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.

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.

Continue to STAT+ to read the full story…

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

BackgroundAlthough music listening and transcranial direct current stimulation (tDCS) alone have certain effects in the treatment of insomnia, the sleep regulatory effects and neural mechanisms of the combined treatment in patients with insomnia disorder (ID) are unclear. This study aimed to investigate the efficacy of combined bifrontal-tDCS (F3: anode, F4: cathode) with music listening in patients with ID using functional near-infrared spectroscopy (fNIRS).Methods76 ID patients were randomly divided into an intervention group (n=38) and a control group (n=38), and received 4 weeks of a total of 20 sessions of music + tDCS therapy and music + sham tDCS therapy (30-second stimulation with fade-in/fade-out to mimic somatic sensations), respectively. The Pittsburgh Sleep Quality Index Scale (PSQI), Self-rating Depression Scale (SDS), Self-rating Anxiety Scale (SAS), and Perceived Stress Scale (PSS-14) were compared between the two groups before and after treatment. Oxy-haemoglobin (HbO2) concentration and functional connectivity (FC) were assessed during the verbal fluency task using fNIRS.ResultsCompared with the control group, the PSQI total score (mean difference: -2.57 points, 95% CI: -4.43 to -0.71, p = 0.001), PSQI sub-scores except “sleep disturbance and daytime dysfunction”, SDS and SAS scores of the intervention group improved significantly after treatment. It was observed by fNIRS that the HbO2 concentration in the medial prefrontal cortex (mPFC), left dorsolateral prefrontal cortex (DLPFC), right ventrolateral prefrontal cortex, and right superior frontal cortex (SFC) increased significantly after treatment in the intervention group but was not superior to the control group. In addition, the FC enhancement of left SFC-left DLPFC and left SFC-mPFC after treatment was significantly better in the intervention group than in the control group, and the PSQI improvement was positively correlated with the FC enhancement of channel-averaged and left SFC-right DLPFC.ConclusionsCombining bifrontal-tDCS with music listening is more helpful in improving sleep quality and prefrontal functional connectivity in ID patients compared with music listening alone. For ID patients, music electrical stimulation headphones may be a safe, effective, and convenient new treatment strategy.Clinical trial registrationhttps://www.chictr.org.cn/, identifier ChiCTR2400086233.

PAD-S/CSA as a candidate shared representation layer for computational psychotherapy: minimal architecture and a staged validation roadmap

Psychotherapy schools often describe overlapping process phenomena in non-interoperable vocabularies. This pluralism is clinically valuable but computationally costly: datasets become difficult to compare, clinically load-bearing distinctions are collapsed into convenience labels, and artificial intelligence (AI) systems inherit annotation schemes rather than a clinically interpretable intermediate representation. Building on the Perceive–Assess–Dose–Safeguard (PAD-S) framework and the Conflict-Square Algorithm (CSA), this theory article asks a narrower question than the prior PAD-S and CSA papers: can the same variables be formulated as a candidate shared representation layer between heterogeneous observation models and school-specific intervention policies? The proposed layer projects a high-dimensional biopsychosocial state into four clinically observable process coordinates—defensive/avoidant organization (DEF), anxiety/arousal and tolerance (ANX), progression toward direct experience and action (PRO), and self-attack/shame processes (SUP)—plus a safety threshold that constrains admissible intervention intensity. The contribution is architectural rather than empirical: it isolates the representational role from earlier decision-grammar and transcript-coding roles; clarifies the distinction between observations, representation, and policy; specifies a minimal falsifiable family of state-transition models; illustrates translation across four pragmatic therapy families; and defines a staged validation order from reliability and function linkage to transcript-level predictive operationalization and only then sparse equation discovery. The framework should therefore be read as a candidate shared representation layer for computational psychotherapy and computational psychiatry rather than as a therapy protocol, a fitted predictive model, a complete generative theory, or an autonomous decision system. No new dataset, fitted classifier, transcript-level predictive result, or discovered equation is reported here. The article aims instead to state what would count for or against PAD-S/CSA as a clinically interpretable interface for later empirical modeling.

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.
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Enhancing Sleep and Mental Health: Longitudinal, Observational, Real-World Study From a Digital Mental Health Platform

