Epilepsy, a neurological disease that causes recurring seizures with a wide array of effects, impacts approximately 50 million people across the world. This condition has been recognized for a long time — written records of epileptic symptoms date all the way back to 4000 B.C.E. But despite this long history of knowledge and treatment, the exact processes that occur in the brain during a seizure remain elusive. 

Scientists have observed distinctive patterns in the electrical activity of neuron groups in healthy brains. Networks of neurons move through states of similar behavior (synchronization) and dissimilar behavior (desynchronization) in a process that is associated with memory and attention. But in a brain with a neurological disorder like epilepsy, synchronization can grow to a dangerous extent when a collection of brain cells begins to emit excess electricity. “Synchronization is thought to be important for information processing,” Jennifer Creaser of the University of Exeter said. “But too much synchronization—such as what occurs in epileptic seizures or Parkinson’s disease—is associated with disease states and can impair brain function.” 

Measurements of epileptic seizures have revealed that desynchronization in brain networks often occurs before or during the early stages of a seizure. As the seizure progresses, networks become increasingly more synchronized as additional regions of the brain get involved, leading to high levels of synchronization towards the seizure’s end. Understanding the interactions between the increased electrical activity during a seizure and changes in synchronization is an important step towards improving the diagnosis and treatment of epilepsy.

Jennifer Creaser, Peter Ashwin (University of Exeter), and Krasimira Tsaneva-Atanasova (the University of Exeter, Technical University of Munich, and Bulgarian Academy of Sciences) explored the mechanisms of synchronization that accompany seizure onset in a paper published in December in the SIAM Journal on Applied Dynamical Systems. In their study—which took place at the Engineering and Physical Science Research Council’s Centre for Predictive Modelling in Healthcare at the University of Exeter and the University of Birmingham—the researchers used mathematical modeling to explore the interplay between groups of neurons in the brain that leads to transitions in synchronization changes during seizure onset. “Although this is a theoretical study of an idealized model, it is inspired by challenges posed by understanding transitions between healthy and pathological activity in the brain,” Ashwin said. {module INSIDE STORY}

The authors utilize an extended version of an existing mathematical model that represents the brain as a network connecting multiple nodes of neuron groups. The model network consists of bistable nodes, meaning that each node is able to switch between two stable states: resting (a quiescent state) and seizure (an active and oscillatory state). These nodes remain in their current state until they receive a stimulus that gives them a sufficient kick to escape to the other state. In the model, this stimulus comes from other connected nodes or appears in the form of “noise” — outside sources of neural activity, such as endocrine responses that are associated with an emotional state or physiological changes due to disease. 

The influence between neighboring nodes is governed by a coupling function that represents the way in which the nodes in the network communicate with each other. The first of the two possible types of coupling is amplitude coupling, which is governed by the “loudness” of the neighboring nodes. The second is phase coupling, which is related to the speed at which the neighbors are firing. Although the researchers needed to utilize a simple formulation on a small network to even make their analysis possible—a more complex and realistic system would be too computationally taxing—they expected their model to exhibit the same types of behaviors that clinical recordings of real brain activity have revealed. 

The nodes in the modeled system all begin in the healthy resting state. In previous research, the authors found that adding a small amount of noise to the system caused each node to transition to the active state — but the system’s geometry was such that returning to the resting state took much longer than leaving. Because of this, these escapes can spread sequentially as a “domino effect” when a number of nodes are connected. This leads to a cascade of escapes to the active state—much like a falling line of dominos—that spreads activity across the network. 

Creaser, Ashwin, and Tsaneva-Atanasova’s new paper build upon this previous research on the domino effect to explore the transitions into and out of synchrony that occur during cascades of escapes. The team used their model to identify the circumstances that bring about these changes in synchrony and investigate how the type of coupling in a network affects its behavior. 

When the model incorporated only amplitude coupling, it exhibited a new phenomenon in which the domino effect could accelerate or decelerate. However, this effect had no bearing on synchronization changes in the network; all of the nodes started and remained synchronized. But when the model incorporated more general amplitude and phase coupling, the authors found that the nodes’ synchrony could change between consecutive escapes during the domino effect. They then determined which conditions would cause changes in synchrony under phase-amplitude coupling. This change in synchrony throughout the sequence of escapes was the study’s most novel result. 

