Advanced Neural Probes Reveal Predictable Patterns in Epileptic Brain Activity
In addition to suffering seizures, many people with epilepsy also experience bursts of abnormal brain activity called interictal epileptiform discharges (IEDs). These can happen thousands of times a day and interfere with attention, memory, language, and sleep. New data from a study led by scientists at University of California, San Francisco (UCSF) shows that these brain blips are not random events as once thought. The data shows that they unfold in a predictable pattern that can be detected before they occur, suggesting it may be possible to prevent them.
Details of their work are published in Nature Neuroscience in a paper titled “Laminar organization of cellular microcircuits modulating human interictal epileptiform discharges.” In it, the scientists explain that they used a high-resolution technology recently adapted for humans that records individual neuron activity to track more than 1000 neurons in four patients undergoing surgery for epilepsy. The so-called Neuropixel probes provide “a view into new ways we might address a debilitating aspect of epilepsy that we haven’t been able to tackle,” said Jon Kleen, MD, PhD, an associate professor of neurology at UCSF and co-senior author of the study.
Preventing brain blips would be a boon for patients’ quality of life because over time, the effects of these mental disruptions can be significant and may account for some of the cognitive impairment experienced by about half of people with epilepsy.
Neuropixels probes, which are thin devices lined with hundreds of sensors, are designed to record activity throughout the human cortex. This means that unlike current sensors which are limited to brain signals on the surface of the brain, Neuropixels can provide a three-dimensional view of brain activity. For the study, the scientists implanted the probes seven millimeters deep into the part of the brain where patients’ seizures originate—this is the tissue that surgeons typically remove to reduce epilepsy symptoms.
Inserting the probes here made it possible to observe what happened in the neurons before, during, and after each IED. While seizures appear as a burst of neurons firing in synchrony, when IEDs occur, they unfold sequentially. Specifically, one set of neurons was active about a second before the IED started followed by another set that generated the sharp electrical spike at its peak, and then a third set became active as the IED faded. “We could see individual neurons that were just microns apart from each other playing different roles in the process,” said Alex Silva, the study’s first author and a medical student and doctoral candidate in the UCSF-UC Berkeley Joint PhD program in bioengineering. “It was really striking.”
Previous studies have demonstrated that most neurons involved in IEDs are used in normal cognitive processing. According to this study, nearly 80% of the neurons involved in IEDs were also involved in language and perception. Current implantable devices for epilepsy may be able to help. They include closed loop neurostimulators that can detect abnormal brain activity and deliver electrical pulses that interrupt it. So in the case of IEDs, devices that monitor single neurons could use the activity of the first set of neurons announcing the arrival of the abnormal pattern as a warning signal. “That would be a major step forward, changing treatment from reactively responding to abnormal brain bursts to proactively preventing them in the first place,” Kleen said.
The post Advanced Neural Probes Reveal Predictable Patterns in Epileptic Brain Activity appeared first on GEN – Genetic Engineering and Biotechnology News.
Momentary predictors of dissociation in functional neurological disorder: an ecological momentary assessment-based pilot study
STAT+: New obesity tool aims to predict risk of 18 serious complications
Body mass index has its limitations, but for now it’s the metric medicine often defaults to when predicting weight-related health problems. A new tool promises to better define who’s at risk for obesity complications, based on measures that include BMI but also family history, diet, current illness, and socioeconomic factors culled from medical records.
One aim of the research is to better understand who’s a candidate for an obesity drug, often prescribed based on BMI alone or BMI in combination with another disease. Over time, GLP-1 medications, whose initial target was type 2 diabetes, have revealed their power to ease cardiovascular disease, kidney disease, liver disease, sleep apnea, and osteoarthritis, in addition to promoting significant weight loss. But discerning who’s the best fit for the costly, lifelong treatment has been uncertain.