Background: Poor sleep is closely linked to mental health challenges and workplace burnout. Mental health and workplace stressors can impair sleep, while good sleep quality supports cognitive and emotional resources to cope with daily challenges. Despite positive outcomes of maintaining good sleep, many people struggle to get enough restorative sleep at night. Given the bidirectional relationship between sleep and mental health, evidence-based digital mental health solutions may offer an accessible and scalable approach to improving sleep quality. Objective: This study examines whether engagement with an employer-sponsored, multimodal digital mental health platform is associated with improvements in sleep quality over time, and whether changes in sleep quality are associated with concurrent changes in mental health and burnout outcomes. Methods: This 12-month prospective, observational study followed working adults who were newly registered to an employer-sponsored digital mental health platform (Modern Health). The platform leveraged technology (mobile and web) to connect employees with comprehensive provider-led and self-guided care through therapy, coaching, on-demand digital resources, and group psychoeducational sessions. Participants [N=578; 61.1% (n=353) women; mean age 33.88, SD 8.73 years; 40.3% (n=233) people of color] completed measures of self-rated sleep quality, depression, anxiety, and burnout (exhaustion, cynicism, and professional efficacy) at baseline and after 3 and 12 months of accessing the platform. Upon registering for the platform, participants were given an initial care recommendation, but could flexibly engage in any combination of services. Participants in this study engaged with at least one care modality, including therapy, coaching, psychoeducation sessions, and self-guided mental health resources. We examined perceived sleep quality and associations with other study variables at baseline, changes in perceived sleep quality over time, and whether changes in sleep quality correlated with concurrent changes in mental health and burnout. Results: At baseline, 42% (243/578) reported poor sleep quality and were more likely to have higher levels of depression, anxiety, and burnout. A generalized linear mixed-effects model showed that each additional month of platform access was related to an increased odds of having good sleep quality by 3.7% (=.02). Linear mixed-effects models found that higher sleep quality over time was associated with lower depression, anxiety, exhaustion, cynicism, and efficacy (all <.001). Among participants reporting poor sleep quality at baseline, 44% (62/141) reported good sleep quality at 12 months. Within this subgroup, paired sample tests showed significant reductions in depression (−48.3%) and anxiety (−38.3%), and increased cynicism, burnout, though cynicism levels remained below the cutoff for high burnout (23.9%; all <.01). Conclusions: Use of an employer-sponsored digital mental health platform was associated with meaningful improvements in self-reported sleep quality over 12 months. These gains were associated with significant reductions in depression, anxiety, and burnout symptoms, highlighting broader well-being benefits of comprehensive mental health care.

Digital Phenotyping via Passive Network Traffic Monitoring: Prospective Observational Study in University Students

Background: Digital behaviors such as sleep, social interactions, and productivity reflect how individuals structure their daily lives. Among university students, online activity patterns mirror academic schedules, social rhythms, and lifestyle habits, with disruptions linked to sleep, stress, and well-being. Existing approaches—including wearables, apps, and surveys—depend on self-report or active participation, limiting long-term adherence. Passive sensing of network traffic offers a scalable alternative for the unobtrusive capture of smartphone usage patterns that preserves privacy. Objective: This study evaluated the degree to which encrypted smartphone network traffic, collected via a standard virtual private network (VPN), can capture patterns of digital behavior. We assessed feasibility (sustained data capture) and acceptability (usability, burden, and privacy perceptions) and examined how traffic-derived features reveal aspects of digital behavior—including timing, intensity, and regularity—relevant to health and daily functioning. Methods: We conducted a 2-week prospective observational study at New York University. Participants installed the WireGuard VPN client on personal smartphones, enabling passive capture of encrypted network traffic. Feasibility was assessed using a mixed methods approach combining quantitative measures of user retention and data coverage with qualitative analysis of semistructured exit interviews. Acceptability was evaluated using the System Usability Scale, NASA Task Load Index, and qualitative interview analysis. Exploratory analyses visualized traffic-derived features in relation to digital activity patterns. Results: Thirty-eight students consented, of whom 29 (76.3%) contributed valid network traffic data and formed the analytic cohort. Within this cohort, 93% of participants (27/29; Wilson 95% CI 78%‐98%) contributed at least 5 days of monitoring, corresponding to 71% retention relative to all consented participants (27/38; Wilson 95% CI 55%‐83%). The mean data coverage within the analytic cohort (n=24) was 74.1% (SD 19.3%; median 77.1%, IQR 63.6%-90.0%; bootstrap 95% CI 66.3%‐81.4%). These participants contributed an average of 311.6 (∼13 d, SD 3.5) hours of monitored traffic, ranging from 121 to 496 hours. Acceptability outcomes were evaluated among participants completing the exit survey and interview. Usability ratings were high (System Usability Scale score: mean 78, SD 14.96), and perceived workload was low (NASA Task Load Index scores were minimal). Participants described the system as easy to install, unobtrusive, and generally trustworthy, although some reported temporarily disabling the VPN during activities they considered private. No inferential statistical tests were conducted; analyses were descriptive. Exploratory analyses indicated that traffic-derived features reflected daily digital activity rhythms and revealed distinctive lifestyle patterns, including gaming and irregular late-night food delivery use. Conclusions: VPN-based monitoring of encrypted smartphone traffic was feasible and acceptable, enabling sustained passive data collection with minimal burden. This approach shows promise as a scalable, device-agnostic method for digital phenotyping that captures fine-grained behavioral rhythms while preserving privacy. With broader validation, this technique could expand the toolkit for studying health and well-being in everyday life.
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