Bilal Zonjy

Nonseizure SUDEP: Sudden unexpected death in epilepsy without preceding epileptic seizures

To describe the phenomenology of monitored sudden unexpected death in epilepsy (SUDEP) occurring in the interictal period where death occurs without a seizure preceding it.

Amygdala and hippocampus are symptomatogenic zones for central apneic seizures

Objective: To identify limbic sites of respiratory control in the human brain, and by extension, the symptomatogenic zone for central apnea. Methods: We used direct stimulation of anatomically, precisely placed stereotactic EEG electrodes to analyze breathing responses. We prospectively studied 3 patients who were explored with stereotactically implanted depth electrodes. The amygdala and hippocampus, as well as extralimbic sites (orbitofrontal, temporal tip, and temporal neocortex), were investigated. Results: Individual stimulation of the amygdala and hippocampal head consistently elicited central apnea in the expiratory phase, as did exquisitely focal hippocampal seizures. Conclusions: These findings confirm that hippocampus and amygdala are limbic breathing control sites in humans, as well as the symptomatogenic zone for central apneic seizures.

Left-insular damage, autonomic instability, and sudden unexpected death in epilepsy.

We analyzed the only two sudden unexpected death in epilepsy (SUDEP) cases from 320 prospectively recruited patients in the three-year Prevention and Risk Identification of SUDEP Mortality (PRISM) project. Both patients had surgically refractory epilepsy, evidence of left insular damage following previous temporal/temporo-insular resections, and progressive changes in heart rate variability (HRV) in monitored evaluations prior to death. Insular damage is known to cause autonomic dysfunction and increased mortality in acute stroke. This report suggests a possible role for the insula in the pathogenesis of SUDEP. The presence of intrinsic insular lesions or acquired insular damage in patients with refractory epilepsy may be an additional risk factor for SUDEP.

The incidence and significance of periictal apnea in epileptic seizures

The aim of this study was to investigate periictal central apnea as a seizure semiological feature, its localizing value, and possible relationship with sudden unexpected death in epilepsy (SUDEP) pathomechanisms.

A scalable neuroinformatics data flow for electrophysiological signals using MapReduce.

Data-driven neuroscience research is providing new insights in progression of neurological disorders and supporting the development of improved treatment approaches. However, the volume, velocity, and variety of neuroscience data generated from sophisticated recording instruments and acquisition methods have exacerbated the limited scalability of existing neuroinformatics tools. This makes it difficult for neuroscience researchers to effectively leverage the growing multi-modal neuroscience data to advance research in serious neurological disorders, such as epilepsy. We describe the development of the Cloudwave data flow that uses new data partitioning techniques to store and analyze electrophysiological signal in distributed computing infrastructure. The Cloudwave data flow uses MapReduce parallel programming algorithm to implement an integrated signal data processing pipeline that scales with large volume of data generated at high velocity. Using an epilepsy domain ontology together with an epilepsy focused extensible data representation format called Cloudwave Signal Format (CSF), the data flow addresses the challenge of data heterogeneity and is interoperable with existing neuroinformatics data representation formats, such as HDF5. The scalability of the Cloudwave data flow is evaluated using a 30-node cluster installed with the open source Hadoop software stack. The results demonstrate that the Cloudwave data flow can process increasing volume of signal data by leveraging Hadoop Data Nodes to reduce the total data processing time. The Cloudwave data flow is a template for developing highly scalable neuroscience data processing pipelines using MapReduce algorithms to support a variety of user applications.

Serum serotonin levels in patients with epileptic seizures

Profound cardiovascular and/or respiratory dysfunction is part of the terminal cascade in sudden unexpected death in epilepsy (SUDEP). Central control of ventilation is mediated by brainstem rhythm generators, which are influenced by a variety of inputs, many of which use the modulatory neurotransmitter serotonin to mediate important inputs for breathing. The aim of this study was to investigate epileptic seizure–induced changes in serum serotonin levels and whether there are potential implications for SUDEP. Forty‐one epileptic patients were pooled into 2 groups based on seizure type as (1) generalized tonic–clonic seizures (GTCS) of genetic generalized epilepsy and focal to bilateral tonic–clonic seizures (FBTCS; n = 19) and (2) focal seizures (n = 26) based on clinical signs using surface video‐electroencephalography. Postictal serotonin levels were statistically significantly higher after GTCS and FBTCS compared to interictal levels (P = .002) but not focal seizures (P = .941). The change in serotonin (postictal‐interictal) was inversely associated with a shorter duration of tonic phase of generalized seizures. The interictal serotonin level was inversely associated with a shorter period of postictal generalized electroencephalographic suppression. These data suggest that peripheral serum serotonin levels may play a role in seizure features and earlier postseizure recovery; these findings merit further study.

