Automated segmentation of ventricular volumes and subarachnoid hemorrhage from computed tomography images: Evaluation of a rule-based pipeline approach.

Changes in ventricular size, related to brain edema and hydrocephalus, as well as the extent of hemorrhage are associated with adverse outcomes in patients with subarachnoid hemorrhage (SAH). Frequently, these are measured manually using consecutive non-contrast computed tomography scans. Here, we developed a rule-based approach which incorporates both intensity and spatial normalization and utilizes user-defined thresholds and anatomical templates to segment both lateral ventricle (LV) and SAH blood volumes automatically from CT images. The algorithmic segmentations were evaluated against two expert neuroradiologists on representative slices from 20 admission scans from aneurysmal SAH patients. Previous methods have been developed to automate this time-consuming task, but they lack user feedback and are hard to implement due to large-scale data and complex design processes. Our results using automatic ventricular segmentation aligned well with expert reviewers with a median Dice coefficient of 0.81, AUC of 0.91, sensitivity of 81%, and precision of 84%. Automatic segmentation of SAH blood was most reliable near the base of the brain with a median Dice coefficient of 0.51, an AUC of 0.75, precision of 68%, and sensitivity of 50%. Ultimately, we developed a rule-based method that is easily adaptable through user feedback, generates spatially normalized segmentations that are comparable regardless of brain morphology or acquisition conditions, and automatically segments LV with good overall reliability and basal SAH blood with good precision. Our approach could benefit longitudinal studies in patients with SAH by streamlining assessment of edema and hydrocephalus progression, as well as blood resorption.

Diffuse microglial responses and persistent EEG changes correlate with poor neurological outcome in a model of subarachnoid hemorrhage.

The mechanism by which subarachnoid hemorrhage (SAH) leads to chronic neurologic deficits is unclear. One possibility is that blood activates microglia to drive inflammation that leads to synaptic loss and impaired brain function. Using the endovascular perforation model of SAH in rats, we investigated short-term effects on microglia together with long-term effects on EEG and neurologic function for up to 3 months. Within the first week, microglia were increased both at the site of injury and diffusely across the cortex (2.5-fold increase in SAH compared to controls, p = 0.012). Concomitantly, EEGs from SAH animals showed focal increases in slow wave activity and diffuse reduction in fast activity. When expressed as a fast-slow spectral ratio, there were significant interactions between group and time (p < 0.001) with less ipsilateral recovery over time. EEG changes were most pronounced during the first week and correlated with neurobehavioral impairment. In vitro, the blood product hemin was sufficient to increase microglia phagocytosis nearly six-fold (p = 0.032). Immunomodulatory treatment with fingolimod after SAH reduced microglia, improved neurological function, and increased survival. These findings, which parallel many of the EEG changes seen in patients, suggest that targeting neuroinflammation could reduce long-term neurologic dysfunction following SAH.

Spike-induced cytoarchitectonic changes in epileptic human cortex are reduced via MAP2K inhibition.