“We really wanted to have an integrated model that enables us to look at not one, but 18 different obesity-relevant complications,” Claudia Langenberg, co-author of a study about the new model published Thursday in Nature Medicine, said in a media briefing Tuesday. She is director and professor of medicine and population health at Precision Healthcare University Research Institute of Queen Mary University of London.
Effects of bifrontal-transcranial direct current stimulation combined with music listening on sleep quality, cortical activation and functional connectivity in patients with insomnia: a randomised controlled trial by fNIRS
PAD-S/CSA as a candidate shared representation layer for computational psychotherapy: minimal architecture and a staged validation roadmap
Efficacy and Safety of Daridorexant in Patients With Major Depressive Disorder and Insomnia
Interventions: Drug: Daridorexant 50 mg; Drug: Placebo
Sponsors: Institut d’Investigació Biomèdica de Bellvitge; Idorsia Pharmaceuticals Ltd.; Hospital Universitari de Bellvitge
Not yet recruiting
The Impact of Digital Detox Training on Screen Use, Sleep Quality, and Healthy Lifestyle Behaviors in Adolescents
Interventions: Behavioral: Digital Detox Intervention
Sponsors: Şırnak Üniversitesi
Completed
Benefit of the N-of-1 Approach Versus Aggregate Analysis in Tracking Individual Trajectories During Pregnancy: Comparison of Longitudinal Wearable Observational Studies
Background: Personal digital health technologies (DHTs) enable real-time monitoring of physiological metrics and behavioral data, including heart rate variability (HRV), supporting analysis of pregnancy-related conditions and personalized care throughout the perinatal period. While recent studies demonstrate the utility of personal DHTs in tracking pregnancy-related symptoms, they often rely on aggregate statistical methods that overlook individual variability. Objective: This study aims to compare aggregate and individual-level analyses of DHT data for pregnancy-related conditions, using the comprehensive BUMP (Better Understanding the Metamorphosis of Pregnancy) dataset to highlight the importance of individual variability and data heterogeneity. Methods: We analyzed wearable and self-reported data from 256 participants enrolled in the BUMP study (January 2021 to May 2022), including HRV, sleep, and fatigue measured via Oura Rings and smartphone surveys. Individual-level (N-of-1) trajectories were evaluated and compared with aggregate results to uncover personal and collective trends. A statistical method was developed to assess the influence of adverse events and severe symptoms, while case studies explored confounding and modifying factors underlying heterogeneity. Comprehensive statistical analysis included the coefficient of determination, Kolmogorov-Smirnov tests, likelihood ratio tests, and Welch tests, with interindividual variability flagged based on high-variability thresholds. Results: Substantial interindividual variability was observed across all features. Only 4.76% (12/256) of participants exhibited an HRV inflection at the aggregate week-33 inflection point, with a coefficient of variation of 14.24%. The median value of the gestational week in individual fatigue troughs was 23 (IQR 8; range 8-38) weeks, differing from aggregate estimates. Distributional comparisons showed no statistically significant differences in individual-level model fit (²) by pregnancy complications or age ( values ranging from .06 to .99 across all model fit comparisons). Case studies further highlighted both intraindividual and interindividual differences, emphasizing the importance of considering external factors, such as adverse events and severe symptoms. Conclusions: Our findings show that aggregate wearable data often fail to generalize across populations, oversimplifying pregnancy-related physiological and subjective changes. This simplification can obscure individual trajectories, leading to generalized insights that may not reflect many pregnant women’s experiences. Our results highlight the impact of heterogeneity on pregnancy outcomes, emphasizing the need to move beyond one-size-fits-all models and leverage DHT for personalized care.
<img src="https://jmir-production.s3.us-east-2.amazonaws.com/thumbs/56f195607607607bbdd89930537aae1a" />
Auditory Stimulation for Insomnia and Depression
Interventions: Device: Alpha phase-locked auditory stimulation; Device: Sham Stimulation
Sponsors: Wake Forest University Health Sciences
Not yet recruiting