Cortical Structures Associated With Human Blood Pressure Control

Importance A better understanding of the role of cortical structures in blood pressure control may help us understand cardiovascular collapse that may lead to sudden unexpected death in epilepsy (SUDEP). Objective To identify cortical control sites for human blood pressure regulation. Design, Setting, and Participants Patients with intractable epilepsy undergoing intracranial electrode implantation as a prelude to epilepsy surgery in the Epilepsy Monitoring Unit at University Hospitals Cleveland Medical Center were potential candidates for this study. Inclusion criteria were patients 18 years or older who had electrodes implanted in one or more of the regions of interest and in whom deep brain electrical stimulation was indicated for mapping of ictal onset or eloquent cortex as a part of the presurgical evaluation. Twelve consecutive patients were included in this prospective case series from June 1, 2015, to February 28, 2017. Main Outcomes and Measures Changes in continuous, noninvasive, beat-by-beat blood pressure parameter responses from amygdala, hippocampal, insular, orbitofrontal, temporal, cingulate, and subcallosal stimulation. Electrocardiogram, arterial oxygen saturation, end-tidal carbon dioxide, nasal airflow, and abdominal and thoracic plethysmography were monitored. Results Among 12 patients (7 female; mean [SD] age, 44.25 [12.55] years), 9 electrodes (7 left and 2 right) all in Brodmann area 25 (subcallosal neocortex) in 4 patients produced striking systolic hypotensive changes. Well-maintained diastolic arterial blood pressure and narrowed pulse pressure indicated stimulation-induced reduction in sympathetic drive and consequent probable reduction in cardiac output rather than bradycardia or peripheral vasodilation–induced hypotension. Frequency-domain analysis of heart rate and blood pressure variability showed a mixed picture. No other stimulated structure produced significant blood pressure changes. Conclusions and Relevance These findings suggest that Brodmann area 25 has a role in lowering systolic blood pressure in humans. It is a potential symptomatogenic zone for peri-ictal hypotension in patients with epilepsy.

Automated Detection of Post-ictal Generalized EEG Suppression

Although there is no strict consensus, some studies have reported that Postictal generalized EEG suppression (PGES) is a potential electroencephalographic (EEG) biomarker for risk of sudden unexpected death in epilepsy (SUDEP). PGES is an epoch of EEG inactivity after a seizure, and the detection of PGES in clinical data is extremely difficult due to artifacts from breathing, movement and muscle activity that can adversely affect the quality of the recorded EEG data. Even clinical experts visually interpreting the EEG will have diverse opinions on the start and end of PGES for a given patient. The development of an automated EEG suppression detection tool can assist clinical personnel in the review and annotation of seizure files, and can also provide a standard for quantifying PGES in large patient cohorts, possibly leading to further clarification of the role of PGES as a biomarker of SUDEP risk. In this paper, we develop an automated system that can detect the start and end of PGES using frequency domain features in combination with boosting classification algorithms. The average power for different frequency ranges of EEG signals are extracted from the prefiltered recorded signal using the fast fourier transform and are used as the feature set for the classification algorithm. The underlying classifiers for the boosting algorithm are linear classifiers using a logistic regression model. The tool is developed using 12 seizures annotated by an expert then tested and evaluated on another 20 seizures that were annotated by 11 experts.Although there is no strict consensus, some studies have reported that Postictal generalized EEG suppression (PGES) is a potential electroencephalographic (EEG) biomarker for risk of sudden unexpected death in epilepsy (SUDEP). PGES is an epoch of EEG inactivity after a seizure, and the detection of PGES in clinical data is extremely difficult due to artifacts from breathing, movement and muscle activity that can adversely affect the quality of the recorded EEG data. Even clinical experts visually interpreting the EEG will have diverse opinions on the start and end of PGES for a given patient. The development of an automated EEG suppression detection tool can assist clinical personnel in the review and annotation of seizure files, and can also provide a standard for quantifying PGES in large patient cohorts, possibly leading to further clarification of the role of PGES as a biomarker of SUDEP risk. In this paper, we develop an automated system that can detect the start and end of PGES using frequency domain features in combination with boosting classification algorithms. The average power for different frequency ranges of EEG signals are extracted from the prefiltered recorded signal using the fast fourier transform and are used as the feature set for the classification algorithm. The underlying classifiers for the boosting algorithm are linear classifiers using a logistic regression model. The tool is developed using 12 seizures annotated by an expert then tested and evaluated on another 20 seizures that were annotated by 11 experts.