Interictal spikes are electroencephalographic discharges that occur at or near brain regions that produce epileptic seizures. While their role in generating seizures is not well understood, spikes have profound effects on cognition and behaviour, depending on where and when they occur. We previously demonstrated that spiking areas of human neocortex show sustained MAPK activation in superficial cortical Layers I-III and are associated with microlesions in deeper cortical areas characterized by reduced neuronal nuclear protein staining and increased microglial infiltration. Based on these findings, we chose to investigate additional neuronal populations within microlesions, specifically inhibitory interneurons. Additionally, we hypothesized that spiking would be sufficient to induce similar cytoarchitectonic changes within the rat cortex and that inhibition of MAPK signalling, using a MAP2K inhibitor, would not only inhibit spike formation but also reduce these cytoarchitectonic changes and improve behavioural outcomes. To test these hypotheses, we analysed tissue samples from 16 patients with intractable epilepsy who required cortical resections. We also utilized a tetanus toxin-induced animal model of interictal spiking, designed to produce spikes without seizures in male Sprague-Dawley rats. Rats were fitted with epidural electrodes, to permit EEG recording for the duration of the study, and automated algorithms were implemented to quantify spikes. After 6 months, animals were sacrificed to assess the effects of chronic spiking on cortical cytoarchitecture. Here, we show that microlesions may promote excitability due to a significant reduction of inhibitory neurons that could be responsible for promoting interictal spikes in superficial layers. Similarly, we found that the induction of epileptic spikes in the rat model produced analogous changes, including reduced neuronal nuclear protein, calbindin and parvalbumin-positive neurons and increased microglia, suggesting that spikes are sufficient for inducing these cytoarchitectonic changes in humans. Finally, we implicated MAPK signalling as a driving force producing these pathological changes. Using CI-1040 to inhibit MAP2K, both acutely and after spikes developed, resulting in fewer interictal spikes, reduced microglial activation and less inhibitory neuron loss. Treated animals had significantly fewer high-amplitude, short-duration spikes, which correlated with improved spatial memory performance on the Barnes maze. Together, our results provide evidence for a cytoarchitectonic pathogenesis underlying epileptic cortex, which can be ameliorated through both early and delayed MAP2K inhibition. These findings highlight the potential role for CI-1040 as a pharmacological treatment that could prevent the development of epileptic activity and reduce cognitive impairment in both patients with epilepsy and those with non-epileptic spike-associated neurobehavioural disorders.

Applying Proteomics and Computational Approaches to Identify Novel Targets in Blast-Associated Post-Traumatic Epilepsy.

Traumatic brain injury (TBI) can lead to post-traumatic epilepsy (PTE). Blast TBI (bTBI) found in Veterans presents with several complications, including cognitive and behavioral disturbances and PTE; however, the underlying mechanisms that drive the long-term sequelae are not well understood. Using an unbiased proteomics approach in a mouse model of repeated bTBI (rbTBI), this study addresses this gap in the knowledge. After rbTBI, mice were monitored using continuous, uninterrupted video-EEG for up to four months. Following this period, we collected cortex and hippocampus tissues from three groups of mice: those with post-traumatic epilepsy (PTE+), those without epilepsy (PTE-), and the control group (sham). Hundreds of differentially expressed proteins were identified in the cortex and hippocampus of PTE+ and PTE- relative to sham. Focusing on protein pathways unique to PTE+, pathways related to mitochondrial function, post-translational modifications, and transport were disrupted. Computational metabolic modeling using dysregulated protein expression predicted mitochondrial proton pump dysregulation, suggesting electron transport chain dysregulation in the epileptic tissue relative to PTE-. Finally, data mining enabled the identification of several novel and previously validated TBI and epilepsy biomarkers in our data set, many of which were found to already be targeted by drugs in various phases of clinical testing. These findings highlight novel proteins and protein pathways that may drive the chronic PTE sequelae following rbTBI.

INTUITION: a data platform to integrate human epilepsy clinical care and support for discovery.

To make appropriate clinical decisions, clinicians consider many types of data from multiple sources to arrive at a diagnosis and plan. However, the current health systems have siloed data, making it challenging to develop information platforms that integrate this process into a single place for comprehensive clinical evaluation and research. INTUITION is a human brain integrative data system that facilitates multimodal data integration, unified storage, cohort selection, and analysis of multidisciplinary datasets. In this article, we describe the use of INTUITION to include electronic health records together with co-registered neuroimaging and EEG from patients who undergo invasive brain surgery for epilepsy. In addition to providing clinically useful visualizations and analytics to help guide surgical planning, INTUITION also links a bank of human brain epileptic tissues from specific brain locations to quantitative EEG, imaging, histology, and omics studies in a unique, completely integrated informatics platform. Having a clinically useful platform for integrating multimodal datasets can not only aid in clinical management decisions but also in creating a unique resource for research and discovery when linked to spatially mapped tissue samples.

Atypical Neurogenesis, Astrogliosis, and Excessive Hilar Interneuron Loss Are Associated with the Development of Post-Traumatic Epilepsy.

