semiology, EEG, and neuroimaging findings in temporal lobe epilepsies

Temporal lobe epilepsy (TLE) is the most common type of focal epilepsy. First descriptions of TLE date back in time and detailed portraits of epileptic seizures of temporal origin can be found in early medical reports as well as in the works of various artists and dramatists. Depending on the seizure onset zone, several subtypes of TLE have been identified, each one associated with peculiar ictal semiology. TLE can result from multiple etiological causes, ranging from genetic to lesional ones. While the diagnosis of TLE relies on detailed analysis of clinical as well as electroencephalographic (EEG) features, the lesions responsible for seizure generation can be highlighted by multiple brain imaging modalities or, in selected cases, by genetic investigations. TLE is the most common cause of refractory epilepsy and despite the great advances in diagnostic tools, no lesion is found in around one-third of patients. Surgical treatment is a safe and effective option, requiring presurgical investigations to accurately identify the seizure onset zone (SOZ). In selected cases, presurgical investigations need intracerebral investigations (such as stereoelectroencephalography) or dedicated metabolic imaging techniques (interictal PET and ictal SPECT) to correctly identify the brain structures to be removed.

Arousal and Consciousness in Focal Seizures

Impaired consciousness during seizures severely affects quality of life for people with epilepsy but the mechanisms are just beginning to be understood. Consciousness is thought to involve large-scale brain networks, so it is puzzling that focal seizures often impair consciousness. Recent work investigating focal temporal lobe or limbic seizures in human patients and experimental animal models suggests that impaired consciousness is caused by active inhibition of subcortical arousal mechanisms. Focal limbic seizures exhibit decreased neuronal firing in brainstem, basal forebrain, and thalamic arousal networks, and cortical arousal can be restored when subcortical arousal circuits are stimulated during seizures. These findings open the possibility of restoring arousal and consciousness therapeutically during and following seizures by thalamic neurostimulation. When seizures cannot be stopped by existing treatments, targeted subcortical stimulation may improve arousal and consciousness, leading to improved safety and better psychosocial function for people with epilepsy.

Deep brain stimulation of the centromedian thalamic nucleus for the treatment of FIRES

Febrile infection‐related epilepsy syndrome (FIRES) is a rare, life‐threatening complication of febrile illness in previously healthy individuals followed by super‐refractory status epilepticus. Deep brain stimulation (DBS) has been demonstrated to be a promising therapy for the treatment of intractable epilepsy. Here, we present a pediatric patient with FIRES whose seizures were mitigated by acute DBS of the bilateral centromedian thalamic nucleus (CMTN). This is a previously healthy 11‐year‐old female who presented emergently with altered mental status, fever, and malaise after 1 week of lethargy, anorexia, fever, and abdominal pain. The patient began having seizures shortly after admission. After thorough workup for encephalitis and other potential etiologies, this patient was diagnosed with FIRES due to super‐refractory status epilepticus. Status epilepticus persisted despite pharmacologic management, immunotherapy, and vagus nerve stimulation. DBS of the bilateral CMTN (CM‐DBS) was pursued after 56 days of hospitalization, and she demonstrated considerable improvement in baseline mental status 30 days after DBS insertion. This report highlights application of CM‐DBS for super‐refractory status epilepticus in FIRES. This region is a diffusely connected brain region and has been shown to modulate neural networks contributing to seizure propagation and consciousness; therefore, neurostimulation is a potential therapeutic intervention for patients with super‐refractory status epilepticus.

Self, Me and I in the repertoire of spontaneously occurring altered states of Selfhood: eight neurophenomenological case study reports

This study investigates eight case reports of spontaneously emerging, brief episodes of vivid altered states of Selfhood (ASoSs) that occurred during mental exercise in six long-term meditators by using a neurophenomenological electroencephalography (EEG) approach. In agreement with the neurophenomenological methodology, first-person reports were used to identify such spontaneous ASoSs and to guide the neural analysis, which involved the estimation of three operational modules of the brain self-referential network (measured by EEG operational synchrony). The result of such analysis demonstrated that the documented ASoSs had unique neurophenomenological profiles, where several aspects or components of Selfhood (measured neurophysiologically and phenomenologically) are affected and expressed differently, but still in agreement with the neurophysiological three-dimensional construct model of the complex experiential Selfhood proposed in our earlier work (Fingelkurts et al. in Conscious Cogn 86:103031. 10.1016/j.concog.2020.103031, 2020).

Functional reorganization of brain regions into a network in childhood absence epilepsy: A magnetoencephalography study

OBJECTIVE

Epilepsy is considered as a network disorder. However, it is unknown how normal brain activity develops into the highly synchronized discharging activity seen in disordered networks. This study aimed to explore the epilepsy brain network and the significant re-combined brain areas in childhood absence epilepsy (CAE).

METHODS

Twenty-two children with CAE were recruited to study the neural source activity during ictal-onset and interictal periods at frequency bands of 1-30 Hz and 30-80 Hz with magnetoencephalography (MEG) scanning. Accumulated source imaging (ASI) was used to analyze the locations of neural source activity and peak source strength.

RESULTS

Most of the participants had more active source activity locations in the ictal-onset period rather than in the interictal period, both at 1-30 Hz and 30-80 Hz. The frontal lobe (FL), the temporo-parietal junction (T-P), and the parietal lobe (PL) became the main active areas of source activity during the ictal period, while the precuneus (PC), cuneus, and thalamus were relatively inactive.

CONCLUSIONS

Some brain areas become more excited and have increased source activity during seizures. These significant brain regions might be re-combined to form an epilepsy network that regulates the process of absence seizures.

SIGNIFICANCE

The study confirmed that important brain regions are reorganized in an epilepsy network, which provides a basis for exploring the network mechanism of CAE development. Imaging findings may provide a reference for clinical characteristics.

Short-term changes in cortical physiological arousal measured by electroencephalography during thalamic centromedian deep brain stimulation

OBJECTIVE

The intralaminar thalamus is well implicated in the processes of arousal and attention. Stimulation of the intralaminar thalamus has been used therapeutically to improve level of alertness in minimally conscious individuals and to reduce seizures in refractory epilepsy, both presumably through modulation of thalamocortical function. Little work exists that directly measures the effects of intralaminar thalamic stimulation on cortical physiological arousal in humans. Therefore, our goal was to quantify cortical physiological arousal in individuals with epilepsy receiving thalamic intralaminar deep brain stimulation.

