Seizures are a disruption of normal brain activity present across a vast range of species and conditions. We introduce an organizing principle that leads to the first objective Taxonomy of Seizure ...Dynamics (TSD) based on bifurcation theory. The 'dynamotype' of a seizure is the dynamic composition that defines its observable characteristics, including how it starts, evolves and ends. Analyzing over 2000 focal-onset seizures from multiple centers, we find evidence of all 16 dynamotypes predicted in TSD. We demonstrate that patients' dynamotypes evolve during their lifetime and display complex but systematic variations including hierarchy (certain types are more common), non-bijectivity (a patient may display multiple types) and pairing preference (multiple types may occur during one seizure). TSD provides a way to stratify patients in complement to present clinical classifications, a language to describe the most critical features of seizure dynamics, and a framework to guide future research focused on dynamical properties.
Wearable sensors are receiving a great deal of attention as they offer the potential to become a key technological tool for healthcare. In order for this potential to come to fruition, new ...electroactive materials endowing high performance need to be integrated with textiles. Here we present a simple and reliable technique that allows the patterning of conducting polymers on textiles. Electrodes fabricated using this technique showed a low impedance contact with human skin, were able to record high quality electrocardiograms at rest, and determine heart rate even when the wearer was in motion. This work paves the way towards imperceptible electrophysiology sensors for human health monitoring.
Abstract
There is a crucial need to identify biomarkers of epileptogenesis that will help predict later development of seizures. This work identifies two novel electrophysiological biomarkers that ...quantify epilepsy progression in a rat model of epileptogenesis. The long-term tetanus toxin rat model was used to show the development and remission of epilepsy over several weeks. We measured the response to periodic electrical stimulation and features of spontaneous seizure dynamics over several weeks. Both biomarkers showed dramatic changes during epileptogenesis. Electrically induced responses began to change several days before seizures began and continued to change until seizures resolved. These changes were consistent across animals and allowed development of an algorithm that could differentiate which animals would later develop epilepsy. Once seizures began, there was a progression of seizure dynamics that closely follows recent theoretical predictions, suggesting that the underlying brain state was changing over time. This research demonstrates that induced electrical responses and seizure onset dynamics are useful biomarkers to quantify dynamical changes in epileptogenesis. These tools hold promise for robust quantification of the underlying epileptogenicity and prediction of later development of seizures.
This work identified two novel electrophysiological biomarkers that quantify epilepsy progression in the tetanus toxin rat model: the response to periodic electrical stimulation and features of spontaneous seizure dynamics. Both biomarkers quantify the evolution of epileptogenesis over weeks.
Graphical Abstract
Graphical Abstract
Epilepsy is characterized by spontaneously recurring seizures that severely disrupt quality of life and pose risks of injury and death. Seizures can be seen in a wide range of other diseases ...(Alzheimer’s, autism, Down’s syndrome, etc.), indicating that epilepsy is highly heterogeneous and cannot be constrained to a single source. Yet to date, there is no method of categorizing seizures that can help distinguish the link between pathology and seizures. The aim of this work is to validate and explore a method of categorizing seizures based on their fundamental dynamics to provide a framework for future research to better investigate the underlying mechanisms of seizures and therapeutic approaches to stop them. The first study used predictions from a previously-published computational model to visually classify seizures using dynamical transition features in two large datasets (simulated and real human data). Machine learning was applied to raw signal features to verify the accuracy of the reviewer’s labels. It found that visual classification is consistent and supported by the signal feature analysis. We also investigate the model’s predictions in real human data, finding that most dynamic classifications were observed and patients can have varying seizure dynamics over time. The second study used data mining and machine learning in a long-term rat model of epileptogenesis to investigate the viability of these same dynamic principles as a biomarker of epileptic brain state. It also applied the same rigor to an analysis of the response to electrical stimulation. We found that evoked responses can be used to predict if an injured brain would eventually develop seizures or not. Once seizures began manifesting, both evoked responses and seizure onset dynamics had strong correlation with the progression of epileptogenesis, suggesting they are independent biomarkers. For the final study, we use the same principles of dynamics and machine learning to characterize differences between a low Mg2+ / high K+ mouse brain-slice seizure model with and without different anti-seizure drugs. It found that anti-seizure drugs can change the observed seizure dynamics, and each drug has a different effect on brain dynamics. These three studies provide evidence that seizures can be categorized by their fundamental dynamics. These dynamics can provide mechanistic insights into current brain state, future brain states, and the response to anti-epileptic drugs. The results presented in this dissertation can be used as a framework to further investigate seizure mechanisms and personalize patient treatment and research. Next steps include using this framework to investigate seizure propagation and spatial variability.
Coupled oscillators with shared bias current Crisp, Dakota; Cahill, Benjamin; Yumin Zhang
2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS),
2017-Aug.
Conference Proceeding
This paper is on the study of double coupled oscillators, where two cross-coupled differential oscillators share the same bias current. Without external coupling, competition for bias current ...happens, and the one with a higher Q can grab most of the current, and the other one is starved to death. With external coupling, a beat pattern is generated and the beat frequency is strongly related to the value of the coupling circuit elements. These phenomena are investigated by experiment, circuit simulation, and theoretical analysis. The results agree with one another quite well.
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