BACKGROUND: Traumatic brain injury (TBI) remains a significant risk factor for post-traumatic epilepsy (PTE). The pathophysiological mechanisms underlying the injury-induced epileptogenesis are under investigation. The dentate gyrus-a structure that is highly susceptible to injury-has been implicated in the evolution of seizure development. METHODS: Utilizing the murine unilateral focal control cortical impact (CCI) injury, we evaluated seizure onset using 24/7 EEG video analysis at 2-4 months post-injury. Cellular changes in the dentate gyrus and hilus of the hippocampus were quantified by unbiased stereology and Imaris image analysis to evaluate Prox1-positive cell migration, astrocyte branching, and morphology, as well as neuronal loss at four months post-injury. Isolation of region-specific astrocytes and RNA-Seq were performed to determine differential gene expression in animals that developed post-traumatic epilepsy (PTE+) vs. those animals that did not (PTE-), which may be associated with epileptogenesis. RESULTS: CCI injury resulted in 37% PTE incidence, which increased with injury severity and hippocampal damage. Histological assessments uncovered a significant loss of hilar interneurons that coincided with aberrant migration of Prox1-positive granule cells and reduced astroglial branching in PTE+ compared to PTE- mice. We uniquely identified Cst3 as a PTE+-specific gene signature in astrocytes across all brain regions, which showed increased astroglial expression in the PTE+ hilus. CONCLUSIONS: These findings suggest that epileptogenesis may emerge following TBI due to distinct aberrant cellular remodeling events and key molecular changes in the dentate gyrus of the hippocampus.

Automated sleep detection reveals differences in sleep patterns in an animal model of neocortical epilepsy.

Sleep in epilepsy is best studied in longitudinal preclinical animal models, where state changes can have significant effects on epileptic activities. Voluminous data makes it very difficult to mark sleep stages manually. This demands an automated way to detect sleep and wake states. We developed an approach to characterize sleep-wake states in continuous video-electroencephalography (EEG) recordings in animals. We compared brute force approach based on frequency band-power based thresholding with machine learning algorithms to detect sleep in 600 hours of EEG data from 4 epileptic and 2 control animals. We found that conventional delta and theta band-powers were prominent in sleep; however, this was not sufficient to detect sleep algorithmically. We therefore extracted a set of novel frequency bands to robustly differentiate individual sleep states by using brute-force algorithm and machine learning models, among which k-nearest neighbors (KNN) was the best predictor of sleep with 94% accuracy. We subsequently characterized sleep patterns in animals with chronically induced epileptic spiking in the neocortex from tetanus toxin injections using brute-force algorithm. We found that epileptic spiking animals (without seizures) sleep more frequently, with significantly longer sleep segments and overall daily sleep time, as compared to control animals. This automated algorithm could help expedite sleep studies and help us understand the relationship between sleep and patients with epilepsy.

Multi-modal data integration platform combining clinical and preclinical models of post subarachnoid hemorrhage epilepsy.

Subarachnoid hemorrhage (SAH) is a devastating neurological injury that can lead to many downstream complications including epilepsy. Predicting who will get epilepsy in order to find ways to prevent it as well as stratify patients for future interventions is a major challenge given the large number of variables not only related to the injury itself, but also to what happens after the injury. Extensive multimodal data are generated during the process of SAH patient care. In parallel, preclinical models are under development that attempt to imitate the variables observed in patients. Computational tools that consider all variables from both human data and animal models are lacking and demand an integrated, time-dependent platform where researchers can aggregate, store, visualize, analyze, and share the extensive integrated multimodal information. We developed a multi-tier web-based application that is secure, extensible, and adaptable to all available data modalities using flask micro-web framework, python, and PostgreSQL database. The system supports data visualization, data sharing and downloading for offline processing. The system is currently hosted inside the institutional private network and holds [Formula: see text] of data from 164 patients and 71 rodents. Clinical Relevance-Our platform supports clinical and preclinical data management. It allows users to comprehensively visualize patient data and perform visual analytics. These utilities can improve research and clinical practice for subarachnoid hemorrhage and other brain injuries.

Highly consistent temporal lobe interictal spike networks revealed from foramen ovale electrodes.