METHODS

We recorded scalp electroencephalogram (EEG) during thalamic intralaminar centromedian (CM) nucleus stimulation in 11 patients with medically refractory epilepsy. Participants underwent stimulation at 130 Hz and 300 µs for periods of 5 min alternating with 5 min of rest while stimulus voltage was titrated upward from 1 to 5 V. EEG signal power was analyzed in different frequency ranges in relation to stimulus strength and time.

RESULTS

We found a progressive increase in broadband gamma (25-100 Hz) cortical EEG power (F = 7.64, p < .05) and decrease in alpha (8-13 Hz) power (F = 4.37, p < .05) with thalamic CM stimulation. Topographic maps showed these changes to be widely distributed across the cortical surface rather than localized to one region.

SIGNIFICANCE

Previous work has shown that broadband increases in gamma frequency power and decreases in alpha frequency power are generally associated with states of cortical activation and increased arousal/attention. Our observed changes therefore support the possible role of cortical activation and increased physiological arousal in therapeutic effects of intralaminar thalamic stimulation for improving both epilepsy and attention. Further investigations with this approach may lead to methods for determining optimal deep brain stimulation parameters to improve clinical outcome in these disorders.

Cortical low-frequency power correlates with behavioral impairment in animal model of focal limbic seizures

OBJECTIVE

Impairment in consciousness is a debilitating symptom during and after seizures; however, its mechanism remains unclear. Limbic seizures have been shown to spread to arousal circuitry to result in a "network inhibition" phenomenon. However, prior animal model studies did not relate physiological network changes to behavioral responses during or following seizures.

METHODS

Focal onset limbic seizures were induced while rats were performing an operant conditioned behavioral task requiring response to an auditory stimulus to quantify how and when impairment of behavioral response occurs. Correct responses were rewarded with sucrose. Cortical and hippocampal electrophysiology measured by local field potential recordings was analyzed for changes in low- and high-frequency power in relation to behavioral responsiveness during seizures.

RESULTS

As seen in patients with seizures, ictal (p < .0001) and postictal (p = .0015) responsiveness was variably impaired. Analysis of cortical and hippocampal electrophysiology revealed that ictal (p = .002) and postictal (p = .009) frontal cortical low-frequency 3-6-Hz power was associated with poor behavioral performance. In contrast, the hippocampus showed increased power over a wide frequency range during seizures, and suppression postictally, neither of which were related to behavioral impairment.

SIGNIFICANCE

These findings support prior human studies of temporal lobe epilepsy as well as anesthetized animal models suggesting that focal limbic seizures depress consciousness through remote network effects on the cortex, rather than through local hippocampal involvement. By identifying the cortical physiological changes associated with impaired arousal and responsiveness in focal seizures, these results may help guide future therapies to restore ictal and postictal consciousness, improving quality of life for people with epilepsy.

The role of convulsive seizures in SUDEP

Convulsive seizures are the most consistently reported risk factor for SUDEP. However, the precise mechanisms by which convulsive seizures trigger fatal cardiopulmonary changes are still unclear. Additionally, it is not clear why some seizures cause death when most do not. This article reviews the physiologic changes that occur during and after convulsive seizures and how these may contribute to SUDEP. Seizures activate specific cortical and subcortical regions that can cause potentially lethal cardiorespiratory changes. Clinical factors, including sleep state, medication treatment and withdrawal, positioning and posturing during seizures, and underlying structural or genetic conditions may also affect specific aspects of seizures that may contribute to SUDEP. While seizure control, either through medication or surgical treatment, is the primary intervention that reduces SUDEP risk, unfortunately, seizures cannot be fully controlled despite maximal treatment in a significant proportion of people with epilepsy. Thus specific interventions to prevent adverse seizure-related cardiopulmonary consequences are needed. The potential roles of repositioning/stimulation after seizures, oxygen supplementation, cardiopulmonary resuscitation and clinical treatment options in reducing SUDEP risk are explored. Ultimately, understanding of these factors may lead to interventions that could reduce or prevent SUDEP.

A Recent Investigation on Detection and Classification of Epileptic Seizure Techniques Using EEG Signal

The benefits of early detection and classification of epileptic seizures in analysis, monitoring and diagnosis for the realization and actualization of computer-aided devices and recent internet of medical things (IoMT) devices can never be overemphasized. The success of these applications largely depends on the accuracy of the detection and classification techniques employed. Several methods have been investigated, proposed and developed over the years. This paper investigates various seizure detection algorithms and classifications in the last decade, including conventional techniques and recent deep learning algorithms. It also discusses epileptiform detection as one of the steps towards advanced diagnoses of disorders of consciousness (DOCs) and their understanding. A performance comparison was carried out on the different algorithms investigated, and their advantages and disadvantages were explored. From our survey, much attention has recently been paid to exploring the efficacy of deep learning algorithms in seizure detection and classification, which are employed in other areas such as image processing and classification. Hybrid deep learning has also been explored, with CNN-RNN being the most popular.

Disruption of functional connectivity among subcortical arousal system and cortical networks in temporal lobe epilepsy

Growing evidence has demonstrated widespread brain network alterations in temporal lobe epilepsy (TLE). However, the relatively accurate portrait of the subcortical-cortical relationship for impaired consciousness in TLE remains unclear. We proposed that consciousness-impairing seizures may invade subcortical arousal system and corresponding cortical regions, resulting in functional abnormalities and information flow disturbances between subcortical and cortical networks. We performed resting-state fMRI in 26 patients with TLE and 30 matched healthy controls. All included patients were diagnosed with impaired awareness during focal temporal lobe seizures. Functional connectivity density was adopted to determine whether local or distant network alterations occurred in TLE, and Granger causality analysis (GCA) was utilized to assess the direction and magnitude of causal influence among these altered brain networks further. Patients showed increased local functional connectivity in several arousal structures, such as the midbrain, thalamus, and cortical regions including bilateral prefrontal cortex (PFC), left superior temporal pole, left posterior insula, and cerebellum (P < 0.05, FDR corrected). GCA analysis revealed that the casual effects among these regions in patients were significantly sparser than those in controls (P < 0.05, uncorrected), including decreased excitatory and inhibitory effects among the midbrain, thalamus and PFC, and decreased inhibitory effect from the cerebellum to PFC. These findings suggested that consciousness-impairing seizures in TLE are associated with functional alterations and disruption of information process between the subcortical arousal system and cortical network. Understanding the functional networks and innervation pathway involved in TLE can provide insights into the mechanism underlying seizure-related loss of consciousness.

