Tryptophan modulates the impact of prolactin on insomnia in perimenopausal women: a cross-sectional study

BackgroundInsomnia is highly prevalent among perimenopausal women and exerts detrimental effects on physical health, psychological well-being, and overall quality of life. However, its underlying mechanisms remain incompletely understood. This cross-sectional study aimed to identify factors associated with insomnia in perimenopausal women.MethodsA total of 187 perimenopausal women aged 45–55 years were enrolled. Insomnia, anxiety, and depression severity were assessed using the Insomnia Severity Index (ISI), Generalized Anxiety Disorder-7 (GAD-7), and Patient Health Questionnaire-9 (PHQ-9), respectively. Serum levels of relevant amino acids and hormones were measured. Spearman correlation and linear regression analyses were performed to examine the associations among prolactin levels, tryptophan levels, insomnia, anxiety, and depression. Moderation analysis was further conducted to evaluate the potential moderating role of tryptophan in these relationships.ResultsSerum prolactin levels were positively associated with scores of ISI, GAD-7, and PHQ-9. Furthermore, prolactin levels were positively correlated with the severity of sleep-onset difficulties, sleep maintenance problems, noticeability of impairment, and sleep-related distress. Of note, serum tryptophan levels significantly moderated the association between prolactin levels and ISI scores (β = 0.227, 95% CI = 0.04–0.41, p = 0.0148). To wit, he positive relationship between prolactin levels and insomnia severity was stronger in perimenopausal women with higher serum tryptophan levels compared with those with lower levels.ConclusionsThe moderating effect of serum tryptophan on the relationship between prolactin levels and insomnia in perimenopausal women helps us understand the neuroendocrine mechanisms underlying perimenopausal insomnia and may inform future research on targeted preventive and therapeutic strategies.

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

IntroductionEvidence suggests that dissociation may play a role in the manifestation of functional neurological disorder (FND). Dissociative experiences are frequently reported in FND, yet their dynamic associations with affective and physiological states remain underexplored. This pilot study aimed to examine dissociative symptoms in daily life in FND, to identify predictors of dissociation using ecological momentary assessment (EMA) combined with wearable heart-rate monitoring.MethodsSeventeen individuals with FND (functional seizures/motor symptoms) and seventeen age- and gender-matched healthy controls completed EMA via pseudorandom smartphone prompts eight times daily for one week. This was a secondary analysis of data from a pilot case-control intensive longitudinal observation study. Dissociation (depersonalisation, derealisation, amnesia), negative affect, and subjective arousal were assessed using items modified from validated scales, while heart-rate was continuously recorded via Fitbit devices. Multilevel modelling was conducted to examine between-group differences, and momentary associations between predictors (negative affect, subjective arousal, heart-rate) and dissociative symptoms within the FND group. Time-lagged analyses explored temporal relationships between the predictors and dissociative symptoms.ResultsThe FND group reported significantly higher dissociative symptoms across the week compared to controls; amnesia (F(1,34)=13.09, p <.001), depersonalisation (F(1, 34)=7.35, p = .010), and derealisation (F(1, 34)=8.31, p = .007), these differences withstood correction for anxiety, but not for depression. Negative affect (p-values=<0.001, β=0.094-0.111), subjective arousal (p-values=<0.001, β=0.102-0.124), and higher heart-rate (p-values = 0.01-0.006, β= 0.078-0.091) were each significant concurrent predictors of all three dissociative symptom types. In combined models, negative affect and subjective arousal remained robust predictors across all symptom domains, whereas heart-rate lost significance. Time-lagged analyses did not yield significant associations.ConclusionIndividuals with FND self-reported elevated dissociative symptoms in daily life compared to healthy controls; however, these significant differences did not remain when controlling for depression. Dissociation was consistently associated with subjective arousal and negative affect, but not heart-rate, underscoring the importance of subjective emotional states over physiological influences on self-reported dissociative symptom variability. The absence of temporal effects highlights the transient nature of dissociation in FND. These findings support the possibility of conceptualising FND through a dissociative lens and emphasise the need for larger longitudinal studies to clarify underlying mechanisms.

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.
<img src="https://jmir-production.s3.us-east-2.amazonaws.com/thumbs/56f195607607607bbdd89930537aae1a" />