A Multi-center Physiological Data Repository for SUDEP: Data Curation, Data Conversion and Workflow

For any rare diseases, patient cohorts from individual medical research centers may not have sufficient statistical power to develop and verify/validate disease biomarkers as results of either small sample size or lack of patient-level predictors of the disease often in the form of recorded biological signals integrated with clinical data. Continuous recording is thus becoming a necessary step in the research to identify these biomarkers. The creation of a biological signals repository on top of a clinical data repository from multiple centers is thus a catalyst for current and future research of rare diseases. In this paper, several issues are considered in order to combine recorded physiological measurements from multiple centers to create a collaborative Big Data repository. Practical challenges including standardization of clinical information as well as physiological data are addressed. A case study of the Big-Data challenges associated with creating a large physiological data repository for the study of SUDEP (Sudden Unexpected Death in Epilepsy) as a part of the CSR (Center for SUDEP Research) study is presented. This includes end-to-end workflow from obtaining the source waveform data to storing standardized data files in the multi-center repository. This workflow has been implemented at Case Western Reserve University in partnership with University Hospitals to standardize data from multiple SUDEP centers that include Nihon Kohden, Micromed, and Nicolet physiological signal formats converted to European Data Format (EDF). A combination of existing third party, proprietary, and in-house-developed software tools used in the workflow are discussed.

NeuroPigPen: A Scalable Toolkit for Processing Electrophysiological Signal Data in Neuroscience Applications Using Apache Pig.

The recent advances in neurological imaging and sensing technologies have led to rapid increase in the volume, rate of data generation, and variety of neuroscience data. This “neuroscience Big data” represents a significant opportunity for the biomedical research community to design experiments using data with greater timescale, large number of attributes, and statistically significant data size. The results from these new data-driven research techniques can advance our understanding of complex neurological disorders, help model long-term effects of brain injuries, and provide new insights into dynamics of brain networks. However, many existing neuroinformatics data processing and analysis tools were not built to manage large volume of data, which makes it difficult for researchers to effectively leverage this available data to advance their research. We introduce a new toolkit called NeuroPigPen that was developed using Apache Hadoop and Pig data flow language to address the challenges posed by large-scale electrophysiological signal data. NeuroPigPen is a modular toolkit that can process large volumes of electrophysiological signal data, such as Electroencephalogram (EEG), Electrocardiogram (ECG), and blood oxygen levels (SpO2), using a new distributed storage model called Cloudwave Signal Format (CSF) that supports easy partitioning and storage of signal data on commodity hardware. NeuroPigPen was developed with three design principles: (a) Scalability—the ability to efficiently process increasing volumes of data; (b) Adaptability—the toolkit can be deployed across different computing configurations; and (c) Ease of programming—the toolkit can be easily used to compose multi-step data processing pipelines using high-level programming constructs. The NeuroPigPen toolkit was evaluated using 750 GB of electrophysiological signal data over a variety of Hadoop cluster configurations ranging from 3 to 30 Data nodes. The evaluation results demonstrate that the toolkit is highly scalable and adaptable, which makes it suitable for use in neuroscience applications as a scalable data processing toolkit. As part of the ongoing extension of NeuroPigPen, we are developing new modules to support statistical functions to analyze signal data for brain connectivity research. In addition, the toolkit is being extended to allow integration with scientific workflow systems. NeuroPigPen is released under BSD license at: https://sites.google.com/a/case.edu/neuropigpen/.