Theoretical Models of Consciousness: A Scoping Review

The amount of knowledge on human consciousness has created a multitude of viewpoints and it is difficult to compare and synthesize all the recent scientific perspectives. Indeed, there are many definitions of consciousness and multiple approaches to study the neural correlates of consciousness (NCC). Therefore, the main aim of this article is to collect data on the various theories of consciousness published between 2007-2017 and to synthesize them to provide a general overview of this topic. To describe each theory, we developed a thematic grid called the dimensional model, which qualitatively and quantitatively analyzes how each article, related to one specific theory, debates/analyzes a specific issue. Among the 1130 articles assessed, 85 full texts were included in the prefinal step. Finally, this scoping review analyzed 68 articles that described 29 theories of consciousness. We found heterogeneous perspectives in the theories analyzed. Those with the highest grade of variability are as follows: subjectivity, NCC, and the consciousness/cognitive function. Among sub-cortical structures, thalamus, basal ganglia, and the hippocampus were the most indicated, whereas the cingulate, prefrontal, and temporal areas were the most reported for cortical ones also including the thalamo-cortical system. Moreover, we found several definitions of consciousness and 21 new sub-classifications.

Narrative review of epilepsy: getting the most out of your neuroimaging

Neuroimaging represents an important step in the evaluation of pediatric epilepsy. The crucial role of brain imaging in the diagnosis, follow-up and presurgical assessment of patients with epilepsy is noted and has to be familiar to all neuroradiologists and trainees approaching pediatric brain imaging. Morphological qualitative imaging shows the majority of cerebral lesions/alterations underlying focal epilepsy and can highlight some features which are useful in the differential diagnosis of the different types of epilepsy. Recent advances in MRI acquisitions including diffusion-weighted imaging (DWI), post-acquisition image processing techniques, and quantification of imaging data are increasing the accuracy of lesion detection during the last decades. Functional MRI (fMRI) can be really useful and helps to identify cortical eloquent areas that are essential for language, motor function, and memory, and diffusion tensor imaging (DTI) can reveal white matter tracts that are vital for these functions, thus reducing the risk of epilepsy surgery causing new morbidities. Also positron emission tomography (PET), single photon emission computed tomography (SPECT), simultaneous electroencephalogram (EEG) and fMRI, and electrical and magnetic source imaging can be used to assess the exact localization of epileptic foci and help in the design of intracranial EEG recording strategies. The main role of these "hybrid" techniques is to obtain quantitative and qualitative informations, a necessary step to evaluate and demonstrate the complex relationship between abnormal structural and functional data and to manage a "patient-tailored" surgical approach in epileptic patients.

The dissociative subtype of posttraumatic stress disorder is associated with subcortical white matter network alterations

Posttraumatic stress disorder (PTSD) is characterized by intrusions, avoidance, and hyperarousal while patients of the dissociative subtype (PTSD-D) experience additional dissociative symptoms. A neurobiological model proposes hyper-inhibition of limbic structures mediated by prefrontal cortices to underlie dissociation in PTSD. Here, we tested whether functional alterations in fronto-limbic circuits are underpinned by white matter network abnormalities on a network level. 23 women with PTSD-D and 19 women with classic PTSD participated. We employed deterministic diffusion tractography and graph theoretical analyses. Mean fractional anisotropy (FA) was chosen as a network weight and group differences assessed using network-based statistics. No significant white matter network alterations comprising both frontal and limbic structures in PTSD-D relative to classic PTSD were found. A subsequent whole brain exploratory analysis revealed relative FA alterations in PTSD-D in two subcortical networks, comprising connections between the left amygdala, hippocampus, and thalamus as well as links between the left ventral diencephalon, putamen, and pallidum, respectively. Dissociative symptom severity in the PTSD-D group correlated with FA values within both networks. Our findings suggest fronto-limbic inhibition in PTSD-D may present a dynamic neural process, which is not hard-wired via white matter tracts. Our exploratory results point towards altered fiber tract communication in a limbic-thalamic circuit, which may underlie (a) an initial strong emotional reaction to trauma reminders before conscious regulatory processes are enabled and (b) deficits in early sensory processing. In addition, aberrant structural connectivity in low-level motor regions may present neural correlates for dissociation as a passive threat-response.

Retrosplenial Cortex Contributes to Network Changes during Seizures in the GAERS Absence Epilepsy Rat Model

Resting state-fMRI was performed to explore brain networks in Genetic Absence Epilepsy Rats from Strasbourg and in nonepileptic controls (NEC) during monitoring of the brain state by simultaneous optical Ca2+-recordings. Graph theoretical analysis allowed for the identification of acute and chronic network changes and revealed preserved small world topology before and after seizure onset. The most prominent acute change in network organization during seizures was the segregation of cortical regions from the remaining brain. Stronger connections between thalamic with limbic regions compared with preseizure state indicated network regularization during seizures. When comparing between strains, intrathalamic connections were prominent in NEC, on local level represented by higher thalamic strengths and hub scores. Subtle differences were observed for retrosplenial cortex (RS), forming more connections beyond cortex in epileptic rats, and showing a tendency to lateralization during seizures. A potential role of RS as hub between subcortical and cortical regions in epilepsy was supported by increased numbers of parvalbumin-positive (PV+) interneurons together with enhanced inhibitory synaptic activity and neuronal excitability in pyramidal neurons. By combining multimodal fMRI data, graph theoretical methods, and electrophysiological recordings, we identified the RS as promising target for modulation of seizure activity and/or comorbidities.

