A5091769728- EEG and Brain-Computer Interfaces
- Functional Brain Connectivity Studies
- Epilepsy research and treatment
- Neural dynamics and brain function
- Neurological disorders and treatments
- Neuroscience and Neuropharmacology Research
- Advanced MRI Techniques and Applications
- Transcranial Magnetic Stimulation Studies
- Neuroscience and Neural Engineering
- Non-Invasive Vital Sign Monitoring
- Blood Pressure and Hypertension Studies
- Advanced Neuroimaging Techniques and Applications
- Parkinson's Disease Mechanisms and Treatments
- Cerebrospinal fluid and hydrocephalus
- Healthcare Technology and Patient Monitoring
- Genetic Neurodegenerative Diseases
- Electrochemical Analysis and Applications
- Neonatal and fetal brain pathology
- Atomic and Subatomic Physics Research
- Machine Learning in Healthcare
- Topic Modeling
- Pharmacological Effects and Toxicity Studies
- Fetal and Pediatric Neurological Disorders
University of Pennsylvania
2017-2024
Neurological Surgery
2024
Duke University
2014
Over the past 10 years, drive to improve outcomes from epilepsy surgery has stimulated widespread interest in methods quantitatively guide intracranial EEG (iEEG). Many patients fail achieve seizure freedom, part due challenges subjective iEEG interpretation. To address this clinical need, quantitative analytics have been developed using a variety of approaches, spanning studies seizures, interictal periods, and their transitions, encompass range techniques including electrographic signal...
Patients with drug-resistant epilepsy often require surgery to become seizure-free. While laser ablation and implantable stimulation devices have lowered the morbidity of these procedures, seizure-free rates not dramatically improved, particularly for patients without focal lesions. This is in part because it unclear where intervene cases. To address this clinical need, several research groups published methods map epileptic networks but applying them improve patient care remains a...
Abstract Objective Seizure frequency and seizure freedom are among the most important outcome measures for patients with epilepsy. In this study, we aimed to automatically extract clinical information from unstructured text in notes. If successful, could improve decision-making epilepsy allow rapid, large-scale retrospective research. Materials Methods We developed a finetuning pipeline pretrained neural models classify as being seizure-free containing their date of last annotated 1000 notes...
Abstract Objective Despite the overall success of responsive neurostimulation (RNS) therapy for drug‐resistant focal epilepsy, clinical outcomes in individuals vary significantly and are hard to predict. Biomarkers that indicate efficacy RNS—ideally before device implantation—are critically needed, but challenges include intrinsic heterogeneity RNS patient population variability management across epilepsy centers. The aim this study is use a multicenter dataset evaluate candidate biomarker...
Abstract Planning surgery for patients with medically refractory epilepsy often requires recording seizures using intracranial EEG. Quantitative measures derived from interictal EEG yield potentially appealing biomarkers to guide these surgical procedures; however, their utility is limited by the sparsity of electrode implantation as well normal confounds spatiotemporally varying neural activity and connectivity. We propose that comparing recordings a normative atlas connectivity can...
Patients with drug-resistant focal epilepsy are often candidates for invasive surgical therapies. In these patients, it is necessary to accurately localize seizure generators ensure freedom following intervention. While intracranial electroencephalography (iEEG) the gold standard mapping networks surgery, this approach requires inducing and recording seizures, which may cause patient morbidity. The goal of study evaluate utility interictal (non-seizure) iEEG identify targets treatment. We...
INTRODUCTION: Human cognition involves the complex coordination of interconnected networks, where pre-task spontaneous network states can prime brain performance. Thalamocortical communication is known to be a crucial element attentional processes. METHODS: We employed temporal expectancy task in 24 humans and 6 wild-type mice, consisting an initial cue followed by go separated variable time interval, response cue. subjects were implanted with iEEG, mice injected fluorescent Ca2+ indicator...
Abstract Brain network models derived from graph theory have the potential to guide functional neurosurgery, and improve rates of post-operative seizure freedom for patients with epilepsy. A barrier applying these clinically is that intracranial EEG electrode implantation strategies vary by centre, region country, cortical grid & strip electrodes (Electrocorticography), purely stereotactic depth (Stereo EEG), a mixture both. To determine whether one type study are broadly applicable...
Closed-loop implantable neural stimulators are an exciting treatment option for patients with medically refractory epilepsy, a number of new devices in or nearing clinical trials. These must accurately detect variety seizure types order to reliably deliver therapeutic stimulation. While effective, broadly-applicable detection algorithms have recently been published, these methods too computationally intensive be directly deployed device. We demonstrate strategy that couples cloud computing...