The results of this work could facilitate further studies on seizures and their management. “The mathematical modeling of seizure initiation and propagation can not only help to uncover seizures’ complex underlying mechanisms, but also provide a means for enabling in silico experiments to predict the outcome of manipulating the neural system,” Tsaneva-Atanasova said. Understanding the interplay between synchronized and desynchronized dynamics in brain networks could help identify clinically-relevant measures for seizure treatment. For example, Creaser and Tsaneva-Atanasova recently served as the lead and senior author, respectively, on a paper that utilized a simpler version of the model to classify patterns of seizure onset that were recorded in a clinical setting. In the future, these kinds of modeling studies may lead to the personalization of seizure identification and treatment for individuals with epilepsy.

Less than two days of water quality sampling at local beaches may be all that's needed to reduce illnesses among millions of beachgoers every year due to contaminated water, according to new Stanford research. The study, published in Environmental Science & Technology, presents a modeling framework that dependably predicts water quality at beaches after only a day or two of frequent water sampling. The approach, tested in California, could be used to keep tabs on otherwise unmonitored coastal areas, which is key to protecting the well-being of beachgoers and thriving ocean economies worldwide.

"This work combines knowledge of microbiology, coastal processes, and data science to produce a tool to effectively manage one of our most precious resources and protect human health," said senior author Alexandria Boehm, a Stanford professor of civil and environmental engineering.

Measuring concentrations of fecal indicator bacteria (FIB) - which denote the presence of fecal matter and can lead to unsafe water conditions - at beaches ensures the health and safety of the public. While all ocean water contains some degree of pathogens, such as bacteria or viruses, they're typically diluted to harmless concentrations. However, changes in rainfall, water temperature, wind, runoff, boating waste, storm sewer overflow, proximity to waste treatment plants, animals, and waterfowl can lead to an influx of water contamination. Exposure to these contaminants can cause many ailments, including respiratory diseases and gastrointestinal illnesses, along with skin, eye, and ear infections to swimmers. {module INSIDE STORY}

Protecting coastal waters and the people that use them remains essential for much of California's 840 miles of coastline. Over 150 million people swim, surf, dive, and play at one of the state's 450 beaches annually, generating over $10 billion in revenue. According to the California State Water Resources Control Board, health agencies across 17 counties, publicly owned sewage treatment plants, environmental groups and several citizen-science groups perform water sampling across the state. However, not all waters are routinely checked due to accessibility issues, budget resource constraints, or the season, despite their use by the public.

Another obstacle to safeguarding public health lies in the lag time between sampling and results - up to two-days - leading beach managers to make decisions reflecting past water quality conditions. When monitored waters contain high levels of bacteria and pose a health risk, beach managers post warning signs or close beaches. The delay in current testing methods could unknowingly expose swimmers to unhealthy waters.

To overcome these limitations, the researchers combined water sampling and environmental data with machine learning methods to accurately forecast water quality. While predictive water quality models aren't new, they have generally required historical data spanning several years to be developed.

The team used water samples collected at 10-minute intervals over a relatively brief timeframe of one to two days at beaches in Santa Cruz, Monterey, and Huntington Beach. Among the three sites, 244 samples were measured for FIB concentrations and marked as above or below the acceptable level deemed safe by the state. The researchers then collected meteorological data such as air temperature, solar radiation, and wind speed along with oceanographic data including tide level, wave heights, and water temperature (all factors influencing FIB concentrations) over the same timeframe.

Using the high-frequency water quality data and machine learning methods, they trained supercomputer models to accurately predict FIB concentrations at all three beaches. The researchers found hourly water sampling for 24 hours straight - capturing an entire tidal and solar cycle - proved enough for reliable results. Feeding the framework meteorological and tidal data from longer time periods resulted in future water quality predictions that were dependable for at least an entire season.

"These results are really empowering for communities who want to know what's going on with water quality at their beach," Searcy said. "With some resources to get started and a day of sampling, these communities could collect the data needed to initiate their own water quality modeling systems."

The framework code, which is publicly accessible, could also be developed for accurate predictions of other contaminants such as harmful algae, metals, and nutrients known to wreak havoc on local waters. The researchers point out that more analysis is needed to better determine the exact timeframe these models remain accurate and note that continually assessing and retraining the models remains a best practice for accurate predictions.