Electrophysiological and neurochemical evaluation of the adverse effects of REM sleep deprivation and epileptic seizures on rat's brain

AIM

The current study aims to investigate the impact of paradoxical (REM) sleep deprivation and/or epileptic seizures on rat's cortical brain tissues.

MAIN METHODS

Animals were divided into four groups; control, epileptic, REM sleep deprived and epileptic subjected to REM sleep deprivation. Electrocorticogram (ECoG) signals were recorded and quantitatively analyzed for each group. Concentrations of amino acid neurotransmitters; proinflammatory cytokines; and oxidative stress parameters; and acetylcholinesterase activity were determined in the cortex of the animals in different groups.

KEY FINDINGS

Results showed significant variations in the spectral distribution of ECoG waves in the epilepsy model, 24- and 48-hours of REM sleep deprivation and their combined effects indicating a state of cortical hyperexcitability. Significant increases in NO and taurine and significant decrement in glutamine, GABA and glycine were determined. In REM sleep deprived rats significant elevation in glutamate, aspartate, glycine and taurine and a significant lowering in GABA were obtained. This was accompanied by significant reduction in AchE and IL-β. In the cortical tissue of epileptic rats deprived from REM sleep significant increases in lipid peroxidation, TNF-α, IL-1β, IL-6 and aspartate and a significant reduction in AchE were observed.

SIGNIFICANCE

The present data indicate that REM sleep deprivation induces an increase in lipid peroxidation and storming in proinflammatory cytokines in the cortex of rat model of epilepsy during SRS. These changes are associated with a decreased seizure threshold as inferred from the increase in alpha and Beta waves and a decrease in Delta waves of ECoG.

Shared and distinct global signal topography disturbances in subcortical and cortical networks in human epilepsy

Epilepsy is a common brain network disorder associated with disrupted large-scale excitatory and inhibitory neural interactions. Recent resting-state fMRI evidence indicates that global signal (GS) fluctuations that have commonly been ignored are linked to neural activity. However, the mechanisms underlying the altered global pattern of fMRI spontaneous fluctuations in epilepsy remain unclear. Here, we quantified GS topography using beta weights obtained from a multiple regression model in a large group of epilepsy with different subtypes (98 focal temporal epilepsy; 116 generalized epilepsy) and healthy population (n = 151). We revealed that the nonuniformly distributed GS topography across association and sensory areas in healthy controls was significantly shifted in patients. Particularly, such shifts of GS topography disturbances were more widespread and bilaterally distributed in the midbrain, cerebellum, visual cortex, and medial and orbital cortex in generalized epilepsy, whereas in focal temporal epilepsy, these networks spread beyond the temporal areas but mainly remain lateralized. Moreover, we found that these abnormal GS topography patterns were likely to evolve over the course of a longer epilepsy disease. Our study demonstrates that epileptic processes can potentially affect global excitation/inhibition balance and shift the normal GS topological distribution. These progressive topographical GS disturbances in subcortical-cortical networks may underlie pathophysiological mechanisms of global fluctuations in human epilepsy.

Dissociation and Brain Rhythms: Pitfalls and Promises

Recently, Vesuna et al. proposed a novel circuit mechanism underlying dissociative states using optogenetics and pharmacology in mice in combination with intracranial recordings and electrical stimulation in an epilepsy patient. Specifically, the authors identified a posteromedial cortical delta-rhythm that underlies states of dissociation. In the following, we would like to critically review these findings in the context of the human literature on dissociation as well as highlight the challenges in translational neuroscience to link complex behavioral phenotypes in psychiatric syndromes to circumscribed circuit mechanisms.

Comparison of anaesthetic- and seizure-induced states of unconsciousness: a narrative review

In order to understand general anaesthesia and certain seizures, a fundamental understanding of the neurobiology of unconsciousness is needed. This review article explores similarities in neuronal and network changes during general anaesthesia and seizure-induced unconsciousness. Both seizures and anaesthetics cause disruption in similar anatomical structures that presumably lead to impaired consciousness. Despite differences in behaviour and mechanisms, both of these conditions are associated with disruption of the functionality of subcortical structures that mediate neuronal activity in the frontoparietal cortex. These areas are all likely to be involved in maintaining normal consciousness. An assessment of the similarities in the brain network disruptions with certain seizures and general anaesthesia might provide fresh insights into the mechanisms of the alterations of consciousness seen in these particular unconscious states, allowing for innovative therapies for seizures and the development of anaesthetic approaches targeting specific networks.

EpilepsyGAN: Synthetic Epileptic Brain Activities With Privacy Preservation

Epilepsy is a chronic neurological disorder affecting more than 65 million people worldwide and manifested by recurrent unprovoked seizures. The unpredictability of seizures not only degrades the quality of life of the patients, but it can also be life-threatening. Modern systems monitoring electroencephalography (EEG) signals are being currently developed with the view to detect epileptic seizures in order to alert caregivers and reduce the impact of seizures on patients' quality of life. Such seizure detection systems employ state-of-the-art machine learning algorithms that require a large amount of labeled personal data for training. However, acquiring EEG signals during epileptic seizures is a costly and time-consuming process for medical experts and patients. Furthermore, this data often contains sensitive personal information, presenting privacy concerns. In this work, we generate synthetic seizure-like brain electrical activities, i.e., EEG signals, that can be used to train seizure detection algorithms, alleviating the need for sensitive recorded data. Our experiments show that the synthetic seizure data generated with our GAN model succeeds at preserving the privacy of the patients without producing any degradation in performance during seizure monitoring.

If seizures left speechless: CA-P-S C-A-R-E, a proposal of a new ictal language evaluation protocol

INTRODUCTION

We aimed to create standardized protocol for language examination in patients who underwent video-EEG recording and assessed its efficacy in the characterization of ictal language impairment, its ability to differentiate this from impaired awareness, and interobserver reliability in clinical practice.

METHODS

From our database of video-EEG recordings, we selected a representative sample of 63 focal seizures with presumed language impairment. A multidisciplinary team of epileptologists, EEG technicians, and speech therapists analyzed the selected videos to highlight the critical issues of ordinary ictal language evaluation. We subsequently followed a multi-step process to develop the protocol and assess its interobserver reliability.

