Recent Funded Projects
SCH: Ensemble Logic: A Formal Precision Phenotyping Framework for Cohort Discovery in Epilepsy
PI: Guo-Qiang Zhang, PhD
In medicine, a phenotype is a person's visible traits or behaviors, such as heart rhythm, brain activity, or signs of disease. Accurately describing these traits is important for making correct diagnoses and choosing the right treatment. But clinicians currently write descriptions of these traits in loose, unstructured text, which can create confusion and inconsistency. Two doctors might describe the same condition in different ways, making it harder to communicate clearly and to keep documented data reliable. This lack of clarity has real effects on health. To diagnose problems like heart or brain disorders, doctors often watch how signals in the body, such as an EKG (electrocardiogram) or EEG (electroencephalogram), change over time. If these changing patterns are not clearly defined, it becomes harder to detect disease early, track its course, or plan treatments tailored to each patient. This project tackles this issue by creating a better way to describe phenotypes. This research builds a new logical system, called Ensemble Logic, that allows clinicians to describe these patterns in a way that is clear, consistent, and easy for computers to understand. This logical system is meant to be both intuitive and flexible. It will be used to define and detect complex health conditions using structured rules that help improve patient care and value in healthcare data.
Inhibition of central CO2 chemoreception by seizures: Effects on ventilation, cardiovascular control and postictal recovery of consciousness
PI: George B. Richerson, MD, PhD
Sudden unexpected death in epilepsy (SUDEP) is the leading cause of death in patients with refractory epilepsy and is two to four times as common as sudden infant death syndrome (SIDS). Because the mechanisms responsible for SUDEP have not been clearly defined, there are no specific treatments to prevent it. Serotonin (5-HT) neurons are central CO2 chemoreceptors (CCR) that regulate breathing, autonomic function, and arousal. Patients with low interictal CCR are more hypercapnic following a generalized tonic-clonic seizure (GTCS), and seizures may depress CCR, an effect that can be measured with the hypercapnic ventilatory response (HCVR) test. Impairment of CCR by seizures may contribute to autonomic dysfunction and impaired arousal after GTCS, increasing the risk of SUDEP. The long-term goal is to develop new treatments to prevent SUDEP by elucidating the mechanisms responsible for seizure-induced respiratory depression, autonomic dysfunction, and impaired arousal.
Breathing Rescue for SUDEP Prevention (BreatheS)
PI: Nuria Lacuey Lecumberri, MD, PhD
Sudden unexpected death in epilepsy (SUDEP) is a devastating complication of epilepsy and was thought to be due to cardiovascular failure, but research on patients who died while being monitored in hospital epilepsy units reveal that most SUDEP is due to post-convulsive central apnea. Crucially, a three-minute post convulsive window of opportunity was identified, beyond which the terminal cascade of respiratory and cardiac failure appears irrevocable. By advancing our understanding of forebrain breathing networks, we can develop neuromodulatory strategies for respiratory facilitation and apnea rescue that may prevent SUDEP during this critical time window. The objective of this project is to understand forebrain modulation of breathing by using anatomically precise intracranial stereotactic electroencephalography (SEEG) data that can determine optimal stimulation paradigms for breathing enhancement.
International Seizure and SUDEP Research Repository (InSSURR)
PI: Samden D. Lhatoo MD, FRCP; Guo-Qiang Zhang, PhD
Sudden Unexpected Death in Epilepsy (SUDEP) is the commonest category of direct epilepsy related mortality worldwide. Research into biomarker identification and specific prevention approaches has been hindered by the lack of largescale, prospectively acquired, long term (multi-day or longer), multimodality datasets that allow comprehensive study of seizures for clues to mechanisms of death. Understanding mechanisms will allow development of specific preventive methods. The goal of this data resource project, the International Seizure and SUDEP Research Repository (InSSURR), is to overcome the barriers posed by the lack of high-quality datasets for research. InSSURR will be the single largest resource of its kind, expanded from three foundational US and European resources, a) the previously NINDS funded Center for SUDEP Research (CSR) study; b) the French Health Ministry funded European Réseau national d’Etude des facteurs prédictifs et de la Prévention des Morts Soudaines inattendues dans les Epilepsies partielles pharmacorésistantes (REP02MSE) study; and c) the University of Texas at Houston SUDEP research database. InSSURR will ingest seizure and SUDEP data from ongoing and future studies to create the largest publicly available epilepsy mortality research data resource.