Epilepsy is a neurological disorder characterized by recurrent seizures which vary widely in severity, from clinically silent to prolonged convulsions. Measuring severity crucial for guiding therapy, particularly when complete control not possible. Seizure diaries, the current standard are insensitive duration of events or propagation seizure activity across brain. We present quantitative score that incorporates electroencephalography (EEG) and clinical data demonstrate how it can guide...
Network neuroscience applied to epilepsy holds promise map pathological networks, localize seizure generators, and inform targeted interventions control seizures. However, incomplete sampling of the epileptic brain because sparse placement intracranial electrodes may affect model results. In this study, we evaluate sensitivity several published network measures spatial propose an algorithm using subsampling determine confidence in We retrospectively evaluated EEG data from 28 patients...
Evaluating patients with drug-resistant epilepsy often requires inducing seizures by tapering antiseizure medications (ASMs) in the monitoring unit (EMU). The relationship between ASM taper strategy, seizure timing, and severity remains unclear. In this study, we developed validated a pharmacokinetic model of total load tested its association occurrence EMU.We studied 80 who underwent intracranial electroencephalographic recording for surgery planning. We first order ASMs administered EMU to...
New approaches are needed to interpret large amounts of physiologic data continuously recorded in the ICU. We developed and prospectively validated a versatile platform (IRIS) for real-time ICU monitoring, clinical decision making, caretaker notification.IRIS was implemented neurointensive care unit stream multimodal time series data, including EEG, intracranial pressure (ICP), brain tissue oxygenation (PbtO2), from monitors an analysis server. IRIS applied 364 patients undergoing continuous...
Abstract Objective. To determine the effect of epilepsy on intracranial electroencephalography (EEG) functional connectivity, and ability connectivity to localize seizure onset zone (SOZ), controlling for spatial biases. Approach. We analyzed EEG data from patients with drug-resistant admitted pre-surgical planning. calculated networks determined whether changes in lateralized SOZ using a subsampling method control bias. developed ‘spatial null model’ electrode only sampling information,...
White matter supports critical brain functions such as learning and memory, modulates the distribution of action potentials, transmits neural information between regions. Notably, neuronal cell bodies exist in deeper white tissue, neurotransmitter vesicles are released directly matter, blood-oxygenation level dependent (BOLD) signals detectable across a range different tasks—all appearing to reflect dynamic, active tissue from which recorded can reveal meaningful about brain. Yet, within...
Epilepsy surgery is an effective treatment for drug-resistant patients. However, how different surgical approaches affect long-term brain structure remains poorly characterized. Here, we present a semiautomated method quantifying structural changes after epilepsy and compare the remote effects of two approaches, anterior temporal lobectomy (ATL), selective amygdalohippocampectomy (SAH).
Abstract Intracranial EEG is used for two main purposes: to determine (i) if epileptic networks are amenable focal treatment and (ii) where intervene. Currently, these questions answered qualitatively differently across centres. There a need quantify the focality of systematically, which may guide surgical decision-making, enable large-scale data analysis facilitate multi-centre prospective clinical trials. We analysed interictal from 101 patients with drug-resistant epilepsy who underwent...
Mouse models are widely used in studies of various forms transcranial electric stimulation (TES). However, there is limited knowledge the field distribution induced by TES mice, and computational to estimate this lacking. This study examines current density mouse brain TES. We created a high-resolution finite element model incorporating ear clip electrodes commonly study, for example, electroconvulsive therapy (ECT). The strength an electrode configuration were computed anatomically...
Continuous electroencephalogram monitoring is associated with lower mortality in critically ill patients; however, it underused due to the resource-intensive nature of manually interpreting prolonged streams continuous data. Here, we present a novel real-time, machine learning-based alerting and system for epilepsy seizures that dramatically reduces amount manual review.We developed custom data reduction algorithm using random forest deployed within an online cloud-based platform, which...
Although seizure detection algorithms are widely used to localize onset on intracranial EEG in epilepsy patients, relatively few studies focus activity beyond the zone direct treatment of surgical patients with epilepsy. To address this gap, we develop and compare fully automated deep learning detect single channels, effectively quantifying spread when deployed across multiple channels. Across 275 seizures 71 discover that extent brain timing between temporal lobe regions is associated both...
Intracranial EEG (IEEG) is used for 2 main purposes, to determine: (1) if epileptic networks are amenable focal treatment and (2) where intervene. Currently these questions answered qualitatively sometimes differently across centers. There a need objective, standardized methods guide surgical decision making enable large scale data analysis centers prospective clinical trials. We analyzed interictal from 101 patients with drug resistant epilepsy who underwent presurgical evaluation IEEG....