RESULTS

A protocol based on seven tests in hierarchical succession was created, summed up in the acronym CA-P-S C-A-R-E (Closed Answers, Pro-speak question, Simple orders, Common object denomination, Audio repetition, Reading, Evoke). Following its preliminary administration for 5 months, we assessed the inter-observer reliability of 16 healthcare professionals in distinguishing between language impairment and impaired awareness among a sample of 10 seizures, finding a substantial agreement (kappa 0.61).

CONCLUSION

The proposed protocol, made of simple and easy to memorize tests, is an effective tool that evaluates multiple domains beyond language. Its use could help to recognize ictal aphasia effectively and differentiate it from impaired awareness, minimizing inter-examiner variability.

Post-ictal Generalized EEG Suppression is reduced by Enhancing Dorsal Raphe Serotonergic Neurotransmission

Sudden unexpected death in epilepsy (SUDEP) is the leading cause of death in patients with refractory epilepsy. A proposed risk marker for SUDEP is the duration of post-ictal generalized EEG suppression (PGES). The mechanisms underlying PGES are unknown. Serotonin (5-HT) has been implicated in SUDEP pathophysiology. Seizures suppress activity of 5-HT neurons in the dorsal raphe nucleus (DRN). We hypothesized that suppression of DRN 5-HT neuron activity contributes to PGES and increasing 5-HT neurotransmission or stimulating the DRN before a seizure would decrease PGES duration. Adult C57BL/6J and Pet1-Cre mice received EEG/EMG electrodes, a bipolar stimulating/recording electrode in the right basolateral amygdala, and either a microdialysis guide cannula or an injection of adeno-associated virus (AAV) allowing expression of channelrhodopsin2 plus an optic fiber into the DRN. Systemic application of the selective 5-HT reuptake inhibitor citalopram (20 mg/kg) decreased PGES duration from seizures induced during wake (n = 23) and non-rapid eye movement (NREM) sleep (n = 13) whereas fluoxetine (10 mg/kg) pretreatment decreased PGES duration following seizures induced from wake (n = 11), but not NREM sleep (n = 9). Focal chemical (n = 6) or optogenetic (n = 8) stimulation of the DRN reduced PGES duration following seizures in kindled mice induced during wake. During PGES, animals exhibited immobility and suppression of EEG activity that was reduced by citalopram pretreatment. These results suggest 5-HT and the DRN may regulate PGES.

Altered Functional Connectivity after Epileptic Seizure Revealed by Scalp EEG

Epileptic seizures are considered to be a brain network dysfunction, and chronic recurrent seizures can cause severe brain damage. However, the functional brain network underlying recurrent epileptic seizures is still left unveiled. This study is aimed at exploring the differences in a related brain activity before and after chronic repetitive seizures by investigating the power spectral density (PSD), fuzzy entropy, and functional connectivity in epileptic patients. The PSD analysis revealed differences between the two states at local area, showing postseizure energy accumulation. Besides, the fuzzy entropies of preseizure in the frontal, central, and temporal regions are higher than that of postseizure. Additionally, attenuated long-range connectivity and enhanced local connectivity were also found. Moreover, significant correlations were found between network metrics (i.e., characteristic path length and clustering coefficient) and individual seizure number. The PSD, fuzzy entropy, and network analysis may indicate that the brain is gradually impaired along with the occurrence of epilepsy, and the accumulated effect of brain impairment is observed in individuals with consecutive epileptic bursts. The findings of this study may provide helpful insights into understanding the network mechanism underlying chronic recurrent epilepsy.

Dynamic Temporospatial Patterns of Functional Connectivity and Alterations in Idiopathic Generalized Epilepsy

The dynamic profile of brain function has received much attention in recent years and is also a focus in the study of epilepsy. The present study aims to integrate the dynamics of temporal and spatial characteristics to provide comprehensive and novel understanding of epileptic dynamics. Resting state fMRI data were collected from eighty-three patients with idiopathic generalized epilepsy (IGE) and 87 healthy controls (HC). Specifically, we explored the temporal and spatial variation of functional connectivity density (tvFCD and svFCD) in the whole brain. Using a sliding-window approach, for a given region, the standard variation of the FCD series was calculated as the tvFCD and the variation of voxel-wise spatial distribution was calculated as the svFCD. We found primary, high-level, and sub-cortical networks demonstrated distinct tvFCD and svFCD patterns in HC. In general, the high-level networks showed the highest variation, the subcortical and primary networks showed moderate variation, and the limbic system showed the lowest variation. Relative to HC, the patients with IGE showed weaken temporal and enhanced spatial variation in the default mode network and weaken temporospatial variation in the subcortical network. Besides, enhanced temporospatial variation in sensorimotor and high-level networks was also observed in patients. The hyper-synchronization of specific brain networks was inferred to be associated with the phenomenon responsible for the intrinsic propensity of generation and propagation of epileptic activities. The disrupted dynamic characteristics of sensorimotor and high-level networks might potentially contribute to the driven motion and cognition phenotypes in patients. In all, presently provided evidence from the temporospatial variation of functional interaction shed light on the dynamics underlying neuropathological profiles of epilepsy.

Disrupted alertness and related functional connectivity in patients with focal impaired awareness seizures in temporal lobe epilepsy

OBJECTIVE

Focal impaired awareness seizures are common in temporal lobe epilepsy (TLE). The cognitive impairment associated with this type of seizure is unclear. Alertness is a fundamental aspect of cognition. The locus coeruleus (LC) is closely related to alertness. We aimed to assess the impairment in alertness and LC-related alertness network in patients with focal impaired awareness seizures.

METHODS

Patients with unilateral TLE were grouped into the only focal impaired awareness seizure group (focal group, n = 19) and the focal impaired awareness seizure with focal to bilateral tonic-clonic seizure (FBTCS) group (FBTCS group, n = 19) and compared with matched healthy controls (HC, n = 19). Alertness was assessed with the attention network test. Functional magnetic resonance imaging (fMRI) was used to construct an alertness-related LC-based functional connectivity (FC) network.