OBJECTIVE: A major challenge that limits understanding and treatment of epileptic events from mesial temporal structures comes from our inability to detect and map interictal networks reproducibly using scalp electrodes. Here, we developed a novel approach to map interictal spike networks and demonstrate their relationships to seizure onset and lesions in patients with foramen ovale electrode implantations. METHODS: We applied the direct Directed Transfer Function to reveal interictal spike propagation from bilateral foramen ovale electrodes on 10 consecutive patients and co-registered spatially with both seizure onset zones and temporal lobe lesions. RESULTS: Highly reproducible, yet unique interictal spike networks were seen for each patient (correlation: 0.93 ±â€¯0.13). Interictal spikes spread in both anterior and posterior directions within each temporal lobe, often reverberating between sites. Spikes propagated to the opposite temporal lobe predominantly through posterior pathways. Patients with structural lesions (N = 4), including tumors and sclerosis, developed reproducible spike networks adjacent to their lesions that were highly lateralized compared to patients without lesions. Only 5% of mesial temporal lobe spikes were time-locked with scalp electrode spikes. Our preliminary observation on two lesional patients suggested that along with lesion location, Interictal spike networks also partially co-registered with seizure onset zones suggesting interrelationship between seizure onset and a subset of spike networks. CONCLUSIONS: This is the first demonstration of patient-specific, reproducible interictal spike networks in mesial temporal structures that are closely linked to both temporal lobe lesions and seizure onset zones. SIGNIFICANCE: Interictal spike connectivity is a novel approach to map epileptic networks that could help advance invasive and non-invasive epilepsy treatments.

Modulation of locomotor behaviors by location-specific epileptic spiking and seizures.

INTRODUCTION: Epilepsy is a debilitating neurological condition characterized by spontaneous seizures as well as significant comorbid behavioral abnormalities. In addition to seizures, epileptic patients exhibit interictal spikes far more frequently than seizures, often, but not always observed in the same brain areas. The exact relationship between spiking and seizures as well as their respective effects on behavior are not well understood. In fact, spiking without overt seizures is seen in various psychiatric conditions including attention-deficit hyperactivity disorder. METHODS: In order to study the effects of spiking and seizures on behavior in an epileptic animal model, we used long-term video-electroencephalography recordings at six cortical recording sites together with behavioral activity monitoring. Animals received unilateral injections of tetanus toxin into either the somatosensory or motor cortex. RESULTS: Somatosensory cortex-injected animals developed progressive spiking ipsilateral to the injection site, while those receiving the injection into the motor cortex developed mostly contralateral spiking and spontaneous seizures. Animals with spiking but no seizures displayed a hyperactive phenotype, while animals with both spiking and seizures displayed a hypoactive phenotype. Not all spikes were equivalent as spike location strongly correlated with distinct locomotor behaviors including ambulatory distance, vertical movements, and rotatory movement. CONCLUSIONS: Together, our results demonstrate relationships between brain region-specific spiking, seizures, and behaviors in rodents that could translate into a better understanding for patients with epileptic behavioral comorbidities and other neuropsychiatric disorders.

Sturge-Weber Syndrome Patient Registry: Delayed Diagnosis and Poor Seizure Control.

OBJECTIVE: To examine the symptomatology and treatment of Sturge-Weber syndrome (SWS) from a large patient registry to identify common symptoms, clinical outcomes, and areas of unmet clinical need. STUDY DESIGN: An online patient questionnaire was completed by 628 patients with clinically diagnosed SWS and/or a port-wine birthmark over a 19-year period. Statistical analysis focused on seizures as a primary outcome measure, as well as associated neurologic, ophthalmologic, and dermatologic attributes to understand some of the natural history of the disorder. RESULTS: The majority (92%) of patients had a port-wine birthmark, and 60% of the patients had neurologic symptoms, including seizures and stroke-like episodes. Glaucoma was present in 48% of the patients. Other common symptoms included behavioral (46%) and hearing (or vestibular) disorders (24%). Delayed diagnosis of SWS beyond 1 year after presentation of initial symptoms occurred in 16% of the patients, with 68% having clear preexisting comorbidities, especially headaches. Birthmarks on the forehead and scalp were associated with seizures (P < .001), whereas bilaterality of birthmarks was not. Only 49% of patients being treated for epilepsy were free of seizures. CONCLUSIONS: Seizures and glaucoma were the primary drivers for a diagnosis of SWS in patients with delayed diagnosis, and hearing (or vestibular) and behavioral problems were also prevalent. The diagnosis of SWS was delayed when the predominant symptom was headache. Seizure control was quite poor in many patients with SWS. Our findings highlight an important need for detailed, longitudinal data to improve our understanding of SWS and develop better treatment strategies for patients with this disorder.