SCH: Neurophysiological AI-Ready Data Resource
PI: Guo-Qiang Zhang, PhD
The goal of this project is to create an AI-ready resource of semantically annotated collection of electroencephalogram (EEG) recordings and sleep polysomnography data, called the Neurophysiological AI-Ready Data Resource (NAIRD), to transform the access and sharing of such resources for brain health research. NAIRD will leverage data sources from prior repositories that cover two major brain health domains: epilepsy, as a part of the Center for Sudden Unexpected Death in Epilepsy (SUDEP) Research (CSR), and sleep, as a part of the National Sleep Research Resource (NSRR). NAIRD will make this rich collection of electrophysiological signals with associated individual-level health outcomes not only FAIR (Findability, Accessibility, Interoperability, and Reusable), but also interactable and AI-ready.
Advancing SUDEP risk prediction using a multicenter case-control approach
PI: Orrin Devinsky MD; Samden D. Lhatoo MD, FRCP; Daniel Friedman, MD
This study will combine and harmonize data from 86 epilepsy centers to study a retrospective cohort who succumbed to Sudden Unexpected Death in Epilepsy (SUDEP) to identify clinical, electrophysiological and imaging predictors of SUDEP and develop an individualized model using machine learning methods to predict SUDEP risk. In addition to understanding SUDEP mechanisms through studying a large number of SUDEP cases it will also identify the development and validation of an individual risk calculator which could make a big difference with adult patients trying to gain independence.
Cardiac and Autonomic Pathological Markers for Arrhythmias and Sudden Unexpected Death in Epilepsy Patients
PI: David Auerbach PhD
Patients with epilepsy are at high risk of sudden death from SUDEP. We are not able to predict a patient’s risk of SUDEP. Patients without known SUDEP risk factors account for the largest number of SUDEP cases. In contrast to prior risk factor association studies that are not linked to a proposed cause for SUDEP, this study will develop pathological markers for a proposed SUDEP mechanism. Analytics that are validated in the cardiac field, but are new to the epilepsy/SUDEP field will identify the clinical populations at risk of, and the triggers for, cardiac-mediated SUDEP.
Defining breathing network neuromodulatory approaches for prevention of Sudden Unexpected Death in Epilepsy (SUDEP)
PI: Nuria Lacuey-Lecumberri MD, PhD
Sudden Unexpected Death in Epilepsy (SUDEP) usually occurs in patients with frequent convulsive seizures due to seizure-induced breathing failure. There are currently no technologies or devices available for directly preventing death in patients at high risk. The goal of this project is to improve the overall understanding of breathing control by identifying specific brain areas that are most important for breathing function and stimulation techniques that can be used to prevent seizure-induced breathing failure. The outcome of this research will pave the way for deep brain stimulation devices for breathing rescue as a targeted SUDEP prevention strategy.
An informatics framework for SUDEP risk marker identification and risk assessment
PI: Licong Cui, PhD
The main goal of this project is to develop an informatics approach for automated extraction of SUDEP risk markers from multimodal clinical data to enable individualized SUDEP risk assessment. Success of this study will enable systematic SUDEP risk assessment based on known and putative factors and communication of such risk factors to patients with epilepsy. Ultimately, this study can lead to evidence-based SUDEP risk assessment tools that help clinicians and patients manage potentially modifiable risks, leading to overall reduced SUDEP mortality and improved epilepsy patient care.
The role of central CO2 chemosensitivity in postictal respiratory depression and SUDEP
PI: George B Richerson, MD, PhD
Sudden unexpected death in epilepsy (SUDEP) is the leading cause of death in patients with refractory epilepsy. In many cases, death follows a period of severe seizure-induced respiratory depression, for which the cause is not understood. The proposed research is relevant to public health and the NIH’s mission because a better understanding of the role played by central CO2 chemosensation in the pathogenesis of seizure-induced respiratory depression is expected to lead to the identification of novel candidate biomarkers for SUDEP risk and to the development of new preventive treatments for SUDEP.
Isolating SUDEP risk conferred by genomic co-variation in candidate SUDEP genes
PI: Alica Goldman, MD, PhD
This pilot project aims to evaluate the components of genetic risk in SUDEP in SCN1A gene related epilepsy and in patients with febrile seizure phenotype. Goal of this project is to develop critically needed bioinformatic tools for genomic SUDEP risk stratification. Results will have an immediate impact on patients with epilepsy due to SCN1A gene pathogenic variation and they will inform future bioinformatics and statistical design when analyzing SUDEP risk in patients with the clinically very common febrile seizure phenotype as well as when evaluating causality in existing collections of SUDEP cases.