RESULTS

The focal group exhibited impaired tonic and phasic alertness and exhibited a decreased trend of LC-based FC to the left superior frontal gyrus (SFG). The FBTCS group exhibited impaired tonic alertness, phasic alertness, and alertness efficiency. No significant difference or trend in LC-based FC was found in the FBTCS group.

SIGNIFICANCE

This study reveals disrupted alertness and alertness-related LC-based FC in patients with focal impaired awareness seizures. Our results further demonstrate that the patterns of impaired alertness and of changed LC-based FC were not significantly different between focal impaired awareness seizures and FBTCS.

Conversational analysis of consciousness during seizures

OBJECTIVE

The objectives of the study were to 1) investigate how patients with epilepsy describe the subjective, conscious experience of having a seizure and 2) determine whether certain themes and descriptions correspond to specific types of epilepsy.

METHODS

We interviewed thirteen patients with electroencephalographically confirmed epilepsy about their subjective experience of having a seizure and used conversational analysis (CA) to analyze the language they used to describe this experience.

RESULTS

Seven patients had focal to bilateral tonic-clonic seizures (FBTCS), 7 had focal impaired awareness seizures (FIAS), 1 had focal aware seizures (FAS), and one had generalized onset tonic-clonic (GTC) seizures. Three had multiple types of seizures. Focal seizure origin was frontal in 2 patients, right hemisphere in 1, parieto-occipital in 1, and temporal in 8. Focal to bilateral tonic-clonic and GTC seizures were most frequently associated with descriptions of a total loss of consciousness (n = 8), whereas FIAS were most frequently associated with a perceived loss of consciousness but able to describe some aspects of being unconscious (n = 5). Temporal seizures most frequently accompanied reports of memory loss/impairment (n = 4). Ten patients provided specific descriptions of the transition between the interictal and ictal state or auras. Descriptions consciousness and unconsciousness ranged significantly, resembling a continuum rather than corresponding to distinct levels.

CONCLUSION

The subjective experience of consciousness for patients with epilepsy may differ by seizure type and origin. These may reflect different involvement of brain regions involved in producing consciousness and arousal. Conversational analysis and narrative approaches can significantly aid clinicians in the diagnosis and management of epilepsy.

Time- and frequency-resolved covariance analysis for detection and characterization of seizures from intracraneal EEG recordings

The amount of power in different frequency bands of the electroencephalogram (EEG) carries information about the behavioral state of a subject. Hence, neurologists treating epileptic patients monitor the temporal evolution of the different bands. We propose a covariance-based method to detect and characterize epileptic seizures operating on the band-filtered EEG signal. The algorithm is unsupervised and performs a principal component analysis of intra-cranial EEG recordings, detecting transient fluctuations of the power in each frequency band. Its simplicity makes it suitable for online implementation. Good sampling of the non-ictal periods is required, while no demands are imposed on the amount of data during ictal activity. We tested the method with 32 seizures registered in 5 patients. The area below the resulting receiver-operating characteristic curves was 87% for the detection of seizures and 91% for the detection of recruited electrodes. To identify the behaviorally relevant correlates of the physiological signal, we identified transient changes in the variance of each band that were correlated with the degree of loss of consciousness, the latter assessed by the so-called Consciousness Seizure Scale, summarizing the performance of the subject in a number of behavioral tests requested during seizures. We concluded that those crisis with maximal impairment of consciousness tended to exhibit an increase in variance approximately 40 s after seizure onset, with predominant power in the theta and alpha bands and reduced delta and beta activity.

Voxel-based morphometry analysis and machine learning based classification in pediatric mesial temporal lobe epilepsy with hippocampal sclerosis

Mesial temporal lobe epilepsy with hippocampal sclerosis (MTLE-HS) is a common type of pediatric epilepsy. We sought to evaluate whether the combination of voxel-based morphometry (VBM) and support vector machine (SVM), a machine learning method, was feasible for the classification of MTLE-HS. Three-dimensional T1-weighted MRI was acquired in 37 participants including 22 with MTLE-HS (16 left, 6 right) and 15 healthy controls (HCs). VBM was used to detect the regions of gray matter volume (GMV) abnormalities. The volumes of these regions were then calculated for each participant and used as the features in SVM. The SVM model was trained and tested with leave-one-out cross validation (LOOCV). We performed VBM-based comparison and SVM-based classification between left HS (LHS) and HC as well as between right HS (RHS) and HC. Both GMV increase and reduction were found in the group comparisons with VBM. Using SVM, we reached an area under the receiver operating characteristic curve (AUC) of 0.870, 0.976 and 0.902 for the classification between LHS and HC, between RHS and HC and between HS and HC respectively. The VBM findings were concordant with the clinical findings. Thus, our proposed method combining VBM findings with SVM, were applicable in the classification of padiatric MTLE-HS with high accuracy.

Current Classification of Seizures and Epilepsies: Scope, Limitations and Recommendations for Future Action

The classification of seizures and epilepsies by the International League Against Epilepsy (ILAE), 2017 is the most recent classification model which aimed to simplify terminologies that patients and their caregivers can easily understand, identify seizures that have both focal and generalized onset and incorporate missing seizures. We have exhaustively reviewed the studies, discussed its scope, outlined its limitations and gave recommendations that could help in forming subsequent reviews. We have also described the terminologies that have been replaced, redefined or removed to have a clear view of the previous and the current classification models. We have recommended the use of multidimensional classification model which incorporated the clinical semiology, disease location, etiology and associated comorbidities. The benefits of this model is for prompt diagnosis which will results into early management and then better patient outcomes. It would also have a profound effects on the kind of treatment patients might receive especially in developing countries where there are scarcity of the diagnostic techniques. Overall, in this study we have reviewed the current study on seizures and epilepsy classification model by ILAE, 2017 to clarify the descriptions and coverage, outlined some limitations and suggested recommendations.