Interictal spike connectivity in human epileptic neocortex.

OBJECTIVE: Interictal spikes are a biomarker of epilepsy, yet their precise roles are poorly understood. Using long-term neocortical recordings from epileptic patients, we investigated the spatial-temporal propagation patterns of interictal spiking. METHODS: Interictal spikes were detected in 10 epileptic patients. Short time direct directed transfer function was used to map the spatial-temporal patterns of interictal spike onset and propagation across different cortical topographies. RESULTS: Each patient had unique interictal spike propagation pattern that was highly consistent across times, regardless of the frequency band. High spiking brain regions were often not spike onset regions. We observed frequent spike propagations to shorter distances and that the central sulcus forms a strong barrier to spike propagation. Spike onset and seizure onset seemed to be distinct networks in most cases. CONCLUSIONS: Patients in epilepsy have distinct and unique network of causal propagation pattern which are very consistent revealing the underlying epileptic network. Although spike are epileptic biomarkers, spike origin and seizure onset seems to be distinct in most cases. SIGNIFICANCE: Understanding patterns of interictal spike propagation could lead to the identification patient-specific epileptic networks amenable to surgical or other treatments.

Inflammation is a key risk factor for persistent seizures in neurocysticercosis.

OBJECTIVE: We conducted a retrospective, case-control study of neurocysticercosis patients to ascertain early markers that identify subjects likely to develop treatment-resistant seizures. METHODS: Clinical histories and imaging studies from 38 neurocysticercosis patients who had been followed for 18 months after treatment were evaluated. Both pairwise and multifactorial analyses were conducted to identify factors associated with continued seizures. RESULTS: Eleven of 38 patients continued to have seizures during the follow-up period. On univariate analysis, the number of neurocysticercosis lesions, number of bands on the baseline neurocysticercosis western blot, edema volumes on follow-up MRI scans, edema volume changes between baseline and follow-up images, and proportion of calcified lesions with perilesional edema were all significantly increased in subjects who had persistent seizures during the 18-month follow-up period. On multivariate analyses using recursive partition and random forest algorithms, variables associated with persistent seizures included: the number of total and calcified lesions, presence of perilesional edema, the rate of change in the lesion and edema volumes from baseline to follow-up, and the number of bands on the neurocysticercosis western blot. INTERPRETATION: Measures of both inflammation and disease burden are key risk factors for persistent seizures despite anticonvulsant treatments in patients with neurocysticercosis. Inflammation is therefore a potentially modifiable risk factor for the frequently seen severe seizure disorders in patients with neurocysticercosis.

Estimation of resting state effective connectivity in epilepsy using direct-directed transfer function.

There has been an increasing demand among neuroscientists to understand the complex network of functionally connected neural assemblies in the human brain. For this purpose, computational EEG research is widely used by researchers due to its remarkable advantage in providing high temporal resolution, and ease of analysis across different frequency bands. Here we analyzed Electrocorticographic (ECoG) signals of electrodes placed on frontal-parietal neocortex brain region of 8 pediatric epileptic patients. In order to evaluate the directed causal relationship among different brain regions, we employed a Granger causality based multivariate connectivity estimator named direct Directed Transfer Function (dDTF) to identify signal propagations among the selected set of electrode in the frequency range 1-50Hz. A consistent network pattern emerged that was unique to each patient. The fidelity of such dDTF-derived connectivity patterns can support a clearer understanding of effective connectivity in epileptic networks.