Thalamic Stimulation Improves Postictal Cortical Arousal and Behavior

The postictal state following seizures is characterized by impaired consciousness and has a major negative impact on individuals with epilepsy. Previous work in disorders of consciousness including the postictal state suggests that bilateral deep brain stimulation (DBS) of the thalamic intralaminar central lateral nucleus (CL) may improve level of arousal. We tested the effects of postictal thalamic CL DBS in a rat model of secondarily generalized seizures elicited by electrical hippocampal stimulation. Thalamic CL DBS was delivered at 100 Hz during the postictal period in 21 female rats while measuring cortical electrophysiology and behavior. The postictal period was characterized by frontal cortical slow waves, like other states of depressed consciousness. In addition, rats exhibited severely impaired responses on two different behavioral tasks in the postictal state. Thalamic CL stimulation prevented postictal cortical slow wave activity but produced only modest behavioral improvement on a spontaneous licking sucrose reward task. We therefore also tested responses using a lever-press shock escape/avoidance (E/A) task. Rats achieved high success rates responding to the sound warning on the E/A task even during natural slow wave sleep but were severely impaired in the postictal state. Unlike the spontaneous licking task, thalamic CL DBS during the E/A task produced a marked improvement in behavior, with significant increases in lever-press shock avoidance with DBS compared with sham controls. These findings support the idea that DBS of subcortical arousal structures may be a novel therapeutic strategy benefitting patients with medically and surgically refractory epilepsy.SIGNIFICANCE STATEMENT The postictal state following seizures is characterized by impaired consciousness and has a major negative impact on individuals with epilepsy. For the first time, we developed two behavioral tasks and demonstrate that bilateral deep brain stimulation (DBS) of the thalamic intralaminar central lateral nucleus (CL) decreased cortical slow wave activity and improved task performance in the postictal period. Because preclinical task performance studies are crucial to explore the effectiveness and safety of DBS treatment, our work is clinically relevant as it could support and help set the foundations for a human neurostimulation trial to improve postictal responsiveness in patients with medically and surgically refractory epilepsy.

Cross-database evaluation of EEG based epileptic seizures detection driven by adaptive median feature baseline correction

OBJECTIVE

In long-term electroencephalogram (EEG) signals, automated classification of epileptic seizures is desirable in diagnosing epilepsy patients, as it otherwise depends on visual inspection. To the best of the author's knowledge, existing studies have validated their algorithms using cross-validation on the same database and less number of attempts have been made to extend their work on other databases to test the generalization capability of the developed algorithms. In this study, we present the algorithm for cross-database evaluation for classification of epileptic seizures using five EEG databases collected from different centers. The cross-database framework helps when sufficient epileptic seizures EEG data are not available to build automated seizure detection model.

METHODS

Two features, namely successive decomposition index and matrix determinant were extracted at a segmentation length of 4 s (50% overlap). Then, adaptive median feature baseline correction (AM-FBC) was applied to overcome the inter-patient and inter-database variation in the feature distribution. The classification was performed using a support vector machine classifier with leave-one-database-out cross-validation. Different classification scenarios were considered using AM-FBC, smoothing of the train and test data, and post-processing of the classifier output.

RESULTS

Simulation results revealed the highest area under the curve-sensitivity-specificity-false detections (per hour) of 1-1-1-0.15, 0.89-0.99-0.82-2.5, 0.99-0.73-1-1, 0.95-0.97-0.85-1.7, 0.99-0.99-0.92-1.1 using the Ramaiah Medical College and Hospitals, Children's Hospital Boston-Massachusetts Institute of Technology, Temple University Hospital, Maastricht University Medical Centre, and University of Bonn databases respectively.

CONCLUSIONS

We observe that the AM-FBC plays a significant role in improving seizure detection results by overcoming inter-database variation of feature distribution.

SIGNIFICANCE

To the best of the author's knowledge, this is the first study reporting on the cross-database evaluation of classification of epileptic seizures and proven to be better generalization capability when evaluated using five databases and can contribute to accurate and robust detection of epileptic seizures in real-time.

A model for focal seizure onset, propagation, evolution, and progression

We developed a neural network model that can account for major elements common to human focal seizures. These include the tonic-clonic transition, slow advance of clinical semiology and corresponding seizure territory expansion, widespread EEG synchronization, and slowing of the ictal rhythm as the seizure approaches termination. These were reproduced by incorporating usage-dependent exhaustion of inhibition in an adaptive neural network that receives global feedback inhibition in addition to local recurrent projections. Our model proposes mechanisms that may underline common EEG seizure onset patterns and status epilepticus, and postulates a role for synaptic plasticity in the emergence of epileptic foci. Complex patterns of seizure activity and bi-stable seizure end-points arise when stochastic noise is included. With the rapid advancement of clinical and experimental tools, we believe that this model can provide a roadmap and potentially an in silico testbed for future explorations of seizure mechanisms and clinical therapies.

Serotonin and sudden unexpected death in epilepsy

Epilepsy is a highly prevalent disease characterized by recurrent, spontaneous seizures. Approximately one-third of epilepsy patients will not achieve seizure freedom with medical management and become refractory to conventional treatments. These patients are at greatest risk for sudden unexpected death in epilepsy (SUDEP). The exact etiology of SUDEP is unknown, but a combination of respiratory, cardiac, neuronal electrographic dysfunction, and arousal impairment is thought to underlie SUDEP. Serotonin (5-HT) is involved in regulation of breathing, sleep/wake states, arousal, and seizure modulation and has been implicated in the pathophysiology of SUDEP. This review explores the current state of understanding of the relationship between 5-HT, epilepsy, and respiratory and autonomic control processes relevant to SUDEP in epilepsy patients and in animal models.

Differences Between Interictal and Ictal Generalized Spike-Wave Discharges in Childhood Absence Epilepsy: A MEG Study

Purpose: To investigate the differences between interictal and ictal generalized spike-wave discharges (GSWDs) for insights on how epileptic activity propagates and the physiopathological mechanisms underlying childhood absence epilepsy (CAE).

Methods: Twenty-five patients with CAE were studied using magnetoencephalography (MEG). MEG data were digitized at 6,000 Hz during the interictal and ictal GSWDs. GSWDs were analyzed at both neural magnetic source levels and functional connectivity (FC) in multifrequency bands: delta (1–4 Hz), theta (4–8 Hz), alpha (8–12 Hz), beta (12–30 Hz), gamma (30–80 Hz), ripple (80–250 Hz), and fast ripple (250–500 Hz). Brain FC was studied with the posterior cingulate cortex/precuneus (PCC/pC) as the seed region.

Results: The magnetic source of interictal GSWDs mainly locates in the PCC/pC region at 4–8 and 8–12 Hz, while that of ictal GSWDs mainly locates in the medial frontal cortex (MFC) at 80–250 Hz. There were statistically significant differences between interictal and ictal GSWDs (p < 0.05). The FC network involving the PCC/pC showed strong connections in the anterior-posterior pathways (mainly with the frontal cortex) at 80–250 Hz during ictal GSWDs, while the interictal GSWDs FC were mostly limited to the posterior cortex region. There was no significant difference in the magnetic source strength among interictal and ictal GSWDs at all bandwidths.

Conclusions: There are significant disparities in the source localization and FC between interictal and ictal GSWDs. Low-frequency activation in the PCC/pC during inhibition of seizures possibly relates to the maintenance of consciousness during interictal GSWDs. High-frequency oscillations (HFOs) of the MFC during CAE may associate with the inducing or occurrence of GSWDs. Weakened network connections may be in favor of preventing overexcitability and relates to the termination of GSWDs.

Phase-dependent modulation of cortical and thalamic sensory responses during spike-and-wave discharges

OBJECTIVE

The neuronal underpinnings of impaired consciousness during absence seizures remain largely unknown. Spike-and-wave (SW) activity associated with absences imposes two extremely different states in cortical neurons, which transition from suprathreshold synaptic depolarizations during spike phases to membrane hyperpolarization and electrical silence during wave phases. To investigate whether this rhythmic alternation of neuronal states affects the processing of sensory information during seizures, we examined cortical and thalamic responsiveness to brief sensory stimuli in the different phases of the epileptic cycle.

METHODS

Electrocorticographic (ECoG) monitoring from the primary somatosensory cortex combined with intracellular recordings of subjacent pyramidal neurons, or extracellular recordings of somatosensory thalamic neurons, were performed in the Genetic Absence Epilepsy Rat From Strasbourg. Sensory stimuli consisted of pulses of compressed air applied to the contralateral whiskers.

RESULTS

Whisker stimuli delivered during spike phases evoked smaller depolarizing synaptic potentials and fewer action potentials in cortical neurons compared to stimuli occurring during wave phases. This spike-related attenuation of cortical responsiveness was accompanied by a reduced neuronal membrane resistance, likely due to the large increase in synaptic conductance. Sensory-evoked firing in thalamocortical neurons was also decreased during ECoG spikes as compared to wave phases, indicating that time-to-time changes in the thalamocortical volley may also contribute to the variability of cortical responses during seizures.

SIGNIFICANCE

These findings demonstrate that thalamocortical sensory processing during absence seizures is nonstationary and strongly suggest that the cortical impact of a given environmental stimulus is conditioned by its exact timing relative to the SW cycle. The lack of stability of thalamic and cortical responses along seizures may contribute to impaired conscious sensory perception during absences.

Cortical lesions causing loss of consciousness are anticorrelated with the dorsal brainstem

Brain lesions can provide unique insight into the neuroanatomical substrate of human consciousness. For example, brainstem lesions causing coma map to a specific region of the tegmentum. Whether specific lesion locations outside the brainstem are associated with loss of consciousness (LOC) remains unclear. Here, we investigate the topography of cortical lesions causing prolonged LOC (N = 16), transient LOC (N = 91), or no LOC (N = 64). Using standard voxel lesion symptom mapping, no focus of brain damage was associated with LOC. Next, we computed the network of brain regions functionally connected to each lesion location using a large normative connectome dataset (N = 1,000). This technique, termed lesion network mapping, can test whether lesions causing LOC map to a connected brain circuit rather than one brain region. Connectivity between cortical lesion locations and an a priori coma-specific region of brainstem tegmentum was an independent predictor of LOC (B = 1.2, p = .004). Connectivity to the dorsal brainstem was the only predictor of LOC in a whole-brain voxel-wise analysis. This relationship was driven by anticorrelation (negative correlation) between lesion locations and the dorsal brainstem. The map of regions anticorrelated to the dorsal brainstem thus defines a distributed brain circuit that, when damaged, is most likely to cause LOC. This circuit showed a slight posterior predominance and had peaks in the bilateral claustrum. Our results suggest that cortical lesions causing LOC map to a connected brain circuit, linking cortical lesions that disrupt consciousness to brainstem sites that maintain arousal.

Epilepsy Overview and Revised Classification of Seizures and Epilepsies

PURPOSE OF REVIEW

The classification of seizures, epilepsies, and epilepsy syndromes creates a framework for clinicians, researchers, and patients and their families. This classification has evolved over the years, and in 2017 the International League Against Epilepsy (ILAE) published an operational classification of seizures and epilepsies. Understanding this classification is important in the diagnosis, treatment, and understanding of seizures and epilepsies, including epilepsy incidence.

RECENT FINDINGS

The 2017 ILAE classification system builds on newly formulated definitions of seizures and epilepsy. Seizure classification begins by determining whether the initial manifestations of the seizure are focal or generalized. If the onset of the seizure is missed or unclear, the seizure is of unknown onset. Focal seizures are classified according to the individual's level of awareness, the most prominent motor or nonmotor features of the seizure, and whether the focal seizure evolves to a bilateral tonic-clonic seizure. Similarly, generalized seizures are classified according to motor or nonmotor manifestations. Motor seizures are either tonic-clonic or other motor seizures. Nonmotor generalized seizures primarily refer to absence seizures. Similar to seizure classification, the epilepsies can be classified as focal or generalized. In addition, the new classification system recognizes two new categories: combined generalized and focal epilepsy and unknown epilepsy. The concept of an epilepsy syndrome has been introduced under the new classification system and refers to a cluster of features incorporating seizure types, EEG, imaging, and other features including genetics. The new classification system emphasizes the etiology of seizures and epilepsies.

SUMMARY

The recent ILAE seizure and epilepsy classification system aims to create a framework to better classify seizures and the epilepsies. Universal adoption and implementation of this system will enable patients, their families, clinicians, and researchers to better define and treat the epilepsies. Incidence studies have not generally classified seizures and the epilepsies, and use of this classification system, which emphasizes etiology, will lead to a better understanding of epilepsy incidence.